Advances in Agronomy, Volume 85

Agronomy D VA N C E S I N VOLUME 85 Advisory Board John S. Boyer University of Delaware Paul Bertsch University of...

Author: Donald L. Sparks

103 downloads 1519 Views 4MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

Agronomy

D VA N C E S I N

VOLUME 85

Advisory Board John S. Boyer University of Delaware

Paul Bertsch University of Georgia

Ronald L. Phillips University of Minnesota

Kate M. Scow University of California, Davis

Larry P. Wilding Texas A&M University

Emeritus Advisory Board Members Kenneth J. Frey Iowa State University

Eugene J. Kamprath North Carolina State University

Martin Alexander Cornell University

Prepared in cooperation with the American Society of Agronomy Monographs Committee David D. Baltensperger, Chair Lisa K. Al-Almoodi John M. Baker Kenneth A. Barbarick David M. Burner

Warren A. Dick L. Richard Drees Jeffrey E. Herrick Bingru Huang

Michel D. Ransom Craig A. Roberts David L. Wright

Agronomy D VA N C E S I N

VOLUME 85 Edited by

Donald L. Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware

Elsevier Academic Press 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WCIX 8RR, UK

This book is printed on acid-free paper.

Copyright ß 2005, Elsevier Inc. All Rights Reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the Publisher. The appearance of the code at the bottom of the first page of a chapter in this book indicates the Publisher’s consent that copies of the chapter may be made for personal or internal use of specific clients. This consent is given on the condition, however, that the copier pay the stated per copy fee through the Copyright Clearance Center, Inc. (www.copyright.com), for copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Law. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. Copy fees for pre-2005 chapters are as shown on the title pages. If no fee code appears on the title page, the copy fee is the same as for current chapters. 0065-2113/2005 $35.00 Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (þ44) 1865 843830, fax: (þ44) 1865 853333, E-mail: [email protected]. You may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting ‘‘Customer Support’’ and then ‘‘Obtaining Permissions.’’ For all information on all Elsevier Academic Press publications visit our web site at www.books.elsevier.com ISBN: 0-12-000783-5 PRINTED IN THE UNITED STATES OF AMERICA 05 06 07 08 09 9 8 7 6 5 4 3 2

1

Contents CONTRIBUTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi xiii

ADVANCES IN HYDROPEDOLOGY H. Lin, J. Bouma, L. P. Wilding, J. L. Richardson, M. Kutı´lek and D. R. Nielsen I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Hydropedology as an Intertwined Branch of Soil Science and Hydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Pedology, Soil Physics, and Hydrology. . . . . . . . . . . . . . . . . . . . . B. Hydropedology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Fundamentals and Applications of Hydropedology . . . . . . . . . . . . . . A. Soil Structure and Layering as Indicators of Flow and Transport Characteristics in Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Soil Morphology as Signatures of Soil Hydrology . . . . . . . . . . . . C. Water Movement over the Landscape in Relation to Soil Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Hydrology as a Factor of Soil Formation and a Driving Force of Dynamic Soil System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Future Needs in Advancing Hydropedology . . . . . . . . . . . . . . . . . . . A. Systems Approaches to Understanding and Communicating Landscape–Soil–Water Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . B. From Variability to Pattern and Their Relations to Scale . . . . . . C. From Pedotransfer Functions to Soil Inference Systems and Hydropedoinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Education of the Next Generation of Soil Scientists and Hydrologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 6 6 13 20 20 28 37 47 54 54 59 68 72 74 75 76

BIOINDUSTRIAL AND BIOPHARMACEUTICAL PRODUCTS PRODUCED IN PLANTS John A. Howard and Elizabeth Hood I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Technology Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

92 93

vi

CONTENTS A. Generation of Transgenic Material . . . . . . . . . . . . . . . . . . . . . . . . B. Protein Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III. Production Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Harvesting/Transport/Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Tissue Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Extraction/Purification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. High-Purity Human Health Products . . . . . . . . . . . . . . . . . . . . . . B. Orally Delivered Products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Industrial Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Public Acceptance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Conclusions and Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

93 96 98 100 101 102 103 110 111 113 114 116 118 119

ASSESSING THE POTENTIAL FOR PATHOGEN TRANSFER FROM GRASSLAND SOILS TO SURFACE WATERS D. M. Oliver, C. D. Clegg, P. M. Haygarth and A. L. Heathwaite I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Pathogens in Livestock Wastes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Protozoa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Detection and Enumeration Techniques . . . . . . . . . . . . . . . . . . . . . . A. Culture-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Direct Counting Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Molecular Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Transfer from Soil to Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Lateral Surface Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Matrix Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Soil Retention Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Bypass Mechanisms in Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Movement via Growth and Motility. . . . . . . . . . . . . . . . . . . . . . . F. The Role of Soil Mesofauna. . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. The Role of Colloids in Facilitating Transfer . . . . . . . . . . . . . . . . . .

126 127 128 131 131 132 132 137 137 138 140 144 146 149 149 150 151

CONTENTS VI. Factors Affecting Survival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Survival in Livestock Wastes . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Survival in Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Survival in Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii 153 153 156 164 168 169

DEVELOPING EXISTING PLANT ROOT SYSTEM ARCHITECTURE MODELS TO MEET FUTURE AGRICULTURAL CHALLENGES L. Wu, M. B. McGechan, C. A. Watson and J. A. Baddeley I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The Future Agronomic Challenge . . . . . . . . . . . . . . . . . . . . . . . . B. Why Model Roots? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Environment/Root Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . II. Current Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Available Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Selected Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Model Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. General Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Branching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Root Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Data Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Extending the Scope of Current Models . . . . . . . . . . . . . . . . . . . . . . A. Root Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Interaction with the Environment . . . . . . . . . . . . . . . . . . . . . . . . . C. Water Uptake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Nutrient Uptake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Photoassimilate Availability and Root Development . . . . . . . . . . F. Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Structure of an Integrated Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

182 182 184 185 186 186 188 191 191 192 197 201 204 204 205 206 207 208 209 210 211 212 212 213

viii

CONTENTS

LABILE ORGANIC MATTER FRACTIONS AS CENTRAL COMPONENTS OF THE QUALITY OF AGRICULTURAL SOILS: AN OVERVIEW R. J. Haynes I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Total Soil Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Attainment of Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Effects of Agricultural Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Particulate Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Method of Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Nature of the Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Amounts Present in Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Management-Induced Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Seasonal Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Significance to Soil Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Dissolved Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Method of Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Nature of the Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Biodegradability of DOM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Adsorbed Organic Matter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Quantities of DOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Management-Induced Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Seasonal Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Significance to Soil Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Extractable Forms of Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . A. Hot Water-Extractable Organic Matter . . . . . . . . . . . . . . . . . . . . B. Dilute Acid-Hydrolyzable C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Permanganate-Oxidizable C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Potentially Mineralizable C and N. . . . . . . . . . . . . . . . . . . . . . . . . . . A. Method of Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Nature of the Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Relationship with Other Pools . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Amounts Present in Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Management-Induced Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Seasonal Flunctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Significance to Soil Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Synthesis and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Significance of Labile Organic Matter Fractions . . . . . . . . . . . . . B. Practical Value of Labile Organic Matter Fractions . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

222 223 224 226 228 229 229 231 231 233 234 235 235 237 238 239 240 240 240 241 243 244 246 246 247 247 248 248 250 250 251 251 252 252 257 258

CONTENTS

ix

CROP RESIDUE MANAGEMENT FOR NUTRIENT CYCLING AND IMPROVING SOIL PRODUCTIVITY IN RICE-BASED CROPPING SYSTEMS IN THE TROPICS Yadvinder-Singh, Bijay-Singh and J. Timsina I. II. III. IV.

V.

VI.

VII. VIII. IX. X.

XI.

XII. XIII.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Availability of Crop Residues in Rice-Based Cropping Systems . . . . Management Options for Crop Residues . . . . . . . . . . . . . . . . . . . . . . Crop Residue Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Kinetics of Crop Residue Decomposition . . . . . . . . . . . . . . . . . . . B. Factors Affecting Residue Decomposition . . . . . . . . . . . . . . . . . . C. Fallow Period and Crop Residue Management . . . . . . . . . . . . . . Crop Residue Management Effects on Nutrient Availability in Soils A. Nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Potassium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Sulfur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Micronutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Crop Residues on Soil Properties . . . . . . . . . . . . . . . . . . . . A. Soil Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Chemical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Physical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Biological Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Crop Residues for Reclamation of Salt-Affected Soils . . . . . . . . . Biological Nitrogen Fixation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phytotoxicity Associated with Crop Residue Incorporation into the Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weed Control and Herbicide Efficiency . . . . . . . . . . . . . . . . . . . . . . . Emission of Greenhouse Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Methane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Nitrous Oxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Mitigation Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agronomic Responses to Crop Residue Management . . . . . . . . . . . . A. Rice–Wheat Cropping System . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Rice–Rice Cropping System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Rice–Legume Cropping System . . . . . . . . . . . . . . . . . . . . . . . . . . D. Other Rice-Based Cropping Systems . . . . . . . . . . . . . . . . . . . . . . Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

270 272 274 277 277 282 290 293 294 308 312 312 313 315 315 327 328 335 338 339 341 344 345 346 350 351 353 353 365 371 372 373 377 380

x

CONTENTS

ASPECTS OF JOJOBA AGRONOMY AND MANAGEMENT G. J. Ash, A. Albiston and E. J. Cother I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Plant Description and Characteristics . . . . . . . . . . . . . . . . . . . . . . . . III. Plant Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Seed Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Vegetative Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Environmental Effects and Heritability . . . . . . . . . . . . . . . . . . . . B. DNA Markers for Jojoba Clones . . . . . . . . . . . . . . . . . . . . . . . . . C. Genetics of Wax Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Agronomic Practices and Plant Adaptation . . . . . . . . . . . . . . . . . . . . A. Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Water Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Irrigation and Fertilizer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . E. Salt Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Soil pH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Temperature Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Frost Susceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Mycorrhizal Status of Jojoba . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Diseases and Insect Pests of Jojoba . . . . . . . . . . . . . . . . . . . . . . . . . . A. Endophytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Insect and Arthropod Pests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

410 411 412 412 413 416 416 417 418 418 418 420 420 421 423 424 424 425 426 427 427 427 428 430 431 431

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

439

Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

A. Albiston (409), Farrer Centre, School of Agriculture, Charles Sturt University, Wagga Wagga NSW 2678, Australia G. J. Ash (409), Farrer Centre, School of Agriculture, Charles Sturt University, Wagga Wagga NSW 2678, Australia J. A. Baddeley (181), Crop and Soil Research Group, SAC, Craibstone Estate, Aberdeen AB21 9YA, United Kingdom J. Bouma (1), Laboratory of Soil Science and Geology, Wageningen University and Research Center, 6701 AR Wageningen, The Netherlands C. D. Clegg (125), Soil Science and Environmental Quality Team, North Wyke Research Station, Okehampton, Devon EX20 2SB, United Kingdom E. J. Cother (409), NSW Agriculture, Orange Agricultural Institute, Orange NSW 2800, Australia P. M. Haygarth (125), Soil Science and Environmental Quality Team, North Wyke Research Station, Okehampton, Devon EX20 2SB, United Kingdom R. J. Haynes (221), Discipline of Soil Science, School of Applied Environmental Sciences, University of Natal, Pietermaritzburg, Scottsville 3209, South Africa A. L. Heathwaite (125), Centre for Sustainable Water Management, The Lancaster Environmental Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom Elizabeth Hood (91), Arkansas State University, Jonesboro, Arkansas 72403, USA John A. Howard (91), Applied Biotechnology Institute, College Station, Texas 77845, USA M. Kutı´lek (1), Faculty of Civil Engineering, Czech Technical University, 16636 Prague, Czech Republic H. Lin (1), Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA M. B. McGechan (181), Land Economy Research Group, SAC, Bush Estate, Penicuik EH26 0PH, United Kingdom D. R. Nielsen (1), Department of Land, Air and Water Resources, University of California, Davis, California 95616, USA D. M. Oliver (125), Soil Science and Environmental Quality Team, North Wyke Research Station, Okehampton, Devon EX20 2SB, United Kingdom, and Department of Geography, University of Sheffield, Sheffield S10 2TN, United Kingdom J. L. Richardson (1), Department of Soil Science, North Dakota State University, Fargo, North Dakota 58105, USA Bijay-Singh (269), Department of Soils, Punjab Agricultural University, Ludhiana 141 004, India xi

xii

CONTRIBUTORS

Yadvinder-Singh (269), Department of Soils, Punjab Agricultural University, Ludhiana 141 004, India J. Timsina (269), CSIRO Land and Water, Griffith NSW 2680, Australia C. A. Watson (181), Crop and Soil Research Group, SAC, Craibstone Estate, Aberdeen AB21 9YA, United Kingdom L. P. Wilding (1), Department of Soil and Crop Sciences, Texas A & M University, College Station, Texas 77843, USA L. Wu (181), Crop and Soil Research Group, SAC, Craibstone Estate, Aberdeen AB21 9YA, United Kingdom

Preface Volume 85 contains seven excellent and state-of-the-art reviews on topics that will be of great interest to crop and soil scientists. Chapter 1 is a comprehensive review on advances in hydropedology. Topics that are discussed include: hydropedology as an intertwined branch of soil science and hydrology, fundamentals and applications of hydropedology, and future needs in advancing hydropedology. Chapter 2 is a timely treatise on bioindustrial and biopharmaceutical products produced in plants. Key factors such as transformation, expression, growth, harvest, transport, storage, processing, and purification of the plant material are included. Chapter 3 assesses the potential for pathogen transfer from grassland soils to surface waters. Topics include pathogens in livestock waste, detection and enumeration techniques, transfers from soil to water, the role of colloids in facilitating transfer, and factors affecting survival. Chapter 4 is an interesting review on plant root-system architecture models including discussions on model processes and evaluation of various models with respect to growth, structure, root mortality, and water and nutrient uptake. Chapter 5 deals with labile organic matter fractions and impacts on the quality of agricultural soils. Discussions on total particulate dissolved, and extractable forms of organic matter, as well as potentially mineralizable C and N, are included. Chapter 6 is a comprehensive review on crop residue management for nutrient cycling and improving soil productivity in rice-based cropping systems in the tropics. Topics covered include availability of crop residues in rice-based cropping systems, management options and crop residue decomposition effects of management on nutrient availability and soil properties, biological nitrogen fixation, phytotoxicity associated with crop residue incorporation into soil, and emission of greenhouse gases. The final Chapter deals with agronomic and management aspects of jojoba, a new crop that is well suited for hot, dry climates. The seed of jojoba contains a wax that is used in lubricants, pharmaceuticals and cosmetics. I appreciate the excellent reviews of the authors. DONALD L. SPARKS

xiii

ADVANCES IN HYDROPEDOLOGY H. Lin,1 J. Bouma,2 L. P. Wilding,3 J. L. Richardson,4 M. Kutı´lek5 and D. R. Nielsen6 1

Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA 2 Laboratory of Soil Science and Geology, Wageningen University and Research Center, 6701 AR Wageningen, The Netherlands 3 Department of Soil and Crop Sciences, Texas A & M University, College Station, Texas 77843, USA 4 Department of Soil Science, North Dakota State University, Fargo, North Dakota 58105, USA 5 Faculty of Civil Engineering, Czech Technical University, 16636 Prague, Czech Republic 6 Department of Land, Air and Water Resources, University of California, Davis, California 95616, USA

I. Introduction II. Hydropedology as an Intertwined Branch of Soil Science and Hydrology A. Pedology, Soil Physics, and Hydrology B. Hydropedology III. Fundamentals and Applications of Hydropedology A. Soil Structure and Layering as Indicators of Flow and Transport Characteristics in Soils B. Soil Morphology as Signatures of Soil Hydrology C. Water Movement over the Landscape in Relation to Soil Cover D. Hydrology as a Factor of Soil Formation and a Driving Force of Dynamic Soil System IV. Future Needs in Advancing Hydropedology A. Systems Approaches to Understanding and Communicating Landscape–Soil–Water Dynamics B. From Variability to Pattern and Their Relations to Scale C. From Pedotransfer Functions to Soil Inference Systems and Hydropedoinformatics D. Education of the Next Generation of Soil Scientists and Hydrologists V. Concluding Remarks Acknowledgments References

1 Advances in Agronomy, Volume 85 Copyright 2005, Elsevier Inc. All rights reserved. 0065-2113/05 $35.00

2

H. LIN ET AL. Hydropedology is an intertwined branch of soil science and hydrology that encompasses multiscale basic and applied research of interactive pedological and hydrological processes and their properties in the unsaturated zone. The synergistic integration of classical pedology with soil physics, hydrology, and other related bio- and geosciences into hydropedology suggests a renewed perspective and a more integrated approach to studying landscape–soil–water dynamics across scales. Pedality, layering of soil horizons, and soil–landscape relationships are three essential characteristics of soils as occurring on the landscape. Fundamental issues of hydropedology include (1) soil structure and layering as indicators of flow and transport characteristics in field soils; (2) soil morphology as signatures of soil hydrology; (3) water movement over the landscape; and (4) hydrology as a factor of soil formation and a driving force of dynamic soil system. Hydrology affects and is affected by all of the five natural soil-forming factors and the four general soil-forming processes. Hence, hydropedology offers potential opportunities for quantifying soil-forming processes. Future needs in advancing hydropedology are encapsulated in the philosophy of ‘‘bridging disciplines, scales, data, and education.’’ These include (1) systems approaches to understanding and communicating landscape–soil–water dynamics; (2) addressing variability using patterns at various scales; (3) enhancing pedotransfer functions and developing soil inference systems and hydropedoinformatics; and (4) education of the next generation of soil scientists and hydrologists. Hydropedology calls for adequate attention to soil morphology (including soil structure) in the field and soil patterns over the landscape to guide optimal soil physical and hydrological measurements, field monitoring and experimental designs, and understanding and modeling of flow and transport in the critical zone. Identification and prediction of patterns (spatial-temporal organizations) across multiple scales are coming to the forefront in soil science and hydrology, which offer rich and comprehensive insights regarding variability and the underlying processes. We suggest various hydropedological approaches to address diverse knowledge gaps. Given its links to a wide array of environmental, ecological, geological, agricultural, and natural resource issues of societal importance, hydropedology is emerging as a promising field that could contribute significantly to the study of the pedosphere, the hydrological cycle, the earth’s critical zone, and ß 2005 Elsevier Inc. the earth system.

I. INTRODUCTION The U.S. National Research Council (NRC) recently identified integrated studies of the earth’s critical zone as a compelling research area for the 21st century (NRC, 2001a). The critical zone, as defined by the NRC (2001a), extends through the root zone, deep vadose zone, and ground water zone and includes the land surface and its canopy of vegetation, rivers, lakes, and shallow seas (Figs. 1 and 2) (See Color Insert). Interactions at this interface between the solid earth and its fluid envelopes determine the availability of

ADVANCES IN HYDROPEDOLOGY

3

nearly every life-sustaining resource, hence the term ‘‘critical zone.’’ The critical zone encompasses the pedosphere—the thin skin of soils on the earth’s surface that represents a geomembrane across which water and solutes, as well as energy, gases, solids, and organisms, are actively exchanged with the atmosphere, biosphere, hydrosphere, and lithosphere to create a life-sustaining environment (Fig. 1). Soil and water are two critical components of the critical zone. In fact, soil–water interaction is a key interface between the biotic and abiotic and thus is a key determinant of the state of the earth system. Water controls a variety of soil physical, chemical, and biological processes that lead to the formation of diverse soils that support an array of land uses and biological communities. On the other hand, soils play a key role in the global hydrological and biogeochemical cycles, contribute to the maintenance of water quality and ecosystem functions, and act as a living filter and remediation medium for waste materials. The interactions of soil and water are so intimate and complex that they cannot be studied in a piecemeal manner, but rather as a system across spatial and temporal scales. In this spirit, hydropedology is suggested for synergistic integration of knowledge from relevant disciplines. Hydropedology is defined here as an intertwined branch of soil science and hydrology that encompasses multiscale basic and applied research of interactive pedological and hydrological processes and their properties in the unsaturated zone. Lin (2003) suggested that hydropedology functions as a bridge that could address (1) knowledge gaps between pedology, soil physics, hydrology, and other related bio- and geosciences; (2) scale differences in microscopic, mesoscopic, and macroscopic studies of soil– water interactions; and (3) data translations from soil survey databases into soil hydraulic properties. Such a bridging signifies the potential unique contributions that hydropedology can make to integrated soil and water sciences. Hydropedology also shifts the focus of geology-rooted classical pedology—a branch of soil science that integrates and quantifies the morphology, formation, distribution, and classification of soils as natural landscape entities—to a hydrology-driven approach with a landscape perspective, reflecting the crucial role of water in many environmental, ecological, geological, agricultural, and natural resource issues of societal importance. Because interactions between the solid earth and its fluids control almost every life-sustaining activity, hydropedology holds significant potential to enhance our understanding of the earth’s critical zone and to improve the modeling of flow and transport phenomena occurring in the earth’s surface and subsurface environments. From a broader perspective, hydropedology plays an increasing role in interdisciplinary teams and panels formed to address complex environmental research and policy issues (Bouma, 2005). As highlighted in the following list, seven working models or perceptions of soils may be used to evaluate the

4

H. LIN ET AL.

relevancy of hydropedology to the study of the earth’s critical zone—within each of these models, soil–water interaction plays a critical role. 1. Soil as a natural body: V. V. Dokuchaev’s (1893) theory of soil formation (i.e., the soil has been formed under the influence of climate, organisms, relief, parent material, and time) gave birth to modern soil science. Soil science evolved at the beginning as a branch of geoscience (i.e., soil viewed as a superficial geological entity and a weathered crust). In the process of soil formation, water plays an important role, both directly, through chemical weathering of rock and physical leaching and erosion processes, and indirectly, through life support of soil biota and vegetation. Soil water is also an active agent in the transformation and translocation of organic and mineral materials and influences the deposition and resistance of soils to erosion. 2. Soil as a water reservoir and transmitting mantle: Soil is an important fresh water reservoir and a living filter that impacts water quantity and quality as well as the overall global hydrological cycle. Without soil, noncontinuous rainfall or snow could not be transformed into a continuous flow of water to plant roots. Together with ground water, soil also transforms discontinuous precipitation into continuous discharges recognized as streams and rivers (Kutı´lek and Nielsen, 1994). Transport of water soluble or suspended materials over the soil surface and through the soil profile ultimately impacts the quality of surface and ground waters, the global biogeochemical cycles, and the efficiency and fate of anthropogenically applied chemicals. 3. Soil as a gas and energy regulating geoderma: Like the skin of human bodies, the porous soil layer essentially functions as the skin of the earth (termed ‘‘geoderma’’). It regulates the gas (including water vapor) exchange between the land and the atmosphere, greenhouse gas emissions from the land, the temperature of the land and plant community, and the energy balance on the earth’s surface. For example, the phase changes of water in soil involve storage and release of latent heat that drives the atmospheric circulation and redistributes both water and heat globally. Water vapor is the most important of the greenhouse gases, acting to regulate the earth’s surface temperature by absorbing and returning to the earth much of the thermal radiation emitted there (NRC, 1991). The soil and related land uses also play an important role in regulating global carbon fluxes and sequestration that are related to global warming. 4. Soil as a component in ecosystems: Soil is the life-giving substance for vegetation and ecosystems, supporting and regulating the fluxes of air, water, and nutrients for macro- and microorganisms. For instance, the water in the uppermost 1–2 m of the earth’s crust (i.e., soil moisture) is


5

directly linked to the type and functionality of ecosystems and controls much of the soil–plant–atmosphere continuum. Through its influences on physical, chemical, and biological processes in the root zone, the soil moisture regime often dictates the pattern of plant distribution over the landscape (Eagleson, 2002). The soil microbial population across the landscape is also strongly influenced by soil water availability and its distribution, which in turn impacts microbially mediated biogeochemical reactions such as nitrification and denitrification (Nielsen et al., 1996; Wagenet, 1998). 5. Soil as a medium for plant growth: Soil is an essential natural resource for agricultural production and other diverse land uses. Soil–water flow mediates nutrient cycling in agroecosystems, influences crop yield variability, and determines the need for drainage or irrigation. Precision agriculture, for instance, requires accurate mapping of soil and moisture variability across the landscape in order to apply chemicals precisely in the right location at the right time. Many best management practices in agriculture (e.g., wetland protection or construction, riparian buffer strips to reduce nonpoint source pollution entering into streams) often require appropriate understanding of soil–water interactions across the landscape. 6. Soil as a material for engineering: Soil is widely utilized in engineering for various applications, such as construction material for buildings, roads, highways, and dams and as a disposal and remediation medium for wastes of all kinds. It has been reported that the most expensive hazard in the United States is not earthquakes or flooding, but the one caused by the enormous structural damages due to soil shrink-swell (NRC, 2001a). Soil shrink-swell is a phenomenon that is directly caused by soil–water interactions. Soil mechanical properties as influenced by water content are also crucial in hillslope stability, landslide or mudflow prevention, and other structural protections. 7. Soil as an integral part of the environment: Encompassing all of the preceding models of soil is the overarching environmental arena that is cross-cutting and multidisciplinary in nature. Soil–water interactions are significant in a variety of environmental issues of societal importance, including, for example, sustainable land use planning, watershed management, water quality protection, contaminant fate, waste remediation, and global environmental change. Water fluxes into and through soils in the landscape are the essence of life, and resemble in a way the manner in which blood circulates in a human body (Bouma, 2005). We could even compare blood pressure with the pressure potential of water in soil: when it is too high or too low, soil functioning is clearly hampered. We could therefore speak of a throbbing landscape in which water enters and leaves, on the timeframe of hours, weeks, years, and centuries. Once water regimes have been characterized, physical, chemical, and biological

6

H. LIN ET AL.

processes can be added, as they strongly depend on the water regime and on interaction processes with the soil. In fact, the hydrological cycle is viewed as the integrating process for the fluxes of water, energy, and the chemical elements (NRC, 1991). Richardson et al. (2001) also suggested that the study of water and its effects on soil is a unifying principle in soil investigations. The objectives of this chapter are three-fold: (1) to suggest hydropedology as a promising interdisciplinary area that could contribute significantly to the integrated studies of the earth’s critical zone; (2) to explore the fundamental issues of hydropedology and its various applications; and (3) to propose some needs for the future advancement of hydropedology.

II. HYDROPEDOLOGY AS AN INTERTWINED BRANCH OF SOIL SCIENCE AND HYDROLOGY A. PEDOLOGY, SOIL PHYSICS, AND HYDROLOGY Pedology, soil physics, and hydrology have been identified as the ‘‘cornerstones’’ of hydropedology (Fig. 2), although hydropedology is also linked to other related bio- and geosciences such as geomorphology, hydrogeology, ecohydrology, hydroclimatology, and other branches of soil science (Lin, 2003). The three cornerstone disciplines share many common interests in the interdisciplinary environmental arena, particularly in areas related to water flow and solute transport through field soils and over the landscape. Although traditionally the three disciplines have had contrasting focuses and approaches in their investigations, the time is now ripe for synergistic integration of the three to address complex flow and transport processes in nature and landscape–soil–water dynamics across scales. Synergies are expected from integrating pedologists’ expert knowledge of soil–landscape relationships with soil physicists’ and hydrologists’ mathematical rigor of flow and transport theory. 1. Different Views of Soil and Scales of Investigation a. Pedology. Pedologists study soils in their natural settings, so they view soil as a natural entity and traditionally have focused on field soil profiles (called pedons) as observed in the landscape. Pedologists describe soil profiles in situ using observable morphological characteristics and related landscape features; then they collect bulk soil samples, undisturbed clods, and sometimes small intact box samples based on soil generic horizons for laboratory characterizations of various physical, chemical, and micromorphological properties (Fig. 3; See Color Insert). The pedon that pedologists


7

examine is commonly 1–2 m deep with an area ranging from 1 to 10 m2 (about 1 m wide and several meters long). When an auger or a probe truck is used in examining field soils (e.g., in soil surveys), the actual volume of soil being observed is much smaller, but the view of the soil–landscape relationship is much larger. In the investigations of pedons, pedologists have placed a much greater emphasis on the vertical relationships of horizons and soil processes (Fig. 4; See Color Insert) than on the horizontal relationships (Fig. 5), although the horizontal relationships are what they attempt to delineate in soil mapping (Buol et al., 2001). Moreover, vertical investigations have been limited to the upper 2 m beneath the earth’s surface (with an emphasis on the root zone). In terms of time scale, much of pedological characterizations has been based on deeper soils on static or static/temporal differentia formed over a long period of time (up to a geological time scale). Most of the data in soil survey databases have been collected at a window in time, and there is precious little soil survey data that are actually of a dynamic basis. One reason that pedology has received continued attention over the years from scientists and land users is the success of pedology-based soil survey interpretations, which have been used extensively. Indicating the relative limitations for various land uses for any given soil type has been quite useful for broad land use planning and land evaluation purposes. Of course, modern problems require more quantitative procedures, as pointed out elsewhere in this chapter, but it is often forgotten that the alternative for qualitative or descriptive land use limitations is often nothing at all when there are no opportunities for extensive measurements. Thus, descriptive characterizations could still be very valuable, as has been well documented, and may offer new opportunities as highlighted later in this chapter (e.g., Section IV.B). The emphasis of pedology is now shifting from classification and inventory to understanding and quantifying spatially-temporally variable processes upon which the water cycle and ecosystems depend. While a huge success in its own right, the exclusive focus on Soil Taxonomy (see illustrations in Fig. 4) in the pedology community over the last four decades or so has become an introspective exercise and has resulted in some unintended consequences: (1) Soil Taxonomy does not relate soils to landscapes well; (2) Soil Taxonomy does not consider dynamic soil properties (such as hydraulic properties and those affected by short-term land management); (3) Soil Taxonomy is viewed by many as too complex for non-pedologists; and (4) soil survey has focused on classifying soils and thus has neglected the quantification of variability (or specific range of soil properties) within taxonomic categories and soil map units, thus leading to a common assumption of ‘‘homogeneity’’ within soil taxa and map units by non-pedologists. However, the purpose of soil surveys is to partition soils and landforms into stratified subsets that are less variable (Soil Survey Division Staff, 1951,

8

H. LIN ET AL.


9

1993). Quantification of map unit purity for different scales of soil maps is an area needing improvements in modern soil surveys (Arnold and Wilding, 1991; Lin et al., 2004). An understanding of how soil maps are made, the map scale involved, and the variability within map units is desirable for integrating pedology with soil physics and hydrology. b. Soil Physics. Soil physics deals with the physical properties of the soil, with an emphasis on the state and transport of matter (especially water) and energy in the soil. Soil physicists view soil as a porous medium through which water, solute, gas, and heat move. Traditionally, soil physicists have emphasized theoretical studies using mathematical models and laboratory investigations using small samples (often 0.05–0.3 m in diameter and height, and often consisting of ground-sieved soil materials instead of intact soils). Small field plots have been used to verify laboratory or theoretical findings or to understand flow and transport processes at a local scale. For instance, most of our present understanding of infiltration stems from theoretical investigations made in the laboratory and on 1 m2 field plots isolated from many of the factors that are relevant in natural and larger area environments. The contemporary challenge is to extend such small-scale understanding to larger domains. In terms of time scale, classical soil physics research has typically been in the order of minutes or hours to days, with few studies lasting months or years. Emphasis in soil physics over the past four decades has shifted from laboratory and local scale processes to the field scale and, more recently, watershed scale transport of water and chemicals (Corwin et al., 1998). The importance of soil heterogeneity across a field and its impacts on soil physical and hydrological properties were recognized by Nielsen et al. (1973) and many others thereafter. However, many of the classical soil physics theories rely on the assumption of uniform and inert porous materials. Complicating physical factors that occur in the field, such as soil heterogeneity, shrink-swell, soil aggregation, and various macropores, often make classical soil physical theories too simplistic or invalid (e.g., Bouma, 2005; Kutı´lek and Nielsen, 1994; Young, 1988). A further inadequacy is that these Figure 5 Classical soil survey block diagrams showing soil mapping based on soil–landscape relationships. Also illustrated are two contrasting cases of landscape hydrology in relation to soil-landscapes. (A) Aerial photo map of a section (6 miles on a side) in Sheboygan Co., WI and a block diagram showing landscape positions of major soils. (From Hole, 1976.) (B) A block diagram showing landscape position of a soil catena on a drumlin and adjacent lowlands in Dodge Co., WI, illustrating water flow direction and dynamic seasonal water table. (From Hole, 1976.) (C) Pattern of soil catena and parent materials in the Clarion-Nicollet-Canisteo association in Kossuth Co., IA. In this soil landscape, water and solutes in the uplands move down into the Clarion soil profile where they either enter the ground water or move laterally until they are discharged further down slope as return flow. (Courtesy of L. Steffen.)

10

H. LIN ET AL.

theories have mostly ignored the effect of temperature gradients on water movement. With the reality of field heterogeneous and structured soils, at least three aspects would warrant a close alliance between soil physicists and pedologists: (1) quantitative soil structural parameters (including macropores) need to be incorporated into the modeling of various types and scales of preferential flow and transport; (2) landscape features should be incorporated into the field and watershed scale soil physical and hydrological models; and (3) scale transfer and input data required for modeling must be adequately addressed. As Kutı´lek and Nielsen (1994) pointed out, models of soil hydrology should be related to the reality of a field or watershed by at least two links: proper characterization of the physical parameters and the scale of the model. They further stressed that ‘‘without properly taken field data all our effort is futile.’’ c. Hydrology. Hydrology deals with the hydrological cycle, including continental water processes at all scales and the global water balance. Hydrologists traditionally often view soil as a more-or-less homogeneous layer at the earth’s surface. The evolution of hydrology has been in the direction of ever-increasing scale, from small catchment to large river basin to the earth system, and from storm event to seasonal cycle to climatic trend (NRC, 1991). Historically, the interest in catchment hydrology has been more related to temporal patterns, in particular, that of streamflow. Because of the focused interest in streamflow (an integrator of spatial responses), hydrologists have managed to avoid confronting the challenges of spatial heterogeneity (Grayson and Blo¨schl, 2000). A similar history is apparent in ground water hydrology, where pumping tests have long provided a measure of integrated aquifer response and thus distracted researchers from the quantification of aquifer heterogeneity (Anderson, 1997). According to Grayson and Blo¨schl (2000), the past few decades have heralded an explosion of interest in spatial variability in hydrology, from the pioneering work on spatial heterogeneity in runoff producing processes during the 1960s and early 1970s (e.g., Betson, 1964; Dunne and Black, 1970a,b), through the development of spatially distributed hydrological models (now often coupled with geographic information system, or GIS) that provide a way to interpret spatial response (e.g., Abbott and Refsgaard, 1996; Beven, 2002), to the ever-increasing capabilities of remote-sensing methods that provide information on state variables of fundamental importance to catchment hydrology (e.g., Jackson and Le Vine, 1996). In view of the enormous variability encountered in field soils, deterministic models of flow and transport processes are giving way to spatially stochastic concepts. In the hydrology community, interest now centers on how to describe the random distribution functions of soil hydraulic properties and the extent of their spatial correlations at various scales. A conceptual framework defining the


11

expectation and variance structure of the spatial and temporal heterogeneity of soil water properties within each soil series or soil map unit from local to global scales is greatly needed (NRC, 1991). Soil physical processes play an important role in the hydrological cycle, because the unsaturated zone is the interface between the atmosphere and the ground water zone. It is important, therefore, that hydrologists have an adequate understanding of soil physical processes so that appropriate theory is used in hydrological models, and that soil physicists continue their endeavors to elucidate the complications that abound in the field and that often vitiate predictions obtained through unmodified classical theory (e.g., Beven, 1986; Kutı´lek and Nielsen, 1994; Young, 1988). In this regard, pedology could assist soil physicists and hydrologists in understanding the natural variability and soil structure in the field. Advances in basic hydrological research will depend on soil hydrological research, field characterizations of soil hydraulic properties, and a close connection of mathematical modeling with adequate field observations. In addition, connecting soil physics, hydrology, and pedology to landscape features would prove to be a meaningful way to integrate the three. For example, topography provides a strong clue to both the hydrological regime and pedological characteristics in a landscape and may help the scaling up or down of observed phenomena. With the increasing availability of the digital elevation model (DEM), along with many other geospatial data and sophisticated computer software, the integration of pedology, soil physics, and hydrology is greatly facilitated. 2.

Debunking Stereotypical Visions and Creating Synergies Through Integration

To truly ally pedology with soil physics and hydrology, there are some stereotypical visions that require debunking: . To many blue-blooded hydrologists and soil physicists, the activities of

pedologists are difficult to judge from a scientific point of view. In their view, pedologists use ‘‘funny’’ names to describe soils and they make too many empirical statements about soil behavior that are not necessarily supported by measurements. On the other hand, pedologists are taken back by the representations of natural soils in terms of homogeneity and isotropy that soil physicists and hydrologists often make in their models, which to pedologists clearly do not reflect real conditions being experienced in the field. . Pedology has its roots in soil survey, which considers landscape processes and soil structure descriptions that have been somewhat neglected in the period in which soil classification received the most attention. These two aspects are critical for soil physics and hydrology to improve their

12

H. LIN ET AL.

characterization of flow regimes in the field. At the same time, pedologists can benefit from flow theories in soil physics and hydrology when transforming their qualitative descriptions into quantitative expressions that are increasingly necessary to respond to the demands from diverse uses of soil survey information and to provide inputs to environmental policy decision-making. The developments of pedology, soil physics, and hydrology are now converging at several fronts, including, for example, (1) facing the field reality, (2) addressing spatial-temporal variability across scales, (3) understanding the various processes involved, and (4) using quantitative modeling appropriately. These common grounds lead to the synergies that could be expected out of integrating the three disciplines, as suggested by recent literatures and professional activities (e.g., Lin, 2003). For example, combining pedological and hydrological expertise can be particularly attractive when presenting soils information to interdisciplinary panels and teams formed to address complex global environmental issues and policies (Bouma, 2005). Pedology is a rich discipline that has been overshadowed in the past by the descriptive and qualitative approaches. But pedology has much to offer to soil physics and hydrology, and vice versa. For instance, soil mapping provides the classical foundation for our understanding of soil variation over the landscape and its underlying causes; soil profile descriptions have been the major source of information on in situ soil structure and various soil hydromorphological features that are signatures of soil hydrology; soil survey databases provide a wealth of information that soil physicists and hydrologists could use in their modeling; soil classification offers a hierarchical system for organizing, modeling, and transferring our knowledge about different soils around the globe; and soil genesis provides insights regarding soil–landscape evolution over time. On the other hand, soil hydrology is a major driving force behind pedogenesis, soil morphology, and soil distribution. It controls a variety of soil physical, chemical, and biological processes that lead to the formation of different soils and diverse land uses. Soil moisture regimes play a critical role in classifying soils, and the spatial-temporal distribution of water provides clues regarding soil variability and mapping. Furthermore, with increasing emphasis on human impacts and land management practices, the rising interest in dynamic soil properties would require more attention to soil physical and hydraulic properties and their relations to soil taxonomic or map units. Hence, many knowledge gaps may be closed by integrating classical pedology, soil physics, and hydrology. Such examples include the following: . soil structure quantification and modeling its impacts on flow and trans-

port processes; . preferential flow prediction at different scales and the determination of its

mechanisms and patterns;


13

. soil hydromorphology quantification and its relations to soil hydrology

(such as water table fluctuation); . water movement over the landscape and its relations to the soil cover; . soil–water mapping and soil–landscape modeling across scales; . soil spatial-temporal variability and the underlying causes, and pattern

identifications and predictions of various soil and hydrological properties and processes; . scale bridging from laboratory to field, landscape, region, and globe; . data bridging through approaches such as pedotransfer functions, including the understanding of the fundamental mechanisms and practical enhancements.

B. HYDROPEDOLOGY The promotion of hydropedology as a new interdisciplinary field suggests a renewed perspective and a more integrated approach to studying landscape–soil–water interactions across spatial and temporal scales. Although many topics related to hydropedology have been studied considerably in the past, many unresolved issues remain and future opportunities abound. From a historic development point of view, some major activities are acknowledged here. Kutı´lek (1966, 1978) recognized the need for combining soil physical theories with theories of soil genesis. He indicated that we could not deal with soil hydrology and soil physics without considering the soil properties within the complexity of soil genesis. Later, Kutı´lek and Nielsen (1994) suggested that the objectives of hydropedological studies are similar to soil hydrology by a broad spectrum of scales, ranging from the soil pore scale up to the regional and soil mapping scale. Bouma and Hole (1971) and Bouma and Anderson (1973) started the investigations of the relationships between field soil structure and hydraulic conductivity and suggested that soil morphometric analysis has a specific function in improving field estimates of soil hydraulic conductivity. Thereafter, Bouma published extensively on linking soil morphology to soil physics (e.g., Bouma, 1984, 1990, 2005). Fritsch and Fitzpatrick (1994) used a pedo-hydrological method to construct conceptual landscape–soil–water models that linked soil–landscape features to soil– water processes, with an emphasis on soil–water flow systems and soil-forming/soil-change processes. Galusky (1997) and Galusky et al. (1998) referenced hydropedology as relating soil morphological indicators to water table behavior. Rabenhorst et al. (1998), in a collective work done by pedologists in the 1990s, suggested that soil morphological features could be quantitatively linked to hydrological or biogeochemical parameters associated with soil wetness. Richardson and Vepraskas (2001) further demonstrated that soil morphology is a valuable field tool for evaluating soil hydrology.

14

H. LIN ET AL.

In the following sections, we further develop the concept of hydropedology that builds upon the strengths of historic developments as well as modern scientific and technologic advances.

1.

Back to the Essence of Natural Soils

‘‘What is soil?’’ To answer this seminal question, a good understanding and appreciation of this gift from nature and its essential characteristics in the landscape are required. In the context of hydropedology, we suggest that the following three aspects warrant a close examination—all three point to the essential differences between natural soils and engineered soil materials or other types of porous media, and all three are in the domain of pedology. 1. Pedality: Peds are naturally formed soil aggregates (heterogeneous masses of solid particles bound together) with various strengths, sizes, and shapes (e.g., strong, very fine granular or weak, coarse prismatic) (see illustrations in Fig. 3). Ped strength, size, and shape combined are termed pedality. These are soil structural features routinely described by pedologists in the field. Pedality, along with the interrelated pore space, is the natural soil ‘‘architecture’’ (soil structure) that is influenced by the five interacting soil-forming factors at the landscape scale and is governed by interrelationships between inorganic-organic constituents and physical, chemical, and biological processes at the meso- or microscopic scales. A hierarchical organization of soil structure (Fig. 6) seems to be characteristic of most soils, where larger aggregates are often composed of an agglomeration of smaller aggregates (Soil Survey Division Staff, 1993; Tisdall and Oades, 1982). Water and chemical movement or retention, mineral weathering or synthesis, plant root or insect activities, and microorganism habitats all are influenced strongly by such soil architecture. Indeed, the word ‘‘ped’’ is well reflected in the term ‘‘pedology.’’ This first essence of natural soils may be summarized as the need to get ‘‘back to soil structure.’’ 2. Layering of soil horizons: Pedons are three-dimensional (3-D) bodies of soil showing an arrangement of soil horizons that are the results of soil-forming processes over time. Various kinds and thicknesses of soil horizons and how they organize in soil profiles reflect long-time pedogenesis and the past and current landscape processes (Figs. 4 and 5). Soil horizonation is the basis for soil classification and mapping. In the U.S. Soil Taxonomy, 18 diagnostic surface horizons and 30 diagnostic subsurface horizons have been identified, along with many additional diagnostic features (Soil Survey Staff, 1999). The fact that natural soils are layered has three significant implications for soil physics and hydrology: (1) any


15

Figure 6 Structure at different scales in a silty soil obtained with different instruments. The fields-of-view differ by about one order of magnitude. Top left: photograph of a vertical profile. Top middle: X-ray tomography of the A-horizon, resolution 0.5 mm/pixel (pores are dark). Top right: X-ray micro-tomography, resolution 0.04 mm/pixel (pores are dark). Below, the images are segmented into the structural units at the corresponding scales. Bottom left: two different horizons. Bottom middle: dense aggregates (gray) within a loose matrix (white) and a few macropores (black). Bottom right: pores (gray) within a porous matrix. (Modified from Vogel and Roth, 2003.)

interface between soil layers slows down water downward movement; (2) soil layering promotes lateral flow, especially in sloping landscapes with water-restricting layers underneath; and (3) soil horizons of different textures and structures often lead to preferential flow. This second essence of natural soils may be summarized as the need to get ‘‘back to the field.’’ 3. Soil–landscape relationships: Landscape is the portion of the land surface (a population of landforms) that human eyes can comprehend in a single view (Ruhe, 1969, 1975). Like landscape architecture, the word ‘‘landscape’’ emphasizes a visual aspect, so it is important that we glance around the surroundings when dealing with soil and water issues in the field. Landscape encompasses soil, water, vegetation, topography, geomorphology, geology, climate, human activities, and other factors. Landscape evolution has a lot to do with the throughflows of water, chemicals, and energy (i.e., the hydrological cycle) and the movement of soils,

16

H. LIN ET AL.

sediments, and other materials. Soil distribution over the landscape is closely related to landforms and geomorphological processes (Fig. 5). Soil–landscape relationships are the foundation for mapping and modeling soils. Depending on which soil-forming factor might dominate in a given region, there are climosequences, biosequences, toposequences, lithosequences, or chronosequences of soil distributions over the landscape at a given scale. As human and hydrological impacts on soil distribution are being increasingly recognized, there are also anthroposequences (i.e., related soils that differ primarily due to the influence of humans such as land uses) and hydrosequences (similar to the concept of catena but with a focus on water as the dominant factor, and its scale is more than hillslope). A systematic understanding of soil–landscape relationships would facilitate the formulation of conceptual and mathematical models of landscape hydrology. This third essence of natural soils could be summarized as the need to get ‘‘back to the landscape.’’ 2.

Catalysts for Promoting Hydropedology

Three factors seem to be the catalysts for promoting hydropedology at the present time. These factors, however, are not unique to hydropedology; rather, they reflect the global trends in modern environmental scientific research and integrated natural resources management: 1. Interdisciplinarity and systems approach: It is well recognized that the progress of science depends increasingly on an advanced understanding of the interrelationships among different fields and their components (AAAS Council, 2001). A number of recent reports of the U.S. National Research Council have highlighted the significance of integrated soil and water studies in the context of agriculture (NRC, 1993a), ground water vulnerability (NRC, 1993b), watershed management (NRC, 1999), earth sciences (NRC, 2001a), water resources (NRC, 2001b), and environmental sciences (NRC, 2001c). In addition, watershed approaches to natural resources management have become a dominant concept at both local and landscape scales. Watersheds are the logical features in ecosystems within which to consider the integration of soil, water, landscape, agricultural and forest productivity, and social-economic factors. To address diverse soil and water issues at various spatial and temporal scales, bridging traditional pedology with soil physics, hydrology, and other related disciplines is necessary as well as synergistic. This bridging is justified not only by the interrelationship among these disciplines but also by the complex nature of the problems. 2. Landscape perspective and multiscale bridging: Over the past decades, there has been a significant increase in the number of field studies


17

conducted at the landscape or watershed levels in order to study processes at a scale relevant to the issue at hand (such as nonpoint source pollution, precision agriculture, sustainable land management, wetland protection, and watershed management). For instance, the development of site-specific farming forces researchers to address questions at the larger scale rather than at the small plot scale that most classical agronomic researchers use (van Kessel and Wendroth, 2001). With landscape perspective comes the need to address inherent variability in the field and scale transfer from laboratory or small plot to the larger field and watershed scales, as well as the requirement of meaningful experimental design and data analysis that take into account the spatial ‘‘scale triplet’’ (i.e., spacing, support, and extent) (Blo¨schl and Sivapalan, 1995) and corresponding temporal variability (sampling time interval, smoothing or averaging interval, and length of record) (e.g., Blo¨schl and Grayson, 2000). The controlling factors by which abiotic and biotic processes occur along the continuum of a landscape should also be taken into account in modeling and prediction. Translating information about soil and hydrological properties and processes across scales has emerged as a major theme in contemporary soil science and hydrology (e.g., Hoosbeek et al., 1998; Pachepsky et al., 2003). 3. Advancements in geospatial technologies and computer modeling: The era of information technologies has had a significant impact on modern soil science and hydrology. Especially relevant is the increasing availability and wide use of geospatial technologies (including GIS, global positioning systems [GPS], and remote sensing) and simulation modeling. For example, an integrated system of a geospatial database coupled with solute transport modeling has been widely sought to address nonpoint source pollution (e.g., Corwin et al., 1999). Spatially distributed hydrological modeling has become easier to use, and visualization of the results has greatly improved. However, there has been little change in the concepts on which the models are based and the ways in which they are calibrated and used (Beven and Feyen, 2002). A sharp increase in the quality and quantity of geospatial data, including voluminous remote-sensing images, coupled with improved data-mining tools and enhanced database management and distribution systems, will significantly improve our abilities to analyze the vast amount of environmental data being collected and stored. In addition, new concepts of nonlinear dynamics (such as fractals, chaos, and fuzzy logic) and new tools (such as spatial-temporal geostatistics, neural networks, and uncertainty analysis) will help improve the extraction of useful information out of large databases. Such advancements make it feasible to integrate pedology, soil physics, and hydrology to understand spatially variable and temporally dynamic processes at various scales.

18

H. LIN ET AL.

3.

Domain and Characteristics of Hydropedology

Bearing in mind the aspects discussed previously, we venture to suggest the domain and characteristics of hydropedology to include the following: . Landscape–soil–water systems: Taking a holistic view of the landscape,

with the root in pedology and a focus on water as a driving force, hydropedology emphasizes the system linkages, the state and pattern of its component parts, interfacial fluxes, and dynamic changes including those caused by human activities. . Soil–water interactions across spatial-temporal scales: Hydropedology attempts to characterize integrated physical, chemical, and biological processes of soil–water interactions at all scales, including the transport of chemicals and energy by the water flow, and the interrelationships between soil distributions and hydrologic and geomorphic processes. As illustrated in Fig. 2, hydropedology, in combination with hydrogeology in the vertical dimension, suggests an integrated approach to studying the interactions of solid earth (soil and rock) and water. Soil investigations should no longer be limited to the top 2 m of the earth’s surface but extend well into the deeper vadose zone (including the contact zone with the aquifer and perhaps the fluxes within that aquifer to the extent that they affect the dynamic level of the water table). Hydropedology thus requires a concerted effort to study the soil and underlying material to whatever depth is needed to meet our scientific needs. Geologists are extending their investigations to the surface and are including the biosphere and surficical processes, so it is paramount that soil scientists redirect their efforts to interface with other geoscientists in making hydropedology an earth, environmental, ecological, and agronomic science. In the horizontal perspective (Fig. 2), as a bridge connecting pedology, soil physics, and hydrology, hydropedology integrates the pedon and landscape paradigms to link phenomena occuring at the microscopic scale (e.g., pores and aggregates) to mesoscopic (e.g., pedons and catenas) and macroscopic (e.g., watersheds, regional, and global) scales. Hydropedology brings the landscape back to pedology, which has been lost a bit in the soil classification frenzy. In terms of time dimension, hydropedology deals with both short- and long-term characterizations of landscape–soil–water systems, from hourly to annual and to geological time scales, in order to systematically understand the role of soils in the hydrological cycle and the role of hydrology in pedogenesis, soil morphology, soil survey, pedodiversity, and biogeochemical processes.


4.

19

Four Challenges to be Addressed

There are four overarching themes along the philosophy of ‘‘bridging disciplines, scales, data, and education’’ that hydropedology attempts to address: 1. Good science is not enough; we need useful science as well. While great strides have been made in soil and water sciences in the last century, several critical areas still badly need to be further studied, including scaling issues and human impacts (Hornberger and Boyer, 1995; NRC, 1999). Related to scaling are the complex spatial heterogeneity and temporal dynamics and our limited understanding of landscape–soil–water processes across scales. On the other hand, it is believed that much of the science and technology needed to provide the underpinnings necessary for integrated soil and water management already exists. We have, however, fallen short in effectively interacting with stakeholders and in translating our understanding of soil and water systems and the benefits of integrated management into action (NRC, 1999). Integrated soil and water sciences in general have yet to develop effective interfaces between what we know and how we deliver that knowledge. Bouma (2005) suggested a ‘‘joint learning’’ approach that is essential for such interactive processes. 2. Most current computer models are either ‘‘too good to be real’’ or ‘‘too real to be good.’’ In the first case, oversimplification undercuts the accuracy or generality of the results. In the second case, the need for detailed input data renders the model impractical to apply except in a research setting. Nevertheless, we recognize that no ideal model exists. Thus, compromises between the quest for perfection and the complex reality, compounded by our limited knowledge and/or data, plus natural uncertainty, are facts of life. Therefore, there is a need for elegant and robust models that can be based on reliable existing data (NRC, 1999). It is also becoming clearer that hierarchical approaches may be effective ways to incorporate scales into models (e.g., Cushman, 1990; Lin and Rathbun, 2003; Vogel and Roth, 2003). 3. Data rich, information poor. The term ‘‘information’’ here connotes interpretation, synthesis, and utilization of data. The problem is largely due to data fragmentation, incompleteness, incomparability, or inaccessibility in spite of past extensive and costly data collections. However, it has been pointed out that reliable long-term monitoring of data across disciplines is perhaps the most fundamental in terms of overarching research needs in the earth’s critical zone (e.g., Hornberger and Boyer, 1995; NRC, 1991, 1999, 2001a; Sposito and Reginato, 1992). On the other hand, advances in automated sampling and analytical equipment, new remote sensing and GPS, and computer models tend to change the perceived ‘‘data crisis’’ of the past into a ‘‘data avalanche’’ for the future, burying scientists and

20

H. LIN ET AL.

stakeholders alike (Bouma, 1999). Therefore, we need to develop better database management, analysis, and distribution systems for effective archiving, comparing, analyzing, visualizing, and modeling of collected datasets. There is also a need for innovative data mining and knowledge discovery methods and tools that can intelligently transform database into useful information. 4. Inspiring classroom teaching is influential; effective public education is equally critical. New technologies have created many teaching and learning challenges as well as excitements. The fusion of GIS, remote sensing, computer modeling, multimedia, and the Internet into the classroom will become more prominent. On the other hand, if we are to put wise soil and water management practices into action, to enhance the image of our profession, to increase the necessary funding for basic and applied research, and to implement collaborative interdisciplinary efforts in environmental research and policy making, public education is essential. Scientists can and ought to help improve the transfer of knowledge about soil and water resources to our stakeholders and assist in promoting the public’s awareness and appreciation of the land and water ethics. 5.

Fundamental Scientific Issues of Hydropedology

We believe that the fundamental scientific issues of hydropedology could be summarized in the following four interrelated areas: 1. soil structure and layering as indicators of flow and transport characteristics in soils; 2. soil morphology as signatures of soil hydrology; 3. water movement over the landscape in relation to soil cover; 4. hydrology as a factor of soil formation and a driving force of dynamic soil system. We now elaborate each of these fundamental issues and their related applications in the following sections.

III. FUNDAMENTALS AND APPLICATIONS OF HYDROPEDOLOGY A. SOIL STRUCTURE AND LAYERING AS INDICATORS OF FLOW AND TRANSPORT CHARACTERISTICS IN SOILS The term soil structure has been used in U.S. soil surveys, and elsewhere, to refer to the natural organization of soil particles into individual units (peds) separated by planes of weakness (Fig. 3). In addition to the shape,


21

size, and grade of peds, the internal surface features of peds are also described, consisting of (1) coats of a variety of substances unlike the adjacent soil material and covering part or all of the surfaces, (2) concentration of material on surfaces caused by the removal of other materials, and (3) stress formations in which thin layers at the surfaces have undergone reorientation or packing by stress or shear (Soil Survey Division Staff, 1993). The structural surface features include clay films, clay bridges, sand or silt coats, other coats, stress surfaces, and slickensides. All of them have significant impacts on flow and transport processes in field soils. Pores are considered separately in the U.S. soil survey’s concept of soil structure; however, in Europe, Canada, Australia, and some other countries, pore-related features (e.g., pore-size distribution, connectivity, and turtuosity) are an integral part of soil structure (e.g., Brewer, 1976; Hodgson, 1997; McKeague et al., 1986). Thus, the U.S. concept of soil structure is sometimes referred to as ‘‘pedality.’’ Pedality and soil pore space are interrelated, but many soils have interpedal, intrapedal, and/or transpedal pores that are not necessarily represented by pedality. These pores, formed by biological activities (e.g., root channels and worm borrows), physical processes (e.g., desiccation cracking and freezing-thawing), or chemical reactions (e.g., dissolution or binding of soluble chemicals and organic matter), are critical in determining flow and transport in field soils. Therefore, in the context of hydropedology, we use the term ‘‘soil structure’’ to encompass both pedality and pore space. Because soil structure generally refers to a specific soil horizon, soil layering is thus treated separately here to reflect the overall organization of a soil profile. Soil structure and layering must be adequately addressed when measuring, modeling, and interpreting hydrological processes and properties in field soils. For example, by visualizing flow patterns in soils using dye-staining techniques, Bouma (1992), Flury et al. (1994), Lin et al. (1996), and many others have demonstrated for a large variety of soils that structural units are critical (Fig. 7; See Color Insert). That natural soils are structured to various degrees at different scales seems to be the rule (Fig. 6), whereas the existence of a macroscopic homogeneity seems to be the exception (Vogel and Roth, 2003). Indeed, it is the natural structure that reveals the essential difference between field soils and disturbed soil materials.

1. Soil Structure Formation and Representative Elementary Volume for Measuring Soil Hydraulic Properties Pedological processes produce heterogeneity and usually enhance discontinuities inherited from parent materials. One important result of pedogenesis is the formation of various soil structures under the influence of various soil-forming factors (Figs. 3 and 4). Strength and expression of soil structure

22

H. LIN ET AL.

generally increase with time. However, quite disparate processes are responsible for soil structure formation, and each of them may introduce a scale of its own. Examples for such processes are formation of organo-clay complexes, desiccation cracks, animal burrows, plant root channels, and the formation of landscape-scale soil structural features due to either topography (such as depressions) or biological and/or chemical processes (such as calcic pipe or tree pipe). Pedality is usually expressed strongest at the surface and decreases with depth, resulting in a general trend of increasing ped size and decreasing ped grade with soil depth. However, tillage and other human activities could significantly alter soil structure. Changing soil moisture also changes the expression of soil structure (especially in shrink-swell soils) and the relative volumes of peds and pores, adding to the complexity of finding mathematical solutions for modeling water movement in structured soils. The concept of the representative elementary volume (REV) is well known (Bear, 1972). However, a long-lasting question remains: What is the actual size of REV for various soils? We believe that the REV for measuring soil hydraulic properties should be a function of soil structure: the larger the peds, the larger the REV. Standard fixed sample volume for diverse soils could lead to incomparable data. For example, Anderson and Bouma (1973) showed that any Ksat could be measured in the Bt2 of a Wisconsin Hapludalf by varying the height of the sample. This was due to the well-developed blocky structure in that soil horizon, where vertical continuity of the cracks between the peds decreased as the sample became longer, resulting in lower Ksat values even though the sample was completely saturated in all cases. This suggests that soil structure is essential for choosing proper REV for measuring Ksat and other soil hydraulic properties. Some studies (e.g., Cushman, 1990; Vogel et al., 2002) have suggested a discrete hierarchy of the REV, where the REV is a local property related to a given level of soil structural unit (Fig. 8). This is consistent with the hierarchical organization of soil aggregates (Fig. 6) that is characteristic of most soils (e.g., Oades and Waters, 1991; Tisdall and Oades, 1982).

2.

Preferential Flow in Relation to Soil Structure and Layering

Preferential flow is the process whereby water and dissolved chemicals move by preferred pathways at an accelerated speed through a fraction of a porous medium. Preferential flow encompasses macropore flow (also called bypass flow or short-circuiting), funnel flow, fingering, and others. Vervoort et al. (1999) suggested that preferential flow might be related to soil structural differences (macropore flow and fractional flow) or textural differences (fingering flow and funnel flow). Nieber (2000) grouped preferential flow into macropore flow, gravity-driven unstable flow, heterogeneity-driven

ADVANCES IN HYDROPEDOLOGY Figure 8 Different concepts of scales and spatial heterogeneity in the unsaturated zone: (A) A conceptual integrated-system model in pedology. (From Wilding, 2000.) (B) Five quantitative models in hydrology/soil physics: (1) macroscopic homogeneity (thin line), (2) discrete hierarchy (dashed line), (3) continuous hierarchy (dashed dotted line), (4) classical fractal (thin straight line), and (5) multi-fractal (thick straight lines). (Modified from Vogel and Roth, 2003.)

23

24

H. LIN ET AL.

flow, oscillatory flow, and depression-focused recharge. Hendrickx and Flury (2001) recognized the scale dependence of preferential flow and classified preferential flow mechanisms into three scales: pore scale, Darcian scale, and areal scale, with each scale having a distinct conceptual and physical basis (Fig. 7 and Table I). The presence of preferential flow in soils leads to spatial concentration of water flow through unsaturated soil that is not well described by Darcy’s approach to flow through porous media. Various pedological features are indicative of possible preferential flow in field soils, especially if combined with landscape observations (Figs. 4, 7, 9, and 10) (See Color Insert). For example, these include (1) soil structural features (such as pedality, coatings, macropores, and slickensides); (2) sloping lamellae (suggesting funnel flow likely to occur); and (3) lithologic discontinuities (indicating significant changes in particle size distribution or mineralogy and thus possible finger flow or other types of preferential flow). A simple field technique was devised by Bouma (1997) to measure bypass flow as a function of rain intensity and quantity and soil moisture content. Deriving cracking patterns from theoretical soil swell-shrink characteristics turned out to be impossible, and very small pores (such as slickenside fissures shown in Fig. 7B), with a volume that cannot be measured with physical methods, can conduct large volumes of water (Bouma, 2005; Lin et al., 1996). A procedure is attractive whereby macropores are first morphologically studied in the field in terms of types, sizes, and vertical continuity, preferably also functionally characterized by dye-staining. Next, such data can be fed into models whereby bypass flow is incorporated as a separate module into existing physical flow models. This represents an effective combined procedure of hydropedology that avoids purely qualitative descriptions by soil morphology and not realistic model representations by soil physics (e.g., Hoogmoed and Bouma, 1980). Significant progress has been made in the past two decades or so in understanding preferential flow. However, our ability to predict preferential flow dynamics, velocity, and pathway is unsatisfactory (Jury, 1999). Quantitative and scalable relationships between preferential flow and soil structure/ texture/layering remain elusive. Although numerous simulation models for water and chemical movement in soils have been developed, few models have been tested with adequate field data at multiple scales, especially in combined macropore–micropore systems. Several approaches that have been taken to incorporate preferential flow into models include (1) the mobile and immobile water concept, which was the first attempt to deal with the problem (van Genuchten and Wierenga, 1976), later elaborated into dual-porosity, dual-permeability, and multiregion approaches (e.g., Ahuja and Hebson, 1992; Gerke and van Genuchten, 1993; Gwo et al., 1995; Othmer et al., 1991); (2) the kinematic wave approach, which was used by German and Beven (1985, 1986) to describe water flow in macropores that

Table I Three General Scales of Water Flow in Soils and Their Relations to Soil Structure and Preferential Flow

Spatial scale

Temporal Conceptual scale model

Governing equation

Pore (Microscopic)

Seconds Fluid Hagento days continuum Poisseuille’s law

pR4 DP Q¼ 8Lh

Pedon (Mesoscopic)

Hours to Repremonths sentative volume

Landscape Days to (Macroyears scopic)

Mass balance

z

Macropores, fractures (Basic fabric, pedality)

Preferential flow Macropore flow, film flow

Critical parameters

Major measurements{

Pore diameter, Thin section, fracture NMR, CT width

Fingering Hydraulic Soil columns, soil Soil profile (unstable conductivity, profiles, small description, flow), funnel hydraulic plots (Horizons, Ksat, TDR, flow gradient trans-layer pore tensiometer connectivity) Precipitation, Weather, Funnel flow, P þ I = R þ ET þ Hillslopes, infiltration, topography, depressionlandforms (Variability D þ DWk drainage, vegetation, focused flow, within and cross water table, geology, pipe flow soil map units, soilGPR, antecedent landscape structural remote soil moisture features) sensing

DarcyBuckingham’s Jw ¼ law

Mass conservation law

Domain features (soil structure)

@H x KðhÞ @z


Classical physical law

{

NMR: Nuclear Magnetic Resonance; CT: Computer-assisted Tomography; TDR: Time Domain Reflectometry; GPR: Ground Penetrating Radar. Q is the volume of water flowing through a cylindrical tube of radius R per unit time, DP is hydrostatic pressure difference across the length L of the cylindrical tube, and h is water viscosity; x Jw is water flux density (also called specific discharge), K(h) is the unsaturated hydraulic conductivity as a function of soil water matric potential h (in head unit), H is hydraulic head, and z is vertical distance in soil profile; k P is precipitation (including dew and frost), I is irrigation water, R is surface runoff, ET is evaportranspiration, D is drainage or deep percolation, and DW is the water storage change in the soil profile. (Sources: Hendrickx and Flury, 2001; Lin and Rathbun, 2003). {

25

26

H. LIN ET AL.

allows flow down the sides of the pores that are not filled with water; (3) the transfer function model, based on the probability density function of solute travel time through a soil unit that was suggested by Jury (1982) and Jury et al. (1986); (4) the morphometric approach, based on soil morphology and dye-staining that was used by Bouma (1984, 1989, 1990) to model bypass flow, in which subprocesses were defined, including vertical infiltration at the soil surface, lateral infiltration from the macropores into the matrix, and internal catchment from discontinuous macropores; (5) the functional models, generally capacity type approaches, which have been developed to work with relatively simple preferential flow models that require only a few parameters (e.g., Addiscott, 1977; Corwin et al., 1991); and (6) a single-porosity model that distinguishes between actual and equilibrium water contents, which was proposed by Ross and Smetten (2000). Sˇimunek et al. (2003) reviewed various models for describing preferential or nonequilibrium flow and transport in the vadose zone. They stressed the need for intercode comparison, especially against field data.

3.

Quantification of Soil Structure and Fractal Scaling

Traditionally, soil structure has been evaluated by pedologists in the field using morphological descriptions or thin sections (Fig. 3), while soil physicists have employed wet and dry sieving, elutriation, and sedimentation to conduct aggregate analysis, aiming to measure the percentage of water-stable aggregates in the soil and the extent to which the finer mechanical separates are aggregated into coarser fractions. In the absence of direct quantification, soil structure has also been frequently evaluated by methods that correlate it to the properties or processes of interest (such as Ksat, water retention, infiltration rate, and gas diffusion rate). In recent years, noninvasive methods that permit soils to be investigated without undue disturbance of their natural architecture and that allow 3-D visualization of internal soil structure and its interactions with water have become increasingly attractive. These methods include X-ray computing tomography, soft X-ray, nuclear magnetic resonance, gamma-ray tomography, and others (e.g., Anderson and Hopmans, 1994; Perret et al., 1999). Image analysis has brought new opportunities for analyzing soil structure, especially that of the pores, their sizes, shapes, connectivity, and tortuosity (e.g., Vervoort and Cattle, 2003; Vogel et al., 2002). Although numerous attempts have been made to find either statistical relations or deterministic links between soil structural data and hydraulic properties, a great need still exists to relate in situ soil structure to field soil hydraulic properties across scales. Soil structure has been suggested as having fractal characteristics, meaning self-similarity over a range of scales (e.g., Anderson et al., 1998; Bartoli


27

et al., 1998; Perrier et al., 1999). Yet it is not obvious that soils should exhibit fractal properties. As Vogel and Roth (2003) pointed out, the self-similarity of soil structure would suggest some self-similarity in the formation of soil structure. However, quite different processes are generally involved in soil structure formation; each of them may introduce a scale of its own. Nevertheless, fractal mathematics (geometrical fractals or probabilistic fractals; cf. Baveye and Boast, 1998) has been applied to soil particle size, aggregate size, and pore size, as well as water retention, hydraulic conductivity, preferential flow, and other soil properties (e.g., Baveye et al., 1999; Pachepsky et al., 2000). The fundamental equation applying to all fractals is the following number–size relationship (Mandelbrot, 1982): NðrÞ ¼ kr

D

ð1Þ

;

where N(r) is the number of elements of size equal to r (unit length, or yardstick), k is the number of initiators of unit length, and D is the fractal dimension. A log-log plot of N(r) vs r yields a straight line (see Fig. 8). In spite of an impressive body of literature on fractal applications in soil science (particularly related to soil structure and hydraulic properties), this field of research still seems in its infancy (Baveye and Boast, 1998). One way to quantitatively model soil structure is to identify structural units as form-elements (Vogel and Roth, 2003). For example, at the intermediate scale in Fig. 6 (middle), three structural units could be identified: ‘‘dense aggregates’’ (gray), ‘‘loose matrix’’ (white), and ‘‘macropores’’ (black). Perrier et al. (1999) proposed a generalized approach to modeling soil structure, called the pore-solid-fractal (PSF) model, which is shown to exhibit either a fractal or nonfractal pore surface depending on the model parameters. In the PSF model, the fractal dimension, D, is expressed as D ¼ d þ logð1

P

SÞ=logn;

ð2Þ

where d is a given Euclidean dimension, P is the proportion of pore phase (like ‘‘macropores’’ in Fig. 6), S is the proportion of solid phase (like ‘‘dense aggregates’’ in Fig. 6), and n is the inverse of the similarity ratio. A third phase, labeled as fractal (F) phase (like ‘‘loose matrix’’ in Fig. 6, where P þ S þ F ¼ 1), is the proportion for the next stage of partitioning that exhibits a selfsimilar manner. With the exception of two special cases corresponding to a solid mass fractal and a pore mass fractal, the PSF model displays symmetric power law or fractal pore size and solid size distributions (Perrier et al., 1999). 4.

Soil Layering and Lateral Flow

Soil layering has three significant hydrological implications, as described in Section II.B.1. Soil layering, especially restrictive horizons, promotes lateral flow and/or preferential flow that are often poorly represented in

28

H. LIN ET AL.

hydrological models. The occurrence of slowly permeable or irregular subsoil horizons or geological formations also could strongly alter flow patterns. Hence, pedology can have important inputs to improve hydrological models by taking into account soil layers and landscape features. For example, numerous water restrictive soil horizons and features have been identified in Soil Taxonomy, such as fragipan, duripan, ortstein, petrocalcic, petrogypsic, and placic diagnostic horizons, as well as petroferric contact, densic contact, glacic layer, lithic contact, and paralithic contact (Soil Survey Staff, 1999). Other subsoil horizons might also gradually develop such that they increasingly act as an aquitard or aquiclude to downward moving water, ultimately resulting in water moving laterally within the soil as subsurface throughflow (Johnson and Hole, 1994). Stratified or dense geological materials (such as glacial till) also often set up a hydrologically restrictive layer that results in a perched near-surface water table and lateral water movement. Another type of lateral flow is caused by hydrophobicity. Dekker et al. (1984) showed that assumed lateral flow of water on top of a compact spodic subsurface horizon in the Netherlands did not occur but that lateral movement of water was due to surface runoff originating from hydrophobicity of the soil surface. Extensive field studies have shown that many soils are susceptible to hydrophobicity under dry conditions, although land use history is another important factor (e.g., Dekker and Ritsema, 2003). Soil survey can provide useful information regarding soil hydrophobicity. The change in soil hydraulic properties over the boundary of soil layers is also noteworthy. Jury and Roth (1990) and Hamlen and Kachanoski (1992) showed that the correlation of hydraulic properties across horizon boundaries is one of the key impediments to modeling realistically solute transport at the soil profile scale. Deurer et al. (2003) found that the scaling factors of measured soil water characteristic functions have a distinct pattern across the soil horizon boundaries. In layered or stratified parent materials, water flow and storage in the landscape, and the consequent formation of wetlands, are influenced by discontinuities in soil hydraulic properties between layers (Mausbach and Richardson, 1994; Richardson et al., 1992).

B. SOIL MORPHOLOGY AS SIGNATURES OF SOIL HYDROLOGY Soil morphology is the basis for mapping soils in the landscape, classifying soils into taxonomic categories, and interpreting soil genesis. Soils record spatial and temporal distribution and circulation of water because actions of water on soils result in the formation of distinctive morphological features. Soil morphology reflects both profile hydrology and landscape hydrology by integrating soil changes over time. Soil horizons, for instance, often develop


29

in response to water movement (such as leaching or accumulation of certain materials). A subset of soil morphological characteristics, known as ‘‘hydric soil indicators’’ (USDA-NRCS, 1998), are directly related to a specific set of hydrological parameters. Soil morphology is also the testimony of long-term persistent flow and transport processes occurring in nature, resulting in visible pedological features such as clay films, tonguing, plinthites, and diverse soil structures that are hydrologically significant features (Figs. 4, 7, 9, and 10). Soil macro- and micromorphology thus have long been used to infer soil moisture regimes and hydraulic properties and to provide a basis for hydrology-related soil genesis and classification (e.g., Bouma, 1992; Lilly and Lin, 2005). With growing public interests in wetlands and hydric soils, several works (e.g., Rabenhorst et al., 1998; Richardson and Vepraskas, 2001; USDANRCS, 1998; Vepraskas and Sprecher, 1997) have underlined the importance of using soil morphology in interpreting soil hydrology. In a historic review of redox features in relation to soil moisture, Veneman et al. (1998) indicated that much of the early work was largely qualitative, followed by efforts to quantify environmental observations with soil morphological features, leading to current efforts to understand pedogenical processes in the genesis of seasonally wet soils, and to link soil morphology to quantifiable hydrological or biogeochemical parameters associated with soil wetness (Rabenhorst et al., 1998).

1.

Redox Features for Identifying Aquic Conditions and Hydric Soil Indicators

Soil hydromorphology deals with soil morphological features (especially redoximorphic, or redox, features) caused by water and their relations with soil hydrology. Redox features (formerly called mottles and low-chroma colors) are formed by the processes of alternating reduction-oxidation due to saturation-desaturation and the subsequent translocation or precipitation of Fe and Mn compounds in the soil (Soil Survey Staff, 1999). Types of redox features include (see illustrations in Figs. 9 and 10) (1) redox concentrations as accumulations of Fe/Mn oxides (e.g., nodules, concretions, masses, and pore linings), (2) redox depletions as low-chroma ( 2) features formed by removal of Fe oxides (including Fe depletions and clay depletions), (3) reduced matrix that changes color upon exposure to air due to Fe(II) oxidation to Fe(III), and (4) a reaction to an alpha, alpha-dipyridyl solution if the soil has no visible redox features (Soil Survey Staff, 1999; Vepraskas, 1992). Redox features, which are usually considered hydric soil indicators, exclude those hydric soil indicators composed of carbon and sulfur (USDA-NRCS, 1998).

30

H. LIN ET AL.

Hydric soils are defined as soils that formed under conditions of saturation, flooding, or ponding that lasted long enough during the growing season (repeated periods of more than a few days) to develop anaerobic conditions in the upper part (usually 0.15–0.3 m) of soil profiles (USDA-NRCS, 1998). Hydric soils are one of the three requirements (along with hydrophytic vegetation and wetland hydrology) for identifying jurisdictional wetlands in the United States. They are identified and delineated in the field using soil morphological features (i.e., hydric soil indicators). These include a variety of features that are regional and texture-based, but all are formed predominantly by the accumulation or loss of Fe, Mn, C, or S compounds (USDA-NRCS, 1998). While indicators related to Fe/Mn concentrations or depletions are the most common, other features (e.g., sulfide and various combinations of carbon accumulations) have been used in specific kinds of soils that do not develop redox features. There are also so-called problem soils that seem to be hydric soils but whose morphologies are difficult to interpret or seem inconsistent with the current landscape, vegetation, or hydrology (Veneman et al., 1998). These include soils formed in grayishor reddish-colored parent materials, soils with high pH or low organic matter, Mollisols with thick dark A horizon, Vertisols with shrink-swell, soils with relict redox features, and disturbed soils such as cultivated soils and filled areas (Rabenhorst et al., 1998; USDA-NRCS, 1998). Relict redox features do not reflect contemporary or recent hydrological conditions of saturation and anaerobiosis; rather, they were likely formed during past geological wetter climates. Typically, contemporary and recent hydric soil morphologies have diffuse boundaries, while relict redox features have abrupt boundaries (USDA-NRCS, 1998). Certain redox patterns occur as a function of the patterns in which the ion-carrying water moves through the soil and as a function of the location of aerated zones in the soil. Characteristic color patterns are thus created by the reduced Fe and Mn ions removed from a soil if vertical or lateral water flow occurs, or the oxidized Fe and Mn precipitated in a soil if lack of sufficient water flux. Consequently, the spatial relationships of redox depletions and redox concentrations may be used to interpret water and air movement in soils (Vepraskas, 1992). Interpreting directions of water movement from redox patterns is easiest when the features have a consistent relationship with soil structure including macropores. Vepraskas (1992) provided four examples that illustrated the basic principles: (1) redox depletions occur around macropores and redox concentrations occur within matrix (Fig. 9A and B), suggesting that water infiltration along macropores and reducing condition developed there because of perched saturated layers; (2) redox concentrations occur around macropores and redox depletions occur within matrix (Fig. 9C and D), indicating that soil matrix is wet for periods long enough for reducing condition to be maintained while


31

macropores become aerated because of faster drainage or plant roots (such as in rice plant) transport air to macropores when the soil is still flooded; (3) redox depletions and concentrations have no consistent relationship to macropores, such as those found in sands or materials with small aggregates where macropores either are not stable or are relatively small and closely spaced such that water and air movement into the soil does not follow the same macropores after each infiltration event; and (4) redox features have a distribution that combines two of the preceding scenarios, resulting from the soil profiles where one horizon having one group of features is overlain or underlain by another horizon containing another group.

2.

Quantification of Soil Hydromorphology

Some recent studies have attempted to quantitatively relate soil morphological features found in soil profiles to quantifiable hydrological parameters associated with soil wetness (Figs. 10 and 11). These studies, covering a broad spectrum of geomorphic and climatic conditions across the United States, were largely associated with the USDA-NRCS Wet Soils Monitoring Project, which was initiated in 1990 in conjunction with the International Committee on Aquic Moisture Regime (Rabenhorst et al., 1998). Designed to collect factual data on the wet properties of various soil climatic regions, this project amassed field data on water table head, shallow ground water depth, soil matric potential, soil temperature, redox potential, and presence of ferrous iron. One facet of the project was to test and comment on hydric soil indicators and wetland delineations. In many cases, a catena of soils that provided trends in properties relative to soil wetness was used in the monitoring (e.g., Jenkinson et al., 2002; Reuter and Bell, 2003; Thompson et al., 1998). Such monitoring efforts provide a foundation for integrating hydropedology concepts into soil survey programs. To illustrate, soil morphology is sensitive to long-term, average monthly water table depths in hydric soils and thus could be used to estimate statistical (e.g., monthly average) and stochastic (e.g., probabilistic) properties of the monthly water table regime (Galusky et al., 1998). The depth to gleying has long been used as a crude indicator of the mean position of the wet season water table (Franzmeier et al., 1983). Soil chroma of 3 to 4 also has been found to be associated with prolonged periods of water table saturation (Evans and Franzmeier, 1986; Franzmeier et al., 1983). Similarly, the depths to redox concretions and depletions have been associated with water table fluctuations (Vepraskas, 1992). In the United Kingdom, in the absence of direct measurement, soils can be assigned to one of six soil wetness classes that describe the height and duration of water logging based on soil profile features such as the depth to a slowly permeable layer

32

H. LIN ET AL.

Figure 11 Quantitative soil morphological indicators for predicting water table behavior: (A) Regression-estimated and measured monthly water table depths for a costal plain hydric soil (Aquic Quartzipsamment) in Maryland. (B) Average long-term, monthly water table hydrograph, estimated using the sine function model of Eq. [4] vs actual data. Sine fn 1 is based on known average March and average annual water-table depths. Sine fn 2 is based on average March and average annual water table depths that were estimated using soil morphology. (From Galusky et al., 1998.)

and depth to gleying (Lilly and Matthews, 1994; Lilly et al., 2003). In an attempt to develop quantitative soil hydromorphological indicators of water table behavior, Galusky et al. (1998) examined the depth to gleying (d-gley), depth to soil chroma of 3 to 4 (d-34), and the depths to redox concretions (d-conc) and depletions (d-depl) in 29 sites in the coastal plain of Maryland. They found that d-34 correlated the most highly with average monthly water table levels. This correlation was greatest for the month of March (when seasonal water table levels in Maryland are generally at their highest) and decreased during the summer months. The d-gley was also highly correlated with late winter/early spring water table levels. However, they did not


33

find good correlations between d-conc or d-depl and average monthly water table levels. Galusky et al. (1998) proposed a first-order autoregressive model to enhance (extend into the past) existing water table records (Fig. 11A): wti ¼ a þ b wti

1

þ c pri þ d evi ;

ð3Þ

where wti is the average monthly water table depth (cm) for month i, wti-1 is the average monthly water table depth (cm) for month i 1, pri is the cumulative precipitation (cm) for month i, evi is the estimated cumulative pan evaporation (cm) for month i, and a, b, c, and d are coefficients estimated from the data. Galusky et al. (1998) also used a sine function to estimate long-term, average monthly water table hydrograph (Fig. 11B): est wti ¼ est ann wt þ ampl sin ½2p ðmonthi =12Þ;

ð4Þ

where est wti is the average estimated monthly water table for month i, est ann wt is the average estimated annual water table, ampl is the difference between the estimated seasonal high water table level and the annual mean, and 2p/12 is the angular frequency of the function. While progress has been made, insufficient data exist on the duration and frequency of high water tables in different soils. There is a need to determine the dynamics of the water table in benchmark and other important soils so that the duration of saturation and reduction required for creating aquic conditions may be specified. Currently, aquic conditions as used in Soil Taxonomy (including endosaturation, episaturation, and anthric saturation) are not yet quantitatively defined (Soil Survey Staff, 1999). Similarly, soil drainage classes as used in soil surveys (Soil Survey Division Staff, 1993) have also been qualitatively determined. There are a great number of applications for water table data once a significant volume of quantitative data has been accumulated, particularly if such data are collected with associated landscape features. One of the best uses of such data would be to more fully develop the relationships between soil profile descriptions and water movement in soil profiles and landscapes. This information could vastly improve the value of soil surveys and their updates. 3.

Soil Morphology as a Guide to Field Hydrological Measurements

Pedology expertise, especially related to visible soil morphological features, helps to guide and interpret physical and hydrological measurements in field soils. Morphological descriptions often yield information that cannot be easily obtained by other methods, such as the shape and strength of peds, type of macropores, and macropore continuity and connectivity. There

34

H. LIN ET AL.

are also examples in which soil morphological data uniquely characterize flow regimes that would be very difficult to document with classical soil physical or hydrological techniques, such as redox patterns discussed in Section III.B.1. Such information can be quite helpful when placing monitoring or measurement equipment in field soils. For example, although many methods are now available for measuring Ksat (e.g., Dane and Clark, 2002), the effect of sample volume and pore continuity of interpedal voids on measured Ksat is still ignored (Bouma, 2005). A study made by Bouma et al. (1989) in a silty clay soil with glossic features indicated that measurements in the bleached cracks yielded a Ksat value of 6.9 m/day while values were 0.3 m/day inside the compact peds (Fig. 12; See Color Insert). Placing samples at random in this soil led to a very high variability that could not be reduced by applying statistics, but could be reduced by making a morphological analysis before samples were taken and by estimating the relative importance of the two flow regimes in the ped matrix and the glossic features. Thus, studying soil structure with morphological methods is important when choosing proper measurement methods and locations for determining Ksat and other soil hydraulic properties. Various preferential flow patterns as illustrated in Fig. 7 also suggest that significant variation could result when sampling soil cores for physical/hydrological measurements in the laboratory. When working in clay soils, measurement of water table depth has often presented problems. Levels may fluctuate wildly, particularly after rainfall, when levels indicated in piezometers and readings of tensiometers may differ significantly at short distances. Bouma et al. (1980) studied this phenomenon, showing the effects of water flowing along cracks and ped faces, both in the unsaturated and saturated zone in the soil (Fig. 13). After rainfall, the water level in the cracks rises very rapidly to subside slowly as water moves slowly into the unsaturated peds. During that time, the water table level is not defined. When piezometers intercept these cracks, they show high fluctuations. When they are inside the peds, they are very stable, unless they are not well sealed on the outside, which is likely to result in crack-flow into the piezometer, incorrectly suggesting a ‘‘perched’’ water table inside the peds. This is true when using unlined augerholes for measuring water table levels in well-structured soils. Then, free water levels are observed at every level to which an augerhole has been drilled (Fig. 13). When the water moves out of the cracks after rain, the peds may remain saturated for a while, suggesting overall saturation of the soil as measured by tensiometry, but the cracks are already filled with air. Placement of tensiometers with a downward angle into a vertical profile wall may induce flow along the sides, incorrectly suggesting saturation, while upward placement avoids this problem. When tensiometers intercept cracks, they may register temporary ‘‘saturation’’ that is not measured when the cups occur inside peds (Fig. 13). Understanding the flow regime, as influenced by macropore patterns, helps explain what


35

Figure 13 A diagram showing water tables, boreholes (A), piezometers (P), and tensiometers (T) in structured clay soils, illustrating the effects of water flowing along cracks and ped faces on instrument readings.

otherwise would be highly confusing hydrological measurement results. Such an understanding can contribute to better instrument design and measurement protocols. Bouma (1989) summarized this well as ‘‘look first, then measure.’’ Soil morphology is also helpful in determining water accessibility. Soils with large compact peds, such as prisms and clods formed by tillage under adverse conditions, often show concentrations of roots at the ped surface (Fig. 14; See Color Insert), indicating that the roots were unsuccessful in penetrating the peds or clods and that preferential flow along roots and ped surfaces is commonly expected. Field conditions have been observed in which plants were wilting even though water contents of the root zone were

36

H. LIN ET AL.

well above the wilting point. This phenomenon has been attributed to limited accessibility (Bouma, 2005). Modeling studies, implicitly assuming unlimited accessibility, thus could yield poor results. Droogers et al. (1997) studied these processes in large undisturbed field samples and defined accessibility as a function of ped sizes. This, in turn, could be incorporated into existing simulation models for more realistic prediction of plant growth and crop yields.

4.

Soil Morphological Attributes for Inferring Soil Hydraulic Properties

Soil scientists have been successful in using descriptive morphological information to make qualitative judgments about a number of soil hydraulic properties, notably Ksat (e.g., Coen and Wang, 1989; King and Franzmeier, 1981; McKeague et al., 1982; O’Neal, 1949; Soil Survey Division Staff, 1993). Soil morphological data are also suited to grouping soils by their hydrological functioning. For example, Quisenberry et al. (1993) devised a descriptive system to classify soils based mainly on water flow pathways and patterns (uniform flow or different types of preferential flow) using surface soil texture, subsoil structure, and clay mineralogy. A soil hydrological classification (termed hydrology of soil types, or HOST) based on soil morphological attributes has also been developed in the United Kingdom to predict water movement through soils and substrates (Boorman et al., 1995; Lilly et al., 1998). The attributes used include the presence or absence of an organic surface layer, substrate hydrogeology, the depth to a slowly permeable layer, the depth to gleying, and air capacity values. Grouping or classifying soils in terms of both soil morphology and hydraulic properties is also a valuable means of developing simple and more reliable predictive pedotransfer functions (PTFs) for field soils. For example, Franzmeier (1991) grouped Ksat of some Indiana soils by soil classes, called lithomorphic classes, based on origin of parent material, type of soil horizon, and soil texture. Batjes (1996) used hierarchical pedotransfer rules and functional grouping to predict available water capacity for the main soil types of the FAO-UNESCO world soil map using soil unit type, horizon textural class, and organic matter class. Wo¨sten et al. (1990) demonstrated that class PTFs that use well-defined soil horizons as ‘‘carriers’’ of physical/hydraulic information allow efficient use of soil morphological data because soil horizons can be determined easily and reproducibly by pedologists (Bouma, 1992). However, as noted by Wo¨sten et al. (1985), Breeuwsma et al. (1986), and Bouma (1992), pedogenic differences, as expressed by horizon designations, do not necessarily correspond with functional differences. Bouma (1992) suggested a more promising threestage protocol that attempts to calculate hydraulic parameters directly


37

from physical or morphological data. First, good measurements of soil hydraulic properties that take soil morphological attributes (such as soil horizon and soil structure described in the field) into account should be made. Second, measured hydraulic properties should be expressed in terms of coefficients as defined by, say, the van Genuchten equation and, third, relate those coefficients by regression analysis to readily available soil properties (such as texture, bulk density, and organic matter content) or more qualitative groupings of soil horizons. While qualitative soil morphological attributes have been widely applied, quantification of such data is generally lacking. Rawls et al. (1993) noted that a quantitative description of the effects of soil morphological properties on soil water movement is yet to be established. So far, limited studies have demonstrated the potential of quantifying soil macromorphology through field observations or soil micromorphology through thin sections (Lilly and Lin, 2005). As illustrated by Bouma et al. (1979), Lin et al. (1999b), Vervoort and Cattle (2003), and Kutı´lek (2004), soil micro- and macromorphometric data could be used to quantitatively derive soil hydraulic parameters. For example, in a study conducted by Lin et al. (1999b), field soil hydraulic conductivity at zero tension (K0) was reasonably predicted from morphometric indices (MI) of soil textural class (MIt), initial moisture state (MIm), ped grade (MIsg), ped shape (MIst), macropore quantity (MIpq), macropore size (MIps), and root abundance (MIrq): K0 ¼

22:4 38:3MIt þ 33:8MIm þ 21:1MIsg þ 47:5MIst þ 102:4MIpq þ 45:5MIps þ 33:3MIrq :

ð5Þ

The utilization of soil structural descriptors in a quantitative fashion, as used in the preceding example, would be a step forward toward incorporation of soil structure into PTFs and the modeling of flow and transport in field soils. Quantification of soil morphology would also enhance the understanding of the relationships among different soil morphological features (as demonstrated by Lin et al., 1999a) and thus permit a better assessment of soil profile descriptions in relation to water movement in soils and over the landscape.

C. WATER MOVEMENT OVER THE LANDSCAPE IN RELATION TO SOIL COVER Conceptual models of water movement over the landscape are key aspects of contaminant transport, watershed management, wetland delineation, and terrestrial ecosystem functions. Where, when, and how water moves through various landscapes of different sizes and how water flow impacts soil processes and subsequently soil spatial-temporal patterns need to be better

38

H. LIN ET AL.

understood. It has been pointed out that ‘‘how water moves through soil at the landscape scale’’ is a major research need of the U.S. National Cooperative Soil Survey (NCSS) program (USDA-NRCS, 2001) and that ‘‘a focused research program to understand soil–water interactions at the landscape level and at whatever depths needed must be an integral part of national soil survey in the 21st century’’ (Smith and Hudson, 1999). In 2003, the NCSS program further recommended that hydropedology be promoted as a useful framework for modern soil surveys and updates and that whole landscape hydropedological study be listed as a priority research in the NCSS program (USDA-NRCS, 2003). In this regard, traditional 3-D block diagrams used in soil surveys that show conceptual models of soil–landscape relationships could be useful in developing conceptual models of water movement over the landscape and in linking dynamic soil properties to landscape positions. Enhanced 3-D block diagrams of landscape–soil–water relationships with added information of water table dynamics, water flow paths, hydric soils, restrictive layers, and other relevant information could significantly increase the values of soil survey products. As illustrated in Fig. 5, classical block diagrams of soil– landscape relationships could be used to indicate landscape hydrology, illustrating water flow direction and water table dynamics. These block diagrams could be further linked to watersheds or physiographic regions to provide valuable conceptual frameworks of water movement over the landscape in different major land resource areas (MLRAs). MLRAs are geographically associated land resource units that are geographical areas (usually several thousand acres in extent) characterized by a particular pattern of soils, water, climate, and land use (USDA-NRCS, 1997). The MLRA approach to soil inventory is becoming more and more recognized in the United States as a focal point for modernizing soil survey information. Hence, hydropedological study in benchmark areas in various MLRAs could provide fundamental insights regarding water movement over diverse landscapes of various scales.

1.

Landscape Hydrology

Landscape perspective is essential in examining interactive hydrological and pedological processes. Landscape shape controls water flow and ultimately soil distribution over an area. For example, landscapes with numerous depressions, termed ‘‘hummocky landscapes,’’ have landform level and local ground water flow as well as landscape level surface and subsurface flow (Lissey 1971; Richardson et al., 2001; Toth, 1963; Winter, 1988). Hummocky till landscapes display an array of flow patterns in a flownet; in contrast, smooth landscapes have even and long flow patterns that display


39

upland recharge and lowland discharge (Fig. 15). Recharge hydrology removes material from a soil horizon, and water moves to the ground water, while discharge hydrology adds material to a soil horizon, and water moves from the ground water (Richardson et al., 2001). Climate also plays a role in landscape hydrology, as noted by Richardson et al. (1992). In humid regions, the local highs have a water table that is a subdued replica of the topography. The higher areas are recharge areas with leached soils, and depressions are discharge areas often with calcareous soils (Fig. 16A). The subhumid climate has more variety in the depressions: the higher areas are distinctly recharged, and the water flow in recharge depression reflects the episaturation; the flowthrough depression has soils that are calcareous or unleached within thick A horizons; and the discharge depression may have saline soils (Fig. 16B). In semiarid regions, the recharge depression becomes more common (Fig. 16C). Wetlands are the interface between land and water. The study of wetland hydrology and wetland soils is thus intimately linked to landscape–soil–water interactions and hence hydropedology. Wetland hydrology involves the spatial and temporal distribution, circulation, and physiochemical characteristics of surface and subsurface water in the wetland and its catchment over time and space (Richardson et al., 2001). The edges of jurisdictional wetlands in the United States are identified by noting the point at which hydric soils and hydrophytic plants end and the upland characteristics begin. The hydrological nature of a wetland is the result of the balance between inflows and outflows of water, the soil and topography in a wetland, and subsurface conditions. Major hydrological inflows include precipitation, flooding rivers, surface flows, ground water,

Figure 15 Flownet representation of (A) a smooth landscape and (B) a hummocky landscape and their related flow patterns as indicated by arrows. (After Toth, 1963, and Winter, 1988.)

40

H. LIN ET AL.

Figure 16 Two-dimensional landscape diagrams showing recharge-discharge hydrology, equipotential lines, flow lines, and flow direction in hummocky landscapes in different climatic settings. (From Wysocki et al., 2000.)

and in coastal wetlands, tides (Fig. 17). Most upland areas that have wetland hydrology occur on landscape positions that receive run-on water from surrounding landscapes to cause wetness above normal precipitation. Other upland areas have a seasonal high water table due to high rainfall and impermeable layers below the soil surface or a ground water table that seasonally rises close to the surface. Most movement of ground water is the result of topographic relief, and discharge of ground water at topographically lower elevations results in wetlands or stream flows (Figs. 16 and 17).


41

Figure 17 A landscape–soil–water system that links soil profile hydrology and hillslope hydrology. The major processes involved in the landscape hydrologic cycle include: precipitation, infiltration, vegetation interception and return to the atmosphere, surface runoff (overland flow), subsurface throughflow, upward flow, deep percolation, and ground water flow. One form of overland flow from a saturated soil is called reflow (or return flow). A concentrated subsurface flow through a chain of connected macropores nearly parallel to the soil surface is called pipeflow. Also shown is a likely catena of soils along the hillslope.

Hillslope is a fundamental landscape unit and an intermediate scale that connects point observations to watershed phenomena. Watersheds are comprised of sub-watersheds, which in turn are comprised of multiple hillslopes. Hillslope processes thus are closely linked to landscape and watershed hydrology (Fig. 17). In the past decades, hillslope hydrology has received considerable attention, particularly by hydrologists (e.g., Anderson and Burt, 1990; Kirkby, 1978; Western et al., 1999). As pointed out by Ridolfi et al. (2003), hillslope hydrology is challenging because a number of processes interact at different scales, significantly contributing to the complexity of the system that hampers the possibility of a general theory. Some of the most important issues in hillslope hydrology include the following (Ridolfi et al., 2003): . horizontal and vertical heterogeneity of soil types and various properties; . lateral redistribution of water along the hillslope due to the formation of a

saturated zone in the soil and lateral subsurface flow in the vadose zone;

42

H. LIN ET AL.

. type and spatial pattern of vegetation along a hillslope and its impacts on

runoff and infiltration; . different types of climate and precipitation events, which, although possi-

. . .

. .

bly spatially uniform over a hillslope, may trigger other mechanisms that generate spatial dynamics; infiltration of runoff generated in the uphill part of a slope that occurs at the rainstorm time scale; longitudinal hillslope profile and form, and 3-D hillslope geometry and the presence of spurs and hollows; geographic position of a hillslope and exposure to the sun and wind, which may strongly affect evapotranspiration, vegetation distribution, and soil properties; boundary conditions, especially at the bottom of a hillslope and the underlying geological formations; various land uses and anthropogenic activities.

There have been numerous attempts to relate topographic variability to soil properties and hillslope hydrology. In the pedology community, for example, many studies have examined the spatial variations of soil horizon thickness, particle size distribution, organic carbon, depth to carbonates, base saturation, depth to redox features, and other soil properties as a function of hillslope position (e.g., Gerrard, 1981; Kleiss, 1970; Lin et al., 2004; Mausbach and Richardson, 1994; Moore et al., 1993; Pennock and de Jong, 1990; Thompson et al., 1998; Walker and Ruhe, 1968). In the hydrology community, the influence of terrain on hillslope hydrology has been widely investigated (e.g., Anderson and Burt, 1990; Beven, 1997a,b; Kirkby, 1978). A common belief regarding soil moisture distribution over a hillslope or landscape is that topography becomes increasingly important in wet periods, but during dry periods soil moisture patterns depend primarily on soil properties with little effect from topography (e.g., Grayson and Blo¨schl, 2000). Particularly useful terrain attributes, which are now routinely calculated from a DEM, include topographic wetness index, slope gradient, slope curvature, specific catchment area, relative elevation, and others. For instance, the topographic wetness index (TWI), also known as the compound topographic index, is an index of hydrological similarity based on topography and is related to the Horton model and Darcy’s law (Burt and Butcher, 1985; Kirkby, 1975): TWI ¼ lnða=tanbÞ;

ð6Þ

where a is the area draining through a point from upslope (called specific catchment area), and tanb is the local slope angle. High TWI areas in a catchment tend to saturate first and therefore indicate potential surface or subsurface contributing areas. The expansion and contraction of such areas


43

as a catchment wets and dries is then indicated by the pattern of the TWI (Beven, 1997a). Based on the TWI, a popular rainfall runoff model, called TOPMODEL (topography based hydrological model) (Beven and Kirkby, 1979), has been widely used in the hydrology community (Beven, 1997a). Various studies have attempted to correlate the TWI with actual soil wetness or zones of surface saturation, but the results vary widely (e.g., Sulebak et al., 2000; Western et al., 1999; Yeh and Eltahir, 1998). There have been many improvements to Eq. [6], such as (1) incorporating soil transmissivity at saturation (T0), leading to what is called the soil topographic index, ln(a/T0 tanb) (Beven, 1986), and (2) considering a as a variable effective upslope contributing area instead of a fixed value, leading to what is called the dynamic TOPMODEL (Beven and Freer, 2001). For a more detailed account of the topographic wetness index and other related aspects of hillslope hydrology, readers are referred to Beven (1997a,b) and Kirkby (1978). 2.

Importance of Geomorphology and Stratigraphy

It is well recognized in the pedology community that geomorphology and stratigraphy are determinant variables to pedogenesis, soil–landscape patterns, and soil behavior, particularly at the large-area scale (Wilding, 1994). Geomorphology is the study of the classification, description, nature, origin, and development of landforms on the earth’s surface, while stratigraphy deals with rock strata (i.e., soil parent materials in residual soils), especially the distribution, deposition, and age of sedimentary rocks. At the beginning, geomorphology was concerned essentially with producing time-dependent models of landscape evolution. The form of the land was the major focus, with little mention of process and scant attention to the soil and regolith materials (Gerrard, 1981). Investigations of drainage basins and storm hydrographs demonstrated the influence exerted on these phenomena by the surface covering soil and vegetation. Modern research is increasingly demonstrating the close dependence of soils, landforms, and geomorphological processes. Geomorphological and pedological processes interact on hillslopes, especially where the movement of soil and water is considered. Patterns of landforms are matched, often on a one-to-one correspondence, by soil patterns (Gerrard, 1981; Wysocki et al., 2000). The characteristic suite of landforms and soils created by glacial and fluvioglacial deposition is a classic example. Fluvial and marine processes also produce a characteristic assemblage of landforms that is paralleled by the soil types. Similar to soil geomorphology (or pedogeomorphology), hydrogeomorphology is recognized in the hydrology community to address the interrelationships between landforms and processes involving water. Water erosion and deposition influence the genesis and characteristics of landforms.

44

H. LIN ET AL.

Conversely, characteristics of the landform influence surface and subsurface water movement in the landscape. Water sculpts the landscape through the processes of runoff, erosion, transport, and deposition, resulting in a treelike network of channels into which the flow becomes concentrated. Networks at the structural basis for interpreting the transport of water and solutes are best seen at the catchment scale (Horton, 1945; Rodrı´guez-Iturbe and Rinaldo, 1997; Scheidegger, 1967). However, although empirical laws describing the 2-D geometry of these networks have existed for about half a century, there is little quantitative understanding of the dynamics of channel formation or of the causal relationship between the 3-D network structure and the precipitation driving the erosion (NRC, 1991). Such an understanding would reveal fundamental scaling relationships of surface water hydrology over a broad range of spatial scales (NRC, 1991). Similarly, water also shapes the vertical soil profile, through the processes of leaching, translocations, fluctuating water table, shrink-swell, freezethaw, and other processes, that result in various soil structures and a network of preferential flow pathways. It appears that there is a similarity between the stream network and the network of water flow pathways in soil profiles. For example, Deurer et al. (2003) recently suggested a concept of drainage networks to describe bypass flow pathways in soils at the soil profile scale. They found that the drainage network in a sandy soil under a coniferous forest in north Germany closely resembled one of mountainous streams, and that the fractional area of the entire profile occupied by the network was found to decrease exponentially with depth. They thought such a network was related to the law of energy dissipation, which causes a specific tree-like structure for flow paths in the soil profile, as well as at the catchment scale. Soil hydrological processes and properties are closely linked to landforms and parent materials. For instance, a few studies have reported the relation of soil hydrology to geomorphology and stratigraphy on Wisconsinan-age till plains that are common in the Midwest (e.g., Evans and Franzmier, 1986; Jenkinson et al., 2002; Thorp and Gamble, 1972). Jenkinson et al. (2002) reported that on a dissected till plain underlain with dense till, water was held up by the low Ksat till. Water thus moved from the interior of the till plain to the dissected bevel of the plain, where it caused relatively high water tables in soils that have no redox features.

3.

Soil Variability over the Landscape and within Soil Map Units

The spatial variability of physical soil properties is particularly critical in hydrology, yet we have relatively poor ways of estimating it at the landscape and watershed scales. A number of case studies in catchment hydrology have scrutinized the reliability of soils data and their effect on the representation of


45

catchment response. For example, Houser et al. (2000) reported that the addition of spatially variable soil properties based on the Order II soil map (including clay %, sand %, Ksat, ur, us, and feff, estimated using PTFs from the literature) produced unrealistic polygon artifacts in the soil moisture patterns simulated using the TOPMODEL-based Land Atmosphere Transfer Scheme (Famiglietti and Wood, 1994). In comparison, simulations based on uniform soil hydraulic properties produced soil moisture patterns that were more consistent with the observations from airborne push broom microwave radiometer (Houser et al., 2000). They suggested that it might be possible to develop a smoothing algorithm that would use the soil polygons to approximate continuously varying, spatially distributed soil parameters. Western and Grayson (2000) used soil type to spatially distribute hydraulic conductivity measurements in Tarrawarra watershed, assuming uniform conductivity within each soil type polygon. This produced artificially high soil moisture values at the interface of the soil types when compared with soil moisture patterns measured by TDR. Vertessy et al. (2000) indicated that the assumption of uniform conductivity in each of the three land types (differentiated by topography and soil properties) in the La Guenca catchment was not appropriate as suggested by a large number of soil core Ksat measurements across the catchment. Instead, they added a random component to the deterministic pattern imposed by land type in their runoff model simulation. Grayson and Blo¨schl (2000) claimed that the variability of soil physical properties within soil types can be as large as or larger than the variability between soil types. This suggests that caution should be exercised in distributed hydrological modeling when allocating soil hydraulic properties on the basis of soil types as indicated by soil maps (using either PTFs or direct measurements). On the other hand, Duffy et al. (1981) demonstrated that when a soil map was properly used it could help sort out the spatial variability of soil hydraulic properties. They measured quasi-steady state infiltration rates on surface soils at 20 locations scattered throughout a 100 ha farm in New Mexico. If the seven soil series on the farm were ignored, there was basically no relation between measured and estimated infiltration rates. But when the infiltration rates were grouped by soil series based on the Order I soil map, the measured and estimated geometric mean values were highly correlated. In discussing emerging technologies for scaling field soil–water behavior, Nielsen et al. (1998) expected that new paradigms for local and regional scales of homogeneity in pedology and soil classification would emerge, with soil map units containing spatial and temporal soil–water scale factors. However, spatial and temporal variability of soil resulting from natural and man-made processes reduces the certainty, as indicated on existing static soil maps and in soil survey reports. Although currently available soil maps and the related databases are often considered as the very best data one can

46

H. LIN ET AL.

obtain in environmental and natural resource assessments (Merchant, 1994), the proper use of existing soil maps and databases is not necessarily warranted if within map unit variability is not well understood and quantified. For example, in regional ground water vulnerability assessments, the uses of the soil associations in the State Soil Survey Geographic Database (STATSGO) (Order IV soil map) are often considered obscure (Merchant, 1994). There are also significant ambiguities with regard to scaling issues of soil maps (Loague and Green, 1990). Soil surveys have traditionally overlooked spatial variability within map units for a variety of reasons, including scale limitations, lack of appropriate sampling design, and inadequate quantitative data (Lin et al., 2004). Although acknowledged, variation within soil map units is generally described qualitatively in vague terms. With the growing use of digital soil maps and related databases for diverse applications, the variability of soil taxa and of map units has become more recognized. For example, the nationwide Order II Soil Survey Geographic Database (SSURGO) is believed to be of little use in site-specific applications if within map unit variability cannot be quantified. It appears that more detailed Order I soil mapping would be in great demand for site-specific applications such as precision agriculture and landscape hydrology. However, virtually every delineation of a map unit in all soil surveys includes other soil components or miscellaneous areas that are not identified in the name of a map unit. Many of these components are either too small to be delineated separately at a given soil survey scale or deliberately included in delineations of another map unit to avoid excessive detail in the map or the legend (Soil Survey Division Staff, 1993). These inclusions reduce the homogeneity or purity of map units and thus require appropriate quantification for use in modeling. Indeed, soil map units are better considered as landscape units rather than individual soil types (Wysocki et al., 2000). Hall and Olson (1991) challenged current soil maps: ‘‘Much effort has been expended on taxonomic classification of soils during the last few years, but the importance of proper representation of landscape relations within and between soil mapping units has been virtually ignored. The same mapping unit is often delineated on convex, concave, and linear slopes. This mapping results in the inclusion of areas of moisture accumulation, moisture depletion, and uniform moisture flow within a given mapping unit.’’ While many studies have suggested the need for a reliable estimate of the proportionate extent of map unit components within a soil map unit for probabilistic assessment of soil properties (e.g., Brown and Huddleston, 1991; Foussereau et al., 1993; Lammers and Johnson, 1991; Lin et al., 2004; Nordt et al., 1991), such information is still largely lacking. Hence, quantification of map unit purity for different scales of soil maps is a needed area of improvement in modern soil surveys (Arnold and Wilding, 1991; Lin et al., 2004). It is encouraging that


47

the USDA-NRCS has begun such attempts to quantitatively document the variability of representative mapping units. We would like to point out that there is a mentality in soil science today stating that we really do not need to map soils anymore; all we need to do is to develop variogram functions using geostatistics to estimate spatial variability from one point to the next. We would challenge this mentality in dealing with soil spatial-temporal dynamics. First of all, geostatistics provides powerful interpolative tools after an extensive dataset has been gathered on a particular soil. However, geostatistics is not a very powerful extrapolative tool with soil properties, especially to extrapolate from one tested area to a new area for which the database has not been collected. This makes geostatistics a rather costly, inefficient method to extrapolate knowledge from one area to the next. Furthermore, geostatistical functions should be derived from landscape stratified units such as soil types, slope gradients, geology, land uses, parent materials, and vegetation, and not indiscriminately across a broad landscape without prior partitioning of the sources of variability. In this regard, pedological expertise and various geospatial data such as DEM are helpful in assisting the appropriate application of geostatistics to landscape analysis.

D. HYDROLOGY AS A FACTOR OF SOIL FORMATION AND A DRIVING FORCE OF DYNAMIC SOIL SYSTEM As the circulatory system is to the body, the fluvial system is to the landscape. Water is of critical importance to soil morphology, genesis, classification, and mapping. All of the five natural soil-forming factors affect and are affected by hydrology. The flux factors of soil formation (climate and vegetation) as well as site factors (topography and parent materials) can be linked to landscape hydrology, which is further modified by the soil internal hydrological environment. For instance, climate influences the amount and timing of soil water availability, and soil moisture in turn influences climate. The biota growing on and in soils are strongly influenced by water’s presence, both directly, because organisms require water to live, and indirectly, because the amount of soil water influences oxygen availability, the temperature regime, and nutrient transport in soils. Topography frequently directs and controls the flow of both surface and subsurface water over the landscape. Parent materials affect the flow of water because they are the sources of the matrix through which surface water infiltrates and may reflect the materials through which ground water flows. Time is required for both soil development and change and for water to flow through soils and landscapes. Much like ‘‘one cannot ignore the role of ground water in performing geologic

48

H. LIN ET AL.

work’’ (Domenico and Schwartz, 1998), water in the unsaturated zone cannot be ignored in soil formation and soil dynamic changes. Another way to look at the essential role of water in soil formation and soil dynamic changes is Simonson’s (1959) theory of generalized processes of soil formation, in which processes and systems linkages were emphasized over factors. Simonson (1959) suggested four general soil-forming processes: additions, deletions, transformations, and translocations. All of these processes involve water in significant ways. Water adds material through deposition of eroded sediment and precipitation of dissolved minerals. Water can also entirely remove soil materials through leaching and erosion. Water transforms soil material through weathering reactions, and translocates solid and dissolved materials in mass flow within soil profiles. 1.

Catena Concept and Hydrosequences

Milne’s (1935) catena concept stated that soils along a hillslope are interconnected. A catena was originally defined as ‘‘a unit of mapping convenience . . . a grouping of soils which, while they fall wide apart in a natural system of classification on account of fundamental and morphological differences, are yet linked in their occurrence by conditions of topography and are repeated in the same relationship to each other wherever the same conditions are met’’ (Milne, 1935). Since then, catenas have been recognized in a variety of areas under a variety of climatic conditions and have played an important role in soil and landform studies (Gerrard, 1981). The wide applications of the catena concept have been complicated by considerations of parent material variations and climatic differences. The temporal as well as the spatial aspects of the soils are also important. The real significance of catenas lies in the recognition of the soil processes and geomorphic processes—especially those driven by water movement downslope—that are involved in catenary differentiation rather than in the formal appearance of its product. From Milne’s original example of East African catena to the latest catenas studied throughout the United States (e.g., Jenkinson et al., 2002; Reuter and Bell, 2003), hydrology plays the central role. Soil profile changes from point to point in accordance with conditions of drainage and past history of the land surface, and soil differences are brought about by ‘‘drainage conditions, differential transport of eroded materials, and leaching, translocation, and redeposition of mobile chemical constituents’’ (Milne, 1936a,b). Catenarey soil development occurs in response to the way water moves through soils and over the landscape (e.g., Hall and Olson, 1991; Lin et al., 2004; Moore et al., 1993; Thompson et al., 1997). Catenas developed from similar parent materials, thus are often hydrosequences of related soils with


49

difference primarily in drainage, especially in depositional landscapes. Soil hydrosequences have been more quantitatively studied in recent years, mainly linked to the UDSA-NRCS Wet Soil Monitoring Project (e.g., Jenkinson et al., 2002; Reuter and Bell, 2003). As an example, Reuter and Bell (2003) investigated quantitative relationships between soil hydrology and morphology in seven landscape positions along a 125 m summit-towetland transect. They installed nested piezometers, observation wells, Pt electrodes, and thermocouples at multiple depths to monitor water flow direction, water table, and redox potential on a weekly to biweekly basis for 4 years, with more intense sampling during spring and fall to capture the effects of snow melt and plant senescence. Their major findings included the following: (1) thickness and color of surface horizons in this landscape were strong indicators of landscape hydrology, especially when redox features associated with normal water table levels were masked by thick mollic epipedons; (2) profile darkness index (a ratio of A horizon thickness to Munsell color value and chroma for A horizon) had strong correlation with the duration of saturation; (3) equal piezometric head with soil depth suggested a throughflow environment with potential for lateral water movement toward the wetland at the toeslope; and (4) depending on the precipitation, overland flow, and subsurface flow, the wetland fluctuated between recharge and discharge hydrology. Spatial distribution of topographic attributes that characterize water flow paths also captures spatial variability of soil attributes at the hillslope scale. Because of the processes that occur along a hillslope, soils may be quite different in different portions of a landscape, but these processes and relationships may be similar across a larger area, particularly if geomorphology and stratigraphy are similar. Numerous investigations of catenas have been completed in order to address this phenomenon (e.g., Evans and Franzmeier, 1986; Khan and Fenton, 1994; Pennock and de Jong, 1990; Stolt et al., 1993; Thompson et al., 1998; Veneman and Bodine, 1982). It follows that the greatest variability in certain soil properties within a physiographic region may occur along a hillslope rather than from one side of the region to the other (e.g., Lin et al., 2004; Pennock and de Jong, 1990; Wilding et al., 1994). This warrants a careful investigation and understanding of hillslope soil variability and hydrosequences before making broad generalizations about soil variability within a physiographic region. 2.

Hydrology/Hydropedology as a Potential Means of Quantifying Soil-Forming Processes

Jenny’s (1941) soil-forming factorial model states that soil (S) is a function of climate (cl ), organisms (o), topography (r), parent materials ( p), and time (t). It is expressed as

50

H. LIN ET AL.

S ¼ f ðcl; o; r; p; t; . . .Þ;

ð7Þ

where the dots indicate additional unspecified factors (such as anthropogenic effects). The factors define the soil in terms of controls on pedogenesis and soil distribution factors—‘‘an environmental formula’’ that defines the ‘‘state and history of a soil’’ (Jenny, 1941, 1980). Jenny believed that Eq. [7] could be solved under ideal conditions and that the variables were independent, though he recognized that the factors also may be interrelated (Jenny, 1941, 1980). Interestingly, while the conceptual framework of Eq. [7] has had a profound impact on pedological research and has been well received by the soil and earth sciences communities (e.g., Amundson et al., 1994), Jenny himself stated that ‘‘the fundamental equation of soil formation is of little value unless it is solved’’ (Jenny, 1941). He further stated that the model had been presented before (e.g., Hilgard, 1921) but that ‘‘I can solve the equation. That was the new approach’’ (Jenny, 1980, p. xii). According to Jenny (1941), the ultimate goal of functional analysis is the formulation of quantitative laws that permit mathematical treatment. However, no correlation had been found between controlling factors and soil properties under field conditions that ‘‘satisfied the requirement of generality and rigidity of natural laws’’ (Jenny, 1941, p. xii). Wilding (1994) summarized the difficulties encountered in solving the Eq. [7], including the following: . assumption of independence of state factors with no or minimal interac. . . . . . . . . .

tions; factor interchangeability and feedback mechanism; problems in obtaining partial differentials with nonoverlapping factors; anthropogenic influences confounding factor variables; spatial and temporal variability causing high noise in factorial analysis; difficulty in rigorously reconstructing the time effects on pedogenesis; lack of time-incremental datasets for developing pedogenic rate changes; polygenetic pathways of soil genesis and multiple origins of soil properties; lack of knowledge of precise processes; lack of suitable database with geographical and geomorphological controls; difficulty in testing and validating the model.

Since Jenny’s (1941) state-factor model, the progression of pedogenic model developments has incorporated all, some, or none of the factorial approach (Wilding, 1994), including the extended state-factor model (Jenny, 1961, 1980), systems mass-balance process model (Chadwick et al., 1990; Simonson, 1959), energy flux model (Runge, 1973; Smeck and Runge, 1971), chemical equilibrium residua model (Chesworth, 1973a,b), soil–landscape systems model (Hugget, 1975; McSweeney et al., 1994; Ruhe, 1969), progressive-regressive evolutionary model (Johnson and


51

Watson-Stegner, 1987; Johnson et al., 1990), coupled reactions-factors-processes model (Ciolkosz et al., 1989), and various simulation models (Bryant and Olson, 1987; Hoosbeek and Bryant, 1992; Levine and Ciolkosz, 1986; Minasny and McBratney, 1999, 2001). While most models of soil formation have been conceptual or qualitative, quantitative and systems models are the hope of the future (Wilding, 1994). Even for many simulation models offered today, the factorial approach is used as a control that governs the direction and magnitude of specific pedogenic processes being simulated. To quantify the factorial model of Eq. [7], there is much to be done and many opportunities along the way. Nielsen et al. (1996), in discussing the opportunity to strengthen soil science from surface soil moisture regimes derived from increasingly available remote-sensing imagery, pointed out that reduction of partial differential equations to ordinary differential equations for describing soil physical, chemical, and biological processes is an observational and theoretical challenge to both practitioners and theoreticians. As discussed at the beginning of Section III.D, hydrology affects and is affected by all of the five natural soil-forming factors and the four general soil-forming processes. It is believed that pedogenic processes are mainly driven by the presence and flow of water (e.g., Fritsch and Fitzpatrick, 1994). Hence, hydrology/hydropedology may offer new perspectives into solving the factorial Eq. [7]. While we do not yet know the answers to this challenging task, we speculate that hydrology/hydropedology could potentially offer a more quantitative way to translate the conceptual model of pedogenic processes into operational mathematical formulae. As addressed throughout this chapter, it is the goal of hydropedology to be able to quantify interactive pedological and hydrological processes across scales.

3.

Dynamic Soil Properties

The classical five natural soil-forming factors, plus hydrology and human activities, contribute to the temporal variation of the soil. Flux factors (including hydrology) and human activities are more influential in relatively shorter term dynamic soil properties over site factors. It appears that Jenny (1941) considered climate as the major pedogenic driving vector acting through time, with vegetation, topography, and parent material serving as secondary (Wilding, 1994). The Soil Quality Institute of the USDA-NRCS considers dynamic soil properties to be those that change with land use and management (Fig. 18) or natural disturbances and cycles (such as seasonal and diurnal changes), and that are important for characterizing soil functions and ecological processes and for predicting soil behavior. Grossman et al. (2001) also suggested use-dependant properties to be those

52

H. LIN ET AL.

Figure 18 Combining dynamic soil properties (or use-dependent) and properties inherent from natural soil-forming processes (or use-invariant) to form a composite record for soil interpretations in soil survey databases. The control section used in the Soil Taxonomy is generally below the dynamic surface soil. The distinction between major soil management types within the same soil series using the concepts of ‘‘genoform’’ and ‘‘phenoform’’ (Droogers and Bouma, 1997) separates the morphogenetic properties used in taxonomic units while near surface temporal properties used in cartographic units that are management driven soil survey units. (Modified from Grossman et al., 2001.)

soil properties that show change and respond to soil use and management (such as soil organic matter levels and aggregate stability), and use-invariant properties to be those soil properties inherent from natural soil-forming processes that show little change over time and are not affected by soil use and management (such as mineralogy and particle size distribution). In the context of hydropedology, hydrology is apparently a major driving force of dynamic soil systems, including changes in soil types and soil properties. For example, soil type changes could result from erosion, deposition, and altered hydrological conditions (Ashby, 1987). Fitzpatrick et al. (1992) have shown that yellow and grey duplex soils (Natraqualfs) in Australia have transformed to saline sulfidic march soils (Sulfaquents) in some sub-catchments where rising saline water tables have resulted from land clearing. Soil moisture regimes play significant roles in classifying soils in Soil Taxonomy and other soil classification systems (Soil Survey Staff, 1999; Buol et al., 2001). In terms of hydrodynamics of soil properties, an example from a Vertisol is used here to illustrate the importance of hydrology (Fig. 19). This clay soil was very dry in August, and many extremely coarse (1–3 cm wide) cracks appeared even in the tillage pan of the Ap2 horizon, thus in situ measured apparent steady-state infiltration rates were high in both Ap1 (0–0.1 m) and Ap2 (0.1–0.27 m) horizons. At the end of August, heavy


53

Figure 19 An illustration of temporal dynamics of apparent steady-state infiltration rates in the Ap1 and Ap2 horizons of the Ships clay (Chromic Hapludert) at supply potentials of 0 (i0) and 0.03 m (i 0.03) for a period of about 2 months. The initial gravimetric soil moisture content (Wi) at each measurement occasion is also indicated. (Modified from Lin et al., 1998.)

rains from a tropical storm occurred for about a week. As a result, soil moisture content increased significantly, and consequently, soil macroporosity decreased drastically (e.g., many cracks closed). The infiltration rates measured in early September became very low in both Ap horizons. After the rains, as the soil dried up under high evaporative demand in hot summer, surface crusts formed, and intercrust cracks gradually reappeared at the soil surface. Consequently, infiltration rates in the Ap1 horizon gradually increased. The drying process in the tillage pan was

54

H. LIN ET AL.

much slower, and the re-opening of cracks in the Ap2 horizon was not evident even a month after the rains, thus its infiltration rates remained fairly low. Besides precipitation, ground water could also have a significant impact on dynamic soil properties. For example, Henry et al. (1985) investigated the role of ground water discharge as a factor in soil salinization under Saskatchewan conditions and reported that the salt content in the soils was linearly proportional to the sodium percentage of the aquifer due to upward water movement. Salinity over an artesian marine aquifer in the Glacial Lake Agassiz in North Dakota created a large (about 77,000 ha) unproductive area surrounding by prime farmland (Doering and Benz, 1972; J. Richardson, personal communication).

IV.

FUTURE NEEDS IN ADVANCING HYDROPEDOLOGY

Future needs in advancing hydropedology may be encapsulated in a philosophy termed ‘‘bridging disciplines, scales, data, and education.’’ This philosophy is critical in promoting the integration of relevant disciplines and in educating the next generation of soil scientists and hydrologists.

A. SYSTEMS APPROACHES TO UNDERSTANDING AND COMMUNICATING LANDSCAPE–SOIL–WATER DYNAMICS Soil and water spatial-temporal distribution is both driven by and contributes to the landscape. Thus, a systems approach to understanding and communicating landscape–soil–water dynamics is needed. Such an approach would facilitate the development of conceptual and mathematical models of landscape hydrology and pedogenesis. Considering the unfolding research landscape of the future, Bouma (2005) pointed out the essential role of a systems approach to solving complex environmental problems. He stated that specialistic input is not always effective when working in international panels or interdisciplinary teams. He introduced a joint learning trajectory that can be effective in creating true cooperation and interchange. Bouma (2005) further suggested that the role of hydropedology in formulating environmental policies can best be considered from two points of view: first, ‘‘up to global’’—its role in the international panels in which policy issues are discussed and negotiations take place. The issue is not so much that pedologists and hydrologists should be present in such panels but, rather, that what they have to offer in terms of expertise should be taken into account when formulating policy options. This requires effective communication and a critical analysis of our own discipline by taking a broad view of the issues at hand and attempting to understand the state of mind not


55

only of colleagues in the natural sciences such as climatology and geology but also, and more importantly, of economists and political scientists. Second, ‘‘down to local’’—once international treaties have been agreed upon, they have to be followed by implementation on the national or local level. This requires input from science, but on a level that recognizes local conditions and that is prepared to fine-tune its approach accordingly. Work is ideally done by interdisciplinary teams and a systems approach.

1.

What It Takes to Study Landscape Phenomena

Several aspects that would facilitate holistic studies of landscape-oriented flow and transport phenomena in natural soils are the following: 1. Soil–landscape mapping is important for capturing patterns of variability, which can give us a much better handle on the space-time dynamics of flow systems than point data. For example, Klemes (1986) pointed out that ‘‘It . . . seems obvious that search for new measurement methods that would yield areal distributions, or at least reliable areal totals or averages, of hydrologic variables such as precipitation, evapotranspiration, and soil moisture would be a much better investment for hydrology than the continuous pursuit of a perfect massage that would squeeze the nonexistent information out of a few poor anemic point measurements. . .’’ 2. Pattern identification at different scales is a key concept that needs to be emphasized in landscape studies. Sivapalan (2003), in addressing the connection between process complexity at hillslope scale and process simplicity at the watershed scale, suggested that ‘‘One way to achieve this reconciliation is to focus on common concepts, features, or patterns that have physical meanings that transcend the range of scales in question, and which are easily scalable. . . . They will lead to parsimonious models and, over time, can also assist us in the development of a new theory of hydrology at the watershed scale by shifting the focus away from smallscale theories at the hillslope (or lower) scales and towards new hydrologic concepts that transcend spatial scales, which are also worthy of study in their own right.’’ 3. Essential to future landscape hydrology is a concerted program of extensive and thorough experimental research on watershed scale dynamics (e.g., Baveye and Boast, 1999; Grayson and Blo¨schl, 2000; Hornberger and Boyer, 1995). This clearly calls for a change of attitude in the scientific community. The collection and analysis of field data have been undervalued in the present computer modeling frenzy. In the past, many research publications were devoted to field data collection, analysis, and interpretation. Indeed, these provided some of the fundamental insights

56

H. LIN ET AL.

into pedogenic and catchment processes. Yet today such publications are very limited (Grayson and Blo¨schl, 2000; Hornberger and Boyer, 1995). 4. An iterative loop and interaction of ‘‘understanding, sampling, and modeling’’ is central to enhanced studies of landscape–soil–water dynamics. As noted by Grayson and Blo¨schl (2000), we generally begin with some process understanding, do some sampling to improve that understanding, and when we have enough understanding to be able to attempt a conceptualization, we build a model. Hopefully this model increases our understanding of the processes and, with some more sampling for proper testing, we iteratively refine our modeling and understanding. Because we can rarely sample densely enough to fully capture the spatial-temporal variability of the system, we must exploit our understanding of dominant processes at different scales, identify patterns that link point observations to areal phenomena, and use such knowledge to implement optimal design of sampling. 2.

Hydropedological Approaches to Landscape–Soil–Water Dynamics Across Scales

Hydropedological approaches call for exploration of the most effective manner to integrate pedological and hydrological expertise and the use of the state-of-the-art techniques in mapping, monitoring, and modeling. It is also important to ‘‘look first, then measure’’ in designing monitoring and measurement protocols based on soil morphology and soil distribution. Integration of geostatistics with geospatial techniques, coupled with understanding from pedological and hydrological expertise, would enhance interpolation and extrapolation of point observations to areal coverages. Many previous studies in soil hydrology have undersampled in space or time, or both. Hydropedological approaches would require adequate spatial coverage and a sufficiently long monitoring period to understand the system well. Three hydropedological approaches to landscape–soil–water studies are suggested here: 1. Mapping, monitoring, and modeling (‘‘3M’’) of landscape–soil–water systems: Much effort by non-pedologists is hampered because soil distribution and processes are not well understood such that site selection for sampling or monitoring and the design of modeling do not represent actual distribution and processes. To connect pedon and landscape phenomena, one of the keys lies in the distribution of various soils over the landscape (i.e., soil patterns). We normally monitor pedons to collect point data and model landscapes trying to understand areal distributions. The key connecting the two is the mapping of various soil and other landscape features. Relatively static properties of soil and landscape features (such as topography and soil type) may be mapped out to assist


57

in scaling and modeling of landscape–soil–water dynamics, while more dynamic properties (such as hydrology and land use) could be monitored to refine model predictions. The fabric of soil over the landscape could also help sampling design in both horizontal and vertical directions. For instance, it is more meaningful for the vertical layout of monitoring devices to correspond to soil horizons rather than equal depth increments that ignore soil vertical layering. Mapping also provides a means of diagnosing the landscape (e.g., identifying some patterns using geospatial maps, remote-sensing imagery, historical records, or soil–landscape surveys) before designing experiments and selecting monitoring sites (we may call this ‘‘map first, then design’’ in landscape studies). 2. Integrating geostatistical and geospatial techniques (‘‘2GS’’) into a Bayesian hierarchical multiscale modeling framework: Lin and Rathbun (2003) proposed a Bayesian hierarchical multiscale modeling framework as an infrastructure for linking soil properties to climatic, topographical, geological, and vegetative processes, and to bridge data collected at multiple scales of spatial support. In this framework, the importance of soil map, DEM, land use, and other geospatial data is emphasized in enhancing the use of geostatistics and in predicting the spatial-temporal patterns of soil properties. Enhanced predictions can be achieved through a combined use of ground-based point observations, GIS-based vector or raster maps of various scales, and remote-sensing imagery, together with pedological expertise about soil–landscape distribution. Combining data collected at different scales of spatial support is achieved by partitioning the modeling effort into separate process modeling and data modeling stages. The process modeling stage consists of modeling the joint probability distribution of all variables at the point scale. Given such a model, the joint distribution of the data collected at different scales of spatial support may be obtained in the data modeling stage. Under the Bayesian inferential paradigm, the effects of all sources of variation, including those attributed to model components and those attributed to the process of data collection, on the uncertainty of model predictions are readily quantified. Moreover, prior beliefs regarding the spatial distribution of landscape–soil–water systems may be readily incorporated into the Bayesian hierarchical modeling framework. 3. Strategic spatial modeling and scaling: There are several routes to move from point scale input at sampling sites to arial coverage of block scale output using a process-based simulation model (Heuvelink and Pebesma, 1999). The routes depend on the sequence of three separate steps involved: interpolating, aggregating, and running the model. This issue has been referred to as the choice between ‘‘calculate first, interpolate later’’ or ‘‘interpolate first, calculate later’’ (Heuvelink and Pebesma, 1999; Stein et al., 1991). The preferred route suggested by Heuvelink and Pebesma

58

H. LIN ET AL.

(1999) is to interpolate point input data first, then run the model at point locations within a desired block, and last aggregate model outputs spatially for areal coverage, thereby avoiding direct application of the model at a larger spatial extent. This approach has been used in many coupled GIS simulation modeling systems to address both spatial and temporal dimensions (e.g., Clarke et al., 2002; Goodchild et al., 1996).

3.

Sustainable Land Use Planning and Proactive Design

Land use planning provides an excellent basis for joint work of pedologists and hydrologists. Soil and water professions must become more heavily involved in land use planning across the spectrum of applications of societal importance. For example, the Dutch ‘‘layer-model’’ planning considers three ‘‘layers’’ (Bouma, 2005). The first one represents the natural dynamics of land and water; the second one is all the networks of roads, railways, and waterways; and the third one is human settlements. Ideally, new land use plans should consider the sequence from one to three, taking into account first the dynamics of land and water, which is the most difficult to affect or should not be affected in sensitive areas in which substantial damage could occur. Next, infrastructure networks have a higher degree of permanence than settlements, which readily expand and contract. This approach offers an attractive platform for applied hydropedology, working with other professions as well. The manner in which the natural landscape ‘‘throbs’’ offers clues as to what can best be done and where it can be done with the lowest risks and the greatest opportunities. Most work in pedology and hydrology in the past has been rather reactive in character. Either questions raised by others were answered or given conditions were characterized, more often than not representing problems caused by poor land use (Bouma, 2005). This has, of course, allowed excellent research, but why not also take an occasional more proactive approach? Why not take a given soil, consider climate conditions and landscape features, and design a soil structure that would best satisfy conflicting demands, for instance, a structure that would allow optimal rooting, supply a relatively high amount of moisture, be trafficable, and avoid bypass flow of agrochemicals? Bouma et al. (1999) have made an attempt to do this. The focus could also be shifted to the landscape scale, taking into account the ‘‘layer-model’’ mentioned previously. As is, we dutifully document errors that engineers and architects have made. Why not proactively design optimal soil and landscape structures based on comparing effects of different flow patterns and deliver these to engineers and architects with an invitation to realize them in practice?


59

B. FROM VARIABILITY TO PATTERN AND THEIR RELATIONS TO SCALE Pedologists are foremost among those basic soil scientists who help develop integrated system models to scale up knowledge from small samples to the global pedosphere (Fig. 8) (Sposito and Reginato, 1992). Pedologists have studied both the mechanisms and the magnitudes of spatial variability of soils and landforms as a basis for broad generalizations about soil genesis, classification, and mapping, while soil physicists and hydrologists have studied scaling theories such as similitudes and fractals and quantify spatial variability using methods such as geostatistics and temporal variability using time series analysis (Lin, 2003). As pointed out by Nielsen and Wendroth (2003), while there exists a versatile and powerful set of statistical tools for diagnosing spatially and temporally variable field observations, we have to explore the cause of variation and improve and expand soil classification concepts. The efforts made by pedologists, soil physicists, and hydrologists on soil variability and scaling do not seem to have converged well in the past. It is to be hoped that hydropedology will generate new opportunities for such needed synergistic efforts. There are several possible ways to help de-mystify the mind-boggling variability of field soils, especially if the synergies are put together jointly by pedologists, soil physicists, and hydrologists. These include (1) systematic understanding of soil variability as a function of various space-time factors, (2) using pattern identification and the concept of pedodiversity, and (3) organizing soil variability based on hierarchical multiscale frameworks. Each of these approaches is further discussed in the following sections. 1.

Soil Variability as a Function of Space-Time Factors

Except for some possible scale-invariant soil properties and processes, different spatial or temporal variations are generally observed depending on the scale. In a general conceptual framework, the magnitude of soil variability (SV ) is influenced by at least five space-time factors, i.e., spatial extent (e) or area size, spatial resolution (r) or map scale, spatial location (l ) and physiographical region, specific soil property or process ( p), and time factor (t). Conceptually, this may be expressed as: SV ¼ f ðe; r; l; p; t; . . .Þ;

ð8Þ

where the dots indicate additional unspecified factors. Unlike the soilforming factorial model of Eq. [7], Eq. [8] is intended to be a functional expression rather than a causational relationship. The exact expression of Eq. [8] is very difficult, if not impossible, to establish, in part because of the

60

H. LIN ET AL.

diversity and complexity of the relationship. Nevertheless, Eq. [8] may serve as a useful conceptual framework for organizing our knowledge about soil variability. Broadly speaking, it may be expected that as spatial extent (e), spatial resolution (r), or time scale (t) increase, the magnitude of soil variability would increase, reaching a possible maximum and then would start to stabilize or decrease as space or time dimensions continue to increase; however, the mode and magnitude of such changes would depend on where the soil is located, in what landscape (i.e., spatial location l ), and which soil type or specific soil property (i.e., p) is of concern. Numerous publications have provided evidence that supports the conceptualization of Eq. [8] (e.g., Burrough, 1993; Heuvelink and Webster, 2001; Lin et al., 2004; Wilding and Dress, 1983; Wilding et al., 1994). However, there is still a great need to further the understanding of the complexity, diversity, interactions, and quantification related to Eq. [8]. For example, the magnitude of soil variability generally increases with increasing spatial extent from individual pedons to pedons that meet the soil series concept to mapping units of a given series to all soils within a survey area. But for some soils, the variability may occur at a limited segment of the landscape, while for others, maximum variability may occur at long-range intervals corresponding to mapping units, geomorphic units, or physiographic regions. Spatial variability with increasing spatial extent may be linear, curvilinear, or other forms depending on soil types and landscape features. It is apparent that when soil sample size is changed from small cores to field plots, soil structure becomes more influential; when sample size is further enlarged from field plots to watersheds, variation in topography, land use, and other landscape features will have a more significant impact on soil variability. Spatial resolution or map scale indicates the level of detail that can be discerned during the survey process or in cartographic representation of soils information. As spatial resolution or map scale increases (like ‘‘zoom in’’), new levels of detail are realized, while general patterns may be lost. On the other hand, implicit representation of soil variability decreases as data are aggregated from field-collected information to more generalized soil maps. Some studies have suggested scale independence or self-similarity of pattern and form in thematic maps of soil (e.g., Burrough, 1981, 1983a,b, 1993). Burrough (1989) reviewed the evidence for fractals in soil variation and concluded that while some properties showed linear log-log variograms over a range of spatial scales, thus suggesting fractal scaling, many plots displayed clear breaks of slope that implied a transition from one pattern of variation at one scale to another pattern at a larger scale. Such departures from the ideal fractal model led Burrough (1983a) to propose a nested model of stochastic


61

functions as a basis for understanding multiscale spatial variation. However, fractal or multifractal models need to be further investigated and elaborated for potential bridging multiscales of diverse soil and landscape properties and processes (Crawford et al., 1999; McBratney, 1998). Not all soil map units at a given scale share the same range of soil properties. Some parts of the landscape are more variable (such as areas with steep slopes), and thus a map unit delineating such an area may have a broader range of characteristics than the one delineated on a more level portion of the same landscape. However, in addition to topography, the regional and local scale impacts of climate, hydrology, organisms, parent materials, and human activities on soil variability can also be significant. For example, soils on nearly level fluvial flood plains are some of the most spatially variable soils in a landscape, but if soils have developed from loess superposed on top of a terrace position, then these soils would be quite uniform. Wilding et al. (1994) suggested that soil spatial variability increases with the nature of parent materials in the following order: loess < till < fluvial deposits < phroclastic and tectonic rocks < drastically disturbed materials. Not all soil properties or processes vary in a similar manner. More stable soil properties such as texture, mineralogy, soil thickness, and color are less variable than more dynamic properties such as moisture content, infiltration rate, hydraulic conductivity, redox state, biological activity, and organic matter content. For soil hydraulic properties, the observed coefficients of variation are often much higher, commonly over 100% (e.g., Jury, 1986; Wilding and Drees, 1983). Surface soils have more dynamic changes and thus tend to have a higher magnitude of variability, while subsoils tend to have smaller variability. However, such differentiation depends on many other factors, such as human activities, hydrology, geology, and landforms. In terms of the time factor, Beckett (1987) pointed out that the temporal variability of soil nutrient status may equal or exceed spatial variability. Burrough (1993) suggested that if one soil-forming process dominates for a long time, it usually leads to a reduction of soil variability. For example, tropical weathering can give rise to large areas of apparently uniform soils (e.g., Oxisols), in which variations caused by differences in parent materials and relief can be reduced by long periods of deep weathering under a tropical humid climate. Temporal variability of soil physical and hydrological properties has been studied, but dynamic change of soil types (taxonomic units) used in soil survey and mapping has received little attention (excluding the change of Soil Taxonomy and taxonomists over time). Although it generally takes thousands of years to form a natural soil, the growing recognition of man-made changes to soils has elevated the importance of dynamic soil changes, especially those related to land use and management.

62

H. LIN ET AL.

2.

Pattern Identification at Various Scales

Patterns are everywhere in nature, from the ‘‘blue marble’’ view of the earth from space, to ‘‘fractal tree-like’’ channels of streams vivid in remotesensing imagery, to dyed pictures of preferential flow observed in field soils. Pattern, or spatial-temporal organization, offers rich and comprehensive insights regarding many phenomena in nature. Observation and interpretation of spatial-temporal patterns are thus fundamental to many areas of the earth sciences such as geology, geomorphology, pedology, and hydrology. Indeed, soil mapping is based on identifying soil–landscape patterns at various scales, often depicted in 3-D block diagrams (e.g., Fig. 5) or 2-D soil maps. We believe that to advance the knowledge base of hydropedology, and to answer many questions regarding the earth’s critical zone, we need to explore the information that resides in the myriad of patterns observable in the pedosphere and the hydrosphere at different space-time scales. We should emphasize the importance of spatial pattern identification in combination with long-term monitoring in our scientific investigations. For example, as demonstrated in an impressive volume of work complied by Grayson and Blo¨schl (2000), there is rich information in spatial patterns that provides much more stringent tests of hydrological models and much greater insights into hydrological behavior than traditional methods. They pointed out two catalysts that brought the issues of patterns to the forefront of hydrologists’ minds (and, we believe, to soil scientists’ minds as well): 1. The readily available DEMs and an array of analysis that is possible with these data, accelerated by the ever–decreasing cost of computing power and vastly available geospatial technologies and databases; 2. The rise in environmental awareness of the broader community and its subsequent impact on the research and management of natural resources. We now want to know not only the quantity and quality of soil and water resources, but also from where and when any contaminants come and where and when best to invest scarce resources to help rectify the problem. In principle, we now have the tools available to undertake spatialtemporal modeling of environmental response, and the spatially distributed and temporally dynamic models in combination with attractive color maps that geospatial technologies generate can seduce even the most skeptical of politicians and administrators (Grayson et al., 1993). However, while our ability to generate patterns using computers might be impressive, it is not of use by itself. What is important is the extent to which these patterns represent reality and to which they provide us with new insights into natural processes (Grayson and Blo¨schl, 2000). Observed spatial-temporal patterns of pedologically and hydrologically important variables are not very common (other than terrain, land use, and in some cases general soil types). To


63

progress, we will need to make measurements different from those used in the past, requiring the development of new instruments and approaches. We will also need to develop more sophisticated methods to analyze spatialtemporal patterns and to correlate measured patterns against model predictions. Grayson and Blo¨schl (2000) predicted that testing models by comparing simulated and observed patterns will eventually become commonplace and will provide a quantum advance in the confidence we could place on predictions from distributed hydrological models. Pattern identification may also offer another way of de-mystifying soil variability. This could be represented either explicitly (e.g., mapped spatial pattern) or statistically (e.g., geostatistical distribution functions). A number of recent catchment hydrology field investigations demonstrate how the understanding and modeling of hydrological processes can be improved by the use of observed spatial patterns. For example, in the humid climate of the Tarawarra catchment in Australia, through extensively observed TDR soil moisture data, spatial-temporal patterns of soil moisture were revealed (Fig. 20; See Color Insert): in a wet winter, surface and lateral flow was dominant, producing a topographically organized spatial pattern, while in a dry summer, there was minimum lateral redistribution and fluxes were essentially vertical, thus producing a random pattern that was not related to topography (Grayson et al., 1997; Western et al., 1999). Analysis of remotely sensed soil moisture patterns in the semi-arid Walnut Gulch watershed in Arizona indicated that, following a rainstorm, these patterns were organized but this organization faded away after the storm, and the pattern became random (Houser et al., 2000). The authors suggested that this change-over was a reflection of the changing control on soil moisture by rainfall vs soil characteristics during the dry-down process. However, some spatial patterns of soil moisture are temporally persistent (the notion of ‘‘time stability’’) (Vachaud et al., 1985). Evidence for time stability has been found by Grayson and Western (1998), Kachanoski and de Jong (1988), Mohanty and Skaggs (2001), and others. However, time stability of spatial pattern may be a function of spatial scale and may vary across a landscape with different soil types, as shown by Kachanoski and de Jong (1988) and by Zhang and Berndtsson (1991). This implies that soil water variability needs to be analyzed simultaneously in both space and time. In this regard, pattern recognition offers special advantages when analyzing time-dependent properties in space (e.g., Fu, 1982; Zhang and Berndtsson, 1991). Grayson and Blo¨schl (2000) illustrated the implications of different patterns on hydrological response in a watershed. Figure 21 (See Color Insert) shows two simulated patterns of soil moisture deficit, each with the same properties of mean, variance, and correlation length, but one spatially random and the other ‘‘organized’’ by a wetness index. These two patterns

64

H. LIN ET AL.

produce different responses to a given rainfall input: the organized pattern gives higher and earlier runoff peaks than the random case for small rainfall events, while the reverse is true for larger precipitation events (Fig. 21). Four types of spatial patterns may be identified based largely on the source and nature of data. Such patterns could be obtained through direct measurements (with or without interpolations), indirect interpretations, or surrogate correlations (Grayson and Blo¨schl, 2000): 1. ‘‘Lots of points’’ pattern: When there is a sufficiently dense array of point measurements, a pattern could be generated by interpreting point data (Grayson et al., 2002). The array may be based on random, grid, or various stratified designs. The quality of such an interpolated pattern depends on the number and distribution of point data, the accuracy of the original point measurements, and how well the interpolation/extrapolation method reflects the underlying spatial structure of the measured property. 2. Vector pattern: This pattern includes stream and road networks, geological faults, soil polygons, clay cracking pattern, preferential flow patterns, and many other line features depicted on a map. This type of pattern can be easily stored and analyzed in a GIS. 3. Raster pattern: This type of pattern comes largely from remote-sensing imagery. It could be qualitative binary (e.g., snow or no snow interpreted from SPOT satellite), multinary (e.g., various land use/land cover interpreted from Landsat satellite), or quantitative (e.g., brightness temperature obtained from airborne electronically scanned thinned array radiometer, or ESTAR). Even for a binary pattern, studies have demonstrated that a wealth of information can be revealed, such as saturated or unsaturated conditions (Troch et al., 2000), whether runoff occurred or not (Vertessy et al., 2000), recharge or discharge (Salvucci and Levine, 2000), and with or without snow cover (Tarboton et al., 2000). This led Grayson and Blo¨schl (2000) to call for a change in attitude toward ‘‘non-quantitative’’ data and hydrological model structures. As suggested by Grayson et al. (2002), remotely sensed data might be used to assist in reducing the degrees of freedom in distributed hydrological models by providing patterns rather than absolute values of important inputs. Such a philosophy could also be applied to many pedological and soil survey data. 4. Surrogate pattern: This refers to surrogate data showing correlation to the pattern of interest but uses data that are much easier to collect in a spatially distributed fashion (Grayson et al., 2002). For example, we cannot directly obtain a map of soil hydraulic conductivity or bulk density from remote sensing, yet it is such parameters that hydrological models need. As demonstrated by Mattikalli et al. (1998), some of the


65

remote-sensing instruments (e.g., ESTAR and synthetic aperture radar, or SAR) can provide information on characteristics related to these variables, but not the variables themselves. This presents a major challenge for hydrologists and soil scientists, i.e., to build models or pedotransfer functions that are able to exploit the information that is coming from remote-sensing platforms (Grayson et al., 2002). Once pattern information is obtained, it can be used in many beneficial ways, such as (1) designing experimental setup and field data collection strategy; (2) stratified interpolations/extrapolations of sparse point data; (3) characterizing and modeling variability such as spatial correlation and connectivity; (4) refining model structure for enhanced modeling of landscape–soil–water dynamics; and (5) use in combination with time series data to provide more realistic space-time simulations of soil moisture and many other pedological and hydrological phenomena. Related to pattern identification is yet another positive way of looking at soil variability, that is, the concept of pedodiversity (or soil diversity), which is analogous to biodiversity (Ibanez et al., 1995, 1998). There are two essential components of biodiversity: the variety (or number) of species (richness) and their spatial distribution or pattern (evenness) (Magurran, 1988). Indices of diversity often attempt to incorporate both components of diversity into a single figure, or else they tend to neglect one or another (Ibanez et al., 1995). However, unlike plants, animals, or other organisms, for which each individual is a discrete entity clearly separated from each other, soils are a continuum over the landscape, often without a clearcut distinction between individual soils. Moreover, specific soil properties are often more of concern to a particular application rather than generic soil types that are more suited for general land use planning. For example, precision agriculture is more focused on spatial distribution of soil nutrients and pH values over a farm field, nonpoint source pollution modeling is more interested in soil hydraulic properties distribution over the landscape, and carbon sequestration is more concerned with soil organic matter and inorganic carbon dynamics. Therefore, it is important that pedodiversity addresses not only the number of soil types, but also specific soil properties that are often of practical concern. While the concept of pedodiversity is still in its infancy, we suggest that four components of pedodiversity be differentiated: (1) the number of soil types and their relative abundance within an area, (2) the variation of specific soil properties, (3) the spatial distribution or pattern of soil types and various properties, and (4) the temporal dynamics of soil types and specific properties. The first two components are directly linked to the development and use of soil maps and the latter two are related to the identification of soil spatial-temporal patterns.

66

H. LIN ET AL.

3.

Hierarchical Frameworks for Bridging Multiscales in Hydropedology

Scale transfer or multiscale bridging remains the heart of many hydrological and pedological studies (e.g., Baveye and Boast, 1999; Lin, 2003; Sposito, 1998). At present, no single theory that is ideal for spatial aggregation or upscaling and disaggreagation or downscaling of soils information has emerged. The major contenders seem to be either scaling via a naturally defined or human-defined hierarchy or through potential continuous hierarchies as suggested by fractal theory (Fig. 8) (e.g., Cushman, 1990; Lin and Rathbun, 2003; McBratney, 1998; Nielsen and Wendroth, 2003; Wagenet, 1998; Vogel and Roth, 2003). Hierarchical frameworks have been conceptualized by soil scientists as a means for organizing multiple spatial and temporal scales from the soil pore to the pedosphere (e.g., Hoosbeek and Bryant, 1992; Sposito and Reginato, 1992; Wilding, 2000). Hierarchical complexity has been studied in pedology, which has long recognized self-organized complexity in the processes of soil formation, with taxonomic frameworks constructed to summarize that ordering (Buol et al., 2001). If properly constructed, a hierarchy of soil systems should reflect logical links and quantitative relationships among scales. It can be argued, however, that the soil scientists’ hierarchy of scales is more an operational or observational device, based on the ability or feasibility to measure, rather than fundamental differences in basic processes (Wagenet, 1998). As suggested by Wagenet (1998), an examination of ecological hierarchy theory (Haigh, 1987; O’Neill et al., 1986, 1989) should present some valuable philosophical and practical concepts pertaining to the translation of information across scales in soil systems. Hierarchy theory in ecology defines ‘‘holons,’’ which are nested spatial units characterized by integrated biological, physical, and chemical processes (Haigh, 1987). In comparison, soil science uses entities that are less well defined and procedures that are less integrated. Lin and Rathbun (2003) discussed two hierarchical frameworks for bridging multiscales in hydropedology through either a data-driven or a processbased approach (Fig. 22). In the first, the soil mapping hierarchy depicts soil spatial distribution over landscapes of varying sizes, considering five orders of soil surveys, spatial aggregations of soil map units, and various applications of geostatistics. The merger of geostatistics with traditional soil mapping has led to encouraging new developments of environmental correlation modeling and landscape-guided soil mapping. In the second, the soil modeling hierarchy deals with soil process models at different scales. While the current generation of surface and subsurface process models is strongly scale dependent because of process representations, parameter requirements, and changes of support in model variables, several approaches for scale bridging

ADVANCES IN HYDROPEDOLOGY 67

Figure 22 Two hierarchical frameworks for bridging multiscale: Hierarchies of (A) soil mapping (for soil distributions) and (B) soil modeling (for soil processes). SSURGO, STATSGO, and NATSGO are county-, state-, and national-level soil maps, respectively. (From Lin and Rathbun, 2003.)

68

H. LIN ET AL.

are available, including upscaling, downscaling, upscaling with downscaling embedded, strategic cyclical scaling, and strategic spatial scaling (e.g., Lin and Rathbun, 2003; Mulla and Addiscott, 1999; Root and Schneider, 1995). In moving beyond the notion of ‘‘trying to model everything,’’ we should be developing methods to identify dominant processes that control pedological and hydrological responses in various environments at different scales, and then develop models to focus on these dominant processes (a notion called the ‘‘dominant processes concept’’) (Grayson and Blo¨schl, 2000). Vogel and Roth (2003) discussed different approaches for incorporating spatial heterogeneity into modeling flow and transport in soils, including many concepts for the organization of heterogeneities, such as macroscopic homogeneity, discrete hierarchy, continuous hierarchy, and fractals (Fig. 8). They further suggested a ‘‘scaleway’’ as a promising tool for predictive modeling of flow and transport in the subsurface at any scale. This conceptual approach is based on the explicit consideration of spatial structure that is assumed to be present at any scale of interest, while the microscopic heterogeneities are replaced by averaged, effective description. The three ingredients needed in their approach are (1) the structure of the medium, which must be known, (2) the corresponding effective material properties, and (3) a process model at the scale of interest. They demonstrated the scaleway concept for the prediction of a breakthrough curve in an undisturbed soil column using structural information from two scales. In view of a common limitation of deterministic approaches to quantify multiscale dynamics of hydropedological processes, Lin and Rathbun (2003) also suggested a Bayesian hierarchical multiscale modeling framework, which has been successfully applied to several environmental applications (e.g., Gotway and Young, 2002; Wikle et al., 2001). However, such a framework, as highlighted in Section IV.A.2, has yet to be applied to soil science and hydrology.

C. FROM PEDOTRANSFER FUNCTIONS TO SOIL INFERENCE SYSTEMS AND HYDROPEDOINFORMATICS Pedotransfer functions (PTFs) relate simple soil characteristics often found in soil surveys to more complex parameters that are needed in modeling and that are relatively difficult to measure (Bouma, 1989; Bouma and van Lanen, 1987). The basic idea of PTFs may be generalized to include the derivation of any needed soil attribute, which is not directly available, based on available soils data. McBratney et al. (2002) further broadened the concept of PTF to soil inference systems (SINFERS), in which PTFs form knowledge rules for inference engines and uncertainty analysis is included in prediction. Bouma (2005) suggested that SINFERS is a promising approach


69

that extends the PTF concept beyond the derivation of soil moisture retention and hydraulic conductivity. Many publications on the subject of PTFs have appeared in the past two decades or so, largely centered around the estimation of soil hydraulic properties using basic soils data (e.g., Pachepsky and Rawls, 2005; van Genuchten et al., 1999; Wo¨sten et al., 2001). This illustrates the significance of the combined approach of hydropedology. The USDA-NRCS and other national and international organizations are also pursuing PTFs for populating data in soil survey databases. While various degrees of success have been achieved with different PTFs (e.g., Pachepsky et al., 1999; Wo¨sten et al., 2001), limitations, uncertainties, and risks remain. Uncritical application of automatically generated PTFs is likely to produce poor results when used with no feeling for what may broadly be expected or when the functions were derived from data obtained for other soils than the ones being characterized (Bouma, 2005). 1. Enhancements of Soil Databases and Pedotransfer Functions Lin (2003) assessed the current status and future opportunities of PTFs. He pointed out several areas needing improvements, including the need for (1) exploring fundamental mechanisms underlying PTFs, (2) linking pedon data to landscape features, (3) incorporating soil structural information, (4) considering spatial and temporal scales, and (5) improving practicality of PTFs to enhance the value of soil survey databases. In the following, we suggest additional areas for which enhancement of soil databases and PTFs would be worth pursuing: 1. One critical point made by Lilly and Lin (2005) and Wo¨sten et al. (2001) is that major progress in PTFs is not to be expected from new statistical methods, but rather from better data. While empirical, regression, or functional approaches continue to be used in PTFs, new methods for developing and using PTFs are increasingly being explored, including artificial neural networks (e.g., Minasny et al., 1999; Schaap and Bouten, 1996), regression trees (e.g., McKenzie and Jacquier, 1997; Rawls and Pachepsky, 2002a), and the group method of data handling (e.g., Pachepsky and Rawls, 1999). However, the success of any mathematical or statistical techniques will be heavily dependent on the quality, quantity, comparability, and transferability of the original data stored in the databases. Without the foundation of reliable and systematic databases, no matter how sophisticated the techniques used in deriving or using PTFs, the outputs would be futile and misleading. 2. As alluded to by Lin (2003), it would be more beneficial if flow patterns (related to flow mechanisms and pathways) could also be determined from soil survey and related landscape databases. In this regard, classification

70

3.

4.

5.

6.

7.

H. LIN ET AL.

or functional grouping of soils based on flow and transport characteristics would be worthwhile, particularly if linked to soil map units. This could provide a means of estimating a priori how important preferential flow phenomenon is in a given soil or location (Jury, 1999). As illustrated by Moore et al. (1993), Rawls and Pachepsky (2002b), and others, topography helps the understanding of causation and correlation of soil properties with landscape positions. Hence, ‘‘toporectifying’’ PTFs through taking into account topographic attributes would improve landscape-based PTFs. Most data in traditional soil survey databases are collected during a specific time window. Recognizing the importance of use-dependent and other dynamic soil properties, the USDA-NRCS is now considering the development of a dynamic soil properties database. Such a database, once developed, would significantly enhance the utility of soil survey databases and the development of dynamic PTFs. We believe hydropedology is a helpful framework that can provide a bridge connecting dynamic soil properties and traditional soil survey databases. Pedology traditionally has focused on natural processes that do not reflect the effects of short-term soil management. This was done on purpose to avoid frequently changing classifications of a given soil following different types of soil management. Droogers and Bouma (1997) suggested the term ‘‘genoform’’ for genetically defined soil series and the term ‘‘phenoform’’ for soil types resulting from a particular form of management in a given genoform (Fig. 18). Such a distinction between major soil management types within the same soil series facilitates the incorporation of management effects on soil properties and could potentially enhance PTFs that involve soil series and land uses as carriers of soil hydraulic information (e.g., Pulleman et al., 2000; Sonneveld et al., 2002). Remote-sensing techniques offer significant opportunities for soil scientists to infer the state of soil based on surface-oriented patterns and to extend these sensor techniques both laterally and vertically to describe the dynamic 3-D nature of the soil with spatially variable properties across landscapes (Nielsen et al., 1996). Thus, PTFs utilizing remote-sensing inputs would be attractive. As alluded to in Section IV.B.2, ‘‘nonquantitative’’ data such as binary or multinary remotely sensed data could be used to assist in reducing the degrees of freedom in models by providing patterns rather than absolute values of important inputs. Soil maps can no longer be static documents. Rather, derivative and dynamic maps, created for specific purposes or functions, must be generated from original soil maps and tailored to particular applications. Thus, PTFs, in combination with computer models and geospatial databases, need to be integrated into expert systems to derive such maps. Until now,


71

there has been a lack of appropriate means of producing derivative and dynamic maps such as soil hydraulic properties through space and time. 2.

Soil Reference Systems and Hydropedoinformatics

McBratney et al. (2002) proposed the concept of SINFERS, in which a set of properly and logically conjoined PTFs serve as the knowledge rules for inference engines. Such a SINFERS takes known measurements with a given level of (un)certainty and infers desirable unknown data with minimal inaccuracy allowed in the system. The SINFERS has a source, an organizer, and a predictor; together they serve as a decision support system for appropriate use of PTFs with uncertainty estimation. The sources are collections of soil databases and PTFs, which could also include geospatial data. The organizer arranges and categorizes the PTFs with respect to their required inputs and soil types from which they were generated. The inference engine is a collection of ‘‘if-then’’ type of logical rules for selecting appropriate PTFs with the minimum variance possible. The uncertainty of the prediction is assessed using Monte Carlo simulations, which can be quantified in terms of the model uncertainty and input data uncertainty (McBratney et al., 2002). Sommer et al. (2003) presented an integrated method for soil–landscape analysis, in which a hierarchical expert system for multidata fusion of inquires, relief analysis, geophysical measurements (such as EM38), and remote-sensing data was developed. They further combined the soil-forming factorial model with the scaleway of Vogel and Roth (2003) to address soil variability across scales. In their system, soil variability is separated at every scale into (1) a scale-typical and predictable part and (2) a random part, which becomes structure at the next lower scale level. To integrate knowledge, scales, and databases of interactive pedological and hydrological processes and properties and to streamline information capture, storage, visualization, modeling, and decision-making, we suggest hydropedoinformatics. The term is coined from hydroinformatics and pedometrics, both of which have received growing interest in recent years. Hydroinformatics is the study of the flows of knowledge and data related to water flow and all it transports, together with interactions with both natural and man-made environments (Abbott, 1991). It is a discipline that has strong ancestry in computational sciences and artificial intelligence, where GIS and data mining (artificial neural networks and genetic algorithms specifically) are the new technologies with probably the widest applicability to the water industry (Savic and Walters, 1999). Pedometrics is the application of mathematical, statistical, numerical, and artificial intelligence methods to soil science in general and soil surveys in particular (McBratney, 1986; Webster, 1994). Pedometricians treat soil properties as spatially correlated random

72

H. LIN ET AL.

processes and tap the richness of geostatistics and artificial intelligence for analysis and prediction. However, although pedometrics has helped in elucidating pedogenesis by quantifying relations between individual soil properties and controlling soil-forming factors, solving the full system of multivariate equations needed to describe soil genesis at different scales remains one of the biggest challenges for pedometricians (Webster, 1994). To make hydropedoinformatics meaningful, one critical need is a network of well-designed and carefully maintained natural laboratories for systematic field data collections. Soil science and hydrology communities have long recognized the fundamental need for multiscale, multidisciplinary, and longterm field experiments, including better archiving and sharing of field data across geographic regions (e.g., NRC, 1999, 2001a). The natural laboratory concept is the basis for the U.S. National Science Foundation’s Long-Term Ecological Research Network, established in 1980 for investigating ecological processes operating over extended periods (months to centuries) at a variety of spatial scales (from 10 m to continental) (NRC, 2001a). The existing NCSS program and several other national field experimental networks (such as the land-grant universities’ experimental stations, the USDAARS’s experimental watersheds, the USGS’s large basin gauging stations, and the DOE’s waste disposal sites) could serve as good starting points for exploring such coordinated efforts. The NCSS has provided over 100 years of soil inventory, measurement, and evaluation, and currently maintains several national databases (such as SSURGO and STATSGO, official soil series descriptions, soil characterization laboratory database, soil climate monitoring network, and wet soil monitoring network). These databases need to be well coordinated and better utilized in both the development of PTFs and the construction of integrated hydropedoinformatic systems.

D. EDUCATION OF THE NEXT GENERATION OF SOIL SCIENTISTS AND HYDROLOGISTS Effective education in the 21st century takes on two new emphases— integrated multidisciplinary and technology enhanced (e.g., Boyer Commission, 1998; NRC, 1998). It is believed that constructivist-based education must begin to replace transmission-dominated education if we are to train professionals who can solve interdisciplinary problems. In this respect, integrated broad-based education and new technologies promise opportunities to infuse cognitive learning and problem solving into curricula. For example, a wave of ‘‘watershed thinking’’ is spreading across the United States and around the globe as watershed-based approaches offer a more integrated way to address natural resources and environmental issues holistically. Accordingly, new


73

educational programs should provide training for holistic interdisciplinary and technology-enhanced approaches to soil and water studies. Effective watershed management also requires the integration of theory, field data, simulation models, expert judgments, policies and regulations, socio-economic factors, and ethics in solving practical problems (NRC, 1999). 1.

Interdisciplinary and Integrative Knowledge Base and Skills

The interdisciplinary emphasis of education in the 21st century makes hydropedology a timely addition to the education of the next generation of soil scientists and hydrologists. Hydropedology by its very nature is interdisciplinary. As such, future hydropedologists must have an integrated knowledge base in pedology, soil physics, and hydrology as well as in other related bio- and geosciences such as geomorphology, stratigraphy, hydrogeology, hydroclimatology, ecohydrology, landscape ecology, and other branches of soil science. In a sense, pedology itself is an integrative earth science. Hence, hydropedology education should cover a broad spectrum of topics dealing with the earth’s critical zone (Fig. 1). Quantitative hydropedology requires the use of mathematics/(geo)statistics and simulation modeling. Thus, pedometrics including spatial-temporal statistics (e.g., Nielsen and Wendroth, 2003) should be an integral part of hydropedological education. It is also important that future hydropedologists possess skills in geospatial and other emerging information technologies, as well as advanced instrumentation, to enable them to collect, visualize, analyze, and model spatial-temporal patterns of landscape–soil–water dynamics across scales. 2.

Fundamental Importance of Field Work

Field work is a distinct aspect in geosciences, including hydropedology. Field work provides the basis for understanding a variety of earth processes and validating model, laboratory, and remote-sensing results (NRC, 2001a). In particular, field survey and mapping of landscape–soil–water systems are fundamental skills for the next generation of hydropedologists. Butler (1980) pointed out that soil survey is ‘‘one of the basic technologies of soil science.’’ Kutı´lek and Nielsen (1994) suggested that a modeler should work in or at least supervise the experimental activity in the field. Vice versa, effective field experimentation requires the theoretical knowledge of the modeler. Hydropedology blends together field work, laboratory experiments, and computer modeling into an integrated approach to understand landscape–soil–water dynamics. Sometimes experimental activities take place in the laboratory; other times they take place in the field, where the comfort of laboratory

74

H. LIN ET AL.

control is lost. However, field work (observations, monitoring, experiments, and verifications) ultimately serves as the initial drive of identifying real-world problems, formulating theories of processes and events, and validating models and their predictions. Field work is the foundation of all, and the logical sequence of hydropedological research is from the field to laboratory and to modeling, and then back to the field. An adequate understanding of this sequence has a profound impact on how we educate future generations of hydropedologists.

V. CONCLUDING REMARKS The critical zone is perhaps the most heterogeneous and complex region of the earth and the only region of the solid earth readily accessible to direct observations (NRC, 2001a). Integrated studies of the critical zone require interdisciplinary and multiscale approaches. Because soil and water are integral parts of the earth’s critical zone, hydropedology is important to address interactive pedological and hydrological processes and their properties in the unsaturated zone. Hydropedology represents a paradigm shift in our basic thinking and approach to ped, pedon, landscape, watershed, regional, and global scale analysis of soil and water interactions. The birth of modern soil science started with the recognition of soilforming factors and related processes (Dokuchaev, 1893). The soil as a natural resource has historically been extensively explored for agricultural production in order to provide food, feed, fiber, and fuel for the ever-growing human population. In past decades, awareness of environmental protection and ecosystem sustainability has driven much of soil science development. In the 21st century, with the need for integrated approaches to study the earth’s critical zone, it is paramount that soil science become an integral part of the earth, environmental, and ecological sciences, in addition to continuing the services to agricultural communities. Getting back to the root in geosciences, and hence completing a full circle, provides a more realistic picture of what soil science can and ought to contribute to our science and society. In examining the opportunities in the hydrological sciences, a group of experts under the auspices of the National Research Council claimed that ‘‘We cannot build the necessary scientific understanding of hydrology at a global scale from the traditional research and educational programs that have been designed to serve the pragmatic needs of the engineering community’’ (NRC, 1991, p. 33). Similarly, we believe that we cannot build the necessary scientific understanding and appreciation of hydropedology (and perhaps soil science as a whole) at a global level from the traditional


75

research and educational infrastructures that have been designed to serve the pragmatic needs of the agricultural community. The area of hydrogeosciences has emerged as a compelling discipline given its links to a broad area of environmental, ecological, geological, agricultural, and natural resource issues. This area has substantial potential as an area of tremendous growth. Hydrogeoscientists are encountering a new intellectual paradigm that emphasizes connections between the hydrosphere and other components of the earth system (Fig. 1). For example, Entekhabi et al. (1999) proposed an agenda for land surface hydrology research in the 21st century as hydrological research at the interface between the atmosphere and land surface is undergoing a dramatic change in focus, driven by new societal priorities, emerging technologies, and better understanding of the earth system. They also called for the second International Hydrological Decade to open the debate for more comprehensive prioritization of science and application activities in the hydrological sciences. Another example is the U.S. Department of Energy’s formulation of the National Roadmap for Vadose Zone Science and Technology, which calls for a national science program to implement, fund, and coordinate interdisciplinary research into vadose zone fluid flow and contaminant transport and fate (Stephens et al., 2002). It is becoming more and more recognized that to understand fully the distribution of contaminants in the surface and subsurface environments, one must consider the movement of water and chemicals in the vadose zone, especially the flow and transport processes occurring in structured soils and fractured rocks that are of vital concern in nuclear waste disposal and toxic chemical sites. The third example is the emerging ecohydrology that addresses the interface between the hydrosphere and the biosphere and that examines the mutual interaction between the hydrological cycle and ecosystems (Eagleson, 2002; Rodrı´guez-Iturbe, 2000). Soil moisture, a key variable in ecohydrology, modulates the complex dynamics of the climate–soil–water–vegetation system and controls the spatial and temporal patterns of vegetation. We believe that hydropedology is a timely addition to this exciting era of interdisciplinary and systems approaches to study the pedosphere, the hydrological cycle, the earth’s critical zone, and the earth system.

ACKNOWLEDGMENTS H. L. thanks Dr. Donald Sparks for his invitation to contribute this manuscript to Advances in Agronomy. H. L.’s contribution to this work was partially supported by a grant from the USDA-CSREES National Research Initiative (#2002-35102-12547).

76

H. LIN ET AL.

REFERENCES Abbott, M. B. (1991). ‘‘Hydroinformatics—Information Technology and the Aquatic Environment’’. Avebury Technical, Aldershot, UK. Abbott, M. B., and Refsgaard, J. C. (1996). ‘‘Distributed Hydrological Modelling’’. Kluwer, Dordrecht, The Netherlands. Addiscott, T. M. (1977). A simple computer model for leaching in structured soils. J. Soil Sci. 28, 554–563. Ahuja, L. R., and Hebson, C. (1992). ‘‘Root Zone Water Quality Model’’. GPSR Technical Report No. 2, USDA-ARS, Fort Collins, CO. American Association for the Advancement of Science (AAAS) Council (2001). Report of the 2001 AAAS Council meeting. Science 292, 2509. Amundson, R., Harden, J., and Singer, M. (Eds.) (1994). In ‘‘Factors of Soil Formation: A Fiftieth Anniversary Retrospective.’’ Special Publ. #33, Soil Sci. Sco. Am. Madison, WI. Anderson, A., McBratney, A. B., and Crawford, J. W. (1998). Applications of fractals to soil studies. Adv. Agron. 63, 1–76. Anderson, J. L., and Bouma, J. (1973). Relationships between hydraulic conductivity and morphometric data of an argillic horizon. Soil Sci. Soc. Am. Proc. 37, 408–413. Anderson, M. G., and Burt, T. P. (Eds.) (1990). ‘‘Studies in Hillslope Hydrology’’. Wiley, New York. Anderson, M. P. (1997). Characterization of geological heterogeneity. In ‘‘Subsurface Flow and Transport’’ (G. Dagan and S. P. Neuman, Eds.), pp. 23–43. Cambridge Univ. Press, Cambridge, UK. Anderson, S. H., and Hopmans, J. W. (Eds.) (1994). ‘‘Tomography of Soil-Water-Root Processes’’. SSSA Special Pub. #36. Soil Sci. Soc. Am., Inc., Madison, WI. Arnold, R. W., and Wilding, L. P. (1991). The need to quantify spatial variability. In ‘‘Spatial Variabilities of Soils and Landforms’’ (M. J. Mausbach and L. P. Wilding, Eds.), pp. 1–8. SSSA Special Pub. #28. Soil Sci. Soc. Am., Inc., Madison, WI. Ashby, W. C. (1987). Forests. In ‘‘Restoration Ecology: A Synthetic Approach to Ecological Research’’ (W. R. Jordan III, M. E. Gilpin, and J. D. Aber, Eds.), pp. 89–109. Cambridge Univ. Press, Cambridge, UK. Bartoli, F., Dutartre, P., Gomendy, V., Niquet, S., Dubuit, M., and Vivier, H. (1998). Fractals and soils structure. In ‘‘Fractals in Soil Science’’ (P. Baveye, J.-Y. Parlange, and B. A. Stewart, Eds.), Advances in Soil Science, pp. 203–232. CRC Press, Boca Raton, FL. Batjes, N. H. (1996). Development of a world data set of soil water retention properties using pedotransfer rules. Geoderma. 71, 31–52. Baveye, P., and Boast, C. W. (1998). Concepts of ‘‘fractals’’ in soil science: Demixing apples and oranges. Soil Sci. Soc. Am. J. 62, 1469. Baveye, P., and Boast, C. W. (1999). Physical scales and spatial predictability of transport processes in the environment. In ‘‘Assessment of Non-Point Source Pollution in the Vadose Zone’’ (D. L. Corwin, K. Loague, and Ellsworth, Eds.), pp. 261–280. Geophysical Monograph 108. American Geophysical Union, Washington, DC. Baveye, P., Parlange, J.-Y., and Stewart, B. A. (1999).‘‘Fractals in Soil Science’’. Advances in Soil Science. CRC Press, Boca Raton, FL. Bear, J. (1972). ‘‘Dynamics of Fluids in Porous Media’’. Elsevier, New York, NY. Beckett, P. H. T. (1987). Spatial variability of soil K—Status. In ‘‘Proceedings of the 20th Colloquium’’, pp. 357–380. International Potash Institute, Bern. Betson, R. P. (1964). What is watershed runoff? J. Geophys. Res. 69, 1541–1552.


77

Beven, K. (1986). Runoff production and flood frequency in catchment of order n: An alternative approach. In ‘‘Scale Problem in Hydrology’’, (V. K. Gupta, I. Rodriguez-Iturbe, and E. F. Wood, Eds.), pp. 107–131. Reidel, Dordrecht. Beven, K. (1997a). TOPMODEL: A critique. Hydro. Process 11, 1069–1085. Beven, K. (Ed.) (1997b). In ‘‘Distributed Modelling in Hydrology: Application of the TOPMODEL concept’’. Wiley, Chichester. Beven, K. (2002). Towards an alternative blueprint for a physically based digitally simulated hydrologic response modeling system. Hydrol. Process 16, 189–206. Beven, K., and Feyen, J. (2002). The future of distributed modeling. Hydro. Process 16, 169–172. Beven, K., and Freer, J. (2001). A dynamic TOPMODEL. Hydro. Process 15, 1993–2011. Beven, K., and Kirkby, M. J. (1979). A physically based variable contributing area model of basin hydrology. Hydro. Sci. Bull. 24, 43–69. Blo¨schl, G., and Sivapalan, M. (1995). Scale issues in hydrological modelling—A review. Hydro. Process 9, 251–290. Blo¨schl, G., and Grayson, R. (2000). Spatial observations and interpolation. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Blo¨schl, Eds.), pp. 17–50. Cambridge University Press, Cambridge, UK. Boorman, D. B., Hollis, J. M., and Lilly, A. (1995). ‘‘Hydrology of Soil Types: A Hydrologically-Based Classification of the Soils of the United Kingdom’’. Institute of Hydrology Report No. 126, Institute of Hydrology, Wallingford. Bouma, J. (1984). Using soil morphology to develop measurement methods and simulation techniques for water movement in heavy clay soils. In ‘‘Proceedings of the ISSS Symposium on Water and Solute Movement in Heavy Clay Soils’’ (J. Bouma and Raats P. A. C., Eds.), pp. 298–316. ILRI Pub. 37, Wageningen. Bouma, J. (1989). Using soil survey data for quantitative land evaluation. Adv. Soil Sci. 9, 177–213. Bouma, J. (1990). Using morphometric expressions for macropores to improve soil physical analyses of field soils. Geoderma 46, 3–13. Bouma, J. (1992). Effects of soil structure, tillage, and aggregation upon soil hydraulic properties. In ‘‘Interacting Processes in Soil Science’’ (R. J. Wagenet, P. Bavege, and B. A. Stewart, Eds.), pp. 1–36. Lewis Publishers, Boca Raton, FL. Bouma, J. (1997). Long-term characterization: Monitoring and modelling. In ‘‘Methods for the Assessment of Soil Degradation’’ (R. Lal, W. H. Blum, C. Valentin, and B. A. Stewart, Eds.), pp. 339–358. Advances in Soil Science. Bouma, J. (1999). The strength of the past is the key to the future. In ‘‘Proceedings of the International Symposium on Soil Resources: Their Inventory, Analysis, and Interpretation for Use in the 21st Century’’. Minneapolis, MN. Bouma, J. (2005). Hydropedology as a powerful tool to environmental policy research. Geoderma, in press. Bouma, J., and Anderson, J. L. (1973). Relationships between soil structure characteristics and hydraulic conductivity. In ‘‘Field Soil Water Regime’’ (R. R. Bruce, K. Flach, and H. M. Taylor, Eds.), pp. 77–105. SSSA special publication No. 5. Madison, WI. Bouma, J., and Hole, F. D. (1971). Soil morphology and hydraulic conductivity of adjacent virgin and cultivated pedons at two sites: A Typic Argiudoll (silt loam) and a Typic Eutrochrept (clay). Soil Sci. Soc. Am. Proc. 35, 316–319. Bouma, J., and van Lanen H. A. J. (1987). Transfer functions and threshold values: From soil characteristics to land qualities. In ‘‘Proceedings of the ISSS/SSSA Workshop on Quantified Land Evaluation’’ (K. J. Beek et al., Eds.), pp. 106–110. Washington, DC. April 27–May 2, 1986. Int. Inst. for Aerospace Surv. and Earth Sci. Publ. No. 6. ITC Publ. Enschede, The Netherlands.

78

H. LIN ET AL.

Bouma, J., Dekker, L. W., and Haans, J. C. F. M. (1980). Measurement of depth to water table in a heavy clay soil. Soil Sci. 130, 264–270. Bouma, J., Droogers, P., and Peters, P. (1999). Defining the ‘‘ideal’’ soil structure in surface soil of a Typic Fluvaquent in the Netherlands. Soil Sci. Soc. Am. J. 63, 343–348. Bouma, J., Jongerius, A., and Schoonderbeek, D. (1979). Calculation of saturated hydraulic conductivity of some pedal clay soils using micromorphological data. Soil Sci. Soc. Am. J. 43, 261–264. Bouma, J., Jongmans, A. G., Stein, A., and Peek, G. (1989). Characterizing spatially variable hydraulic properties of a boulder clay deposit. Geoderma 45, 19–31. Boyer Commission (on Educating Undergraduates in the Research University) (1998). ‘‘Reinventing Undergraduate Education—A Blueprint for America’s Research Universities’’. http://notes.cc.sunysb.edu/Pres/boyer.nsf. Breeuwsma, A., Wo¨sten, J. H. M., Vleeshouwer, J. J., van Slobbe, A. M., and Bouma, J. (1986). Derivation of land qualities to assess environmental problems from soil survey. Soil Sci. Soc. Am. J. 50, 186–190. Brewer, R. (1976). ‘‘Fabric and Mineral Analysis of Soils’’. Robert E. Krieger Publishing Company, Huntington, NY. Broderson, W. D. (2003). ‘‘From the Surface Down: An Introduction to Soil Surveys for Agronomic Use’’. USDA-NRCS, Washington, DC. Brown, R. B., and Huddleston, J. H. (1991). Presentation of statistical data on map units to the user. In ‘‘Spatial Variabilities of Soils and Landforms’’ (M. J. Mausbach and L. P. Wilding, Eds.), pp. 127–147. SSSA Special Pub. #28. Soil Sci. Soc. Am., Inc., Madison, WI. Bryant, R. B., and Olsen, C. G. (1987). Soil genesis: Opportunities and new directions for research. In ‘‘Future Developments in Soil Science Research’’ (L. L. Boersma, Ed.), pp. 301–311. SSSA, Madison, WI. Buol, S. W., Southard, R. J., Graham, R. C., and McDaniel, P. A. (2001). ‘‘Soil Genesis and Classification’’, 5th ed. Iowa State University Press, Ames. IA. Burrough, P. A. (1981). The fractal dimensions of landscapes and other environmental data. Nature 294, 240–242. Burrough, P. A. (1983a). Multiscale sources of spatial variation in soil. I. The application of fractal concepts to nested levels of soil variation. J. Soil Sci. 34, 577–597. Burrough, P. A. (1983b). Multiscale sources of spatial variation in soil. II. A non-Brownian fractal model and its application to soil survey. J. Soil Sci. 34, 599–620. Burrough, P. A. (1989). Fuzzy mathematical methods for soil survey and land evaluation. J. Soil Sci. 40, 477–492. Burrough, P. A. (1993). Soil variability: A late 20th century view. Soils Fert. 56, 529–562. Burt, T. P., and Butcher, D. P. (1985). A basic hillslope runoff model. J. Soil Sci. 36, 469–486. Butler, B. E. (1980). ‘‘Soil Classification for Soil Survey’’. Clarendon Press, Oxford, UK. Chadwick, O. A., Brimhall, G. H., and Hendricks, D. M. (1990). From a black to gray box—A mass balance interpretation of pedogenesis. Geomorphology 3, 369–390. Chesworth, W. (1973a). The parent rock effect in the genesis of soil. Geoderma 10, 215–225. Chesworth, W. (1973b). The residua system of chemical weathering: A model for the chemical breakdown of silicate rocks at the surface of the earth. J. Soil Sci. 24, 69–81. Ciolkosz, E. J., Waltman, W. J., Simpson, T. W., and Dobos, R. R. (1989). Distribution and genesis of soils of the northeastern United States. Geomorphology 2, 285–302. Clarke, K. C., Parks, B. O., and Crane, M. P. (2002). ‘‘Geographic Information Systems and Environmental Modeling’’. Prentice Hall, NJ. Coen, G. M., and Wang, C. (1989). Estimating vertical saturated hydraulic conductivity from soil morphology in Alberta. Can. J. Soil Sci. 69, 1–16. Corwin, D. L., Loague, K., and Ellsworth, T. R. (1998). Assessing nonpoint source pollution in the vadose zone. Eos. 79, 219–220.


79

Corwin, D. L., Loague, K., and Ellsworth, T. R. (1999). Advanced information technologies for assessing nonpoint source pollution in the vadose zone: Conference overview. J. Environ. Qual. 28, 357–365. Corwin, D. L., Waggoner, B. L., and Rhoades, J. D. (1991). A functional model of solute transport that accounts for bypass. J. Environ. Qual. 20, 647–658. Crawford, J. W., Baveye, P., Grindrod, P., and Rappoldt, C. (1999). Application of fractals to soil properties, landscape patterns, and solute transport in porous media. In ‘‘Assessment of Non-Point Source Pollution in the Vadose Zone’’ (D. Corwin, K. Loague, and T. Ellsworth, Eds.), pp. 151–164. American Geophysical Union, Washington, DC. Cushman, J. H. (1990). An introduction to hierarchical porous media. In ‘‘Dynamics of Fluids in Hierarchical Porous Media’’ (J. H. Cushman, Ed.), pp. 1–6. Academic Press, London. Dane, J., and Clark, T. (Eds.) (2002). In ‘‘Methods of Soil Analysis: Part 4, Physical Methods’’. SSSA Book Series No. 5 Soil Sci. Soc. Am., Inc., Madison, WI. Dekker, L. W., and Ritsema, C. J. (2003). Wetting patterns in water repellent Dutch soils. In ‘‘Soil Water Repellency’’ (C. J. Ritsema and L. W. Dekker, Eds.), pp. 151–166. Elsevier, Amsterdam. Dekker, L. W., Wosten, J. H. M., and Bouma, J. (1984). Characterizing the soil moisture regime of a Typic Haplohumod. Geoderma 34, 27–42. Deurer, M., Green, S. R., Clothier, B. E., Bo¨ttcher, J., and Duijnisveld, W. H. M. (2003). Drainage networks in soils: A concept to describe bypass-flow pathways. J. Hydro. 272, 148–162. Doering, E. J., and Benz, L. C. (1972). Pumping an artesian source for water table control. J. Irrig. Drain. Div. ASCE 98, 275–287. Dokuchaev, V. V. (1893). ‘‘The Russian Steppes and Study of Soil in Russia, Its Past and Present’’ (Translated by J. M. Crawford). Ministry of Crown Domains, St. Petersburg, Russia. Domenico, P. A., and Schwartz, F. W. (1998). ‘‘Physical and Chemical Hydrogeology’’, 2nd ed. Wiley, New York. Droogers, P., and Bouma, J. (1997). Soil survey input in exploratory modelling of sustainable land management practices. Soil Sci. Soc. Am. J. 61, 1704–1710. Droogers, P., van der Meer, F. B. W., and Bouma, J. (1997). Water accessibility to plant roots in different soil structures occurring in the same soil type. Plant Soil 188, 83–91. Duffy, C., Wierenga, P. J., and Kselik, R. A. (1981). Variations in infiltration rate based on soil survey information and field measurements. New Mexico Agric. Exp. Sta. Bull. 680, 1–39. Dunne, T., and Black, R. D. (1970a). An experimental investigation of runoff production in permeable soils. Water Resour. Res. 6, 478–490. Dunne, T., and Black, R. D. (1970b). Partial areal contributions to storm runoff in a small New England watershed. Water Resour. Res. 6, 1296–1311. Eagleson, P. S. (2002). ‘‘Ecohydrology: Darwinian Expression of Vegetation Form and Function’’. Cambridge University Press, Cambridge, UK. Entekhabi, D., Asrar, G. R., Betts, A. K., Beven, K. J., Bras, R. L., Duffy, C. J., Dunne, T., Koster, R. D., Lettenmaier, D. P., McLaughlin, D. B., Shuttleworth, W. J., van Genuchten, M. Th., Wei, M.-Y., and Wood, E. F. (1999). An agenda for land surface hydrology research and a call for the second international hydrological decade. Bull. Am. Metero. Soc. 80, 2043–2058. Evans, C. V., and Franzmeier, D. P. (1986). Saturation, aeration, and color patterns in a toposequence of soils in North-central Indiana. Soil Sci. Soc. Am. J. 50, 975–980. Famiglietti, J. S., and Wood, E. F. (1994). Multiscale modeling of spatially variable water and energy balance processes. Water Resour. Res. 30, 3061–3078. Fitzpatrick, R. W., Naidu, R., and Self, P. G. (1992). Iron deposits and microorganisms occurring in saline sulfidic soils with altered soil water regime in the Mt. Lofty Ranges, South Australia. Catena Suppl. 21, 263–286.

80

H. LIN ET AL.

Flury, M., Fluhler, H., Jury, W. A., and Leuenberger, L. (1994). Susceptibility of soils to preferential flow of water: A field study. Water Resour. Res. 30, 1945–1954. Foussereau, X., Hornsby, A. G., and Brown, R. B. (1993). Accounting for variability within map units when linking a pesticide fate model to soil survey. Geoderma 60, 257–276. Franzmeier, D. P. (1991). Estimation of hydraulic conductivity from effective porosity data for some Indiana soils. Soil Sci. Soc. Am. J. 55, 1801–1803. Franzmeier, D. P., Yahner, J. E., Steinhardt, G. C., and Sinclair, Jr., H. R. (1983). Color patterns and water-table levels in some Indiana soils. Soil Sci. Soc. Am. J. 47, 1196–1202. Fritsch, E., and Fitzpatrick, R. W. (1994). Interpretation of soil features produced by ancient and modern processes in degraded landscapes. I. A new method for constructing conceptual soil-water-landscape models. Aust. J. Soil Res. 32, 889–907. Fu, K. S. (1982). ‘‘Applications of Pattern Recognition’’. CRC Press, Boca Raton, FL. Galusky, L. P. F., Jr. (1997). Toward the development of quantitative soil morphological indicators of water table behavior. Ph. D. dissertation. University of Maryland. Galusky, L. P., Rabenhorst, M. C., and Hill, R. L. (1998). Toward the development of quantitative soil morphological indicators of water table behavior. In ‘‘Quantifying Soil Hydromorphology’’ (M. C. Rabenhorst, J. C. Bell, and P. A. McDaniel, Eds.), pp. 77–93. SSSA Special Pub. #54, Soil Sci. Soc. Am., Inc., Madison, WI. Gerke, H. H., and van Genuchten, M. Th. (1993). A dual-porosity model for simulating the preferential movement of water and solutes in structured porous media. Water Resour. Res. 29, 305–319. German, P. F., and Beven, K. (1985). Kinematic wave approximation to infiltration into soils with sorbing macropores. Water Resour. Res. 21, 990–996. German, P. F., and Beven, K. (1986). A distribution function-approach to water-flow in soil macropores based on kinematic wave theory. Journal of Hydrology 83, 173–183. Gerrard, A. J. (1981). ‘‘Soils and Landforms—An Integration of Geomorphology and Pedology’’. George Allen & Unwin, London. Goodchild, M. F., Steyaert, L. T., Parks, B. O., Johnson, C., Maidment, D., Crane, M., and Glendinning, S. (Eds.) (1996). In ‘‘GIS and Environmental Modeling: Progress and Research Issues’’. GIS World, Inc., Fort Collins, CO. Gotway, C. A., and Young, L. J. (2002). Combining incomplete spatial data. J. Am. Stat. Assoc. 97, 632–648. Grayson, R., and Blo¨schl, G. (Eds.) (2000). In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’. Cambridge University Press, Cambridge, UK. Grayson, R. B., and Western, A. W. (1998). Towards areal estimation of soil water content from point measurements: Time and space stability of mean response. J. Hydro. 207, 68–82. Grayson, R., Blo¨schl, G., Barling, R. D., and Moore, I. D. (1993). Process, scale and constraints to hydrological modeling in GIS. In ‘‘Applications of Geographic Information Systems in Hydrology and Water Resources’’ (K. Kovar and H. P. Nachtnebel, Eds.), pp. 83–92. Proc. Vienna Symp. IAHS Pub. #211. Grayson, R., Blo¨schl, G., Western, A. W., and McMahon, T. A. (2002). Advances in the use of observed spatial patterns of catchment hydrological response. Adv. Water Res. 25, 1313–1334. Grayson, R., Western, A. W., Chiew, F. H. S., and Blo¨schl, G. (1997). Preferred states in spatial soil moisture patterns: Local and nonlocal controls. Water Resour. Res. 33, 2897–2908. Grossman, R. B., Harms, D. S., Seybold, C. A., and Herrick, J. E. (2001). Coupling usedependent and use-invariant data for soil quality evaluation in the United States. J. Soil Water Conserv. 56, 63–68. Gwo, J. P., Jardine, P. M., Wilson, G. V., and Yeh, G. T. (1995). A multiple-pore-region concept to modeling mass transfer in subsurface media. J. Hydro. 164, 217–237. Haigh, M. J. (1987). The holon: Hierarchy theory and landscape research. Catena 10, 181–192.


81

Hall, G. F., and Olson, C. G. (1991). Predicting variability of soils from landscape models. In ‘‘Spatial Variabilities of Soils and Landforms’’ (M. J. Mausbach and L. P. Wilding, Eds.), pp. 9–24. Special Pub. #28. Soil Sci. Soc. Am., Madison, WI. Hamlen, C. J., and Kachanoski, R. G. (1992). Field solute transport across a soil horizon boundary. Soil Sci. Soc. Am. J. 56, 1716–1720. Hendrickx, J. M. H., and Flury, M. (2001). Uniform and preferential flow mechanisms in the vadose zone. In ‘‘Conceptual Models of Flow and Transport in the Fractured Vadose Zone’’, pp. 149–187. National Academy Press, Washington, D.C. Henry, J. L., Bullock, P. R., Hogg, T. J., and Luba, L. D. (1985). Groundwater discharge from glacial and bedrock aquifers as a soil salinization factor in Saskatchewan. Can. J. Soil Sci. 65, 749–768. Heuvelink, G. B. M., and Pebesma, E. J. (1999). Spatial aggregation and soil process modeling. Geoderma. 89, 47–65. Heuvelink, G. B. M., and Webster, R. (2001). Modelling soil variation: Past, present, and future. Geoderma. 100, 269–301. Hilgard, E. W. (1921). ‘‘Soils: Their Formation, Properties, Composition, and Plant Growth in the Humid and Arid Regions’’. Macmillan Co., London. Hodgson, J. M. (1997). ‘‘Soil Survey Field Handbook: Describing and Sampling Soil Profiles’’, 3rd ed. Soil Survey Technical Monograph #5, Soil Survey and Land Research Centre, Silsoe, UK. Hole, F. D. (1976). Soils of Wisconsin. Wis. Geol. Nat. Hist. Surv. Bull. 87, Soils Ser. 62, University of Wisconsin Press, Madison, WI. Hoosbeek, M. R., and Bryant, R. B. (1992). Towards the quantitative modeling of pedogenesis—A review. Geoderma 55, 183–210. Hoosbeek, M. R., van Es, H. M., and Stein, A. (Eds.) (1998). In Special issue—Modeling spatial and temporal variability as a function of scale Geoderma 85, 111–254. Hoogmoed, W. B., and Bouma, J. (1980). A simulation model for predicting infiltration into cracked clay soil. Soil Sci. Soc. Amer. J. 44, 458–461. Hornberger, G. M., and Boyer, E. W. (1995). Recent advances in watershed modeling. Rev. Geophys. 33, 949–957. Horton, R. E. (1945). Erosional development of streams and their drainage basins: Hydrophysical approach to quantative morphology. Bull. Geol. Soc. Am. 56, 275–370. Houser, P., Goodrich, D., and Syed, K. (2000). Runoff, precipitation, and soil moisture at Walnut Gulch. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Bloschl, Eds.), pp. 125–157. Cambridge University Press, Cambridge, UK. Hugget, R. J. (1975). Soil landscape systems: A model of soil genesis. Geoderma. 13, 1–22. Ibanez, J. J., De-Alba, S., Bermudez, F. F., and Garcia-Alvarez, A. (1995). Pedodiversity: Concepts and measures. Catena 24, 215–232. Ibanez, J. J., De-Alba, S., Lobo, A., and Zucarello, V. (1998). Pedodiversity and global soil patterns at coarse scales (with discussion). Geoderma 83, 171–192. Jackson, T. J., and Le Vine, D. E. (1996). Mapping surface soil moisture using an aircraft-based passive instrument: Algorithm and example. J. Hydro. 184, 85–99. Jenkinson, B. J., Franzmeier, D. P., and Lynn, W. C. (2002). Soil hydrology on an end moraine and a dissected till plain in west-central Indiana. Soil Sci. Soc. Am. J. 66, 1367–1376. Jenny, H. (1941). ‘‘Factors of Soil Formation—A System of Quantitative Pedology’’. McGrawHill, NY. Jenny, H. (1961). Derivation of state factor equations of soils and ecosystems. Soil Sci. Soc. Am. J. 25, 385–388. Jenny, H. (1980). ‘‘The Soil Resource, Origin, and Behavior’’. Springer-Verlag, NY. Johnson, D. L., and Hole, F. D. (1994). Soil formation theory: A summary of its principal impacts on geography, geomorphology, soil-geomorphology, quaternary geology, and

82

H. LIN ET AL.

paleopedology. In ‘‘Factors of Soil Formation: A Fiftieth Anniversary Retrospective’’ (R. Amundson, J. Harden, and M. Singer, Eds.), pp. 111–126. Soil Sci. Soc. Am., Special Publ. #33, Madison, WI. Johnson, D. L., and Watson-Stegner, D. (1987). Evolution model of pedogenesis. Soil Sci. 143, 349–366. Johnson, D. L., Keller, E. A., and Rockwell, T. K. (1990). Dynamic pedogenesis: New views of some key soil concepts and a model for interpreting quaternary soils. Quat. Res. 33, 306–319. Jury, W. A. (1982). Simulation of solute transport using a transfer function mode. Water Resour. Res. 18, 363–368. Jury, W. A. (1986). Spatial variability of soil properties. In ‘‘Vadose Zone Modeling of Organic Pollutants’’ (S. C. Hern and S. M. Melancon, Eds.), pp. 245–269. Lewis Publishers, Chelsea, MI. Jury, W. A., Sposito, G., and White, R. E. (1986). A transfer function model of solute movement through soil. I. Fundamental concepts. Water Resour. Res. 22, 243–247. Jury, W. A. (1999). Present directions and future research in vadose zone hydrology. In ‘‘Vadose Zone Hydrology—Cutting across Disciplines’’ (M. B. Parlange and J. W. Hopmans, Eds.), pp. 433–441. Oxford University Press, NY. Jury, W. A., and Roth, K. (1990). ‘‘Transfer Function and Solute Movement through Soil: Theory and Applications’’. Birkha¨user, Basel. Kachanoski, R. G., and de Jong, E. (1988). Scale dependence and the temporal persistence of spatial patterns of soil water storage. Water Resour. Res. 24, 85–91. Khan, F. A., and Fenton, T. E. (1994). Saturated zones and soil morphology in a Mollisol catena of Central Iowa. Soil Sci. Soc. Am. J. 58, 1457–1464. King, J. J., and Franzmeier, D. P. (1981). Estimation of saturated hydraulic conductivity from soil morphological and genetic information. Soil Sci. Soc. Am. J. 45, 1153–1156. Kirkby, M. J. (1975). Hydrograph modelling strategies.. In ‘‘Process in Physical and Human Geography’’ (R. Peel, M. Chisholm, and P. Haggett, Eds.), pp. 69–90. Heinemann, London. Kirkby, M. J. (Ed.) (1978). In ‘‘Hillslope Hydrology’’. Wiley and Sons, New York. Kleiss, H. J. (1970). Hillslope sedimentation and soil formation in northeastern Iowa. Soil Sci. Soc. Am. J. 34, 287–290. Klemes, V. (1986). Dilettantism in hydrology: Transition or destiny? Water Resour. Res. 22, 177s–188s. Kutı´lek, M. (1966). ‘‘Soil Science in Water Management’’ (In Czech: ‘‘Vodohospodarska Pedologie’’). SNTL, State Publisher of Technical Literature, Prague. Kutı´lek, M. (1978). ‘‘Soil Science in Water Management’’, 2nd ed. (In Czech: ‘‘Vodohospodarska Pedologie’’). SNTL, State Publisher of Technical Literature, Prague. Kutı´lek, M. (2004). Soil hydraulic properties as related to soil structure. Soil Tillage Res., in press. Kutı´lek, M., and Nielsen, D. R. (1994). ‘‘Soil Hydrology’’. Catena Verlag, Cremlingen-Destedt, Germany. Lammers, D. A., and Johnson, M. G. (1991). Soil mapping concepts for environmental assessment. In ‘‘Spatial Variabilities of Soils and Landforms’’ (M. J. Mausbach and L. P. Wilding, Eds.), pp. 149–160. SSSA Special Pub. #28. Soil Sci. Soc. Am., Inc., Madison, WI. Levine, E. R., and Ciolkosz, E. J. (1986). A computer simulation model for soil genesis applications. Soil Sci. Soc. Am. J. 50, 661–667. Lilly, A. and Lin, H. S. (2005). Using soil morphological attributes and soil structure in pedotransfer functions. In ‘‘Development of Pedotransfer Functions in Soil Hydrology’’ (Y. Pachepsky and W. Rawls, Eds.), Elsevier. in press. Lilly, A., and Matthews, K. B. (1994). A soil wetness class map for Scotland: New assessments of soil and climate data for land evaluation. Geoforum 25, 371–379.


83

Lilly, A., Ball, B. C., McTaggart, I. P., and Horne, P. L. (2003). Spatial and temporal scaling of nitrous oxide emissions from the field to the regional scale in Scotland. Nutr. Cycl. Agroecosyst. 66, 241–257. Lilly, A., Boorman, D. B., and Hollis, J. M. (1998). The development of a hydrological classification of UK soils and the inherent scale changes. Nutr. Cycl. Agroecosyst. 50, 299–302. Lin, H. S. (2003). Hydropedology: Bridging disciplines, scales, and data. Vadose Zone J. 2, 1–11. Lin, H. S., and Rathbun, S. (2003). Hierarchical frameworks for multiscale bridging in hydropedology. In ‘‘Scaling Methods in Soil Physics’’ (Y. Pachepsky, D. Radcliffe, and Selim, Eds.), pp. 353–371. CRC Press, Boca Raton, FL. Lin, H. S., McInnes, K. J., Wilding, L. P., and Hallmark, C. T. (1996). Effective porosity and flow rate with infiltration at low tensions into a well-structured subsoil. Trans. ASAE 39, 131–135. Lin, H. S., McInnes, K. J., Wilding, L. P., and Hallmark, C. T. (1998). Macroporosity and initial moisture effects on infiltration rates in Vertisoles and vertic intergrades. Soil Sci. 163, 2–8. Lin, H. S., McInnes, K. J., Wilding, L. P., and Hallmark, C. T. (1999a). Effects of soil morphology on hydraulic properties: I. Quantification of soil morphology. Soil Sci. Soc. Am. J. 63, 948–954. Lin, H. S., McInnes, K. J., Wilding, L. P., and Hallmark, C. T. (1999b). Effects of soil morphology on hydraulic properties: II. Hydraulic pedotransfer functions. Soil Sci. Soc. Am. J. 63, 955–961. Lin, H. S., Wheeler, D., Bell, J., and Wilding, L. (2004). Assessment of soil spatial variability at multiple scales. Ecolog. Model. in press. Lissey, A. (1971). Depression-focused transient groundwater flow patterns in Manitoba. Geol. Assoc. Can. Spec. Paper 9, 333–341. Loague, K., and Green, R. E. (1990). Comments on ‘Mapping Pesticide Contamination Potential’ by M. Akram Khan and Tung Liang. Environ. Management 14, 149–150. Magurran, A. E. (1988). ‘‘Ecological Diversity and Its Measurement’’. Croom Helm, London. Mandelbrot, B. B. (1982). ‘‘The Fractal Geometry of Nature’’. W. H. Freeman, New York. Mattikalli, N. M., Engman, E. T., Jackson, T. J., and Ahuja, L. R. (1998). Microwave remote sensing of temporal variations of brightness temperature and near-surface soil water content during a watershed-scale field experiment, and its application to the estimation of soil physical properties. Water Resour. Res. 34, 2289–2299. Mausbach, M. J., and Richardson, J. L. (1994). Biogeochemistry processes in hydric soil formation. Curr. Top. Wetland Biogeochem. 1, 68–127. McBratney, A. B. (1986). ‘‘Introduction to Pedometrics: A Course of Lectures’’. CSIRO Australia, Division of Soils Technical Memo. 53/1986, Private Bag 2, Glen Osmond, S.A. 5064. McBratney, A. B. (1998). Some considerations on methods for spatially aggregating and disaggregating soil information. Nutr. Cycl. Agroecosys. 50, 51–62. McBratney, A. B., Minasny, B., Cattle, S. R., and Vervoort, R. W. (2002). From pedotransfer functions to soil inference systems. Geoderma 109, 41–73. McKeague, J. A., Wang, C., and Coen, G. M. (1986). ‘‘Describing and Interpreting the Macrostructure of Mineral Soils—A Preliminary Report’’. Land Resource Research Institute, Ottawa, Ontario, Research Branch Agricultural Canada. McKeague, J. A., Wang, C., and Topp, G. C. (1982). Estimated saturated hydraulic conductivity from soil morphology. Soil Sci. Soc. Am. J. 46, 1239–1244. McKenzie, N. J., and Jacquier, D. W. (1997). Improving the field estimation of saturated hydraulic conductivity in soil survey. Aust. J. Soil Res. 35, 803–825. McSweeney, K., Gessler, P. E., Slater, B., Hammer, R. D., Bell, J., and Petersen, G. W. (1994). Towards a new framework for modeling the soil-landscape continuum. In ‘‘Factors of Soil

84

H. LIN ET AL.

Formation: A Fiftieth Anniversary Retrospective’’ (R. Amundson, J. Harden, and M. Singer, Eds.), pp. 127–145. Special Publ. #33, Soil Sci. Soc. Am., Madison, WI. Merchant, J. W. (1994). GIS-based groundwater pollution hazard assessment: A critical review of the DRASTIC model. Photogram. Eng. Remote Sensing 60, 1117–1127. Milne, G. (1935). Some suggested units of classification and mapping, particularly for East African soils. Soil Res. 4, 183–198. Milne, G. (1936a). Normal erosion as a factor in soil profile development. Nature 138, 548–549. Milne, G. (1936b). ‘‘A Provisional Soil Map of East Africa’’. East Africa Agric. Res. Stn., Amani Memoirs, No. 34. Tanganyika Terr. Minasny, B., and McBratney, A. B. (1999). A rudimentary mechanistic model for soil production and landscape development. Geoderma 90, 3–21. Minasny, B., and McBratney, A. B. (2001). A rudimentary mechanistic model for soil formation and landscape development II. A two-dimensional model incorporating chemical weathering. Geoderma 103, 161–179. Minasny, B., McBratney, A. B., and Bristow, K. L. (1999). Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma 93, 225–253. Mohanty, B. P., and Skaggs, T. H. (2001). Spatio-temporal evoluation and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation. Adv. Water Resour. 24, 1051–1067. Moore, I. D., Gessler, P. E., Nielsen, G. A., and Peterson, G. A. (1993). Soil attribute prediction using terrain analysis. Soil Sci. Soc. Am. J. 57, 443–452. Mulla, D. J., and Addiscott, T. M. (1999). Validation approaches for field-, basin-, and regionalscale water quality models. In ‘‘Assessment of Non-Point Source Pollution in the Vadose Zone’’ (D. Corwin, K. Loague, and T. Ellsworth, Eds.), pp. 63–78. American Geophysical Union, Washington, DC. National Research Council (NRC) (1991). ‘‘Opportunities in the Hydrologic Sciences’’. National Academy Press, Washington, DC. National Research Council (NRC) (1993a). ‘‘Soil and Water Quality: An Agenda for Agriculture’’. National Academy Press, Washington, DC. National Research Council (NRC) (1993b). ‘‘Ground Water Vulnerability Assessment— Contamination Potential Under Conditions of Uncertainty’’. National Academy Press, Washington, DC. National Research Council (NRC) (1998). ‘‘Developing a Digital National Library for Undergraduate Science, Mathematics, Engineering, and Technology Education’’. National Academy Press, Washington, DC. National Research Council (NRC) (1999). ‘‘New Strategies for America’s Watersheds’’. National Academy Press, Washington, DC. National Research Council (NRC) (2001a). ‘‘Basic Research Opportunities in Earth Science’’. National Academy Press, Washington, DC. National Research Council (NRC) (2001b). ‘‘Envisioning the Agenda for Water Resources Research in the Twenty-First Century’’. National Academy Press, Washington, DC. National Research Council (NRC) (2001c). ‘‘Grand Challenges in Environmental Sciences’’. National Academy Press, Washington, DC. Nieber, J. L. (2000). The relation of preferential flow to water quality, and its theoretical and experimental quantification. In ‘‘Preferential Flow—Water Movement and Chemical Transport in the Environment’’, pp. 1–10. Proceedings of the 2nd International Symposium. American Society of Agricultural Engineers, St. Joseph, MI. Nielsen, D. R., and Wendroth, O. (2003). ‘‘Spatial and Temporal Statistics: Sampling Field Soils and Their Vegetation’’. Catena Verlag, Cremlingen-Destedt.


85

Nielsen, D. R., Biggar, J. W., and Erh, K. T. (1973). Spatial variability of field-measured soilwater properties. Hilgardia 42, 215–260. Nielsen, D. R., Hopmans, J. W., and Reichardt, K. (1998). An emerging technology for scaling field soil-water behavior. In ‘‘Scale Dependence and Scale Invariance in Hydrology’’ (G. Sposito, Ed.), pp. 136–166. Cambridge University Press, UK. Nielsen, D. R., Kutilek, M., and Parlange, M. B. (1996). Surface soil water content regimes: Opportunities in soil science. J. Hydro. 184, 35–55. Nordt, L. C., Jacob, J. S., and Wilding, L. P. (1991). Quantifying map unit composition for quality control in soil survey. In ‘‘Spatial Variabilities of Soils and Landforms’’ (M. J. Mausbach and L. P. Wilding, Eds.), pp. 183–197. SSSA Special Pub. #28, Soil Sci. Soc. Am., Inc., Madison, WI. Oades, J. M., and Waters, A. G. (1991). Aggregate hierarchy in soils. Aust. J. Soil Res. 29, 815–828. O’Neal, A. M. (1949). Some characteristics significant in evaluating permeability. Soil Sci. 67, 403–409. O’Neill, R. V., DeAngelis, D. L., Waide, J. B., and Allen, T. F. H. (1986). ‘‘A Hierarchical Concept of Ecosystems’’. Monographs in Population Biology 23. Princeton University Press, Princeton, NJ. O’Neill, R. V., Johnson, A. R., and King, A. W. (1989). A hierarchical framework for the analysis of scale. Landscape Ecol. 3, 193–205. Othmer, H., Diekkru¨ger, B., and Kutilek, M. (1991). Bimodal porosity and unsaturated hydraulic conductivity. Soil Sci. 152, 139–150. Pachepsky, Y., and Rawls, W. J. (1999). Accuracy and reliability of pedotransfer functions as affected by grouping soils. Soil Sci. Soc. Am. J. 63, 1748–1757. Pachepsky, Y. A., and Rawls, W. (Eds.) (2005). ‘‘Development of Pedotransfer Functions in Soil Hydrology’’. Elsevier, Amsterdam, The Netherlands. in press. Pachepsky, Y. A., Crawford, J. C., and Rawls, W. J. (Eds.) (2000). ‘‘Fractals in Soil Science’’. Developments in Soil Science, 27. Elsevier, Amsterdam, The Netherlands. Pachepsky, Y. A., Radcliffe, D. E., and Selim, H. M. (Eds.) (2003). ‘‘Scaling Methods in Soil Physics’’. CRC Press, Boca Raton, FL. Pachepsky, Y. A., Rawls, W. J., and Timlin, D. J. (1999). The current status of pedotransfer functions: Their accuracy, reliability, and utility in field- and regional-scale modeling. In ‘‘Assessment of Non-point Source Pollution in the Vadose Zone’’ (D. Corwin, K. Loague, and T. Ellsworth, Eds.), pp. 223–234. American Geophysical Union, Washington, DC. Pennock, D. J., and de Jong, E. (1990). Regional and caternary variations in properties of Borolls of southern Saskatchewan, Canada. Soil Sci. Soc. Am. J. 54, 1697–1701. Perret, J., Prasher, S. O., Kantzas, A., and Langford, C. (1999). Three-dimensional quantification of macropore networks in undisturbed soil cores. Soil Sci. Soc. Am. J. 63, 1530–1543. Perrier, E., Bird, N., and Rieu, M. (1999). Generalizing the fractal model of soil structure: The pore-solid fractal approach. Geoderma 88, 137–164. Pulleman, M. M., Bouma, J., van Essen, E. A., and Meijles, E. W. (2000). Soil organic matter content as a function of different land use history. Soil Sci. Soc. Amer. J. 64, 689–694. Quisenberry, V. L., Smith, B. R., Phillips, R. E., Scott, H. D., and Nortcliff, S. (1993). A soil classification system for describing water and chemical transport. Soil Sci. 156, 306–315. Rabenhorst, M. C., Bell, J. C., and McDaniel, P. A. (1998). ‘‘Quantifying Soil Hydromorphology’’. SSSA Special Pub. #54, Soil Sci. Soc. Am., Inc., Madison, WI. Rawls, W. J., and Pachepsky, Y. A. (2002a). Soil consistence and structure as predictors of water retention. Soil Sci. Soc. Am. J. 66, 1115–1126. Rawls, W. J., and Pachepsky, Y. A. (2002b). Using field topographic descriptors to estimate soil water retention. Soil Sci. 167, 423–435.

86

H. LIN ET AL.

Rawls, W. J., Ahuja, L. R., Brakensiek, D. L., and Shirmohammadi, A. (1993). Infiltration and soil water movement. In ‘‘Handbook of Hydrology’’ (D. R. Maidment, Ed.). Chapter 5. McGraw-Hill Inc., New York. Reuter, R. J., and Bell, J. C. (2003). Hillslope hydrology and soil morphology for a wetland basin in south-central Minnesota. Soil Sci. Soc. Am. J. 67, 365–372. Richardson, J. L., and Vepraskas, M. J. (2001). ‘‘Wetland Soils: Genesis, Hydrology, Landscapes, and Classification’’. LEWIS, CRC Press, Boca Raton, FL. Richardson, J. L., Arndt, J. L., and Montgomery, J. A. (2001). Hydrology of wetland and related soils. In ‘‘Wetland Soils: Genesis, Hydrology, Landscapes, and Classification’’ (J. L. Richardson and M. J. Vepraskas, Eds.), pp. 35–84. LEWIS, CRC Press, Boca Raton, FL. Richardson, J. L., Wilding, L. P., and Daniels, R. B. (1992). Recharge and discharge of ground water in aquic conditions illustrated with flownet analysis. Geoderma 53, 65–78. Ridolfi, L., D’Odorico, P., Porporato, A., and Rodriguez-Iturbe, I. (2003). Stochastic soil moisture dynamics along a hillslope. J. Hydro. 272, 264–275. Rodrı´guez-Iturbe, I. (2000). Ecohydrology: A hydrologic perspective of climate-soil-vegetation dynamics. Water Resour. Res. 36, 3–9. Rodrı´guez-Iturbe, I., and Rinaldo, A. (1997). ‘‘Fractal River Basins: Chance and SelfOrganization’’. Cambridge Univ. Press, Cambridge, UK. Root, T. L., and Schneider, S. H. (1995). Ecology and climate: Research strategies and implications. Science 269, 334–341. Ross, P. J., and Smettem, K. R. (2000). A simple treatment of physical nonequilibrium water flow in soils. Soil Sci. Soc. Am. J. 64, 1926–1930. Ruhe, R. V. (1969). ‘‘Quaternary Landscapes of Iowa’’. Iowa State Univ. Press. Ruhe, R. V. (1975). ‘‘Geomorphology’’. Houghton Mifflin, Boston, MA. Runge, E. C. A. (1973). Soil development sequences and energy models. Soil Sci. 115, 183–193. Salvucci, G., and Levine, J. (2000). Groundwater-vadose zone interactions at Trochu. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Blo¨schl, Eds.), pp. 304–328. Cambridge University Press, Cambridge, UK. Savic, D. A., and Walters, G. A. (1999). Hydroinformatics, data mining and maintenance of UK water networks. Anti-Corrosion Meth. Materials 46, 415–425. Schaap, M. G., and Bouten, W. (1996). Modelling water retention curves of sandy soils using neural networks. Water Resour. Res. 32, 3033–3040. Scheidegger, A. E. (1967). A stochastic model for drainage patterns into an intramontane trench. Bull. Int. Assoc. Sci. Hydrol. 12, 15–20. Simonson, R. W. (1959). Outline of a generalized theory of soil genesis. Soil Sci. Soc. Am. Proc. 23, 152–156. Sˇimunek, J., Jarvis, N. J., van Genuchten, M. T., and Gardenas, A. (2003). Review and comparison of models for describing non-equilibrium and preferential flow and transport in the vadose zone. J. Hydro. 272, 14–35. Sivapalan, M. (2003). Process complexity at hillslope scale, process simplicity at the watershed scale: Is there a connection? Hydro. Proc. 17, 1037–1041. Smeck, N. E., and Runge, E. C. A. (1971). Phosphorus availability and redistribution in relation to profile development in an Illinois landscape segment. Soil Sci. Soc. Am. Proc. 35, 952–959. Smith, H., and Hudson, B. D. (1999). Soil survey in the twenty-first century. ‘‘Proceedings of the International Symposium on Soil Resources: Their Inventory, Analysis, and Interpretation for Use in the 21st Century, June 10–12.’’ Minneapolis, MN. Soil Survey Division Staff (1951). ‘‘Soil Survey Manual’’. U.S. Department of Agriculture Handbook No. 18. U.S. Government Printing Office, Washington, DC. Soil Survey Division Staff (1993). ‘‘Soil Survey Manual’’. U.S. Department of Agriculture Handbook No. 18. U.S. Government Printing Office, Washington, DC.


87

Soil Survey Staff (1999). ‘‘Soil Taxonomy—A Basic System of Soil Classification for Making and Interpreting Soil Surveys’’, 2nd ed. USDA-NRCS Agricultural Handbook No. 436. U.S. Government Printing Office, Washington, DC. Sommer, M., Wehrhan, M., Zipprich, M., Weller, U., Zu Castell, W., Ehrich, S., Tandler, B., and Selige, T. (2003). Hierarchical data fusion for mapping soil units at field scale. Geoderma 112, 179–196. Sonneveld, M. P. W., Bouma, J., and Veldkamp, A. (2002). Refining soil survey information for a Dutch soil series using land use history. Soil Use Manage 18, 157–163. Sposito, G. (Ed.) (1998). ‘‘Scale Dependence and Scale Invariance in Hydrology’’. Cambridge University Press, Cambridge, UK. Sposito, G., and Reginato, R. J. (Eds.) (1992). ‘‘Opportunities in Basic Soil Science Research’’. Soil Sci. Soc. Am., Inc., Madison, WI. Stein, A., Staritsky, I. G., Bouma, J., van Eijnsbergen, A. C., and Bregt, A. K. (1991). Simulation of moisture deficits and areal interpolation by universal cokriging. Water Resour. Res. 27, 1963–1973. Stephens, D. B., Kowall, S. J., Borns, D., Ellis, D., Everett, L. G., van Genuchten, M. T., Graham, M., Parker, F., Weeks, E., and Wilson, J. (2002). Letter to the editor on a national strategy for vadose zone science and technology. Vadose Zone J. 1, 197–198. Stolt, M. H., Baker, J. C., and Simpson, T. W. (1993). Soil-landscape relationships in Virginia: I. Soil variability and parent material uniformity. Soil Sci. Soc. Am. J. 57, 414–421. Sulebak, J. R., Tallaksen, L. M., and Erichsen, B. (2000). Estimation of areal soil moisture by use of terrain data. Geographical Anal. 82A, 89–105. Tarboton, D., Blo¨schl, G., Cooley, K., Kirnbauer, R., and Luce, C. (2000). Spatial snow cover processes at Ku¨htai and Reynolds Creek. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Blo¨schl, Eds.), pp. 158–186. Cambridge University Press, Cambridge, UK. Thompson, J. A., Bell, J. C., and Butler, C. A. (1997). Quantitative soil-landscape modeling for estimating the areal extent of hydromorphic soils. Soil Sci. Soc. Am. J. 61, 971–980. Thompson, J. A., Bell, J. C., and Zanner, C. W. (1998). Hydrology and hydric soil extent within a Mollisol catena in Southeastern Minnesota. Soil Sci. Soc. Am. J. 62, 1126–1133. Thorp, J., and Gamble, E. E. (1972). Annual fluctuations of water levels in soils of the Miami catena, Wayne County, Indiana. Sci. Bull. 5, Earlham College, Richmond, IN. Tisdall, J. M., and Oades, J. M. (1982). Organic matter and water-stable aggregates in soils. J. Soil Sci. 33, 141–163. Toth, J. (1963). A theoretical analysis of groundwater flow in small drainage basins. J. Geophys. Res. 68, 4197–4213. Troch, P., Verhoest, N., Gineste, P., Paniconi, C., and Merot, P. (2000). Variable source areas, soil moisture and active microwave observations at Zwalmbeek and Coet-Dan. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Bloschl, Eds.), pp. 187–208. Cambridge University Press, Cambridge, UK. USDA-NRCS (1997). ‘‘Part 649—Land Resource Regions and Major Land Resource Areas. National Soil Survey Handbook’’. U S Government Printing Office, Washington, DC. USDA-NRCS (1998). In ‘‘Field Indicators of Hydric Soils in the United States,’’ Ver. 4.0. (Hurt, G. W., Whited, P. M., and Pringle, R. F., Eds.) USDA-NRCS, Ft. Worth, TX. USDA-NRCS (2001). ‘‘National Cooperative Soil Survey Conference Proceedings. Building for the Future: Science, New Technology and People, June 25–29’’. Fort Collins, CO. USDA-NRCS (2003). ‘‘National Cooperative Soil Survey Conference Proceedings. Soil Information for a Changing World, June 16–20’’. Plymouth, MA. Vachaud, G., De Silans Passerat, A., Balabanis, P., and Vauclin, M. (1985). Temporal stability of spatially measured soil water probability density function. Soil Sci. Soc. Am. J. 49, 822–827.

88

H. LIN ET AL.

van Kessel, K., and Wendroth, O. (2001). Landscape research—Exploring ecosystem processes and their relationships at different scales in space and time. Soil Tillage Res. 58, 97–98. van Genuchten, M. T., and Wierenga, P. J. (1976). Mass transfer studies in sorbing porous media. I. Analytical solutions. Soil. Sci. Soc. Am. J. 40, 473–481. van Genuchten, M. T., Feike, L., and Wu, L. (Eds.) (1999). ‘‘Proceedings of the International Workshop on Characterization and Measurement of the Hydraulic Properties of Unsaturated Porous Media’’. University of California, Riverside, CA. Veneman, P. L. M., and Bodine, S. M. (1982). Chemical and morphological soil characteristics in a New England drainage-toposequence. Soil Sci. Soc. Am. J. 46, 359–363. Veneman, P. L. M., Lindbo, D. L., and Spokas, L. A. (1998). Soil moisture and redoximorphic features: A historical perspective. In ‘‘Quantifying Soil Hydromorphology’’ (M. C. Rabenhorst, J. C. Bell, and P. A. McDaniel, Eds.), pp. 1–23. SSSA Special Pub. #54, Soil Sci. Soc. Am., Inc., Madison, WI. Vepraskas, M. J. (1992). ‘‘Redoximorphic Features for Identifying Aquic Conditions’’. North Carolina Agricultural Research Service Technical Bulletin No. 301. NC, USA. Vepraskas, M. J., and Sprecher, S. W. (Eds.) (1997). ‘‘Aquic Conditions and Hydric Soils: The Problem Soils’’. SSSA Special Pub. #50, Soil Sci. Soc. Am., Inc, Madison, WI. Vertessy, R., Elsenbeer, H., Bessard, Y., and Lack, A. (2000). Storm runoff generation at La Cuenca. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Bloschl, Eds.), pp. 247–271. Cambridge University Press, Cambridge, UK. Vervoort, R. W., and Cattle, S. R. (2003). Linking hydraulic conductivity and tortuosity parameters to pore space geometry and pore size distribution. J. Hydrol. 272, 36–49. Vervoort, R. W., Radcliffe, D. E., and West, L. T. (1999). Soil structure development and preferential solute flow. Water Resour. Res. 35, 913–928. Vogel, H. J., and Roth, K. (2003). Moving through scales of flow and transport in soil. J. Hydro. 272, 95–106. Vogel, H. J., Cousin, I., and Roth, K. (2002). Quantification of pore structure and gas diffusion as a function of scale. Eur. J. Soil Sci. 53, 465–473. Wagenet, R. J. (1998). Scale issues in agroecological research chains. Nutr. Cycl. Agroecosys. 50, 23–34. Walker, P. H., and Ruhe, R. V. (1968). Hillslope models and soil formation. II. Closed systems. Trans 9th Int. Cong. Soil Sci. IV, 561–565. Webster, R. (1994). The development of pedometrics. Geoderma 62, 1–15. Western, A. W., and Blo¨schl, G. (1999). On the spatial scaling of soil moisture. J. Hydro. 217, 203–224. Western, A. W., and Grayson, R. (2000). Soil moisture and runoff processes at Tarrawarra. In ‘‘Spatial Pattern in Catchment Hydrology: Observations and Modeling’’ (R. Grayson and G. Bloschl, Eds.), pp. 209–246. Cambridge University Press, Cambridge, UK. Western, A. W., Grayson, R. B., Blo¨schl, G., Willgoose, G. R., and McMahon, T. A. (1999). Observed spatial organization of soil moisture and its relation to terrain indices. Water Resour. Res. 35, 797–810. Wikle, C. K., Milliff, R. F., Nychka, D., and Berliner, L. M. (2001). Spatiotemporal hierarchical Bayesian modeling: Tropical ocean winds. J. Am. Stat. Assoc. 96, 382–397. Wilding, L. P. (1994). Factors of soil formation: Contributions to pedology. In ‘‘Factors of Soil Formation: A Fiftieth Anniversary Retrospective’’ (R. Amundson, J. Harden, and M. Singer, Eds.), pp. 15–30. Soil Sci. Soc. Am., Special Publ. #33, Madison, WI. Wilding, L. P. (2000). Pedology.‘‘Handbook of Soil Science’’ (M. E. Sumner, Ed.-in-Chief), pp. E1–E4. CRC Press, Boca Raton, FL. Wilding, L. P., and Drees, L. R. (1983). Spatial variability and pedology. In ‘‘Pedogenesis and Soil Taxonomy. I. Concepts and Interactions’’ (L. P. Wilding, N. E. Smeck, and G. F. Hall, Eds.), pp. 83–117. Elsevier, Amsterdam, The Netherlands.


89

Wilding, L. P., Bouma, J., and Goss, D. (1994). Impact of spatial variability on modeling. In ‘‘Quantitative Modeling of Soil Forming Processes’’ (R. Bryant and M. R. Hoosbeek, Eds.), pp. 61–75. Soil Sci. Soc. Am., Special Publ. #39, Madison, WI. Winter, T. C. (1988). A conceptual framework for assessing cumulative impacts on hydrology of nontidal wetlands. Environ. Manage 12, 605–620. Wo¨sten, J. H. M., Bouma, J., and Stoffelsen, G. H. (1985). The use of soil survey data for regional soil water simulation models. Soil. Sci. Soc. Am. J. 49, 1238–1245. Wo¨sten, J. H. M., Pachepsky, Y. A., and Rawls, W. J. (2001). Pedotransfer functions: Bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydro. 251, 123–150. Wo¨sten, J. H. M., Schuren, C. H. J. E., Bouma, J., and Stein, A. (1990). Comparing four methods to generate soil hydraulic functions in terms of their effect on simulated soil water budgets. Soil Sci. Soc. Am. J. 54, 827–832. Wysocki, D., Schoeneberger, P., and LaGarry, H. (2000). Geomorphology of soil landscapes. In ‘‘Handbook of Soil Science’’ (M. E. Sumner, Ed.-in-Chief), pp. E5–E39. CRC Press, Boca Raton, FL. Yeh, J.-F., and Eltahir, E. A. B. (1998). Stochastic analysis of the relationship between topography and the spatial distribution of soil moisture. Water Resour. Res. 34, 1251–1263. Young, E. G. (1988). Soil physics and hydrology. J. Hydro. 100, 411–431. Zhang, T., and Berndtsson, R. (1991). Analysis of soil water dynamics in time and space by use of pattern recognition. Water Resour. Res. 27, 1623–1636.

This Page Intentionally Left Blank

BIOINDUSTRIAL AND BIOPHARMACEUTICAL PRODUCTS PRODUCED IN PLANTS John A. Howard1 and Elizabeth Hood2 1

Applied Biotechnology Institute, College Station, Texas 77845, USA 2 Arkansas State University, Jonesboro, Arkansas 72403, USA

I. Introduction II. Technology Options A. Generation of Transgenic Material B. Protein Expression III. Production Options A. Growth B. Harvesting/Transport/Storage C. Tissue Processing D. Extraction/Purification IV. Products A. High-Purity Human Health Products B. Orally Delivered Products C. Industrial Enzymes V. Public Acceptance VI. Conclusions and Future References

Over the past several years there have been many advances in plant biotechnology that have led to the successful commercialization of agricultural products for crop improvement. Plant biotechnology is now being considered as a tool to produce non-food products such as biopharmaceuticals and bioindustrial products. This chapter reviews the status of the field with particular emphasis on different plant systems. Key factors such as transformation, expression, growth, harvest, transport, storage, processing, and purification of the plant material are included. The chapter also evaluates the characteristics of different systems and their utility for different types of products. While no one system stands out as the ideal platform, this chapter does point to systems that have broader appeal and speculates as ß 2005 Elsevier Inc. to future platforms and utilities.


92

J. A. HOWARD AND E. HOOD

I. INTRODUCTION Investigators have been exploring the use of plants as an alternative production system for biologics over the past several years (Daniell et al., 2001; Fischer and Emans, 2000; Fischer et al., 1999; Giddings, 2001; Hood, 2002; Hood and Howard, 1999, 2002; Hood and Jilka, 1999; Hood et al., 2002, 2003a; Kusnadi et al., 1997; Ma et al., 2003). While it seems unlikely that no one production system could meet all potential needs for the diversity of products, plants do offer some clear theoretical advantages over other systems. One obvious advantage is that the biologics can be produced free of animal source tissue, thereby eliminating the fear of transmitting animal pathogens, including prions responsible for such conditions as ‘‘mad cow disease.’’ Plants also offer the potential for a reduced cost of goods, resulting in both a lower cost of raw material and the potential for rapid scale-up with limited facility requirements. Plants offer the least expensive source of biomass. In field environments, the major inputs come from sunlight, rain, and air. Although other inputs are added to many commercial crops, they are a relatively minor expense compared to input requirements for non-plant production systems. Moreover, animal and microbial production systems require plant material as the primary carbon source used in converting energy into useful proteins. Because plants can be rapidly propagated from seeds, they do not have the limitations found in the raising of large herds, as is the case for transgenic animal systems. Nor is it necessary to build production facilities to generate the raw material, as is the case for cell culture systems, for which scale-up can take years and hundreds of millions of dollars. The shorter scale-up times and reduced up-front capital expenditures for facilities for plant production contribute to a lower cost of goods. There are several additional advantages to plant-based products, particularly when used for direct delivery. Direct delivery refers to applications in which the plant material can be used directly in food or feed or as an industrial feedstock without purification. In some cases, the plant material itself may have value in the application independent of the recombinant proteins contained in it. Orally delivered human-health products avoid the cost and safety issues associated with injectables. This has been studied mostly as it relates to oral vaccines (Streatfield, 2002; Streatfield and Howard, 2003a,b). With oral delivery, there is no requirement for needles or syringes, and medically trained personnel are not necessary for administration. The costs of delivery are reduced, and concerns about contamination stemming from incomplete disposal of needles are removed. The lower cost and convenience should result in more patients being vaccinated. This is true not only in developing countries, where access to medical staff may be limited, but also in developed countries, where patient compliance requires booster inoculations.

BIOINDUSTRIAL AND BIOPHARMACEUTICAL PRODUCTS

93

Plant systems are particularly well suited to yielding large amounts of a desired product in a relatively small area. For example, a current corn-based vaccine candidate targeting Enterotoxic Escheria coli (ETEC) can yield over 200,000 doses from a single acre of cultivation. Thus, the world’s supply could be obtained from a single small farm (Streatfield and Howard, 2003b). Moreover, because some plant tissues, such as seeds, can store proteins for years without loss of activity under ambient conditions, a ready supply of material can be manufactured into final form on an as-needed basis. Rapid scale-up, large volumes, and long term storage are particularly advantageous for industrial enzymes. Low cost and the ability to use the raw material directly for industrial processes encourage development in this direction. These potential advantages have led to a recent increase in interest in using this technology for the production of new biologics. This chapter is focused on evaluating the different plant technologies with respect to their characteristics in producing proteins for plant-made pharmaceuticals and plant-made industrial products.

II. TECHNOLOGY OPTIONS The choice of plant to be used depends on a number of factors, including its cultivation, transformability, growing cost, production and processing of the target tissue, existence of wild relatives, and degree of outcrossing. Current systems include corn, soybean, canola, alfalfa, Lemna, tobacco, and safflower. These crops may be wild plants (e.g., Lemna), domesticated non-food crops (e.g., tobacco and alfalfa), or food crops (e.g., rice, corn, soybean, potatos, canola, and safflower). The type of tissue that is used for protein accumulation is often chosen based on the type of plant used, or vice versa. For example, Lemna, alfalfa, and tobacco are leafy crops, thus seed or fruit would not be appropriate for use as production vehicles. Potatoes are a root crop, and thus the tubers are used. Fleshy fruits such as tomatoes or bananas can be used for production. However, grain crops such as corn, soybeans, canola, and safflower have the most stable production vehicle, the seed. While there are multiple interrelated factors affecting protein accumulation and crop choice, for discussion purposes we have divided the technology into two parts, (1) generating the transgenic material and (2) protein expression.

A. GENERATION OF TRANSGENIC MATERIAL Recovery of stably transformed plants exhibiting traits of interest requires the combination of several technologies: (1) appropriate selectable marker genes and selection conditions, (2) an efficient culture system that allows

94


recovery of plants from target tissues, (3) a DNA delivery system, (4) the choice of a genotype that has agronomic or horticultural relevance, and (5) joining of these technologies so that transformed, fertile adult plants can be recovered. Variety is the driver for these technologies because of the plethora of plant species available as targets for plant production of foreign proteins (Hood, 2003). For maximum utility and efficiency, however, DNA delivery systems should be simple, efficient, and preferably inexpensive. This is true whether the methods are used by scientists in industry or in academic institutions. Additionally, particularly for industry, the method must be available for use either because it is in the public domain or because it can be licensed. The transfer of foreign DNA into plant cells has been going on for centuries. The most obvious example of natural DNA transfer to plants with which we are familiar today is in Agrobacterium tumefaciens, the perpetrator of crown galls. Until very recently, the result of this DNA transfer had gone unoticed. However, after some astute observations, the ‘‘tumor-inducing principle’’ was hypothesized in 1958 (Braun, 1958) and its molecular nature was discovered in the mid-1970s (Chilton et al., 1977). The stage was set to begin developing DNA transfer technology for crop improvement. Molecular farming requires that DNA for encoding the protein of choice be introduced at will into the plant of choice. To this end, multiple methods of human-directed foreign DNA transfer into plant nuclei have been developed over the past 20 years, including Agrobacterium-mediated transformation and several direct gene transfer methods, e.g., microprojectile bombardment, electroporation, silicon carbide fibers, electrophoresis, and microinjection (Hood, 1999; Songstad et al., 1995). Microprojectile bombardment has also been used to transform chloroplasts of solanaceous plants (Daniell et al., 2002; Maliga, 2002), and viral vectors are used to transiently express genes in plants, primarily tobacco (Lindbo et al., 2001). Of these methods, the Agrobacterium-mediated nuclear transformation and microprojectile bombardment-mediated nuclear and chloroplast transformation are the methods most often used today. Viral vectors are being used for commercial development of therapeutic proteins from tobacco (Grill et al., 2002; Lindbo et al., 2001). Notable is the lack of recent papers in which the other direct gene transfer methods are used. However, modifications of some of these techniques developed in the early 1980s have gained some favor (Southgate et al., 1998). The operative word here is variety. Crop plants span many species in many genera, families, and classes, and no ‘‘one size fits all’’ method is appropriate for gene transfer. Selection of transformed tissues requires the inclusion of genes that allow identification of the transformed cells. Selectable marker genes come in a variety of types with quite varied substrates (Hood, 2003). Again, the


95

operative word is variety, because a large array of methods and markers are critical to the success of plant biotechnology. Selectable markers can provide selection pressure on plant tissues, resulting in death of nontransformed cells, or through the starvation of unwanted cells because selective growth of transformed cells is supported. Nature provided the first example of selectively different plant cells— hormone independent growth in Agrobacterium-incited tumors. The first engineered plant cell selection system was antibiotic resistance (see Chilton, 2001). In the early 1980s, several labs raced to generate tobacco tissue that could be selected on kanamycin or G418 (Bevan et al., 1983). Subsequent experimentation has focused on refinement of these techniques, broadening of the host range, and broadening of the species amenable to transformation and selection (reviewed in Wilmink and Dons, 1993). Selectable markers have recently been deemed undesirable traits because of their perceived danger to public health due to the potential of allergenicity of the protein or the potential of resistance gene transfer to gut microorganisms (antibiotic resistance). Their maintenance in the plant after establishment of the desired trait is assumed to be unnecessary because these genes have no utility after transformed plant recovery. However, herbicide resistance genes have utility because they confer downstream advantages for selection of transformed plants in the field. These allow growth and recovery of plants prior to establishment of homozygous lines so that early field performance of the transgenic plants can be assessed. Moreover, the pflp gene (Chen et al., 2000; You et al., 2003), a selective trait, confers resistance to plant pathogens, also giving it value. Thus, a discriminating assessment of marker gene value versus risk should be undertaken before wholesale removal of the trait is endorsed. Alternative methods have addressed the development of less objectionable selectable markers that have less perceived risk, such as the pflp gene (Chen et al., 2000; You et al., 2003) and positive selection (Wang et al., 2000). However, when the risk is determined to outweigh the benefit of retaining the selective trait gene, the methods being sought for their removal should be employed. Such methods include (1) co-transformation into unlinked sites then removal through breeding (Komari et al., 1996) and (2) inclusion of recombination sites to selectively remove genes (reviewed by Hare and Chua, 2002). A slate of multiple selectable markers can improve the ability of researchers to maximize recovery of transformed, regenerated plant materials that are close to the final product line. In the future, it may also be desirable for non-food crops to use a selectable marker different from food crops of the same species. This would help to keep the two crops segregated and easily distinguished in the field. While there are options for removal of the selectable marker, it is also possible to express the marker protein in a non-target tissue of the crop. When seeds are used as a source of recombinant protein, markers may be

96


designed to be expressed in cell cultures or leaves without presenting a new protein in the harvested crop. Even when the marker protein may end up in the harvested crop, the presence of the marker gene or protein may be inconsequential if the protein product is used in industrial applications or if it requires purification before being used as a pharmaceutical.

B. PROTEIN EXPRESSION Protein expression is the single most important factor for most recombinant proteins and can dictate the economics of the product as well as regulatory issues. Expression of the protein depends on many factors, including transcription, translation, targeting, and the ability of the plant to accumulate the protein. Native proteins are expressed in all plant tissues and organs. Some basic ‘‘house-keeping’’ proteins are often present in most tissues, e.g., ubiquitin, and are thus expressed from genes regulated by constitutive promoters. In the case of foreign protein production of pharmaceuticals and industrial enzymes, it is desirable to sequester the protein as much as possible into specific target tissues with the use of tissue-specific promoters that are active only in limited tissue types. An example is the globulin-1 promoter from maize (Belanger and Kriz, 1991), which is primarily limited to embryo-specific expression (Hood et al., 2003; Woodard et al., 2003). Localization of native protein into subcellular compartments combined with the tissue-specific expression of their genes allows cells to differentiate with unique identities and collectively form a eukaryotic organism. In addition to promoter specificity, the subcellular compartments that are noteworthy within that tissue present the array of potential subcellular locations that are likely to maximize that promoter’s work. For example, targeting to a glyoxysome in a leaf or root would not be as useful as targeting to this organelle in a cotyledon that stores oil. An example of the effect of alternative targeting on protein accumulation is shown in Table I. Clearly, cell wall targeting effected the highest accumulation of protein 1, whereas the vacuole was the best subcellular location for accumulation of protein 2. The cellular machinery responsible for targeting proteins is under intense study and has been elegantly reviewed (Kermode, 1996; Pyke, 1999; Sanderfoot and Raikel, 1999). General pathways for targeting gene products into specific subcellular compartments are diagrammed in Fig. 1. The rough endoplasmic reticulum contributes protein to the several compartments that receive their member proteins from the membrane/secretion pathway. Secretion to the exterior of the plasma membrane is the default pathway, and a signal sequence that begins this process is generally necessary and sufficient for a protein to arrive on the cell surface (Vitale and Denecke, 1999).


97

Table I Effect of Promoter and Targeting Signal on Protein Accumulation in Maize Seed Protein location

Protein example

Targeting sequence

Highest T1 seedc

Embryo cell walla Constitutive cell walla Constitutive ERa Constitutive cytoplasma Endosperm cell walla Embryo ERa

1

BAASS

0.8%

1

BAASS

0.19%

1

BAASS KDEL

0.12%

1

None

0.07%

1

BAASS

0.0085%

1

BAASS KDEL

Constitutive vacuoleb Constitutive CWb Constitutive ERb Constitutive nucleusb Constitutive cytoplasmicb

2

SS vacuole

No events recovered 10%

2

BAASS

2%

2

BAASS KDEL

0.08%

2

NLS

0.02%

2

None

0.0008%

a

Data from Hood et al. (2003a). Data from Streatfield et al. (2003). c Percentage given as a percent of total soluble protein. b

Figure 1 Targeting pathways for plant proteins.

98


The plastids, mitochondria, nucleus, and cytoplasm receive their proteins from cytoplasmically translated messages, and the proteins have specific transit peptide sequences that allow their import into each organelle (Kermode, 1996). The plastids are an interesting group of organelles that are derived from a single pre-organelle, the proplastid (Esau, 1977); they include chloroplasts, chromoplasts, amyloplasts, and elaioplasts. The import process for chloroplast proteins encoded by nuclear genes was reviewed by Keegstra and Cline (1999). The underlying principle of this process is that a transit peptide is necessary for recognition by a receptor on the surface of the plastid envelope. Some features of this transit peptide are common to all plastids (de Boer et al., 1988; Lawrence et al., 1997). However, each member of the plastid group most likely has features of its import apparatus that limit uptake of proteins to those specifically required for the unique functions of the plastids (Wan et al., 1996). Transit peptides and signal sequences have been used by all players in the field of molecular farming. In most cases, these signals are a part of the strategy to achieve maximal accumulation of the target protein. In very few cases has subcellular in situ localization of the target protein been performed. In one case, Lt B, the bacterial endotoxin from Escherichia coli, was found in an unpredicted compartment (Chikwamba et al., 2003). In order for proteins to accumulate, the protein must be stable to the particular environment. This includes not only the obvious problem of protease attack, but also more subtle attributes. Specific proteins can have different stabilities depending on their specific environments. Carbohydrate, protein, and lipid content, as well as pH and salt, may influence the stability of the protein. Since these will differ in different plants, tissues, and subcellular locations, their ability to accumulate will also vary considerably. While this must be explored empirically today, there is hope that the future may bring predictability to the fate of proteins based on their specific characteristics in plants and tissues to achieve the highest overall accumulation.

III.

PRODUCTION OPTIONS

Production of recombinant proteins refers to the growing, harvesting, transport, storage, and tissue processing of the crop, as well as the extraction and purification (Fig. 2). With thousands of species to select from and a wide variety of possible products, it is highly unlikely that any one system will work best for each of these steps. Each plant production system has its own distinct characteristics that may or may not prove advantageous, depending on the product, so it is important to select the plant system that is best for each product. Since it is impractical to have thousands of different production


Figure 2

99

Steps in production with key considerations.

systems, it is preferable to adapt a given system to the needs of the various products. Fortunately, there are some common features that apply to most products, enabling a few systems to accommodate most products. These key features include a potential for low cost of goods, maintenance of protein

100


integrity, flexibility with regard to time and temperature for harvest, and maintenance of product safety and environmental safety (Delaney, 2002; Nikolov and Hammes, 2002). These are discussed in the following sections as to how they relate to the overall efficiency of the system as well as to the regulatory aspects.

A. GROWTH One of the first decisions to be made when selecting a crop for production is whether working with a cultivated species would be preferred. Cultivated species have several advantages, including a higher yield of biomass compared to their wild relatives. An understanding of agronomic practices will lead to a more reliable supply than that from non-domesticated species, most of which have not undergone selection for higher yields and pest resistance in past centuries. These characteristics practiced on domesticated crops have the net effect of a greatly reduced cost of producing the raw material compared to competing technologies. Another advantage of cultivated species is that many have been evaluated for antinutritional, allergenic, or toxic agents. In addition, ample infrastructure for downstream processing and storage of cultivated crops is established along with the experience of handling the crop. Plants only grow well in specific environments due to light, temperature, and soil conditions. The major commercial crops have been adapted for a wide range of conditions, which extends their geographic boundaries. In many cases only one crop per year can be grown in a geographic area. However, additional production of this same crop can be done in other geographic locations at different times of the year, extending the seasonal growing of the crop to year-round production. Non-cultivated crops have an advantage in that they are unlikely to be mistaken for food crops and therefore are unlikely to be inadvertently mixed with the food supply. Unfortunately, they would be more likely than food crops to outcross with native plants in the environment, which may pose a larger hazard. Matching the yield of cultivated species with that of wild species would require decades of research, assuming the crop had the potential. Finally, the impact of these relatively unknown species on product safety is unknown. Determination of the degree of this impact would undoubtedly require extensive effort and time. One consideration for foreign protein production is whether to select a food crop or a non-food crop. One advantage of food crops is that we know they are safe for consumption. This is a dramatic advantage in cases where the final product can include the plant tissue as well as the recombinant protein. Examples include orally delivered products, such as vaccines, for which the


101

protein product is not purified from the plant tissue; rather, a formulated product made from the food crop is orally administered. The use of non-food crops for orally delivered products would demand that we understand the potential for toxicants, allergens, or antinutritional agents of these uncharacterized systems. In the case of therapeutics where the protein is purified from plant tissue there is also an advantage to using food crops. A greater safety margin can be obtained with known food crops since any host protein that co-purifies with the recombinant protein would already be part of the food chain. The only obvious potential advantage for using a non-food crop is that it may be less likely to inadvertently mix with the food crop. Another choice to be made is whether to use an open-pollinated or a selfpollinating plant. The advantage of using self-pollinating plants is that there is a much lower risk that pollen will unintentionally transfer onto other plants of the same species. Controlled pollen shed of open-pollinated crops can be used to help alleviate this concern by either physical or genetic means to prevent outcrossing onto weedy species or related food or feed crops. For most cultivated crops, self-pollination usually means that the seed planted by the grower can be saved every year and replanted the following season. However, it is more difficult to maintain control of the seed and more likely for a contaminant to accumulate when the same seed source is used to grow crops season after season. Therefore, the advantage of self-pollinating crops is partially offset by the potential for amplification of contaminating plants. This is in contrast to the case of open-pollinated crops that are produced as hybrids, in which subsequent production is not from plants in the production fields but from parent seed stocks. Growers do not save seed because of the poor yield compared to the hybrid seed. Therefore, it is unlikely that any amplification of contaminating plants will occur. One additional possibility that exists for plant production systems is that of plant cell culture systems or hydroponics. These systems have the advantage of being physically contained and avoid the potential disadvantage of outcrossing with species that are grown in an open environment. Unfortunately, other factors such as higher cost and unknown product safety present major hurdles for product development.

B. HARVESTING/TRANSPORT /STORAGE With the exception of some specialty crops, most crops today are harvested mechanically. Therefore, collecting the plant material itself is usually not a problem. The problem with harvesting concerns how time sensitive the plant material is. If fresh fruit is used as the source tissue, it may be critical to harvest the crop within a narrow time window to avoid degradation of the crop and/or the protein product. The next related concern is how to move

102


this tissue into a storage facility without protein degradation. Will the tissue require refrigeration for any length of time? Fresh fruit and green leafy material can be a problem in that even if they are harvested at the right time, they can still degrade during transport or storage. In many cases, protein degradation can be greatly reduced upon immediate refrigeration. Some leafy material can be dried; assuming this drying does not degrade the protein, this option is very appealing. With regard to harvest, seeds are a preferred choice since they are not time sensitive and do not require special handling conditions to prevent degradation of the recombinant proteins. After the plant tissue is harvested and transported to its designated location, it must be stored for some amount of time before it is processed. The storage concerns of the host tissue mimic those for transporting the tissue but may be magnified, since the raw material may be in storage for years until it is fully processed to its final form. The length of time between harvesting and processing can vary significantly depending on the plant tissue. If the protein source is fresh tissue, then the protein will be at risk for degradation due to the plants’ active metabolic machinery. Fruit crops are at a disadvantage with regard to storage because of natural degradation by native enzymes. This limitation can be overcome if the fruit is immediately processed in some form. For example, the fruit can be dried, thus providing a useful storage system. Alternatively, fruit can be made into a juice that could be further processed immediately, thereby eliminating the storage of the host tissue in its native form. This means either that processing must take place shortly after harvest with no storage or that storage will require refrigeration. In many cases, both are required. In contrast, in plant storage organs, e.g., seeds or tubers, the plant part is in a dormant state with little metabolic activity. In this regard seed tissue has a distinct advantage because seeds store proteins for years without degrading proteins. Examples of this phenomenon include the demonstration that recombinant proteins can remain stable in seed tissue for months to years (Kusnadi et al., 1998a; Lamphear et al., 2002; Stoger et al., 2000). This stability may be due to the high concentration of protease inhibitors in seed, the low water content, or the carbohydrate available to stabilize the protein. Thus, seeds can be stored under ambient conditions to allow greater flexibility for processing options.

C. TISSUE PROCESSING Tissue processing after the crop is harvested is necessary whether for direct delivery or for highly purified products. One of the most critical aspects is the amount of total protein present in the harvested tissue. The amount of protein as a percentage of total biomass can range from less than


103

1% to over 40% depending on the plant and tissue source. This feature is critical because in addition to obtaining relatively high percentages of total soluble protein for ease in purification, the overall cost of tissue processing and extraction is directly related to the amount of total biomass. Fresh fruit is generally at a disadvantage because it is relatively low in overall protein content (1–3%) and high in water content. In contrast, most seeds are high in total protein (10–40%) and low in water content (Koehn, 1978). Mechanical processing of seeds to flour is common and suitable for extraction. In the case of leaves, maceration can be employed before grinding the tissue. For fresh fruit or fresh leaf tissue, it may be necessary to extract the product immediately after processing to avoid protein degradation. In this instance, tissue processing and extraction need to be considered together. Therefore, seeds offer protein stability and allow for processing dry tissue and a relatively low biomass to give an overall advantage prior to extraction. Tissue can also be processed and separated into fractions enriched with recombinant proteins by mechanical means. For example, it is possible to generate a germ or endosperm fraction from grains by using standard procedures common in the industry today (Watson, 1988). These fractions can have a much higher protein content than the whole grain due to the expression technology used. Furthermore, the remaining part of the grain can be used for other industrial applications. This has been done for maize for both orally delivered product candidates and purified proteins. In these cases, the germ fraction contained a 5- to 10-fold enrichment of the recombinant protein on a dry-weight basis compared to the whole grain, due in part to the type of promoter used (Kusnadi et al., 1998b; Streatfield et al., 2003). The amount of biomass required for extraction is reduced between 5-fold and 10-fold, thereby reducing the cost of extraction. This leaves 90% of the grain available for other industrial applications, such as ethanol production. In this way, the cost of the raw material can be reduced since it is offset with byproduct credits. Finally, the waste from the process is greatly reduced, thereby reducing the cost of waste disposal. In this example, tissue processing is not only a required step, but also an opportunity to reduce downstream costs.

D. EXTRACTION /PURIFICATION Extraction is relatively easily done in most cases by simply adding an aqueous buffer to ground tissue. The pH and salt content can be optimized on a case-by-case basis to reduce extraction of endogenous proteins and preferentially solubilize the recombinant protein (Bai and Nikolov, 2001; Evangelista et al., 1998). Fresh tissue will most likely require refrigeration to prevent protein degradation. Seeds, however, usually have the advantage of

104


endogenous protease inhibitors, which allow greater flexibility in extraction times and temperatures. Protein can be purified from the aqueous extracts in a manner similar to extracts from other production systems. For pharmaceutical products, this will require a cGMP (current Good Manufacturing Practices) facility. Many control points will be similar to those already used for other systems, but a few characteristics that are more pertinent to plant-based systems will be discussed. The need to test for animal pathogens in the final product should be greatly reduced, if not eliminated, when using plant material. Unfortunately, this concern may be replaced with the need to test or validate protocols to demonstrate that no pesticides used in growing the crop will be present in the final product. Often, pesticides are not present on the harvested portion of the crop. Moreover, the small molecule pesticides will separate easily from proteins, even if they are present on the harvested material, due to their vastly different physical properties. Acceptance that the final product is free of pesticides will most likely require validation before it gains acceptance from regulatory agencies. Plants make a number of phenolics, alkaloids, and other secondary metabolites that can interfere with protein purification. These small molecules sometimes bind to proteins, making purification difficult, or they may interfere with the applications when the proteins are used directly without purification. Green leaf material is generally high in these compounds, although some seeds can also have a high content. Care must be taken either to select material that is low in these compounds or to validate that there is no interference with purification or applications. The advantages and disadvantages for a variety of plant types when considering all of these characteristics are summarized in Table II. No one type of plant rises to the top as the clear choice. This suggests the possibility that one could modify an existing crop specifically for recombinant protein production. As an example, a cultivated crop could be altered to have a higher protein content that could translate into higher amounts of recombinant protein. For industrial feedstocks, selecting a major crop that already is used in industrial applications would be beneficial. For orally delivered therapeutic proteins or vaccines, a food crop that has GRAS (‘‘generally recognized as safe’’) status would be best. For protein stability and ease of transport, a grain would be a good choice. Combining these characteristics into a single crop would require starting with a food crop. Because of the perceived danger of intermixing recombinant protein-containing crops with commodity crops, specific preventive measures can be taken. For example, a colored marker could be used to differentiate between the industrial grain and the commodity crop. The public’s acceptance of the crop may change for open-pollinated crops if a


105

Table II Characteristics of Plant Systems Crop Wild species

Advantages &

Clearly distinguishable from crops

Disadvantages & & &

Domesticated species

& & &

High yields Experience in growing Infrastructure and experience exist

&

Low yield Outcross to native plants Little known about safety Potential to intermix with crops used for other purposes

Food

&

High margin of safety for human health products

&

Greater potential to intermix with food supply

Non-food

&

Less potential to intermix with food supply

&

Greater potential for toxic, antinutritional, or allergenic agents

Fresh tissue Seed or dry tissue

&

Abundant biomass Harvest/transport/storage High protein content

&

Harvest/transport/storage

&

Limited exposure to environment

&

Low cost Infrastructure in place

&

High cost Limited knowledge of product safety Higher potential to intermix

Clearly distinguished by color/shape Non-transferable genetics Low cost Infrastructure and experience transferable from commodity crop

&

Not yet developed

Hydroponics, cell cultures Field grown

& &

& &

Modified food/feed grain designed for industrial applications

&

& & &

&

male sterile system is used. Alternatively, a self-pollinating crop could be used. Chemically induced promoters could also be employed such that expression is only present when an exogenous chemical is applied. By incorporating these measures, the valued experience and product safety attributes of food quality grains can be kept and at the same time, the potential for an unintentional environmental impact can be minimized. While a general discussion of different plant types provides a framework, it is also possible to compare actual systems used today. We have summarized the abilities of several different plant species to be host production systems (Table III). This is a very controversial topic, and a number of assumptions must be made to even begin to compare them. Therefore, this

106

Table III Ratings for Selected Crops as Suitable Protein Production Systems

Crop

C B B B B A A B A C C A A A C B B A

Cost

Environmental safety

Lab ease

Growing ease

Harvest transport storage

Process purification

Byproduct credits

B A C A A C B A A C C A A C C A A A

A B A C C B B A A A A C B B B B B A

A B A B A C A B B B B C B A A C B A

B A B A A B B A A C C A A B C A B A

C A C A A C B B A C C A A C C A A A

B A C B B C C C B C C A A C C A B A

C A C B B C B C C C C A A C C B C A


Tobacco Rice Cell cultures Sunflower Canola Banana Potato Alfalfa Safflower Lemna Hydroponics Sorghum Maize Tomato Arabidopsis Soybean Peanut Modified industrial grain

Product safety


107

should only be used as a general guide. To understand the ratings, the criteria are listed below as they relate to how the art is practiced today. . Product safety refers to what we know about the crop as it relates to

.

.

.

.

.

antinutritional, allergenic, or toxic agents. An ‘‘A’’ rating means a crop is a food source with no known problems. A ‘‘B’’ rating means the crop can be used as a food source; the known toxins and antinutritional or allergenic agents associated with it are not severe. A ‘‘C’’ rating means there are known toxins, allergens, or antinutritional properties or there is no information known. Environmental safety refers to the potential to outcross with weedy species or food crops and the potential to intermix with the food supply. An ‘‘A’’ rating means there is little outcross potential with weeds or food crops. A ‘‘B’’ rating is given to those species that, as practiced today, have reduced this risk to near zero. A ‘‘C’’ rating is given to species for which there is a significant risk as practiced today. Cost is defined as the commodity price of the harvested crop, which was used to provide an index to determine its relative production cost. The percentage of total protein in the harvested tissue was then used to calculate the relative amount of recombinant protein that could be present if expression, based on percent of total protein, was the same in all cases. An ‘‘A’’ rating was given to the low cost crops, ‘‘B’’ ratings refer to species that were significantly higher, and ‘‘C’’ ratings were given to species that were an order of magnitude or more higher than the ‘‘A’’-rated crops. Laboratory ease: This represents the ease of transforming and targeting protein accumulation in the harvested tissue. An ‘‘A’’ was given to crops for which this is routine and can be done in most laboratories. A ‘‘B’’ was given to crops for which protein accumulation is routine but does require additional skills and is only practiced in specialized laboratories. A ‘‘C’’ was given to crops for which this process is known to be difficult. Field knowledge and experience: An ‘‘A’’ rating refers to crops for which we have ample experience and infrastructure to grow the plant and for which we have a series of variants that can be useful if specific problems arise. A ‘‘B’’ rating is given to those for which we have some experience but the crop experiences are not as well characterized. A ‘‘C’’ refers to those crops for which we have little experience in growing them. Harvest/storage/transport: An ‘‘A’’ rating refers to crops that can be harvested at almost any time, and for which transport and storage is stable at ambient temperatures for extended times. A ‘‘B’’ rating is for species that under certain conditions can be stored at cool temperatures but for which there is flexibility in the harvest date. ‘‘C’’ ratings are for those crops that easily spoil and that are time-sensitive to harvest, transport, and storage conditions with or without refrigeration.

108


. Tissue processing/extraction/purification: An ‘‘A’’ rating is for plant tissue

in which processing can be optimized for extraction and purification. The tissue would contain low levels of phenolics that can interfere with protein purification and low protease activity that can degrade the product during purification. ‘‘B’’ ratings are for those crops that are easily processed but require a larger biomass to extract the same amount of protein. ‘‘C’’ ratings are for those crops that are difficult to process, are low in protein content, and have protease activity or phenolics that could interfere with protein purification. While each crop has its unique features, Table III illustrates a few key points. First, no one plant species exists today that is best under all conditions. There has been no attempt to weight each of these categories, because the importance of each category may be different for different product types. Therefore, we will consider each of the categories separately and point out what products are most appropriate for that category and what can be done to improve a perceived disadvantage. While some advocate the use of weeds to produce recombinant protein products, no specific examples have been suggested. Weeds in general score poorly in all categories. The suggestion of using weeds arises from the fact that weeds can be easily differentiated from food crops and serve no other useful purpose. However, they would often be able to outcross with native weeds, which gives them a lower rating for the environment. Little is known about how weeds may fair in downstream processes or product safety. It would require decades of research and cost millions of dollars to fully characterize these systems. At the end of that time, it may be clear if weeds could be a viable production system—but the answer may be that they are unacceptable. Tobacco is one of the favorite plants used in the laboratory; this is undoubtedly why it was one of the first crops used for plant production systems. In addition, there is little need for concern about its effect on the environment or intermixing with food. One disadvantage in using tobacco for the direct delivery of human health products is its high alkaloid content. It may also have some antinutritional properties, and it is generally not palatable to humans. For larger acreage and low-cost products such as industrial enzymes, tobacco would not be a good choice because of its relatively high cost of production. This crop fits best with parenteral products that have higher profit margins and high purification requirements compared to industrial enzymes. In a unique situation, by using a viral vector system, tobacco can be one of the best systems to deliver small-scale therapeutics in record time. This is being explored for single-chain antibodies and other small proteins (Grill et al., 2002). Cell cultures and hydroponics are more suited for containment than all other plant choices. The physical facility required for growing material is


109

analogous to microbial or animal cell culture systems, making this one of the most expensive ways to produce raw material from plants. It is highly unlikely that this cost disadvantage could be overcome any time soon. These systems may be some of the best when expressing proteins that are toxic or deleterious if released. Sunflower and canola are good choices in most categories. There are some known allergenic problems associated with sunflower (Zitouni et al., 2000), which slightly lowers its rating for product safety. This is probably only a significant factor when producing orally delivered products. Sunflower, canola, and sorghum have the potential to outcross with weedy species; therefore, they have the worst rating for safety, which is the ability to intermix with food and the ability to outcross with weedy species. This safety issue can be controlled by management practices, depending on how and where the plants are grown, although they will increase the cost of production slightly and limit the flexibility of areas in which the crops can be grown. While it may be possible to overcome this limitation, it will be difficult to convince the public and regulatory agencies that practices are in place such that these crops pose little risk. The recent entry of safflower as a recombinant protein production vehicle may help to limit some of the unwanted characteristics and provide an alternative to sunflower. Bananas and tomatoes may be good choices for orally delivered products since they are highly palatable, assuming that a dose can be obtained in convenient amounts of plant tissue. These crops fall short in producing parenterals because of their low protein content, which impacts purification costs, and because they are difficult to harvest, transport, store, and extract. They also do not have the infrastructure or the cost advantages needed to grow large volume industrial products. Soybeans are one of the better choices for industrial products, in theory, based on their high protein content and low cost. They can also be used for human health products, although they have a slightly lower rating for product safety because of some known allergens and antinutritional agents (Kleine-Tebbe et al., 2002; Rihs et al., 1999). This should not be a major disadvantage for industrial products, however, and its cost advantages and self-pollination aspects make this crop a good candidate. The major disadvantage of soybeans is that they are very difficult to work with in the laboratory. This limitation will undoubtedly be reduced with time, and soybeans may become one of the preferred crops for industrial products in the future. Maize has been the most used crop to date for the production of recombinant proteins in plants (Hood et al., 1999). The reasons become apparent when examining the data in Table III. Maize is well accepted as a safe product (GRAS) and is widely used in food, feed, and industrial applications today (Watson, 1988). The production cost of maize is very low, and the

110


infrastructure can handle large or small acreages for industrial or pharmaceutical products. Storage and transport of seed and protein purification from flour are very compatible and flexible with current practices without special handling. In addition, maize has an advantage in that the kernels can be mechanically separated to yield a germ fraction with enriched protein and an endosperm fraction with enriched carbohydrate (Watson, 1988). This allows for easy use of the carbohydrate fraction for industrial applications such as ethanol production. In this way, not only is the cost of the raw material reduced but the waste products are handled as well. There are no known agents in maize that generally interfere with protein purification. Finally, the grain can be processed with little or no heat inactivation steps without affecting the nutritional or taste properties, making this a good candidate for orally delivered products. The disadvantage of maize is the potential for intermixing with the food supply. This one feature has brought other crops of lesser strength to the forefront. Intermixing potential can be handled by management practices, but the public’s confidence needs to be gained. While skeptics may claim no food source can ever be used to produce pharmaceutical products, we must remind ourselves that both eggs and yeast are used to make pharmaceuticals. Not only is there not a problem with intermixing in these instances, but also the public has accepted these as distinctive production systems from the food system. Maize as well as other plant systems must build an infrastructure dedicated to pharmaceutical protein production, which is as distinct from food production as edible eggs are from vaccine production.

IV. PRODUCTS When should one plant system be chosen over another system, and for what specific types of products? Table IV summarizes some of the key characteristics of production as they apply to different types of products. Table IV Production Characteristics of Different Product Types Product safety requirement

Relative cost of raw material

Relative overall production cost

Low

Med

Med

High

Low Med–high

High Low

High Low

Med Low

Acreage High purity Human health Oral delivery Industrial


111

The following sections describe progress on these types of products and the features that are most critical in choosing the best production system.

A. HIGH-PURITY HUMAN HEALTH PRODUCTS Most therapeutic proteins were historically derived from animal tissue because they could only be produced by animals. The advent of recombinant DNA technology has led to many new hosts for therapeutic protein production and the design of new therapeutic proteins. The recent explosion of antibodies as a class of therapeutics, a class that did not exist 20 years ago, has propelled these proteins to become the major class of new protein therapeutics. Antibodies also represent a challenge for the industry (Chu and Robinson, 2001; Houdebaine, 2000; Schwartz, 2001). How can these types of products be produced inexpensively to realize their great potential? Currently, the preferred production system is animal cell culture because microbial systems are an inadequate host for making complex mammalian proteins. However, the cost of production from animal cell culture is quite high, resulting in antibody products that are too expensive for many applications. This has opened up the possibility for plants to play a major role for these products because plants can adequately synthesize these complex proteins and their production costs are lower than animal cell culture systems (Schillberg et al., 2003). Another class of proteins that fit the plant technology platform is that of blood proteins. In many cases, extremely large quantities are needed and at a very inexpensive price. Proteins such as human serum albumin have been a target for a low-cost supply for years to increase its applications. In addition, these proteins can be made in an animal-free system, which is a great concern when considering the amount of blood required from unknown sources to obtain the products. Additional proteins, if made in quantity and at a low cost, may be the basis for an eventual blood replacement. Both hemoglobin (Merot et al., 2002) and human serum albumin (Farran et al., 2002; Sijmons et al., 1990) have already been expressed in plants. Glycoproteins have different glycan structures depending on the host used for expression. One significant difference is the addition of sialic acid on some mammalian proteins, which does not occur in other systems. While this represents a very limited number of proteins, it does represent an important class of proteins used in the pharmaceutical industry. Sialic acid allows therapeutic glycoproteins to have a prolonged clearance time in the blood stream (Rasmussen, 1991). One of the major limitations is that plants do not usually make sialic acid, although a recent study demonstrated that at least limited sialylation may be possible (Shah et al., 2003). This option is not practical for recombinant proteins until plants are better equipped with both the specific neuraminic acid transferase and the enzymes required to make

112


sialic acid. While this seems possible, it is not without technical challenges and will require several years to accomplish. Alternatively, if the therapeutic proteins are not toxic, it may be possible to use a higher dose to overcome clearance time and achieve the same physiological results without sialic acid. While this may add cost to the product, the plant source may still cost less than alternative systems and easily accommodate the increased cost needed for a larger dose. In addition to sialic acid, plant glycoproteins have other subtle differences from their mammalian counterparts (Faye et al., 1989; Lerouge et al., 1998), including the addition of xylose and the change in fucose from an alpha(1,6) to an alpha(1,3) linkage. These carbohydrates have been implicated in the allergic response as it relates to pollen allergens (Garcia-Casado et al., 1996), and they may also be responsible for allergic reactions with recombinant proteins. (van Ree et al., 2000). In several cases, recombinant proteins expressed in plants contained carbohydrate structures that have been characterized with no apparent effect on activity (Bakker et al., 2001; CabanesMacheteau et al., 1999; Ma et al., 1998; Samyn-Petit, 2001). In a few examples, these proteins also were examined for their possible role in allergic response. The minor changes in glycosylation, however, did not appear to be implicated in an allergenic response (Chargelegue et al., 2000; van Ree et al., 1994). Commercial high-purity recombinant protein products from plants were first demonstrated with the diagnostic products avidin (Hood et al., 1997) and b-glucuronidase (GUS) (Witcher et al., 1998) expressed in corn grain. The large-scale production of a foreign protein in a recombinant plant has recently been achieved for bovine trypsin, also expressed in corn grain (Woodard et al., 2003). These first products represent the prototype for most high-value therapeutics. In these instances, the raw material is only a small cost of the final product. What is critical is the concentration of the protein in the starting material before extraction. As is the case for all types of products, higher levels of expression and accumulation are the key drivers. Unlike industrial enzymes, however, the price for obtaining the high concentrations can be easily absorbed, even if this causes a significant loss in agronomic yield. This is important because there are several variant lines of crops that could cause an increase in recombinant protein as a percent of dry weight but that may result in a net decrease in overall agronomic yield. One example may be breeding recombinant protein genes into opaque mutants of corn. The yield of opaque corn is significantly lower than normal hybrid maize. However, the concentration of recombinant protein can be as much as 3–5 times higher in this grain, allowing significant savings in extraction costs. This tradeoff is easily acceptable, not only because of the relatively low cost of raw material compared to the final product but also because of the relatively low overall acreage requirement.


B.

113

ORALLY DELIVERED PRODUCTS

In general, oral delivery of vaccines or therapeutics is favored because of the convenience of this method. The economic advantages of oral delivery when using plant-based production systems that eliminate the need for purification are also compelling. The key driver is to obtain sufficient levels of expression to enable oral delivery. The required dose must be in an amount of plant material that is manageable for consumption at a single sitting. If the material is not particularly palatable, it could be presented in pill form. If the material is palatable, a single dose may be in the form of a wafer or other processed food-like substance. There are several processing alternatives depending on the available food or feed industry procedures for the chosen plant material. A few alternatives have been explored, for example, with corn (Watson, 1988). Selecting tissues that contain high levels of protein as measured by the concentration of the protein per gram of tissue is advantageous for oral delivery rather than as a percent of total soluble protein, as is the case for highly purified products. This favors plant tissues that are low in water content, as opposed to tissues such as fresh fruits. Preferred plant tissues include seeds, which are low in water content and rich in protein, and leaf tissue, if it can be dried to reduce water content without interfering with the product. Preferred crops include dried alfalfa and edible grains such as corn or rice (Streatfield et al., 2001, 2002, 2003). Potatoes have been used but may cause some problems with digestion when eaten raw by humans (Tacket et al., 1998), although recent studies using corn germ appeared to overcome these mild adverse symptoms (Tacket et al., 2004). In general, plant tissue that is consumed as food or feed should provide an extra level of comfort with regard to orally delivered product safety. Processing costs for orally delivered products are greatly reduced compared to purified proteins, and if it were not for tight quality control and regulatory standards, the cost would be similar to industrial proteins. Therefore, most plant systems will be economically viable as long as expression is moderately high. The more critical concern is to demonstrate that during whatever limited processing is necessary, the protein product is not altered. Most processing steps for grain products include a high-temperature step that will most likely inactivate the protein. It has been shown, however, that antigens can be processed in plant tissues at reduced temperatures, retaining their native state while at the same time achieving the desired effect (Streatfield et al., 2002). Oral delivery can be used with therapeutic proteins, growth hormones, or vaccines. Because of the large cost advantage in plants, obtaining doses of 1000 times the injectable dose is technically and economically feasible. This means that even if the oral product requires a 1000-fold higher dose to be equivalent to an injectable dose, it can be effective and cost the same or less.

114


Most oral delivery research has focused on vaccines. Immunogenic responses have been observed with several plant-based vaccine candidates in experimental animal systems. Protection from disease symptoms has been observed in target animal trials (Lamphear et al., 2002); this has recently included the possibility of passive immunity for the application to newborns pigs when boosting the lactogenic immunity in sows (Lamphear et al., 2004). Human clinical studies have not yet advanced this far and have focused on inducing an immune response (Kong et al., 2001; Tacket et al., 1998, 2000, 2004). Presumably, orally consumed proteins are protected by surrounding plant tissue that allows a sufficient dose to survive the digestive processes of the stomach and small intestine (Bailey, 2000). This biological encapsulation, or bioencapsulation, has an effect similar to the use of encapsulating agents such as liposomes with oral vaccines produced from alternative sources. It is not yet known if bioencapsulation is true for all plant tissues or if it is restricted to certain types of tissues. Grains offer the potential for native protease inhibitors, carbohydrates for protein stability, and a granular matrix that may account for some of the proteins’ slow release and ability to survive into the gut.

C. INDUSTRIAL ENZYMES Industrial proteins are used for protein processing and purification, diagnostics, and processes relating to food, feed, and industrial applications. Most commercial industrial enzymes are currently derived from microbial sources, either natural or recombinant, primarily because all other systems are cost prohibitive. However, using plants as a production system is a developing industry that could be competitive in the next few years (Hood, 2002; Hood and Woodard, 2002). There are several categories of enzymes, including hydrolases, transferases, oxidation/reduction (redox) enzymes, lyases, isomerases, and ligases. Only a few are routinely used in industrial applications. Hydrolases such as amylases and proteases are the most commonly used industrial enzymes. Other enzyme classes, particularly redox enzymes, will be useful in industry when they become less expensive and thus available for testing in various processes. The principal non-food industrial uses for enzymes are starch hydrolysis (amylases), textile desizing (amylases), leather production (proteases), pulp processing (xylanases), detergent additives (proteases), and animal feeds, which represent substantial markets to date. However, a reason for the limited use of enzymes in certain applications is that no cost-effective source of the enzyme is available. Transgenic plant systems can meet both the scale and cost targets for many new applications.


115

Using an enzyme as a catalyst in a particular process has advantages that result from the enzyme properties of functioning at ambient temperatures, in aqueous solution, and usually at near neutral or physiological pH values. Most enzymes have a very high specificity and function in low concentrations to produce the desired effect. Enzymes can produce a rapid reaction and have a low level of toxicity. However, these highly active proteins are generally unstable and require a degree of care and expertise in their preparation and use. In addition, extremes of pH and temperature can limit activity levels, even if these conditions do not destroy the enzymes. Enzymes may be inactivated by the presence of various ions, organic molecules, or solvents, components that are often present in organic reaction systems and in large-scale industrial processes. Significant changes in the process may be necessary when switching from chemical to enzymatic catalysts. Alternatively, the enzyme of interest could be engineered using a variety of emerging technologies to more closely fit into the reaction conditions of the current target process. Nevertheless, considering the large market opportunity for industrial enzymes and their environmentally friendly benefits, it is well worth the effort to generate an efficient production system and make process or protein changes to accommodate them. For achieving low-cost industrial enzymes, plants as a production system have many advantages over current competing technologies. Plants are an excellent system for cost competition because of their protein expression potential and minimal production costs. Various plant systems can be used to produce enzymes, such as tobacco, alfalfa, barley, canola, and corn (Hood and Woodard, 2002). The first recombinant proteins produced and sold from transgenic plants were avidin and GUS (Hood et al., 1999; Witcher et al., 1998). Avidin, the first plant-produced recombinant protein product to be marketed, was first sold in 1997. Applications of these proteins include using the GUS protein as a research reagent and using avidin as a diagnostic reagent and protein purification tool for biotinylated proteins. Currently, the highest profile plant-based industrial enzyme projects involve enzymes for applications in feed, cleaning agents, processing reagents, the wood products industry, and biomass conversion. These include mainly xylanases and cellulases for the textiles and wood products industries, as well as laccase and trypsin (Bailey et al., 2003; Hood and Woodard, 2002; Hood et al., 2003b; Woodard et al., 2003). For food applications, enzymes are used in the conversion of raw materials to form intermediate products that are more useful in food processing and in food formulations. The treatment of food products with enzymes makes them more palatable or more stable or enables the development of some other desirable property. Enzyme production for the food industry is primarily through microbial fermentation. However, major value from edible plant-based protein or enzyme production is readily apparent. For example, brazzein, a high intensity sweetener, has been

116


expressed in transgenic maize (Lamphear et al., in press). For food applications, the direct addition of corn meal is possible, allowing for both a bulking agent and a low-calorie sugar substitute. The cost is kept to a minimum since there is no added cost in preparing the sweetener, and the corn meal can be processed using methods similar to conventional methods. Two potential new areas for enzyme applications are biomass conversion and the wood products industries. For biomass conversion applications, enzymes that degrade cell walls will be useful. The resulting products from those enzymes are monomeric components of walls, primarily 5- and 6-carbon sugars, but also amino acids and lignin products. These will be substrates for a variety of applications, including fermentation into ethanol and specialty chemicals. Oxidation/reduction enzymes such as laccase (described previously) and peroxidases (Caramelo et al., 1999; De Jong et al., 1992; Jensen et al., 1996) will find many new markets as new production systems are introduced. These enzymes can potentially replace many applications currently using chemicals that are damaging to the environment. Because of the scale-up potential of the plant system and the low cost of goods, plants will likely be the system of choice for large-scale enzyme production for these industries. It is important to explore many plant systems, enzymes, promoters, and targeting sequences to understand the factors that affect expression levels, and hence the economics of industrial enzyme production in plants. To date, such experimentation has been limited.

V. PUBLIC ACCEPTANCE GMO (genetically modified organism) products that enter the food supply are generally not labeled, and thus the public is not given a choice about consumption of these products. This has led to a controversy about whether to label GMO products, resulting in a public that is skeptical about their value and safety. While GMO non-food products can be easily labeled and are not intended for the food supply, the skepticism and lack of trust have carried over to them as well. For pharmaceuticals manufactured from any source, however, the public has a clear choice whether or not to use the drug product. If the product is effective, they experience the direct benefit. In this example, the public has been much more accepting. Therefore, there should not be any major concerns about the plant-produced products themselves. In fact, the animal-free source, lower cost of goods, and oral delivery should make the public enthusiastic about these products. The major public concern is that non-food products produced in plants will not somehow end up in the food supply. The public usually assumes that


117

GMOs will be grown with commodity practices that do not limit the intermixing of the plants. In practice, however, these plants are grown with regulatory standards that are similar to those used for pharmaceutical and industrial products from non-plant systems. There are new regulations for these types of products that are similar to practices used in non-plant systems. It is hoped that in the future the public will have the same confidence for plants as they now have for eggs or yeast, potential food products that are currently used for pharmaceutical and industrial protein production. The regulations and containment practices used for plants, as well as for other hosts, do not overlook the remote possibility that unintended exposure may occur, regardless of how insignificant it is. This situation calls for a safety and risk assessment that would be accepted by the industry, regulatory agencies, special interest groups, and the public. The risk assessment needs to be science based and could be similar to what is used for other systems. A system has been proposed for quantification of unintentional exposure that is based on evaluating the risk that is linked to the hazard and exposure. Formulas exist for regulated articles, and it has been suggested that these equations be modified for non-food products produced in plants, allowing a quantitative method of assessing the risk of unintentional exposure (Howard and Donnelly, 2004). The requirements for producing non-food products in transgenic plants are considerably different from those for producing food products. They include physical isolation, delayed planting times from food crops, agronomic support, dedicated equipment, and frequent monitoring. When taking these practices into account, the amount of a crop that may inadvertently end up in the food supply and the associated risk can be calculated. Aprotinin is an example of a pharmaceutical product that has been produced in plants (Delaney et al., 2003; Zhong et al., 1999). It has been calculated that even without any of the required confinement practices, the amount of aprotinin that could inadvertently end up in the food supply would be well below the level needed to show an effect (Howard and Donnelly, submitted). This means that there is no hazard even if the plants were grown and harvested as a commodity crop. If the required containment practices are used, the calculated level of risk can be a million times below the non-contained exposure levels. In addition to potential toxicity, some proteins can be allergenic. Using the case of aprotinin, we can calculate how much transgenic corn containing aprotinin used for commercial production must be eaten to induce an immune response. In this example, one would have to eat 350 tacos or 350 bowls of cereal at one time on three different occasions just to ingest the minimum dose needed to observe an immune response. The above calculations seem inconsistent with the fact that the general public considers these non-food products to be a grave danger. Aprotinin is one case for which even though the product is intended for non-food

118


applications, it is already in the food supply from natural sources and no problems have ever been documented. Other proteins discussed earlier, trypsin and avidin, are also in the food supply from natural sources, and we would anticipate similar results. Clearly, the fear of products entering the food supply is unwarranted for proteins that are already in the food supply at much higher concentrations than an accidental intermixing would produce. This is not to say that all proteins would have the same risk profile, and it is this that fuels public fears. Thus, a case-by-case assessment is needed to allay public concerns if it is applied to all non-food products. In conclusion, confinement practices can reduce unintentional exposure for most proposed products to a level that is orders of magnitude below the slightest concern for food safety. However, safety assessment models need to be standardized and accepted by the public, regulatory agencies, and special interest groups. Finally, we need to treat plant-made pharmaceuticals and plant-made industrial proteins with the same considerations as other pharmaceutical production systems such as eggs or yeast and not as value-added agriculture.

VI. CONCLUSIONS AND FUTURE Plant production technology is still in its infancy. The key to economic feasibility of products derived from this technology lies first with the expression level of the foreign genes. The best plant expression systems are now approaching 1% of the dry weight of tissue. This would enable plants to be the low-cost producer of most proteins compared to other current systems. However, there is no theoretical reason why plant systems cannot achieve levels of expression that are at least an order of magnitude higher. Microbes can produce proteins that comprise as much as 70% of their energy store. For plants, especially grain with storage proteins, it is reasonable to assume that the recombinant protein can constitute or replace other proteins that supply a sink for amino acids. Many advances in technology remain to be made to take this production system to higher levels. Direct delivery will be essential for many oral vaccines, and in the future this process may deliver nutraceuticals and possibly some therapeutics. The possibility of using products that can be prophylactic, such as specific antibodies, is now approachable using this technology. Feed products easily lend themselves to direct delivery because growers can control the diet and supplements of domesticated animals. Direct delivery of industrial products results in added cost savings for production. The increase in research efforts for plant-made products will also translate into many more clinical and industrial application trials to demonstrate


119

efficacy of these products. There will undoubtedly be additional technologies available that will increase the potency of the orally delivered products. However, while the data to date are promising, the anticipated increase in effort will also show that the technology will have limits. These will become clear as the research is completed. The next several years should also see detailed regulatory guidelines that will pave a clear path for later stage clinical trials and manufacturing of pharmaceutical products. A separate regulatory path for industrial products should be developed. This will help reduce risks and increase acceptance. Regulations currently exist for growing non-food crops, but these will need refinements before large-scale industrial and nutraceutical products can be commercialized. The most likely near-term commercialization possibilities for pharmaceuticaltype proteins from plants will be with proteins currently made from animals or animal cell cultures. These have a much clearer advantage of cost and safety than products made from microbes. This should set the stage for additional orally delivered products and industrial products that can compete with microbial systems for cost. The industry is young, and it is impossible to even imagine the full range of products at this time.

REFERENCES Bakker, H., Bardor, M., Molthoff, J. W., Gomord, V., Elbers, I., Stevens, L. H., Jordi, W., Lommen, A., Faye, L., Lerouge, P., and Bosch, D. (2001). Galactose-extended glycans of antibodies produced by transgenic plants. Proc. Natl. Acad. Sci. USA 98(5), 2899–2904. Bai, Y., and Nikolov, Z. L. (2001). Effect of processing on the recovery of recombinant b-glucuronidase (Gus) from transgenic canola. Biotechnol. Prog. 17, 168–174. Bailey, M. (2000). ‘‘A Model System for Edible Vaccination Using Recombinant Avidin Produced in Corn Seed’’. Master’s Thesis, Texas A&M University. Bailey, M. R., Woodard, S. L., Callaway, E., Beifuss, K., Delaney, D., Magallanes-Lundback, M., Lane, J., Horn, M. E., Ward, M., Van Gastel, F., Howard, J. A., and Hood, E. E. (2003). Improved recovery of active recombinant laccase from maize seed. Appl. Microbiol. Biotech. 63(4), 390–397. Belanger, F. C., and Kriz, A. L. (1991). Molecular-basis for allelic polymorphism of the maize globulin-1 gene. Genetics 129, 863–872. Bevan, M. W., Flavell, R. B., and Chilton, M. D. (1983). A chimeric antibiotic resistance gene as a selectable marker for plant cell transformation. Nature 304, 184–187. Braun, A. C. (1958). A physiological basis for autonomous growth of the crown gall tumor cell. Proc. Natl. Acad. Sci. USA 44, 344–349. Cabanes-Macheteau, M., Fitchette-Laine, A.-C., Loutelier-Bourhis, C., Lange, C., Vien, N. D., Ma, J. K. C., Lerouge, P., and Faye, L. (1999). N-Glycosylation of a mouse IgG expressed ion transgenic tobacco plants. Gycobiology 9(4), 365–372.

120


Caramelo, L., Martinez, M. J., and Martinez, A. T. (1999). A search for lignolytic peroxidases in the fungus Pleurotus eryngii involving alpha-keto-gamma thiomethylbutyric acid and lignin model dimers. Appl. Env. Micro. 65, 916–922. Chargelegue, D., Vine, N. D., van Dolleweerd, C. J., Drake, P. M., and Ma, J. K. (2000). A murine monoclonal antibody produced in transgenic plants with plant-specific glycans is not immunogenic in mice. Transgenic Res. 9, 187–194. Chen, C.-H., Lin, H.-J., Ger, M.-J., Chow, D., and Feng, T.-Y. (2000). The cloning and characterization of a hypersensitive response assisting protein that may be associated with the harpin-mediated hypersensitive response. Plant Mol. Biol. 43, 429–438. Chikwamba, R. K., Scott, M. P., Mejia, L. B., Mason, H. S., and Wang, K. (2003). Localization of a bacterial protein in starch granules of transgenic maize kernels. Proc. Natl. Acad. Sci. USA 100, 11127–11132. Chilton, M. D. (2001). Agrobacterium. A memoir. Plant Phys. 125, 9–14. Chilton, M. D., Drummond, M. H., Merlo, D. J., Sciaky, D., Montoya, A. L., Gordon, M. P., and Nester, E. W. (1977). Stable incorporation of plasmid DNA into higher plant cells: The molecular basis of crown gall tumorigenesis. Cell 11, 263–271. Chu, L., and Robinson, D. K. (2001). Industrial choices for protein production by large-scale cell culture. Curr. Opin. Biotechnol. 12, 180–187. Daniell, H., Khan, M. S., and Allison, L. (2002). Milestones in chloroplast genetic engineering: An environmentally friendly era in biotechnology. Trends Plant Sci. 7, 84–91. Daniell, H., Streatfield, S. J., and Wycoff, K. (2001). Medical molecular farming: Production of antibodies, edible vaccines and biopharmaceuticals in plants. Trends Plant Sci. 6, 219–226. de Boer, D., Cremers, F., Teertstra, R., Smits, L., Hille, J., Smeekens, S., and Weisbeek, P. (1988). In vivo import of plastocyanin and a fusion protein into developmentally different plastids of transgenic plants. EMBO J. 7, 2631–2635. De Jong, E., de Vries, F. P., Field, J. A., van der Zwan, R. P., and de Bont, J. A. M. (1992). Isolation and screening of basidiomycetes with high peroxidative activity. Mycol. Res. 96, 1098–1104. Delaney, D. E. (2002). Choice of crop species and development of transgenic product lines. In ‘‘Plants as Factories for Protein Production’’ (E. E. Hood and J. A. Howard, Eds.), pp. 139–158. Kluwer Academic Publishers, The Netherlands. Delaney, D., Jilka, J., Barker, D., Irwin, P., Poage, M., Woodard, S., Horn, M., Vinas, A., Beifuss, K., Barker, M., Wiggins, B., Drees, C., Harkey, R., Nikolov, Z., Hood, E., and Howard, J. (2003). Production of aprotinin in transgenic maize seeds for the pharmaceutical and cell culture markets. In ‘‘Plant Biotechnology 2002 and Beyond: Proceedings of the 10th IAPTC&B Congress, Orlando, Florida’’ (I. K. Vasil, Ed.), pp. 393–394. Kluwer Academic Publishers. Esau, K. (1977). ‘‘Anatomy of Seed Plants’’. John Wiley and Sons, New York, NY. Evangelista, R. L., Kusnadi, A. R., Howard, J. A., and Nikolov, Z. L. (1998). Process and economic evaluation of the extraction and purification of recombinant b-glucuronidase from transgenic corn. Biotechnol. Prog. 17, 168–174. Farran, I., Sanchez-Serranno, J. J., Medina, J. F., Prieto, J., and Mingo-Castel, A. M. (2002). Targeted expression of human serum albumin to potato tubers. Transgenic Res. 11(4), 337–346. Faye, L., Johnson, K. D., Sturm, A., and Chrispeels, M. J. (1989). Structure, biosynthesis, and function of asparagine-linked glycans on plant glycoproteins. Physiologica Plantarum 75, 309–314. Fischer, R., and Emans, N. (2000). Molecular farming of pharmaceutical proteins. Transgenic Res. 9, 279–299. Fischer, R., Drossard, J., Schillberg, S., and Emans, N. (1999). Towards molecular farming in the future: Moving from diagnostic protein and antibody production in microbes to plants. Biotechnol. Appl. Biochem. 30, 101–108.


121

Garcia-Casado, G., Sanchez-Monge, R., and Chrispeels, M. J. (1996). Role of complex asparagines-linked glycans in the allergenicity of plant glycoproteins. Glycobiology 6, 471–477. Giddings, G. (2001). Transgenic plants as protein factories. Curr. Opin. Biotechnol. 12, 450–454. Grill, L. K., Lindbo, J., Pogue, G. P., and Turpen, T. H. (2002). Viral vector expression of foreign proteins in plants. In ‘‘Plants as Factories for Protein Production’’ (E. E. Hood and J. A. Howard, Eds.), pp. 3–16. Kluwer Academic Publishers, The Netherlands. Hare, P. D., and Chua, N.-H. (2002). Excision of selectable marker genes from transgenic plants. Nature Biotechnol. 20, 575–579. Hood, E. E. (1999). Analysis of plant transformation systems. In ‘‘Application of Transformation Technology in Plant Breeding, Proceedings of the 30th Annual Symposium of the Korean Breeding Society’’, pp. 33–38. Suwon, Korea, November 18–21, 1999. Hood, E. E. (2002). From green plants to industrial enzymes. Enzyme Microbial Technol. 30, 279–283. Hood, E. E. (2003). Selecting the fruits of your labors. Trends Plant Sci. 8, 357–358. Hood, E. E., and Howard, J. A. (1999). Protein products from transgenic plants. Agro-Food Industry Hi-Tech. 3(10), 35–36. Hood, E. E., and Jilka, J. M. (1999). Plant-based production of xenogenic proteins. Curr. Opin. Biotechnol. 10, 382–386. Hood, E. E., and Howard, J. A. (Eds.) 2002. ‘‘Plants as Factories for Protein Production’’. Kluwer Academic Publishers, Dordrecht, The Netherlands. Hood, E. E., and Woodard, S. L. (2002). Industrial proteins produced from transgenic plants. In ‘‘Plants as Factories for Protein Production’’ (E. E. Hood and J. A. Howard, Eds.), pp. 119–138. Kluwer Academic Publishers, Dordrecht, The Netherlands. Hood, E. E., Bailey, M. R., Beifuss, K., Horn, M., Magallanes-Lundback, M., Drees, C., Delaney, D. E., Clough, R., and Howard, J. A. (2003b). Criteria for high-level expression of a fungal laccase gene in transgenic maize. Plant Biotechnol. J. 1, 129–140. Hood, E. E., Horn, M. E., and Howard, J. A. (2003a). Production and application of proteins from transgenic plants. In ‘‘Plant Biotechnology 2002 and Beyond. Proceedings of the 10th IAPTC&B Congress, Orlando, Florida’’ (I. K. Vasil, Ed.), pp. 377–382. Kluwer Academic Publishers, The Netherlands. Hood, E. E., Kusnadi, A., Nikolov, Z., and Howard, J. A. (1999). Molecular farming of industrial proteins in transgenic maize. In ‘‘Chemicals via Higher Plant Bioengineering’’ ( Shahidi et al., Eds.), pp. 127–147. Kluwer Academic/Plenum Publishers, New York, NY. Hood, E. E., Witcher, D. R., Maddock, S., Meyer, T., Baszcynski, C., Bailey, M., Flynn, P., Register, J., Marshall, L., Bond, D., Kulisek, E., Kusnadi, A., Evangelista, R., Nikolov, Z., Wooge, C., Mehigh, R. J., Hernan, R., Kappel, W. K., Ritland, D., Li, C. P., and Howard, J. A. (1997). Commercial production of avidin from transgenic maize: Characterization of transformant, production, processing, extraction and purification. Mol. Breeding 3, 291–306. Hood, E. E., Horn, M. E., and Woodard, S. L. (2002). Monoclonal antibody manufacturing in transgenic plants-myths and realities. Curr. Opin. Biotechnol. 13, 630–635. Houdebaine, L. M. (2000). Transgenic animal bioreactors. Transgenic Res. 9, 279–299. Howard, J. A., and Donnelly, K. C. (2004). A quantitative safety assessment model for nonfood products produced in agricultural crops. J. Agric. and Environ. Ethics 17(6). Jensen, L. G., Olsen, O., Kops, O., Wolf, N., Thomsen, K. K., and von Wettstein, D. (1996). Transgenic barley expressing a protein-engineered, thermostable (1,3-1,4)-b-glucanase during germination. Proc. Natl. Acad. Sci. USA 93, 3487–3491. Keegstra, K., and Cline, K. (1999). Protein import and routing systems of chloroplasts. Plant Cell 11, 557–570. Kermode, A. (1996). Mechanisms of intracellular protein transport and targeting in plant cells. Crit. Rev. Plant Sci. 15, 285–423.

122


Kleine-Tebbe, J., Vogel, L., Crowell, D. N., Haustein, U. F., and Vieths, S. (2002). Sever oral allergy syndrome and anaphylactic reactions caused bya Bet v 1-related PR-10 protein in soybean, SAM22. J. Allergy Clin. Immunol. 110(5), 797–804. Koehn, M. L. (Ed.) 1978. ‘‘Agricultural Statistics, 1978’’. U.S. Department of Agriculture, U.S. Government Printing Office, Washington, DC. Komari, T., Hiei, Y., Saito, Y., Murai, N., and Kumashiro, T. (1996). Vectors carrying two separate T-DNAs for co-transformation of higher plants mediated by Agrobacterium tumefaciens and segregation of transformants free from selection markers. Plant J. 10, 165–174. Kong, Q., Richter, L., Yang, Y. F., Arntzen, C. J., Mason, H. S., and Thanavala, Y. (2001). Oral immunization with hepatitis B surface antigen expressed in transgenic plants. Proc. Natl. Acad. Sci. USA 98, 11539–11544. Kusnadi, A. R., Evangelista, R. L., Hood, E. E., Howard, J. A., and Nikolov, Z. L. (1998a). Processing of transgenic corn seed and its effect on the recovery of recombinant b-glucuronidase. Biotechnol. Bioeng. 60(1), 45–52. Kusnadi, A. R., Hood, E. E., Witcher, D. R., Howard, J. A., and Nikolov, Z. L. (1998b). Production and purification of two recombinant proteins from transgenic corn. Biotechnol. Prog. 14, 149–155. Kusnadi, A. R., Nikolov, Z. L., and Howard, J. A. (1997). Production of recombinant proteins in transgenic plants: Practical considerations. Biotechnol. Bioeng. 56, 473–484. Lamphear, B. J., Jilka, J. M., Kesl, L., Welter, M., Howard, J. A., and Streatfield, D. J. (2004). A corn-based delivery system for animal vaccines: An oral transmissible gastroenteritis in virus vaccine boosts lactogenic immunity in swine. Vaccine 22, 2420–2424. Lamphear, B. J., Streatfield, S. J., Jilka, J. M. et al. (2002). Delivery of subunit vaccines in maize seed. J. Control. Release 85, 169–180. Lamphear, B. J., Barker, D. K., Brooks, C. A., DeLaney, D. E., Lane, J. R., Beifurs, K., Love, R., Thompson, K., Mayor, J., Clough, R., Harkey, R., Poage, M., Drees, C., Horn, M., Strentfield, S., Nikolov , Z., Woodard, S., Hood, E., Jilka, J., and Howard, J. (in press). Expression of the Sweet Protein Brazzein in Maize for production of a new commercial sweetener. Plant Biotechnol. J. Lawrence, S. D., Kline, K., and Moore, G. A. (1997). Chromoplast development in ripening tomato fruit: Identification of cDNAs for chromoplast-targeted proteins and characterization of a cDNA encoding a plastid-localized low-molecular-weight heat shock protein. Plant Mol. Biol. 33(3), 483–492. Lerouge, P., Cabanes-Macheteau, M., Rayon, C., Fischette-Laine, A. C., Gomord, V., and Faye, L. (1998). N-glycoprotein biosynthesis in plants: Recent developments and future trends. Plant Mol. Biol. 38, 31–48. Lindbo, J. A., Fitzmaurice, W. P., and della-Cioppa, G. (2001). Virus-mediated reprogramming of gene expression in plants. Curr. Opin. Plant Biol. 4, 181–185. Ma, J. K.-C., Drake, P. M. W., and Christou, P. (2003). The production of recombinant pharmaceutical proteins in plants. Nat. Rev. 4, 794–805. Ma, J. K.-C., Hikmat, B. Y., Wycoff, K., Vine, N. D., Chargelegue, D., Yu, L., Hein, M. B., and Lehner, T. (1998). Characterization of a recombinant plant monoclonal secretory antibody and preventive immunotherapy in humans. Nat. Med. 4, 601–606. Maliga, P. (2002). Engineering the plastid genome of higher plants. Curr. Opin. Plant Biol. 5, 164–172. Merot, B., Dieryck, W., Philippe, L., Marden, M., Veronique, G., Pagnier, R.-J., Baudino, S., and Poyart, C. (2002). Method for producing human hemoglobin proteins using plant cells. US Patent No. 6,344,600. Nikolov, Z., and Hammes, D. (2002). Production of recombinant proteins from transgenic crops. In ‘‘Plants as Factories for Protein Production’’ (E. E. Hood and J. A. Howard, Eds.), pp. 159–174. Kluwer Academic Publishers, The Netherlands.


123

Pyke, K. A. (1999). Plastid division and development. Plant Cell 11, 549–556. Rasmussen, J. R. (1991). Gycosylation of recombinant proteins. In ‘‘Biology of Carbohydrates’’, pp. 179–285. JAI Press Ltd, London. Rihs, H. P., Chen, Z., Rueff, F., Petersen, A., Rozynek, P., Heimann, H., and Baur, X. (1999). IgE binding of the recombinant allergen soybean profilin (rGly m 3) is mediated by conformational epitopes. J. Allergy Clin. Immunol. 104(6), 1293–1301. Samyn-Petit, B., Gruber, V., Flauhaut, C., Wajda-Dubos, J.-P., Farrer, S., Pons, A., Desmaizieres, G., Slomianny, M.-C., Theisen, M., and Delannoy, P. (2001). N-Glycosylation potential of maize: The human lactoferrin used as a model. Glyconjugate J. 18, 519–527. Sanderfoot, A. A., and Raikhel, N. V. (1999). The specificity of vesicle trafficking: Coat proteins and SNAREs. Plant Cell 11, 629–641. Schillberg, S., Fischer, R., and Emans, N. (2003). Molecular farming of recombinant antibodies in plants. Cell. Mol. Life Sci. 60, 433–445. Schwartz, J. R. (2001). Advances in Escherichia coli production of therapeutic proteins. Curr. Opin. Biotechnol. 12, 195–201. Shah, M. M., Fujiyama, K., Flynn, C. R., and Joshi, L. (2003). Sialylated endogenous glycoconjugates in plant cells. Nature Biotechnol. 21(12), 1470–1471. Sijmons, P. C., Dekker, B. M., Schrammeijer, B., Verwoerd, T. C., van den Elzer, P. J., and Hoekema, A. (1990). Production of correctly processed human serum albumin in transgenic plants. Biotechnology 8(3), 217–221. Songstad, D. D., Somers, D. A., and Griesbach, R. J. (1995). Advances in alternative DNA delivery techniques. Plant Cell Tissue Organ Cult. 40, 1–15. Southgate, E. M., Davey, M. R., Power, J. B., and Westcott, R. J. (1998). A comparison of methods for direct gene transfer into maize (Zea mays, L.). In Vitro Cell Biol. Plant 34, 218–224. Stoger, E., Vaquero, C., Torres, E., Sack, M., Nicholson, L., Drossard, J., Williams, S., Keen, D., Perrin, Y., and Christou, P. (2000). Cereal crops as viable production and storage systems for pharmaceutical scFv antibodies. Plant Mol. Biol. 42, 583–590. Streatfield, S. J., Jilka, J. M., Hood, E. E., Turner, D. D., Bailey, M. R., Mayor, J. M., Woodard, S. L., Beifuss, K. K., Horn, M. E., Delaney, D. D., Tizard, I. R., and Howard, J. A. (2001). Plant-based vaccines: Unique advantages. Vaccine 19, 2742–2748. Streatfield, S. J., Mayor, J. M., Barker, D. K., Brooks, C., Lamphear, B. J., Woodard, S. L., Beifuss, K. K., Vicuna, D. V., Massey, L. A., Horn, M. E., Delaney, D. D., Nikolov, Z. L., Hood, E. E., Jilka, J. M., and Howard, J. A. (2002). 2000 Congress Symposium on Molecular Farming: Development of an edible subunit vaccine in corn against enterotoxigenic strains of Escherichia coli. In Vitro Cell. Dev. Biol. Plant 38, 11–17. Streatfield, S. (2002). The greening of vaccine technology. Curr. Drug Disc. November, 15–18. Streatfield, S. J., Lane, J. R., Brooks, C. A. et al. (2003). Corn as a production system for human and animal vaccines. Vaccine 21, 812–815. Streatfield, S. J., and Howard, J. A. (2003a). Plant-based vaccines. Int. J. Parasitol. 33, 479–493. Streatfield, S. J., and Howard, J. A. (2003b). Plant production systems for vaccines. Expert Rev. Vaccines 2(6), 763–775. Tacket, C. O., Mason, H. S., Losonsky, G., Clements, J. D., Levine, M. M., and Arntzen, C. J. (1998). Immunogenicity in humans of a recombinant bacterial antigen delivered in a transgenic potato. Nat. Med. 4, 607–609. Tacket, C. O., Mason, H. S., Losonsky, G., Este, M. K., Levine, M. M., and Arntzen, C. J. (2000). Human immune responses to a novel Norwalk virus vaccine delivered in transgenic potatoes. J. Infect. Dis. 182, 302–305. Tacket, C. O., Pasetti, M., Edelman, R., Clements, J. D., Howard, J. A., and Streatfield, S. (2004). Immunogenicity of recombinant LT-B delivered orally to humans in transgenic corn. Vaccine 22, 4385–4389.

124


Twyman, R. M., Stoger, E., Schillberg, S., Christou, P., and Fischer, R. (2003). Molecular farming in plants: Host systems and expression technology. Trends Biotechnol. 21, 570–578. van Ree, R., Cabanes-Macheteau, M., Akkerdaas, J., Milazzo, J.-P., Loutelier-Bourhis, C., Rayon, C., Villalba, M., Koppelmans, S., Aalbers, R., Rodriguez, R., Faye, L., and Lerouge, P. (2000). b(1,2)-Xylose and a(1,3)-fucose residues have a strong contribution in IgE binding to plant glycoallergens. J Biol. Chem. 75(15), 11451–11458. van Ree, R., van der Wal, J.-W., Pen, J., and Aalberse, R. C. (1994). Expression of a- amylase from Bacillus licheniformis in transgenic tobacco introduces IgE-binding N-glycans. In ‘‘Specificity of Grass Pollen Allergens’’, pp. 155–142. Doctorate Dissertation, Universite it van Amsterdam. Vitale, A., and Denecke, J. (1999). The endoplasmic reticulum—Gateway of the secretory pathway. Plant Cell 11, 615–628. Wan, J. X., Blakeley, S. D., Dennis, D. T., and Ko, K. (1996). Transit peptides play a major role in the preferential import of proteins into leucoplasts and chloroplasts. J. Biol. Chem. 271(49), 31227–31233. Wang, A. S., Evans, R. A., Altendorf, P. R., Hanten, J. A., Doyle, M. C., and Rosichan, J. L. (2000). A mannose selection system for production of fertile transgenic maize plants from protoplasts. Plant Cell Rep. 19, 654–660. Watson, S. A. (1988). Corn marketing, processing and utilization. In ‘‘Corn and Corn Improvement’’ (G. F. Sprague and J. W. Dudley, Eds.), 3rd ed., pp. 885–942. No. 18, American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc., Madison, WI. Wilmink, A., and Dons, J. J. M. (1993). Selective agents and marker genes for use in transformation of monocotyledonous plants. Plant Mol. Biol. Rptr. 11, 165–185. Witcher, D. R., Hood, E. E., Peterson, D., Bailey, M., Bond, D., Kusnadi, A., Evangelista, R., Nikolov, Z., Wooge, C., Mehigh, R., Kappel, W., Register, J., and Howard, J. A. (1998). Commercial production of B-glucuronidase (GUS): A model system for the production of proteins in plants. Mol. Breeding 4, 301–312. Woodard, S. L., Mayor, J. M., Bailey, M. R., Barker, D. K., Love, R. T., Lane, J. R., Delaney, D. D., McComas-Wagner, J. M., Mallubhotla, H. D., Hood, E. E., Dangott, L. J., Tichy, S. E., and Howard, J. A. (2003). Maize-derived bovine trypsin: Characterization of the first large-scale, commercial protein product from transgenic plants. Biotechnol. Appl. Biochem. 38, 123–130. You, S. J., Liau, C. H., Huang, H. E., Feng, T. Y., Prasad, V., Hsiao, H. H., Lu, J. C., and Chan, M. T. (2003). Sweet pepper ferredoxin-like protein (pflp) gene as a novel selection marker for orchid transformation. Planta 217, 60–65. Zhong, G.-Y., Peterson, D., Delaney, D. E., Bailey, M., Witcher, D. R., Register, J. C. III, Bond, D., Lin, C.-P., Marshall, L., Kulisek, E., Ritland, D., Meyer, T., Hood, E. E., and Howard, J. A. (1999). Commercial production of aprotinin in transgenic maize seeds. Mol. Breeding 5, 345–356. Zitouni, N., Errahali, Y., Metche, M., Kanny, G., Moneret-Vautrin, D. A., Nicolas, J. P., and Fremont, S. (2000). Influence of refining steps on trace allergenic protein content in sunflower oil. J. Allergy Clin. Immunol. 106(5), 962–967.

ASSESSING THE POTENTIAL FOR PATHOGEN TRANSFER FROM GRASSLAND SOILS TO SURFACE WATERS D. M. Oliver,1,2 C. D. Clegg,1 P. M. Haygarth1 and A. L. Heathwaite3 1

Soil Science and Environmental Quality Team, North Wyke Research Station, Okehampton, Devon EX20 2SB, United Kingdom 2 Department of Geography, University of Sheffield, Sheffield S10 2TN, United Kingdom 3 Centre for Sustainable Water Management, The Lancaster Environmental Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom

I. Introduction II. Pathogens in Livestock Wastes A. Bacteria B. Protozoa C. Viruses III. Detection and Enumeration Techniques A. Culture-Based Methods B. Direct Counting Approaches C. Molecular Methods IV. Transfer from Soil to Water A. Lateral Surface Pathways B. Matrix Flows C. Soil Retention EVects D. Bypass Mechanisms in Soil E. Movement via Growth and Motility F. The Role of Soil Mesofauna V. The Role of Colloids in Facilitating Transfer VI. Factors AVecting Survival A. Survival in Livestock Wastes B. Survival in Soil C. Survival in Water VII. Concluding Remarks References

Contamination of surface waters with pathogenic micro-organisms is an area of growing importance in the context of diVuse agricultural pollution. Hydrological pathways linking farmed land to receiving waters may operate 125 Advances in Agronomy, Volume 85 Copyright 2005, Elsevier Inc. All rights reserved. 0065-2113/05 $35.00

126

D. M. OLIVER ET AL. as vectors of disease transmission. RunoV from grassland systems may be particularly important. In this chapter, we synthesize and evaluate recent and contextual studies relating to the issue. The chapter is necessarily wide ranging and interdisciplinary but we have focused largely on the hydrological, soilbased, and microbiological perspectives. The potential for pathogen presence in livestock wastes is demonstrated through prevalence studies, and subsequent loading of grasslands with contaminated wastes generates a potential surface store of pathogens. These microbes may then be transferred to the wider environment when source and transport drivers are combined in, for example, precipitation events. The delivery of contaminated agricultural drainage waters into first order streams may impact the quality and ecological balance of watercourses if the micro-organisms of concern are still viable. This chapter evaluates both die-oV and transfer processes operating from source through to the end point receptors in surface waters. Gaps in knowledge are identified and appear to be due to the contribution of heterogeneity and hydrological complexity of agricultural catchments and the complications of ß 2005, Elsevier Inc. prevalence data derived via a range of methodologies.

I. INTRODUCTION The spread of human pathogens throughout populations is a major health concern (Sharma et al., 2003; Theron and Cloete, 2002), drawing attention from scientific and government bodies in the interest of public welfare (Ketley, 1997; Miettinen et al., 2001). Surface water can function as a vector of disease transmission and so human health may be compromised as a result of contaminated receiving waters being used as sources of potable water, for recreational purposes, and to irrigate food crops. Linked to health issues is absence from work due to resulting gastrointestinal illness, which is estimated to cost the UK economy over £1 billion per annum (Jones, 1999) and the United States approximately $20 billion in lost productivity each year (Gerba, 1996). Calculations using U.S. medical data suggest that Escherichia coli 0157 alone may cost the United Kingdom around £30 M annually in healthcare (Jones, 1999), and water-borne micro-organisms are now responsible for approximately one-quarter of hospital patients throughout the world (Gerba, 1996). Coupled with this problem is the potential impact on commercial enterprises associated with contaminated recreational waters and implications associated with European legislation, such as the EU Bathing Waters Directive. One industry thought to play a significant role in the dissemination of pathogens through the environment is agriculture, in particular, grassland livestock farming, because of the carriage of such micro-organisms within infected animals (Nicholson et al., 2000). Livestock wastes, if used eYciently and managed eVectively, provide a host of benefits to both farmers and the environment. Recycling livestock waste to land curbs excessive fecal waste

TRANSFER OF PATHOGENS FROM GRASSLAND SOILS

127

accumulation in farm storage systems (Unwin et al., 1986), builds soil quality, and returns valuable nutrients back to grassland systems (Hooda et al., 2000). However, there are a number of more serious pollutant problems linked with ‘‘washoV’’ of surface applied wastes that may impact the quality and ecological balance of watercourses. Such problems are increasingly prominent within modern farming, in which ‘‘recycling’’ of farm wastes has more and more become an adopted ‘‘disposal’’ strategy (Chadwick and Chen, 2002). It is this routine procedure of disposal alongside the unavoidable grazing seasons that threatens contamination of drainage waters with enteric pathogens such as, for example, E. coli 0157, Salmonella spp, Campylobacter spp, and Cryptosporidium parvum. Consequently, there is a potential linkage between agricultural practices and pathogen dissemination through the environment, driven by hydrological events. While much eVort has been exerted in studying the food chain links of pathogen movement and epidemiology (Locking et al., 2001), these hydrological drivers of pathogen transfer in agricultural systems are an alternative route of disease transmission that warrant further examination to highlight the potential pathways for contamination other than those via the food chain. As a result, the farm environment has come under increased scrutiny as a vector for disease transmission (Aitken, 2003; Jones, 2001; Mawdsley et al., 1995; Stanley and Jones, 2003; Trevena et al., 1999). This Chapter draws on the available literature to identify the current state of our knowledge with regard to the introduction of micro-organisms to grassland systems via farm waste products. A framework of key research needs, with regard to both transfer and survival, is presented in Fig. 1. This is an area of emerging importance in the context of diVuse pollution associated with disease-causing micro-organisms, and what is certain is that this is a major issue in grassland ecology and catchment water quality.

II. PATHOGENS IN LIVESTOCK WASTES The rumen and digestive tract of agricultural livestock is host to a rich diversity of microflora and therefore can also act as a reservoir for pathogenic micro-organisms (Rasmussen et al., 1993). A consequence of this is that micro-organisms such as E. coli 0157, Salmonella spp., Campylobacter jejuni, Listeria monocytogenes, C. parvum, and Giardia intestinalis may contaminate fecal deposits and livestock wastes that are excreted and applied onto grasslands, respectively. As a result, it is inevitable that the soil system will, at least periodically, harbour fecally derived microbes both at the soil surface and within the network pore structure. In addition to pathogenic bacteria and protozoa, viruses such as Rotavirus have also been recorded in livestock wastes (Lund and Nissen, 1983); however, few zoonotic viruses

128

D. M. OLIVER ET AL.

Figure 1 Framework of key research needs.

infect cattle (Pell, 1997), so this chapter centers on bacterial and protozoan pathogens. The fecally derived micro-organisms of concern within grassland agriculture are detailed in the following.

A. BACTERIA 1. Escherichia coli E. coli are short, Gram-negative, enteric bacteria that are common inhabitants of the mammalian gut. While most strains of E. coli are harmless, a few strains are potentially pathogenic, such as strains 0157, 0111, 026,


129

0103, and 0145. Verotoxin-producing E. coli 0157 is considered to be the most severe strain and was first identified as a major disease-causing microorganism in 1982, symptomatically ranging from mild diarrhea through to, in extreme cases, life-threatening illnesses. E. coli 0157 has been identified in many cattle worldwide, with the incidence of detection varying between studies. For example, up to 16% of cattle in UK (Chapman et al., 1997) and Finnish (Lahti et al., 2003) herds and 1.9–5% of U.S. cattle (Sargeant et al., 2000; Zhao et al., 1995) were reportedly infected, and in the southern hemisphere, the incidence of E. coli infection is as low as 0.2% (Hallaran and Sumner, 2001). However, work by the U.S. Department of Agriculture (USDA) (1997) has identified the sporadic nature and seasonal diVerences in carriage of E. coli 0157 in cattle because of the varying incidence among herds that can range up to 100% if studies repeatedly sample through time. While much information has been gleaned from studies of E. coli 0157 food infections, there is still much to be understood about routes of transmission though soil and water. The current UK annual infection rate in the human population is estimated at approximately 2 cases per 100,000 (Jones, 1999), and is low in comparison with Salmonella and Campylobacter infections. However, the rapid rise in the number of cases and complications associated with this pathogen have focused increased attention on the severity of risk associated with E. coli 0157.

2.

Salmonella spp.

Salmonella spp. are Gram-negative facultative aerobic rods closely related to E. coli and Shigella spp. Salmonella spp. are commonly found in the gut of animals, and many strains are pathogenic to humans. Cells of Salmonella spp. are capable of prolonged survival in unfavorable conditions outside their host (Winfield and Groisman, 2003). Although the main reservoir of Salmonella spp. is generally considered to be poultry, cattle also harbor this pathogen (Huston et al., 2002; McEvoy et al., 2003), with reported shedding rates of up to 109 cells per day in feces of chronically aVected cattle (McGuirk and Peek, 2003). The reported numbers of cattle harboring Salmonella spp. indicate some variability, with infection rates generally between 1% (Zibilske and Weaver, 1978) and 10% (Heinonen-Tanski et al., 1998) of cattle. The increased amount of research associated with Salmonella spp. in poultry has led to an improved understanding and management regarding the control of this pathogen that has consequently resulted in a decrease in the number of reported cases of salmonellosis in the United Kingdom (Mawdsley et al., 1995). However, this bacterium still causes many problems

130

D. M. OLIVER ET AL.

throughout the world and is estimated to cost the U.S. economy up to $3 billion each year (ERS/USDA, 2003).

3.

Campylobacter spp.

Campylobacter spp. are microaerophilic, Gram-negative curved rods and have recently emerged as a major human gastrointestinal pathogen (Ketley, 1997) that is now recognized as one of the most common causes of gastroenteritis. For example, in the United States, Campylobacter spp. are responsible for the greatest number of food-borne diseases, with almost 2 million cases of infection per year (Madigan et al., 2003). Campylobacters are common to the intestinal tract of humans and animals, and the strain associated with most reported human infections, C. jejuni, causes greater than 90% of Campylobacter enteritis in the United Kingdom (Stanley and Jones, 2003). An investigation recently carried out in Denmark determined that 23% of all animals tested within 24 Danish dairy herds were positive for thermophilic Campylobacters (Nielsen, 2002). In U.S. dairy herds, C. jejuni was identified in 37.7% of fecal samples of animals taken across a number of states (Wesley et al., 2000), and 89.4% of British beef cattle at slaughter were reportedly infected with Campylobacter (Stanley et al., 1998). Some caution should be taken with the interpretation of prevalence results relating to Campylobacter infection, as some discrepancies may exist in data because of problems with sample collection and location. For example, the prevalence of Campylobacter spp. within 25 Californian beef herds was 5% for rectal samples; however, the prevalence in fecal samples was only 0.5% (Hoar et al., 1999), suggesting inactivation of cells post-defecation.

4.

Listeria monocytogenes

L. monocytogenes is a Gram-negative facultative anaerobic rod that is commonly found in soil and water and is emerging as an important food-borne pathogen (Bassler et al., 1995). Although L. monocytogenes is more often associated with dairy product contamination, this pathogen has been identified in cattle feces (Pell, 1997) and so may present a potential risk through environmental routes of transmission. Jones (1999) quotes public health laboratory data that state that approximately 130 cases of food poisoning in the United Kingdom during 1997 were related to Listeria spp.


131

B. PROTOZOA 1.

Cryptosporidium parvum

C. parvum is a zoonotic parasite that infects the gastrointestinal tract of warm-blooded animals, including humans, causing the disease Cryptosporidiosis. Cattle feces are a primary source of Cryptosporidium, with estimated maximum oocyst shedding by Californian beef cattle ranging between 3 103 to 2.3 105 oocysts per cow per day (Atwill et al., 2003; Hoar et al., 2000). The prevalence of Cryptosporidium infection among cattle within German and Canadian herds may range from 36% (Joachim et al., 2003) to 100% (O’Handley et al., 1999).

2.

Giardia spp.

Giardia spp. are a single-celled parasite that can cause the disease Giardiasis through the consumption of fecally contaminated water; it is common in domestic and wild animals (Olson et al., 1997). The prevalence of Giardia among livestock can diVer, with 38% and 29% incidence rates reported for sheep and cattle, respectively, and younger animals identified as having a greater incidence of Giardia infection (Olson et al., 1997). Giardia and Cryptosporidium combined were responsible for one-third of drinking waterassociated disease outbreaks during 1993 to 1994 (Kramer et al., 1996), which highlights the importance of the protozoan pathogen community.

C. VIRUSES Viruses are obligate intracellular parasites composed of genetic elements and those of an enteric origin that are shed by grazing animals. They may be of concern in the soil and aquatic habitat, where they may be able to persist extracellularly and remain viable. Enteric viruses in polluted waters are not well documented, although the few studies that have addressed this issue have identified some, such as bovine enterovirus, within livestock polluted sources (Lund and Nissen, 1983). Although viral pathogens sourced from livestock wastes represent a much reduced health risk to humans than bacterial and protozoan pathogens (Nicholson et al., 2000), the real risk remains unclear. Viruses in soils and water sources pose a greater risk to human health when human sewage sludge is applied to land. Routes of transfer of viral particles derived from livestock wastes will therefore be useful to demonstrate the transfer pathways available to human viruses dissipated from surface applied sewage sludge. Such viruses may include

132

D. M. OLIVER ET AL.

Rotavirus, Calicivirus, and Enteroviruses such as Poliovirus and Coxsackievirus. In this chapter we do not consider viruses further (see review by Jin and Flury [2002] for a discussion of virus fate and transport in porous media). While issues of pathogen carriage rates have been discussed, caution should be taken when assessing prevalence data within livestock and herds. Enumeration techniques in any discipline are only as sensitive as their limit of detection, and we should bear in mind that when animals within a herd test negative with respect to a pathogen of concern, this result may potentially be a false negative result, because the target pathogen is actually present in numbers below the detection level of the technique. This is important because the potential to infect other animals in the herd through the subsequent contamination of pasture and drinking water will exist. This demonstrates the advantages of prevalence studies that monitor over long periods.

III.

DETECTION AND ENUMERATION TECHNIQUES

Determining the presence of specific micro-organisms in environmental samples requires reliable enumeration techniques. The techniques currently employed can be divided into methods that rely upon the culturing of viable micro-organisms, direct counting approaches, and those that utilize molecular techniques. These are described briefly here; more comprehensive reviews of detection methods can be found in Rompre et al. (2002) and APHA (1998).

A. CULTURE-BASED METHODS The most commonly used approach to assess bacterial cell numbers in soil and water samples is standard plate-counting techniques through the enumeration of colony forming units (cfu’s), the most probable number (MPN) method, and membrane filtration (MF) techniques. These approaches rely on the ability of the target population in a sample to grow on solid agar or in a liquid culture media. Table I summarizes the advantages and limitations associated with the classic methods of bacterial cell detection. 1. Plate Counts The plate count method provides a quick, easy, and inexpensive method of bacterial enumeration. Soil and water samples are spread plated onto specific solid agar media and usually are incubated for 24 h at temperatures

Table I Summary of the Range of Classical Detection Techniques Available for Bacterial Micro-organisms

Plate count/Indicator organisms

Advantages

Limitations

Massa et al. (2001)

A reliable alternative to MPN and MF method. Method of choice in polluted waters due to its economic advantages in terms of space, time, and materials. Considerable imprecision is inherent in plate counts, therefore common practice to replicate delivery to culture plates. Competition from antagonistic organisms. Poor detection of slowgrowing or VBNC micro-organisms. Unresolved problems associated with identifying most appropriate agar medium.

MPN/Indicator organisms

Advantageous with soil samples as avoids both clogging of membrane pores and colony spread around soil particles. Works well with turbid and colored waters. Particularly useful for low concentrations of organisms.

References

Hedges (2002)

Rompre et al. (2002)

Stoddard et al. (1998)

Stoddard et al. (1998)

Rompre et al. (2002) Shipe and Cameron (1954)


Method

(continued )

133

134

Table I (Continued) Method

Advantages

Limitations

Greater recovery than MF method.

MF/Indicator organisms

Massa et al. (2001) Bissonnette et al. (1977)

Volumes filtered in cases where cell counts are likely to be low. Greater eYciency than MPN in recovery. Produces more reproducable results.

Massa et al. (1989) Brodsky and Schiemann (1975)

Van Poucke and Nelis (2000)

EVorts to further reduce detection time have seen two-step procedures involving fluorescence labelling of colonies on membrane filters. Lower sensitivity in recovering coliforms when compared to MPN.

Unreliable in turbid waters as a result of colloidal clogging of membrane pores. Suspended sediments cause spreading colonies.

Braswell and Hoadley (1974) Jacobs et al. (1986) Maxey (1970) Massa et al. (2001)

D. M. OLIVER ET AL.

Competition from antagonistic organisms. Poor detection of slow growing or VBNC micro-organisms. Unresolved problems associated with identifying most appropriate medium. Demanding in terms of time (48–96 h) and materials.

References Bissonnette et al. (1977) Rompre et al. (2002)

MPN, most probable number; MF, membrane filtration; VBNC, viable but nonculturable.

Rompre et al. (2002) Green et al. (1975) Lin (1976) Tobin and Dutka (1977) Fleisher and McFadden (1980)

Sharpe and Michaud (1975)


Competition from antagonistic organisms. Poor detection of slow growing or VBNC micro-organisms. Recovery performance diVers between various commercial brands of membrane filters. Multiplicative error associated with the conversion of colony numbers per 100 ml of sample when diVerent sample filter volumes are used. Unresolved problems associated with identifying most appropriate agar medium. Randomness of their distribution on the surface of the filter may complicate counts.

135

136

D. M. OLIVER ET AL.

in the range of 35–44 8C, depending on media used, before enumeration. However, the interpretation of viable count data should be considered carefully, as recovery of all target organisms may not occur because of the presence of viable but non-culturable (VBNC) bacteria in samples. In this VBNC state, organisms are still metabolically active but are no longer able to grow and divide on conventional media and therefore are unable to produce colonies (Colwell et al., 1985; Rollins and Colwell, 1986), leading to an underestimate of the true count. This viable technique is generally reliable and is widely employed in both the public and private sectors as a basic routine method for bacterial enumeration.

2.

Most Probable Number

The MPN method is a statistical approach involving the serial dilution of samples to determine the highest dilution yielding growth. Assessment of the number of positive samples is then made to derive the MPN of bacteria in samples through reference to probability tables or computer programs (Briones and Reichardt, 1999). This method is useful in situations of low cell densities in samples (less than one viable organism per milliliter), as the plate count method lacks statistical robustness at such low concentrations (Herbert, 1990). In addition, Rompre et al. (2002) note that this inexpensive method proves useful in circumstances in which turbid or colored water complicates the MF method; however, estimates of bacterial density are known to vary over a 10-fold range for identical samples (Massa et al., 2001), highlighting potential limitations of the method.

3.

Membrane Filtration

The MF technique is a widely used method incorporating media-based specificity to enumerate micro-organisms (APHA, 1998). The method involves the use of a membrane filter (generally 0.45 mm in diameter) that is capable of capturing bacteria from liquids. A sample is filtered, and the filter pad is then transferred to a selective growth media prior to incubation. Herbert (1990) reported that a major advantage of MF over conventional plating methods is the speed with which samples can be processed. More importantly, however, this technique allows large sample volumes to be filtered in cases where cell numbers are likely to be low. At present, the MF technique is the most widely adopted method for routine enumeration of coliforms (Rompre et al., 2002). However, as with other culture-based methods, its ability to recover stressed or injured coliforms is still not clear.


137

In conjunction with plate counts and MF, specific enzymatic activity may be incorporated to enhance their sensitivity. Media containing chromogenic or fluorogenic substrates for the enzymes b-D-galactosidase and b-D-glucuronidase enable the detection of coliforms and E. coli, respectively, and are being used more and more frequently (Fricker and Fricker, 1996). There are a large number of studies that detail specific substrate incorporation into media for the analysis of environmental samples during the culturing stage (Brenner et al., 1993; Byamukama et al., 2000; Clark et al., 1991; Geissler et al., 2000; George et al., 2001; Rice et al., 1990; Sueiro et al., 2001). Color and fluorescence production result from cleavage by specific enzymes of chromogenic and fluorogenic substrates (e.g., indoxyl-b-D-glucuronide, 5-bromo-4-chloro-3-indolyl-b-D-glucuronide, and 4-methylumbelliferyl-b-D-galactopyranoside). For example, E. coli has been detected in river water through the hydrolysis of the fluorogenic substrate 4-methylumbelliferyl-b-D-glucuronide (George et al., 2001). The addition of such substrates to both solid and liquid cultivation media not only improves the sensitivity of culture-based methods but also provides results more easily and rapidly (Geissler et al., 2000; Sueiro et al., 2001).

B. DIRECT COUNTING APPROACHES It is important to acknowledge the availability of direct counting methods in addition to those already discussed. Direct microscopy is an accepted and common methodology for enumerating micro-organisms within soil suspensions, but it is not used routinely for the identification bacteria of fecal origin. However, standard procedures for detecting protozoan pathogens in surface waters involve filtration of large volumes of water, followed by elution with a detergent solution, concentration via centrifugation, and separation of the protozoa using immunomagnetic techniques (Straub and Chandler, 2003). For enumeration and identification, the oocysts can then be stained and visualized using microscopy.

C. MOLECULAR METHODS In addition to conventional detection methods involving selective culturing, biochemical methods, and direct counting approaches, there are also more recently developed molecular techniques, such as DNA hybridizations and polymerase chain reaction (PCR) detection that can identify specific micro-organisms through targeting specific gene sequences. Although molecular techniques are generally not used in routine sampling regimes, they are employed to confirm the identity of isolates for epidemiological studies.

138

D. M. OLIVER ET AL.

Recent advances in hybridization technology using microarrays may allow the rapid screening of environmental samples for the presence of specific pathogens in the near future (Call et al., 2003; Rompre et al., 2002). Currently, PCR amplification of partial or full sequences of genes allows the detection of low numbers of organisms in samples. For example, E. coli 0157 has been detected in soil at concentrations as low as 10 cfu g 1 (Campbell et al., 2001), highlighting the usefulness of PCR amplification in detecting low numbers of pathogens in environmental samples. Through targeting nucleic acid sequences, the need to rely on the expression of specific physiological and biochemical traits associated with other approaches is eliminated, thus PCR may be valuable in the identification of those microorganisms that are in a state of nonculturability. Successful application of PCR has been demonstrated in a number of studies, for example, in the detection of the listeriolysin O gene, allowing a rapid alternative to standard techniques in the detection of L. monocytogenes (Bessesen et al., 1990). The quantification of C. parvum oocysts in municipal water samples has been investigated using quantitative PCR amplification of a partial gene sequence for oocyst cell wall production (Chung et al., 1999). The approach resulted in the enumeration of oocysts within one order of magnitude and demonstrates the diYculties in accurate enumeration of specific micro-organisms in environmental samples using quantitative PCR-based approaches (Rompre et al., 2002).

IV. TRANSFER FROM SOIL TO WATER Fecal loading of grasslands provides the potential for transfer of microorganisms, introduced via livestock wastes, to watercourses. If this surface store of microbes is coupled with hydrological drivers such as storm events, the risk of transfer is increased. However, such transfer is subject to a series of spatial and temporal controls (Fig. 2) that dictate this potential for transfer. There are a variety of available transfer routes through which potential pathogens may be transported from soil to receiving waters, but the factors that control the transfer of microbes through soils are not well understood (Hornberger et al., 1992). The transport mechanisms of microorganisms within soils can be divided into physical, geochemical, and biological processes (Tim et al., 1988). The physical processes include advection, whereby potential pathogens are carried in bulk water and move according to the water velocity, and dispersion, which involves the spreading of micro-organisms as they move along the water path. The geochemical processes act to delay microbial transfer through the soil matrix and consist of filtration, sorption, and sedimentation mechanisms. Finally, biological


139

Figure 2 Controls governing potential pathogen transfers from agricultural settings.

140

D. M. OLIVER ET AL.

processes, such as growth and chemotactic responses, may influence pathogen transfer through the soil habitat as demonstrated with other introduced micro-organisms (e.g., Reynolds et al., 1989). Pathogenic micro-organisms and, similarly, non-pathogens may enter surface water via overland flow pathways, by subsurface transfer routes in highly permeable soils, or through artificial field drainage. The transfer from grassland soils to surface waters is largely facilitated by hydrology and, conceptually, microbial transfer can be associated with two tiers of hydrological energy. Slow flow microbial transfers operating between storms are thought to be associated with the steady percolation of precipitation inputs through the soil profile. This contrasts with overland flows resulting from high-energy precipitation events, which enable the physical movement of soils, manures, and potential pathogens into streams, creating a more rapid and direct transfer route. Hence, hydrological events can aVect water quality within catchments, and this is emphasized during dry periods and summer grazing seasons when heavy rainfall events are capable of increasing stream fecal bacteria levels by 100-fold (Rodgers et al., 2003). Figure 3 shows a conceptual model of microbial transmission through the agricultural environment. It highlights two important components: (1) the routes of transfer available from source to receptor and (2) the continum of micro-organism die-oV from source to receptor. Figure 4 illustrates naturally occurring flow pathways associated with the soil system and shows transfer modes operating within them—as both free and attached microbial consortia. However, as noted by Camper et al. (1993), it is not only porous medium hydrodynamics that govern microbial contaminant transport. Bacterial characteristics such as size and motility and properties of the soil itself such as surface conditions and particle dimensions all interact to determine microbial fluxes. In addition, the direct voiding of excreta into farm streams and ditches by livestock provides a potential transfer of fecally derived micro-organisms independent of energy-driven flow mechanisms (Crabill et al., 1999).

A. LATERAL SURFACE PATHWAYS The generation of surface runoV provides a potential vehicle for the rapid translocation of entrained soil, waste, and biological colloids (see Fig. 4). The physical force applied to the soil surface, resulting from the kinetic energy associated with the overland flow pathway and impacting raindrops, can disrupt the upper soil layer. This may dislodge soil particles along with sorbed microbes, free attached micro-organisms into the overlying water, and physically break down and transfer fecal matter. Microbial


141

Figure 3 Conceptual model of micro-organism transmission from surface applied fecal waste to surface waters.

contaminants transferred within this above-ground lateral flow then eVectively escape the filtering eVects of the subsurface equivalent lateral transfer route. The role of rainfall and the resulting flow signatures in dictating microbial transfer from soil to water has been described by Abu-Ashour and Lee (2000), Fenlon et al. (2000), Cook and Baker (2001), and Vinten et al. (2002). Abu-Ashour and Lee (2000) suggest that precipitation events are a major factor dictating both vertical and horizontal movements of bacteria in soil. Having investigated E. coli transport on sloping soil surfaces via surface runoV, these authors confirmed the importance of this process as a mechanism of bacterial transport on soil surfaces.

142

D. M. OLIVER ET AL.

Figure 4 Natural transfer pathways available to fecally derived micro-organisms applied to soil surfaces.


143

The significance of the overland flow pathway in facilitating microbial movement has been acknowledged with respect to methods of slurry application in a comparison of surface applied versus incorporation techniques (Quinton et al., 2003). Incorporation of slurries into soil under laboratory conditions indicated that a reduced number of fecal bacteria might be transported from the soil system. While this suggests that under field conditions slurry incorporation would be a preferred method of application to protect water courses, Quinton et al. (2003) warned of the potential discrepancies that may be observed as a result of such a scaling up, largely resulting from desiccation of the surface applied slurry, and hence the reduced threat given such conditions. Pathogen transport processes operating during overland flow have recently been summarized in a suite of proposed transport scenarios (Tyrell and Quinton, 2003). These involved the discussion of incorporation of free microbes into overland flow, mobilization of soil or waste particles into overland flow, carrying attached microbes, and detachment of microbes from soil surfaces arising from shearing forces of raindrop or flow action. There is a clear need to quantify the eYciency of overland flow in facilitating the wash-in of fecal material from pasture to stream (Nagels et al., 2002). However, while wash-in of fecal matter has long been recognized as a consequence of overland flows generated within the contributing areas of a catchment (McDonald and Kay, 1981), within large and complex watersheds the bacteriological quality of a stream is the resultant eVect of a variety of indistinguishable sources, and so determination of the loading capability of a particular transfer route is diYcult. In assessing cell movement and deposition in overland flows, consideration should be given to processes such as splash and flow detachment. Work by Vinten et al. (2002) drew attention to the importance of these hydrological processes at the soil surface and highlights the promotion of microbial transfer via the mobilization of slurry colloids following energy transfer from impacting raindrop momentum. Similarly, the energy associated with heavy rain is able to erode the soil and release considerably high numbers of fecally derived pathogens to runoV waters from grasslands (Heinonen-Tanski and Uusi-Kamppa, 2001). If antecedent soil conditions are conducive to generating overland flow pathways and heavy rains occur shortly after slurry application, there is great potential for significant runoV of fecal micro-organisms following their entrainment into the surface flow. This is complementary to studies by Abu Ashour and Lee (2000), who concluded that cells may be carried in surface runoV following detachment from soil particles or alternatively may experience transfer, remaining in the sorbed phase. However, surface runoV does not always contribute heavily to the bacterial loading of receiving waters. Following slurry application at a field experiment in Scotland, losses of E. coli in surface runoV were only

144

D. M. OLIVER ET AL.

0.003% of cells present after 75 mm of rain in the 49 days post-wastespreading (Vinten et al., 2002). This does not solely reflect the eYciency of the transfer pathway and is likely to incorporate a die-oV of introduced cells. So although overland flows may provide an eYcient microbial export route, their impact may be only short lived or of reduced eVect at times. Using a novel soil tilting table apparatus, Mawdsley et al. (1996) simulated the horizontal surface transfer of the protozoan pathogen C. parvum under controlled laboratory conditions. Following the addition of contaminated livestock waste to soil blocks with a slope of 7.5%, the movement of this micro-organism in runoV was detected for a minimum of 21 days, and in one case was still being laterally exported after 70 days. Throughout the duration of the experiment, a significantly higher oocyst concentration was found in leachate as opposed to the runoV across the soil surface, a finding similar to that of Vinten et al. (2002) with respect to E. coli. However, considering the rapidity of the overland flow pathway, possible pollution from occasional runoV events, even on soils where leaching predominates, must still be considered (Mawdsley et al., 1996). Furthermore, Mawdsley et al. (1996) postulated that on true impermeable heavy clay soil, a greater proportion of oocysts would be lost in surface runoV. However, we have no proof that overland flow, once started, actually delivers materials to streams and primary water systems. It may instead provide a pulsing mechanism of transfer or be interrupted by buVers before having an impact on receiving waters.

B. MATRIX FLOWS Much research has focused on the vertical transfer of pathogens in soil leachate and the similarities with colloid filtration theory. Vertical displacement through the soil profile of these micro-organisms has been demonstrated in a variety of soil column experiments (Aislabie et al., 2001; Brush et al., 1999; Gagliardi and Karns, 2000; Warnemuende and Kanwar, 2002; Wollum and Cassel, 1978). The moisture content of the soil determines bacterial movement as the continuous water films permit bacterial transfer because the microbial population of the soil is limited to the aqueous phase and the solid–liquid interface. It has been proposed that appreciable bacterial movement in soil can only occur if there are enough water-filled pores of the diameter required to enable a continuous pathway (Bowen and Rovira, 1999). A lack of movement at moisture tensions below saturation has been shown, and thus with increasing hydraulic conductivity there is a rise in bacterial transport (Rahe et al., 1978).


145

When water drains from pores, microbial movement through the soil structure depends on sieving eVects imposed by pore openings. Gagliardi and Karns (2000) concluded that if soil pores avoid becoming clogged, E. coli 0157 is able to travel below the soil surface layers for periods in excess of 2 months following the initial application. Manure that has remained on the soil surface for extended periods prior to a rain event is still capable of delivering E. coli, and potentially other cells, by transfer through the profile with the onset of hydrological inputs. With successive rain events, more cells are transferred in the resulting leachate, but at levels much reduced in comparison with counts recorded in the first leaching event (Saini et al., 2003). There is a general increase in bacterial movement within saturated soils compared with drier soils; however, the occurrence of percolating water increases the potential for translocation of micro-organisms through the soil matrix. Culley and Phillips (1982) were able to demonstrate that fecal streptococci (FS) were capable of movement through soil profiles into field drains located at a depth of 75 cm, provided water was present to facilitate this downward transfer. The importance of percolating water cannot be stressed enough, and in combination with the action of higher organisms, it represents one of the most important microbial transfer mechanisms in soils (Hekman et al., 1995). Fenlon et al. (2000) demonstrated the delivery of E. coli to drains to reach a cumulative maximum of 11% of applied cells following rainfall events occurring for the 7 days after slurry application. Their study suggested that, following the initial substantial pulse of cells to the drains immediately after slurry spreading, as much as 80–90% of total E. coli was retained within the soil matrix. Fenlon et al. (2000) reached the conclusion that, provided a large enough hydrological event occurred close to the timing of slurry application, bacteria can be flushed from the soil matrix in considerable numbers. Their work also emphasised the importance of storing farm wastes until the soil conditions were suitable for their application given the ability of the matrix to retain such a large proportion of added cells. Soil type and condition also contribute to the determination of the route of cell movement from soil to water. Bacterial travel times are more rapid in coarser textured soils with larger pore spaces as opposed to finer textured soils (Huysman and Verstraete, 1993; Tan et al., 1992). Gannon et al. (1991) stressed that all bacterial species are filtered out to some extent by the soil matrix, with bacterial transport strongly correlated with cell size, and Cook and Baker (2001) showed the soil matrix, within lysimeters, to be eVective in retaining micro-organisms added to soil via a carrier substrate.

146

D. M. OLIVER ET AL.

C. SOIL RETENTION EFFECTS The translocation of pathogens down soil profiles parallels that of other microbial populations and relies on pore size openings, the soil matrix system, and water characteristics. Figure 5 shows the range of diameters for diVerent pathogen classes and highlights some specific examples of cell/ oocyst/particle sizes. The soil sieving eVects experienced by free-flowing microbes include straining, sedimentation, and sorption mechanisms.

Figure 5

Pathogen cell/oocyst/particle dimensions relative to soil particle fractions.


147

1. Straining There is evidence that pore clogging by bacteria and protozoa may restrict microbial transfer by physically blocking a pore entrance and may even alter soil hydraulic conductivity at a localized scale (Thullner et al., 2002). The extent to which this process of bioclogging occurs is a function of the particle size of the porous medium and the diameter of the microbial consortia that transfer through the system. Thus, micro-organisms suspended in flow are eVectively strained by the soil matrix and accumulate on soil particles when pore openings are too small to permit their passage. The hydraulic conductivity of the soil is then reduced not only by the accumulating cells, but also as a function of the subsequent excretion of extracellular polymers. The immobile regions that may exist in the filter matrix in the form of ineVective micropores (Kim and Corapcioglu, 2002) may trap microbes in these dead end pores and can result in a trapping of potential pathogens, from where they may act as a significant reservoir of contamination. It is unclear what risk trapped microbes pose. The concept of filtering capabilities associated with the soil matrix is crucial within the management strategy of buVer strips. It may be considered that sorption and filtration processes are more eVective in protecting surface waters than relying on organism die-oV (Aislabie et al., 2001). The role of buVer strips in removing pathogens, such as C. parvum, from carrying water was evaluated by Atwill et al. (2002). BuVers constructed with silty clay or loam or at lower bulk densities were most successful at filtering the oocysts, in contrast to sandy loams and higher bulk density soils. However, when water is in excess of field capacity, it has been suggested that the main flow of water occurs through pores larger than all colloid sizes except for protozoa. This in turn implies that straining may only be eVective for protozoan pathogens, and even so, under extremely wet conditions protozoa will still be mobile (McGechan, 2002). Brush et al. (1999) asserted that future research still needs to focus on diVerentiating the relative importance of the various removal mechanisms that operate and on oocyst–medium interactions. The natural filtering of microbes as they dissipate through the soil has been likened to that associated with colloidal movements with both, in contrast to dissolved constituents, accommodating a low diVusivity. As a result, the conceptual use of models formulated for colloid transfer has been investigated with respect to microbiological transport, providing theoretical frameworks for modeling bacterial movement (Johnson et al., 1995). However, filtration theory calculations of the distances traveled by bacteria underestimate true translocation lengths within the soil (Simoni et al., 1998), with increased travel distances resulting from heterogeneity in the adhesion properties within the bacterial populations. Furthermore, Harter et al. (2000) noted that the presence of dead end pores and angular particles

148

D. M. OLIVER ET AL.

arranged to form planar contact potentially enhance localized straining unaccounted for by filtration theory. Bearing in mind this size exclusion principle, Camper et al. (1993) addressed the physiological condition of bacterial cells and proposed that, given adverse conditions, cells may become starved, experience a reduction in their diameter, and then successfully infiltrate the previously inaccessible pore entrance. Similarly, Macleod et al. (1988) demonstrated that starvation influenced cell penetration rates and clogging through porous media. Biological factors such as motility, growth, and physiological stress introduce uncertainty in terms of the extent that colloid filtration principles can successfully interpret microbial movements, and consequently, colloid filtration theory should not be used as a rigid predictive model, but rather as a tool for interpreting microbial buVering (McDowell-Boyer et al., 1986).

2. Sorption Soil type may influence microbial transfer because of diVerences in sorption properties relating to the associated colloidal material of the soil (Schijven et al., 2002). The major soil components aVecting sorption of bacteria are clay and organic matter (Aislabie et al., 2001). E. coli sorption within diVerent soil types has enabled verification that soils accommodating higher clay content sorb greater numbers of bacteria due to a greater specific surface area (Ling et al., 2002). Sorption of C. parvum onto suspended particles in soil water has also been observed. Hydrophobicity and zeta potential were highlighted to exert a significant influence in the adhesion mechanisms of the protozoan oocysts (Drozd and Schwartzbrod, 1996). The authors stressed that sorption of this pathogen to soil surfaces cannot be attributed to a single factor and that there is a complex suite of forces interacting to govern microbial retention. This has also been shown in the study of Nielsen et al. (2001), who concluded that bacterial sorption is a function of electrostatic forces, Van der Waal interactions, extracellular polysaccharides, and cell hydrophobicity.

3. Sedimentation Sedimentation of microbes in pore water may also play a role in restricting microbial transfer through soil; however, it is of little significance for the smaller virus particles, which tend to be naturally buoyant and thus are unlikely to settle. Similarly, motile bacteria by their very nature are less likely to undergo sedimentation. Instead, it is larger micro-organisms in excess of 5 mm that are considered most likely to undergo the sedimentation processes (Yao et al., 1971).


149

D. BYPASS MECHANISMS IN SOIL The significance of large pores and voids in facilitating the movement of water and colloidal material through the soil has long been acknowledged (Allaire-Leung et al., 2000; Beven and Germann, 1982; Buttle and Leigh, 1997; Ehlers, 1975; Jacobsen et al., 1997; McLeod et al., 1998; Williams et al., 2000). A number of studies have focused attention on macropore flow as a vector of microbial transfer. This work concluded that macropore flow is a major mechanism for microbial transport within soil (Abu-Ashour et al., 1998; Fontes et al., 1991; Gannon et al., 1991; Harvey, 1997; Mawdsley et al., 1995; Smith et al., 1985). Such preferential flow pathways serve as routes of relatively rapid water flow and allow cells, among other colloids and contaminants, to successfully bypass the sieving and constraining architecture of the soil matrix (see Fig. 4). Although macropores often make up only a small volume of the soil body, they serve as important routes for both the lateral and vertical transfer of cells entrained in the carrying water. Macropores may be formed naturally or through soil fauna activity, plant root presence, or soil shrinkage. The interconnected pore domain carved through the action of earthworms demonstrates the eVectiveness macropores can have in promoting micro-organism transfer (Joergensen et al., 1998). In their absence, the distribution of microbes may be mostly confined to the uppermost zones of the soil profile. Soil column experiments such as those used by Huysman and Verstraete (1993) detail significant micro-organism movement through these larger pores following the reduction of the retardation component imposed by the more tortuous and constricting micropores. Provided that the input of water to a soil system is suYcient to initiate water flow in larger pores, suspended bacteria may move rapidly through the profiles of well structured soils (Smith et al., 1985). Smith et al. (1985) concluded that macropore-facilitated transfer of cells is frequent and stated that any macroporous soil has the potential to rapidly transport cells to the depth to which these pores extend given suYcient hydrological delivery. Macropores may play a vital role in governing microbial movement, especially in respect to long-distance biological contaminant transport, provided that conditions that initiate water film development, and consequently flow, are met (Fontes et al., 1991).

E. MOVEMENT VIA GROWTH AND MOTILITY Not all bacteria and protozoa introduced into soil depend on advection and dispersion mechanisms for transfer through interconnected pores and fissures. Instead, distribution within porous media can result as a direct consequence

150

D. M. OLIVER ET AL.

of micro-organism growth and motility. Self movement of microbes occurs through the action of flagellar rotation. Reynolds et al. (1989) demonstrated transfer rates approaching 0.5 cm h 1 within packed sand cores, and McCaulou et al. (1995) suggested that motile bacteria were possibly able to detach from a solid surface under their own locomotive power. This suggests that bacterial and protozoan travel times calculated in the absence of motility may incorporate a degree of error (McCaulou et al., 1995). In addition, the motility of one particular micro-organism may influence that of another. Brown et al. (2002) draw attention to the role of free living protozoa, which may prey on bacterial cells and consequently act as vehicles for bacterial transmission. Therefore, this intracellular location not only provides a potential protective niche for ingested cells but also may facilitate their transfer through the soil. However, with microbial motility promoting relatively short travel distances, it may be that such active bacterial movement holds importance only within micro-environments and is of little significance in relation to widespread movement at the field scale, where hydrological flows exert a greater influence.

F.

THE ROLE OF SOIL MESOFAUNA

Mesofaunal activity within grassland soils may contribute to the dispersal of microbes, and hence potential pathogens, either directly, through the attachment of micro-organisms to larger soil inhabitants, or indirectly, through the creation of larger pore networks within the soil matrix. Earthworms account for the largest proportion of biomass attributed to soil animals under pasture (Lee, 1985) and so contribute toward important eVects on the physical structure of soil through burrow formation. Earthwormworked soils can promote up to a 50% reduction in the surface runoV of slurries applied to grasslands, most likely as a result of the worms increasing soil porosity and regenerating connections between the soil surface and drainage cracks in deeper horizons (Scholefield, personal communication). Opperman et al. (1987) concluded that Eisenia foetida aided the translocation of coliform bacteria, derived from cattle slurry, to a depth of 17.5 cm, whereas in their absence the bacteria were restricted to the upper zone of the soil. This complements a study that reported significant vertical FC transfer through earthworm burrows to the subsoil following land application of slurry (Joergensen et al., 1998). Thorpe et al. (1996) found that earthworms promoted bacterial transport to a depth of 30 cm in the soil, though they pointed out that burial of inoculated litter rather than an increase in macropore flow, due to the earthworm channels, was a more important mechanism for cell transfer.


151

In clay soils, earthworm tunnels play a less important role because of their inherent instability resulting from soil swelling and shrinking processes (Joergensen and Seitz, 1998) and the lower number of earthworms in clay soils in general. Interestingly, Joergensen et al. (1998) noted that the drilosphere soil (that which lines earthworm burrows), with its increased potential to hold water and sorb suspended cells, was more densely populated with fecal bacteria compared with the bulk soil. The physical alteration of soil structure is not the only means by which indigenous soil organisms aid transfer of introduced microbial populations. Gammack et al. (1992) suggested that earthworms, along with mites and millipedes, act as vehicles for bacterial transport through direct attachment of cells to such mesofauna. They also note a mechanism of transfer associated with the ingestion of organic material and its subsequent movement through the gut, which they propose enhances both horizontal and lateral movement, though this is probably minimal.

V. THE ROLE OF COLLOIDS IN FACILITATING TRANSFER Colloids may play an important role in assisting the successful transfer of introduced micro-organisms from soil to receiving waters. Slurries, manures, and excreta contain vast numbers of particulate and colloidal materials spanning a range of diameters, with many already being host to sorbed microorganisms (McGechan and Lewis, 2002). Large voids facilitate relatively easy colloidal movement, and so any microbe that is associated with a migrating colloid may not only improve its survival chances but also emerge from the soil ahead of the wetting front. However, in their excellent review, Kretzschmar et al. (1999) noted that prediction of the importance of colloid-facilitated contaminant transport is complicated by the at present poor understanding of processes relating to colloid release, transport, and deposition. Their review assessed colloidal transfer in depth and provided the framework from which concepts of biological attachment to suspended colloids may be extrapolated. There are two particularly important properties associated with colloids that enable them to function as important contaminant carriers. The first is that colloids have a very large specific surface area, in excess of 10 m2 g 1 (Kretzschmar et al., 1999). Second, these colloids remain stable in suspension for significant periods, and if bacteria attach to the large available surface area, their dispersal through the soil may be aided considerably. Those colloids that remain more in the center stream of the flow path are likely to remain uncaptured, and thus migrate along these faster, more permeable flow paths. Consequently, those micro-organisms that form

152

D. M. OLIVER ET AL.

a microbe–colloid complex are likely to experience the same transfer or buVering processes of their nonbiological colloid counterparts. A number of factors contribute to the initial sorption of cells to suspended material in soil water. The preferential sorption of specific microbial cells cannot be explained simply by referring to a single force eVect (see Section IV.C.2) and is related also to contact time between cell and colloid, colloid size, and conditions around the soil particle such as wettability and surface texture (Drozd and Schwartzbrod, 1996). In addition, interactions between bacteria may result, and therefore attachment to a colloid does not necessarily occur in a monolayer. Attached bacteria will detach and be deposited back into the flowing suspension as a critical flow velocity is approached. This introduces the concept of reversible and irreversible attachment, which is discussed briefly here but is described further by Palmateer et al. (1993). Permanent attachment involves the anchoring of the micro-organism to the particle with which it is interacting. This therefore suggests a direct mode of contact between microbe and particle. This contact is provided through the association of the microbes pili, fimbriae, or flagella with the colloid in a ‘‘cementing’’ attachment (Palmateer et al., 1993). Reversible attachment does not involve a true physical association; it is a sorption producing a concentrating eVect at the particle surface. Given suYcient turbulence, however, the resulting shear forces are able to desorb the microbe relatively easily. Recently, Dai and Boll (2003) concluded that C. parvum and Giardia lamblia oocysts fail to attach to natural soil particles and that, in instances of overland flow, oocysts would travel independently of the particle load within turbid plumes. The authors suggested that this has relevance to management practices because the way to deal with freely suspended micro-organisms would be to minimize overland flow, whereas if these microbes had been particulate-attached, the emphasis would have been on targeting sediment transport controls. In contrast to this protozoan example, there are quantitative investigations into bacterial accumulation upon suspended particles exported from agricultural fields (Palmateer et al., 1993). Palmateer et al. (1993) stated that the particulate load within agricultural drainage can accommodate cells at levels as high as 103 to 105 per mm2 as a function of the sorption process. As a consequence of particulate attachment, they also demonstrated that the transport of fecally derived E. coli in this sorbed phase may travel kilometers within agricultural drains. Soil colloids and colloid-facilitated transport of contaminants may potentially play a significant role in assisting potential pathogen transfer. Although the apparent capability of colloids to promote contaminant transfer has been acknowledged, it must be emphasized that the actual importance is far from understood (Mills and Saiers, 1993), and very few studies have been published that detail direct evidence for colloid-facilitated transport of contaminants (Kretzschmar et al., 1999).


153

Having demonstrated the potential for pathogen transfer in grassland environments, it becomes evident that the soil system must, in part, act as a temporary store for introduced micro-organisms as well as providing the physical routes of connection between source application and the end point receptor. One of the challenges in grassland research is to understand how soil systems accommodate introduced micro-organisms and influence their persistence or, alternatively, how micro-organisms adapt to the sudden dramatic change in environmental surroundings. The remainder of this chapter deals with this aspect of pathogen presence in agricultural settings.

VI. FACTORS AFFECTING SURVIVAL The survival and epidemiology of pathogens is a key issue when considering the potential for contamination of the surrounding land and water and, as illustrated in Figure 2, is an important temporal component exerting influence over the risk associated with transfer. A logarithmic first order exponential decay equation is often assumed through statistical analysis of bacterial die-oV. The decline of a bacterial population with time can be described mathematically as Mt ¼ M0 e

kt

where Mt is microbial concentration at time t, M0 is initial microbial concentration, k is the first order rate coeYcient for the net mortality rate for organisms/day, and t is time in days. This equation contributes to the population decline illustrated in Fig. 6. Such an equation worked satisfactorily in the study of Stoddard et al. (1998), although it was occasionally complicated through bacterial regrowth. It is possible to extend first order kinetics to model bacterial die-oV rates and include terms for the antagonistic eVects of biotic and abiotic factors (Wilkinson et al., 1995). A variety of other bacterial decay equations were considered by DeGuise et al. (1999) in their discussion of foundations for modelling bacterial contamination.

A. SURVIVAL IN LIVESTOCK WASTES 1.

Excreta

Excretal fecal waste is deposited directly onto grasslands by grazing livestock. A crust may form on deposited excreta within 2 days (Thelin and GiVord, 1983), producing a favorable location for bacterial survival

154

D. M. OLIVER ET AL.

Figure 6

Factors contributing to survival of micro-organisms introduced to soils.

within the deposit. The crusting limits interactions with the soil and the atmosphere, providing a microclimate and protective niche for microbial survival until the next rain event provides a means of transfer. The crusting process can also protect micro-organisms from intense sunlight and heat for at least one summer, implying that fecal indicator bacteria may persist for over 1 year in crusted bovine feces (Buckhouse and GiVord, 1976). This suggests that fecal deposits may provide a long-term continuous source of microbial pollution to surrounding areas. Bacteria on the surface of recently


155

deposited feces are the first to be aVected by UV in sunlight (Scottish Executive and Food Standards Agency Scotland, 2001). Wang et al. (1996) showed that E. coli 0157 survived up to 7, 8, and 10 weeks at 37 8C, 22 8C, and 5 8C, respectively, demonstrating that the pathogen can persist for lengthy periods within the protective niche of the fecal deposit, though temperature and water activity were noted as being influential on E. coli 0157 persistence. There have been complementary reports suggesting that E. coli 0157 may survive for extended periods even under very dry conditions (Jiang et al., 2002), and Fukushima et al. (1999) observed low levels of pathogenic E. coli surviving in bovine feces for over 4 months. Fukushima et al. (1999) also found that pathogenic strains 0111 and 026 survived for periods of up to 18 weeks. The protozoan Giardia survives for much shorter periods, remaining infective for only 1 week in cattle feces (Olson et al., 1999). Fluctuations in salinity within the fecal deposit have been documented as exerting a control on Cryptosporidium survival within excreta (Bradford and Schijven, 2002). A fecal deposit may experience a change in salinity through exposure to either rain (decrease in salinity) or urine (increase in salinity). An increase in the salinity of the fecal waste reduced the electrostatic interactions between charged particles, and Bradford and Schijven (2002) speculated that induced changes in salinity influenced the release of micro-organisms from protective sites and so interfered with survival curves of the associated microbes.

2.

Slurry

The relatively uniform mix of excreta and urine produced by housed livestock, collected in a liquid form, is termed slurry (Chadwick and Chen, 2002). The pathogen content of slurry is a function of dilution, dry matter content, temperature, storage time, animal source, and pH, among other factors (Mitscherlich and Marth, 1984). Slurries accommodate a more uniform microbial contamination than their solid manure counterparts as a result of the greater mobility of micro-organisms in this liquid material (Chadwick and Chen, 2002). McGee et al. (2001) have reported E. coli 0157 persistence in stored cattle slurry in excess of 3 months. However, despite its survival in slurry, McGee et al. (2001) warned that it may not represent a predominant source of transmission in agricultural settings because of substantial decline in numbers observed. The long survival times in their study may be explained by the large inoculation of bacteria received by the slurry, which exceeded the numbers in feces. A greater persistence of cells was seen in slurry of a higher dry matter content, complementing findings in which E. coli 0157 inoculated into slurry reaches undetectable levels over five times quicker than when inoculated into cattle feces (Maule, 2000). Earlier work of Kovacs and Tamasi (1979) detailed survival times of E. coli and Salmonella

156

D. M. OLIVER ET AL.

spp. in slurry, with maximum survival durations of 1 week and 28 weeks, respectively. Surprisingly, Salmonella survived seven times longer at 20 8C than at 4 8C, where it persisted for only 4 weeks; the authors claimed it to be a potential function of the predominance of the Salmonella component in the samples. However, it may be that the lower temperatures induced a VBNC state in the Salmonella spp. and avoided detection, thus leading to underestimates of Salmonella cell counts at low temperatures. A 30–50% reduction in the number of viable C. parvum oocysts within slurry has been recorded for temperatures of 4 8C after 100 days, demonstrating a healthy persistence of the parasite within these liquid wastes (Warnes and Keevil, 2003).

3.

Solid Manure (Farmyard Manure)

DiVerences in the survival of pathogens in diVerent manures have been reported. For example, within ovine manure, Kudva et al. (1998) reported the persistence of E. coli 0157 for a period of 21 months, but that if the manure was aerated, the survival times for 0157 could be reduced to 4 months. In bovine manure, survival was not as lengthy, with aerated bovine manure allowing E. coli 0157 to persist for only 47 days. Within aerated manures, a drying eVect resulting from the mixing was suggested as the causal agent of bacterial decline. Kudva et al. (1998) also observed a lower survival rate of the bacteria within manure under laboratory conditions compared with manure exposed to the environment. The proposed explanation for this was, in part, that physical dimensions of environmental manure piles provided micro-niches that were not reproducible under the conditions of laboratory experiments. As for micro-organism survival in slurry, it was concluded that wastes of a higher solid content prolonged survival (Kudva et al., 1998).

B. SURVIVAL IN SOIL Governing factors of bacterial and protozoan (both indigenous and introduced) survival in soil are well documented (e.g., Acea et al., 1988; England et al., 1993; GriYths and Young, 1994; Heijnen and Vanveen, 1991; Jamieson et al., 2002). Variables such as temperature, moisture content, soil type, pH, sunlight, and presence of indigenous micro-organisms, nutrients, and organic matter all exert an influence on survival within the soil (Cools et al., 2001; Reddy et al., 1981; Van Donsel et al., 1967). As an important first step in developing an understanding of pathogen persistence in agricultural systems, it is essential to address the biotic and abiotic factors that govern the life cycle of any introduced micro-organism in the


157

soil. Table II summarizes the influential controls of resulting micro-organism die-oV curves, which can be extended to encompass pathogenic strains. What complicates the evaluation of die-oV within agricultural settings is the dissemination of micro-organisms from the source material into the soil. Once in the soil, the exact nature of die-oV coeYcients at the field scale are diYcult to determine through the combined eVect of both true organism decline rates and their dilution into the soil system. Table III provides a summary of selected pathogen die-oV rates in a variety of environmental substances. After the introduction of micro-organisms to soil, either through direct deposition of livestock feces or via a carrier substrate such as manure or slurry, most bacteria and protozoa have diYculty surviving. Common inhabitants of the gastrointestinal tract are not adapted to survive in soils; their preferred habitat facilitates optimal growth in warm (37 8C), moist, and highly nutritious conditions such as those found in the mammalian digestive tract. While there are many factors that may individually aVect microbial survival in soils, it is likely that many interactions of biotic and abiotic eVects combine to provide a detrimental environment for pathogen survival (Fig. 6), and thus it becomes diYcult to determine the extent of individual factor influences. The following section briefly discusses some factors that aVect the survival of microbes, including pathogens, entering the soil habitat.

1. Nutrient Availability The availability of nutrients in the soil habitat exerts an influence on the persistence of microbes. Readily utilizable organic carbon (C) is often a limiting growth factor, and so a lack of available nutrients may induce a starvation response within bacteria and may have an impact on their survival. Consequently, the capability of cells to survive starvation may influence their persistence in their new surrounding (Acea et al., 1988) as they undergo both a physiological and morphological change under nutrient limitation. Such population changes may be a result of cells failure to lower their metabolic rate to satisfy the low usable organic C conditions (Klein and Casida, 1967). High soil organic matter content may support survival and potentially even promote regrowth of fecal microbes (Gerba et al., 1975). The increased organic matter not only acts to increase nutrient retention and provide a C source, but also proves beneficial through improved moisture retention. Nutrient availability presents itself as an important control on microbial survival, but it must be remembered that coliforms and gut bacteria prefer simple C rather than complex organic matter. Soil does not contain a large amount of simple sugars but rather more complex C sources, again highlighting the unfavorable nature of the foreign soil environment.

158

Environmental factor Nutrient availability Soil moisture status

Soil temperature

Water temperature

Soil type

Influence on micro-organism survival Cells may become sublethally stressed and physiologically injured in the absence of nutrients in water and soil Increased moisture content favors micro-organism survival. However, in some instances increased water content may be unfavorable, allowing protozoa to transfer and prey on bacteria and enabling larger pores to fill which oVer cells less protection against predators Increasing soil temperature leads to a decline in micro-organism numbers. Persistence is greater in winter months. Freezing conditions can prove lethal Survival of introduced micro-organisms is promoted through lower water temperatures. Cell die-oV is accelerated during summer, though this may be a combined eVect of increased sunlight inactivation Imposes structural and textural influences. Clay soils promote greatest survival. Sandy soils have a poor water holding capacity which limits survival. The structural network creates a habitable pore space. Soil type influences cellular adsorption

Example references Klein and Casida (1967) Entry et al. (2000) Mubiru et al. (2000) Postma et al. (1989) Reddy et al. (1981) Cools et al. (2001) Davenport et al. (1976) Stoddard et al. (1998) McGee et al. (2002) Thomas et al. (1999b) Wang and Doyle (1998) England et al. (1993) Fenlon et al. (2000) Maule (1999) Stotzky and Rem (1966) Young and Ritz (2000)

D. M. OLIVER ET AL.

Table II Influence of Environmental Factors on the Survival of Micro-organisms Introduced into Soil and Water

Predation in soils

pH UV exposure

Organic matter

Micro-organism species Stream bed sediment

Organic matter increases nutrient retention, provides a carbon source and improves moisture retention—all of which are beneficial to micro-organism survival and growth The variety of interacting physical, biological and chemical factors will aVect micro-organisms depending on the susceptibility of the species Survival is greater in stream bed sediments in comparison with the overlying water column. They provide protection against predation and UV inactivation and act as a source of nutrients

Acea et al. (1988) Brown et al. (2002) Tappeser et al. (1998) Artz and Killham (2002) Korhonen and Martikainen (1991) Van Donsel et al. (1967) Barcina et al. (1989) Mofidi et al. (2002) Morita et al. (2002) Sinton et al. (2002) Gerba et al. (1975)

Mitscherlich and Marth (1984) Burton et al. (1987) Craig et al. (2002) Davies et al. (1995) Van Donsel and Geldreich (1971)


Predation in waters

Increased survival in sterile soils. Most predation of bacterial cells occurs via grazing protozoa. However, survival may be increased with an intracellular location within protozoal trophozoites Removal of protoaoan communities by filtration prolongs bacterial survival in aquatic environments. Cells associated with particles are less susceptible to predation A near neutral pH supports micro-organism survival Can cause inactivation of micro-organisms through the formation of lesions in DNA and via photo-oxidation mechanisms. Therefore survival is reduced at the soil surface and in less turbid waters

159

160

Table III Survival of Fecally Derived Micro-organisms within DiVerent Media Micro-organism Fecal coliforms

Medium Soil

Temperature (8C) Survival (days) Autumn

Comment

Reference

5.8

Stoddard et al. (1998) 50% population reduction after manure application

’’ ’’ ’’ ’’ ’’ ’’ ’’

’’ Liquid manure ’’ Nonsterile river water ’’ Nonsterile soil ’’ Sandy soil Loam soil Clay soil Drinking trough water ’’

’’ 4 20 4 20 4 20 Ambient ’’ ’’ Field conditions Lab conditions ’’

Drinking trough water þ feces Bovine feces 5 ’’ 22 ’’ 37 Aerated bovine Environmental manure cond. Ovine manure ’’

13.9 7 7 12 8 100 65 56 175 175 14

Media used ¼ EMB agar

Stoddard et al. (1998) Kovacs & Tamasi (1979) ’’ Bogosian et al. (1996)

’’ ’’ ’’ Decline to 1 log10 CFU ’’ ’’ Inoculation level of 106 CFU ml

’’ ’’ ’’ Fenlon et al. (2000) ’’ ’’ McGee et al. (2002)

1

31

’’

’’

24

Feces added to trough

’’

49 56 70 47

Inoculation level of 105 CFU g ’’ ’’ Aerated by mixing

630

1

Wang et al. (1996) ’’ ’’ Kudva et al. (1998) ’’

D. M. OLIVER ET AL.

Fecal streptococci Escherichia coli ’’ Eschericia coli K12 strain ’’ ’’ ’’ Escherichia coli 0157 ’’ ’’ ’’

’’

Echovirus ’’

’’ Liquid manure ’’ Untreated water Filtered water Silty clay loam ’’ Silt loam ’’ Loamy sand ’’ Water ’’ ’’ ’’ Soil ’’ Cattle feces ’’ Water ’’ Soil ’’ Cattle feces ’’ Liquid manure ’’

120

Aerated by mixing

’’

5

14–98

Fukushima et al. (1999)

15 4 20 4 4 4 20 4 20 4 20 4 20 4 25 4 25 4 25 4 25 4 25 4 25 4 20

7–126 28 196 14 28 2302 622 4063 2302 2228 690 895 231 >84 70 56 28 56 28 77 14 49 7 7 7 42 7

Kovacs & Tamasi (1979) ’’ Korhonen & Martikainen (1991) ’’ Jenkins et al. (2002) ’’ ’’ ’’ ’’ ’’ ’’ ’’ Olson et al. (1999) ’’ ’’ ’’ ’’ ’’ Olson et al. (1999) ’’ ’’ ’’ ’’ ’’ Kovacs and Tamasi (1979) ’’

0.2 micron filter Days to reach 99% inactivation ’’ ’’ ’’ ’’ ’’


Escherichia coli 0157, 011 & 026 ’’ Salmonella ’’ Campylobacter jejuni ’’ Cryptosporidium parvum ’’ ’’ ’’ ’’ ’’ ’’ ’’ ’’ ’’ ’’ ’’ ’’ ’’ Glardia cysts

Aerated ovine manure Bovine feces

161

162

D. M. OLIVER ET AL.

2. Soil Moisture Status The moisture content of the soil habitat may be an important stress factor; a number of studies have reported that microbial survival is dependant upon soil moisture content (Entry et al., 2000; Jenkins et al., 2002; Mubiru et al., 2000; Postma et al., 1989; Reddy et al., 1981). Inextricably linked with moisture content is the oxygen status of the soil, with slightly anaerobic conditions favoring the persistence of microbes. In general, the rate of microbial die-oV increases with a decrease in soil moisture; as an example, the die-oV rate of E. coli 0157 within diVerent soil types has been determined to be dominated by diVerences in soil water availability (Mubiru et al., 2000). Entry et al. (2000) concluded that FC experienced prolonged survival when accompanied by an increase in moisture within grass buVer strips. However, increasing the water content of a soil does not always prove beneficial to an introduced bacterial population. Under conditions of excessive moisture, a considerable dilution of usable organic C may result, creating unfavorable conditions for E. coli survival (Klein and Casida, 1967) if the cells are no longer associated with fecal wastes. However, Jenkins et al. (2002) examined the role of water potential on the inactivation kinetics of C. parvum oocysts and concluded that soil water potential was less important than soil type and temperature eVects on oocyst inactivation.

3.

Soil Temperature

The temperature of British soils is, on average, 15 8C (Cools et al., 2001); however, micro-organisms that are potentially pathogenic to humans have an optimal growth temperature of 37 8C. Interestingly, once in the soil environment, pathogen survival rates can vary inversely, with temperatures below 15 8C (Davenport et al., 1976). Cools et al. (2001) found that a lower incubation temperature combined with a higher soil moisture content prolonged survival, with numbers of E. coli reaching the detection limit at day 80 under 100% field capacity. The survival of E. coli was greater at 5 8C than at 25 8C and the higher soil temperature resulted in limits of detection being reached as early as 26 days after inoculation. Quantitatively, die-oV rate may be defined as doubling with a 10 8C increase in temperature, in the range of 5–30 8C (Reddy et al., 1981). Faust (1982) also showed that FC bacteria can remain viable for lengthy periods, provided soil temperatures are relatively low, but if high temperatures were combined with other unfavorable conditions, die-oV increased. The study of Van Donsel et al. (1967) investigated FC survival throughout a year and determined a quicker rate of die-oV during summer. Freezing conditions will often reduce survival, and this will take greater eVect during times of high soil moisture levels (Stoddard et al.,


163

1998). High temperatures in combination with low moisture levels have been noted as the conditions most detrimental to Salmonella typhimurium survival (Zibilske and Weaver, 1978). Jenkins et al. (2002) concluded that soil temperature was an important factor governing C. parvum survival, observing, at temperatures between 30 and 40 8C, that the oocyst inactivation rate was directly related to increasing temperature, complementing the earlier results of Fayer et al. (1998).

4.

Soil Type

There has been much work addressing soil structural and textural influences on general micro-organism survival (e.g., England et al., 1993; GriYths and Young, 1994; Heijnen and Vanveen, 1991; Postma and Vanveen, 1990; Rogasik et al., 1999; Stotzky and Rem, 1966; Young and Ritz, 1998). Most of these studies have concentrated on nonpathogenic organisms, although it is possible to apply the same principles to pathogenic micro-organisms introduced to soil via animal wastes. Soil texture determines the water content holding capacities of soils and therefore aVects microbial survival through reasons discussed earlier. In addition, soils that are poor at retaining water will accommodate a greater number of discontinuities in water films, thus restricting the movement of grazing protozoa and improving the survival potential of prey cells (Heijnen and Vanveen, 1991). Soil texture, through the amalgamation of soil aggregates and organic matter, also acts as a provider of microhabitats that may aVect the survival of micro-organisms in the soil. The presence of clay in the soil can enhance the retention of micro-organisms, including pathogens, and increase the provision of protective niches. England et al. (1993) claimed that both clay type and content are important in determining microbial persistence. Soil type also dictates the soil structure and physical makeup of the soil pore network. The habitable pore space (Young and Ritz, 2000) that arises through the given soil structure means that organisms of diVerent diameters may only inhabit pores to which they can gain physical access. The influx of cells into a particular size pore is also a function of the water status of the soil, but those cells that inhabit smaller pores become less susceptible to predation by larger micro-organisms, which cannot access the narrow pore networks (Young and Ritz, 2000). Survival times noted in the literature vary according to soil type and the micro-organism under investigation. Maule (1999) observed E. coli 0157 survival in laboratory-based soil and grass microcosms of over 130 days under continual illumination. Fenlon et al. (2000) reported survival of the same pathogenic strain of E. coli in loam and clay soils to exceed, in some

164

D. M. OLIVER ET AL.

instances, 20 weeks, and the study of Ogden et al. (2002) noted 0157 persistence of approximately 105 days in a loamy sand.

5.

Predation

Tappeser et al. (1998) proposed that predation through protozoa is one of the most important factors controlling inoculated bacterial populations. However, its importance with regard to fecally derived micro-organisms is unclear. England et al. (1993) suggested that the size selective feeding behavior of protozoa can assist in microbe survival. For example, within a soil of increased clay content there exists the potential for cells to increase their mass and volume via sorption to clay colloids, resulting in a clay–cell complex that may avoid protozoan ingestion because of the increased size of the cells. More recently, it has been noted that the role of protozoa in terms of acting as bacterial reservoirs has received little attention (Brown et al., 2002). A number of bacterial pathogens are able to survive and replicate in protozoa, many of which are common in soils and water. Brown et al. (2002) proposed that the stressful soil conditions imposed upon E. coli 0157 and other pathogens can be reduced through the protective niche provided by an intracellular location within protozoan trophozoites.

6. Soil pH Reddy et al. (1981) concluded that a soil pH of between 6 and 7 oVered optimal conditions for bacterial survival. Van Donsel et al. (1967) examined the eVect of low pH on bacterial survival and showed that for a very acidic peat soil, organism survival was reduced to 0.1% in less than 10 days. Increasing the pH of the same soil type extended survival times, although multiplication was prevented. Once pHs of 5.6 to 6.3 were approached, E. coli were able to multiply to a very high level and remained in the environment for as long as 110 days. This reduced survival capacity of fecally derived bacteria at lower pHs has been confirmed by Gerba et al. (1975) and more recently by Sjogren (1994).

C. SURVIVAL IN WATER In surface waters, the survival of fecally derived bacteria is a function of their ability to endure physical, chemical, and biological stresses associated with the aquatic habitat. Microbial persistence in water is critical, as it determines the potential for detrimental eVects ‘‘downstream’’ of fecal


165

sources and is a major route of dispersal. Introduced cells may become physiologically debilitated through exposure to environmental stresses, and natural waters have long been noted as unfavorable environments for introduced micro-organisms (Artz and Killham, 2002; Korhonen and Martikainen, 1991; Scarce, 1964). Within agricultural settings, pathogens may persist in ditch and drainage waters, drinking trough water, and stream and river waters, potentially in a VBNC state as an adopted survival strategy (Nilsson et al., 1991; Rollins and Colwell, 1986; Roszak and Colwell, 1985, 1987; Thomas et al., 1999a). Listed in the following sections are a variety of factors that may exert an influence on potential pathogen survival in water.

1. Nutrient Availability A number of studies have documented the role of nutrient availability in modulating micro-organism survival rates. The multiplication rate of bacterial cells is determined by the level of utilizable nutrients made available to them (Lechevallier et al., 1991). Survival times of E. coli are limited in comparison with the native microbial community of aquatic systems (Jones, 1999), suggesting that enteric bacteria have diYculty competing with natural microflora for the low concentration of available nutrients (Burton et al., 1987). It is also important to consider the nutrient availability associated with suspended particles (both soil and waste derived) in surface waters. Maki and Hicks (2002) stated that suspended sediments impose a variety of influences on bacterial survival, and it is claimed that nutrient levels can be 10 to 100 times higher on suspended particle surfaces as opposed to the surrounding aquatic environment (Paerl, 1975). Hence, available nutrients associated with suspended sediments assist bacterial growth of those cells that attach to particles in comparison with cells suspended freely in water (Crump et al., 1998). Subsequently, through the utilization of nutrients bound to suspended particles, there is an increased potential for cells to remain viable and avoid physiological stresses associated with starvation (Maki and Hicks, 2002).

2.

Water Temperature

Increasing water temperatures often reduces the survival of microorganisms in aquatic systems. Campylobacter spp. survival in water has been observed, with low temperatures (5 8C), along with nutrient availability, favoring survival, suggesting that water systems may act as a significant reservoir for Campylobacter infection (Thomas et al., 1999b). Other studies

166

D. M. OLIVER ET AL.

agree that low temperatures encourage micro-organism survival (Rice et al., 1992), although disagreement concerning exactly how long particular microbes persist in the environment is evident (e.g., Personne et al., 1998; Wang and Doyle, 1998). The survival of E. coli 0157 alongside nonpathogenic strains is likewise temperature dependent, hence the ability of pathogenic E. coli serotypes to persist in aquatic environments in combination with low infective doses reinforces the public health concern associated with this bacteria (Jones et al., 2002). Cell concentrations generally decline through summer months, probably reflecting the influence of the rising water temperatures. However, there is a simultaneous increase in UV radiation distributed to surface waters, and so the eVects on micro-organism survival of individual environmental influences cannot be easily defined. Olson et al. (1999) suggested that C. parvum can remain viable at temperatures of 4 8C, and in waters of 25 8C oocysts may remain infectious for up to 12 weeks (Fayer et al., 1998). The literature demonstrates that microorganisms of fecal origin may be suYciently robust to endure aquatic environments for extended periods and thus harbor great potential for the spread of disease through water as a vector. As well as natural waters, water troughs on farms are an important reservoir of pathogenic micro-organisms (LeJeune et al., 2001; Rice and Johnson, 2000; Shere et al., 1998). The study of McGee et al. (2002) concluded that E. coli 0157 survival within trough water located in the field could last as long as 24 days at temperatures varying between 2 8C and 15 8C. Survival was promoted if the water troughs were stored in a shed rather than being left exposed in field conditions. Trough water kept in the laboratory at a constant 15 8C enabled detection of E. coli 0157 cells 31 days after the start of the experiment, highlighting the improved survival characteristics associated with more constant temperatures.

3.

UV Radiation

The bactericidal eVects of sunlight through UV-B may result in photobiological DNA damage (Sinton et al., 2002). Gameson and Gould (1975) suggested that exposure to solar radiation is the most important factor regarding bacterial decline in waters, and more recently its importance with regard to pathogen persistence in aquatic environments has received attention (e.g., Mofidi et al., 2002; Morita et al., 2002; Sinton et al., 2002). In a comparison of E. coli survival in illuminated and nonilluminated systems, Barcina et al. (1989) determined light to be a decisive regulatory factor governing cellular metabolic activity and concluded that those cells exposed to visible light were progressing through defined stages of dormancy. However, the importance of UV eVects must be put into context, as within


167

agricultural settings they have a much reduced consequence on microbial survival within turbid waters. Likewise, the significance of UV eVects is of little relevance with respect to subsurface hydrological pathways, and so the impact exerted by UV on microbial population decline is more a process operating further down the chain of contamination, once potential pathogens enter more clear waters, in a much diluted concentration, downstream of where drainage waters meet streams.

4.

Predation

Biotic factors also exert an influence upon the survival of foreign microorganisms entering aquatic environments. There can be a great deal of antagonistic activity associated with indigenous microbes through predation and competition, and the importance of predation in regulating the survival of all micro-organisms within surface waters is well documented (e.g., Flint, 1987; Korhonen and Martikainen, 1991). Simple experiments comparing survival rates in filtered and unfiltered waters demonstrate the impact of removing protozoan populations from samples (Artz and Killham, 2002; Korhonen and Martikainen, 1991). Not only does this suggest that, through filtration, removal of the protozoan community from the bacterial population minimizes predation, but it also implies that bacterial cells are able to obtain available nutrients much more readily through reduced competition. In the absence of other micro-organisms, E. coli has been shown to survive for periods in excess of 260 days at temperatures ranging between 4 and 25 8C (Flint, 1987).

5.

Survival in Stream Bed Sediments

Stream sediments may support microbial survival (Davies et al., 1995; Van Donsel and Geldreich, 1971). Sorption to, and the subsequent sedimentation of, suspended particles provides increased protection to microbes by limiting interactions with biotic and abiotic antagonistic factors such as predation and sunlight or by increasing nutrient availability (Craig et al., 2002). The significance of this microbial store was highlighted by ObiriDanso and Jones (2000), and by Craig et al. (2002) and Shiaris et al. (1987), in which the numbers of FC bacteria in sediment were reported be 1,000 and 10,000 times greater, respectively, than those found in the overlying water column. This substantiates the earlier work of Grimes (1975), in which channel dredging increased FC counts downstream. The rise in bacterial concentrations was attributed to the disturbance and resuspension of the bed sediments and the FC bound to those sediments.

168

D. M. OLIVER ET AL.

Medema et al. (1998) related the sedimentation of cells to Stoke’s law, whereby the settling velocity is a function of particle size, water viscosity, and the diVerence in density of the particle and water. Subsequently, those cells that attach to suspended sediment upon entering a watercourse are much more likely to settle with the bed sediment in contrast to freely suspended cells in the water column (Wilkinson et al., 1995). This sediment reservoir is capable of harboring potentially pathogenic bacteria for periods amounting to several months because the favorable conditions and their accumulation in the upper layers of the sediment allows for potential resuspension during times of high turbulence within the water body (Burton et al., 1987). Equally, recreational use of surface waters may disrupt the bed sediments and give rise to temporary health hazards associated with surface waters.

VII. CONCLUDING REMARKS This chapter has identified the two major components to consider in relation to the emergence of potentially pathogenic micro-organisms in grassland environments—survival and transfer. Ultimately, characteristic survival curves must be combined with the dynamics of hydrology to appreciate the real extent of risk in terms of pathogen transmission to the wider public. The literature available to the scientific community at present lacks the bridging of these two fundamental components, though recent studies tend to group these factors together. Awareness of the environment as a reservoir for enteric micro-organisms and the potential routes available to them has revealed a number of gaps in our knowledge. In particular, the identification of hydrological connectivity from surface applied sources to the aquatic receptor needs to be investigated. The heterogeneity and hydrological complexity of agricultural catchments means that transfer routes can vary their relative contaminant contribution loads both spatially and temporally. Thus, extrapolation of our understanding of vertical and lateral flux processes observed at smaller scales cannot be readily applied to the catchment scale. We must prioritize the transfer routes of greatest significance in relation to where the maximum risk of hydrological connection between fecal sources and surface waters exists and then act to reduce their potential threat. Hydrology is highlighted as the key component in governing pathogen emergence in receiving waters. Though this may sound obvious, what remains to be fully catalogued, as with other diVuse agricultural pollutants such as N and P, is a more comprehensive understanding of the role of hydrology. In addition to this, the concept of energy exchanges at the soil surface and energies operating within the soil domain itself deserves further investigation in order to explore associations with flow


169

velocities, colloid mobilization thresholds, and the potential particle association of these micro-organisms. The key risks all point to mobilization of surface applied wastes. To protect watercourses, we need to manage the environment and hydrology to work for us in curtailing pollution of our own water and develop improved communication between scientists and farmers. The other area of confusion is the lack of transferability of results relating to cattle and herd pathogen prevalence, which stems from the large and diverse range of microbiological methods available as detection tools. Whether it is the choice of media to culture bacteria or the molecular technique adopted to detect protozoan oocysts, diVerent laboratories across the world use an array of methods that inevitably complicate the comparison of prevalence data from country to country and between regions. However, at the same time our ability to detect pathogens in the environment has increased markedly through development of alternative molecular approaches that now function as incredibly important tools in the arena of environmental detection. While this chapter addresses grassland farming, it must be remembered that arable farming may also act as a vector of disease transmission. In particular, organic arable farming needs to be addressed in terms of its relative risk contribution to the wider population through the direct consumption of contaminated food. The protection of surface waters can only be achieved through development and continuous evolution of recommendations, regulatory guidelines, and legislation once the processes and apparent governing scenarios of pollutant transfer and delivery are understood. The health risks associated with water-borne disease may be kept to a minimum with eVective control of surface water quality, which may be promoted through an improved understanding of sources, distribution, delivery, and survival of pathogens in agricultural settings.

REFERENCES Abu-Ashour, J., Joy, D. M., Lee, H., Whiteley, H. R., and Zelin, S. (1998). Movement of bacteria in unsaturated soil columns with macropores. Trans. ASAE 41, 1043–1050. Abu-Ashour, J., and Lee, H. (2000). Transport of bacteria on sloping soil surfaces by runoV. Environ. Toxicol. 15, 149–153. Acea, M. J., Moore, C. R., and Alexander, M. (1988). Survival and growth of bacteria introduced into soil. Soil Biol. Biochem. 20, 509–515. Aislabie, J., Smith, J. J., Fraser, R., and McLeod, M. (2001). Leaching of bacterial indicators of faecal contamination through four New Zealand soils. Aust. J. Soil Res. 39, 1397–1406. Aitken, M. N. (2003). Impact of agricultural practices and river catchment characteristics on river and bathing water quality. Water Sci. Technol. 48, 217–224. Allaire-Leung, S., Gupta, S. C., and Moncrief, J. F. (2000). Water and solute movement in soil as influenced by macropore characteristics—1. Macropore continuity. J. Contamin. Hydrol. 41, 283–301.

170

D. M. OLIVER ET AL.

American Public Health Association (1998). ‘‘Standard Methods for the Examination of Water and Wastewater’’, 20th ed., APHA, Washington. Artz, R. R. E., and Killham, K. (2002). Survival of Escherichia coli O157:H7 in private drinking water wells: Influences of protozoan grazing and elevated copper concentrations. FEMS Microbiol. Lett. 216, 117–122. Atwill, E. R., Hoar, B., Pereira, M. D. C., Tate, K. W., Rulofson, F., and Nader, G. (2003). Improved quantitative estimates of low environmental loading and sporadic periparturient shedding of Cryptosporidium parvum in adult beef cattle. Appl. Environ. Microbiol. 69, 4604–4610. Atwill, E. R., Hou, L. L., Karle, B. A., Harter, T., Tate, K. W., and Dahlgren, R. A. (2002). Transport of Cryptosporidium parvum oocysts through vegetated buVer strips and estimated filtration eYciency. Appl. Environ. Microbiol. 68, 5517–5527. Barcina, I., Gonzalez, J. M., Iriberri, J., and Egea, L. (1989). EVect of visible-light on progressive dormancy of Escherichia coli cells during the survival process in natural fresh-water. Appl. Environ. Microbiol. 55, 246–251. Bassler, H. A., Flood, S. J. A., Livak, K. J., Marmaro, J., Knorr, R., and Batt, C. A. (1995). Use of a fluorogenic probe in a PCR-based assay for the detection of Listeria monocytogenes. Appl. Environ. Microbiol. 61, 3724–3728. Bessesen, M. T., Luo, Q., Rotbart, H. A., Blaser, M. J., and Ellison, R. T. (1990). Detection of Listeria monocytogenes by using the polymerase chain-reaction. Appl. Environ. Microbiol. 56, 2930–2932. Beven, K., and Germann, P. (1982). Macropores and water-flow in soils. Water Resour. Res. 18, 1311–1325. Bissonnette, G. K., Jezeski, J. J., McFetters, G. A., and Stuart, D. G. (1977). Evaluation of recovery methods to detect coliforms in water. Appl. Environ. Microbiol. 33, 590–595. Bogosian, G., Sammons, L. E., Morris, P. J. L., Oneil, J. P., Heitkamp, M. A., and Weber, D. B. (1996). Death of the Escherichia coli K-12 strain W3110 in soil and water. Appl. Environ. Microbiol. 62, 4114–4120. Bowen, G. D., and Rovira, A. D. (1999). The rhizosphere and its management to improve plant growth. Adv. Agron. 66, 1–102. Bradford, S. A., and Schijven, J. (2002). Release of Cryptosporidium and Giardia from dairy calf manure: Impact of solution salinity. Environ. Sci. Technol. 36, 3916–3923. Braswell, J. R., and Hoadley, A. W. (1974). Recovery of Escherichia coli from chlorinated secondary sewage. Appl. Environ. Microbiol. 28, 328–329. Brenner, K. P., Rankin, C. C., Roybal, Y. R., Stelma, G. N., Scarpino, P. V., and Dufour, A. P. (1993). New medium for the simultaneous detection of total coliforms and Escherichia coli in water. Appl. Environ. Microbiol. 59, 3534–3544. Briones, A. M., and Reichardt, W. (1999). Estimating microbial population counts by ‘most probable number’ using Microsoft Excel (R). J. Microbiolog. Meth. 35, 157–161. Brodsky, M. H., and Schiemann, D. A. (1975). Influence of coliform source on evaluation of membrane filters. Appl. Microbiol. 30, 727–730. Brown, M. W. R., Smith, A. W., Barker, J., Humphrey, T. J., and Dixon, B. (2002). E. coli 0157 persistence in the environment. Microbiology 148, 1–2. Brush, C. F., Ghiorse, W. C., Anguish, L. J., Parlange, J. Y., and Grimes, H. G. (1999). Transport of Cryptosporidium parvum oocysts through saturated columns. J. Environ. Qual. 28, 809–815. Buckhouse, J. C., and GiVord, G. F. (1976). Water quality implications of cattle grazing on a semiarid watershed in southeastern Utah. J. Range Manage. 29, 109–113. Burton, G. A., Gunnison, D., and Lanza, G. R. (1987). Survival of pathogenic bacteria in various fresh-water sediments. Appl. Environ. Microbiol. 53, 633–638.


171

Buttle, J. M., and Leigh, D. G. (1997). The influence of artificial macropores on water and solute transport in laboratory soil columns. J. Hydrol. 191, 290–313. Byamukama, D., Kansiime, F., Mach, R. L., and Farnleitner, A. H. (2000). Determination of Escherichia coli contamination with chromocult coliform agar showed a high level of discrimination eYciency for diVering fecal pollution levels in tropical waters of Kampala, Uganda. Appl. Environ. Microbiol. 66, 864–868. Call, D. R., Borucki, M. K., and Loge, F. J. (2003). Detection of bacterial pathogens in environmental samples using DNA microarrays. J. Microbiolog. Meth. 53, 235–243. Campbell, G. R., Prosser, J., Glover, A., and Killham, K. (2001). Detection of Escherichia coli 0157:H7 in soil and water using multiplex PCR. J. Appl. Microbiol. 91, 1004–1010. Camper, A. K., Hayes, J. T., Sturman, P. J., Jones, W. L., and Cunningham, A. B. (1993). EVects of motility and adsorption rate coeYcient on transport of bacteria through saturated porous-media. Appl. Environ. Microbiol. 59, 3455–3462. Chadwick, D. R., and Chen, S. (2002). Manures. In ‘‘Agriculture, Hydrology and Water Quality’’ (P. M. Haygarth and S. C. Jarvis, Eds.), pp. 57–82. CAB International, Wallingford and New York. Chapman, P. A., Siddons, C. A., Cerdan-Malo, A. T., and Harkin, M. A. (1997). A 1-year study of Escherichia coli 0157 in cattle, sheep, pigs and poultry. Epidemiol. Infect. 119, 245–250. Chung, E., Aldom, J. E., Carreno, R. A., Chagla, A. H., Kostrzynska, M., Lee, H., Palmateer, G., Trevors, J. T., Unger, S., Xu, R., and De Grandis, S. A. (1999). PCR-based quantitation of Cryptosporidium parvum in municipal water samples. J. Microbiolog. Meth. 38, 119–130. Clark, D. L., Milner, B. B., Stewart, M. H., Wolfe, R. L., and Olson, B. H. (1991). Comparativestudy of commercial 4-methylumbelliferyl-beta-d-glucuronide preparations with the standard methods membrane filtration fecal coliform test for the detection of Escherichia coli in water samples. Appl. Environ. Microbiol. 57, 1528–1534. Colwell, R. R., Brayton, P. R., Grimes, D. J., Roszak, D. B., Huq, S. A., and Palmer, L. M. (1985). Viable but non-culturable Vibrio cholerae and related pathogens in the environment—implications for release of genetically engineered microorganisms. Bio-Technology 3, 817–820. Cook, M. J., and Baker, J. L. (2001). Bacteria and nutrient transport to tile lines shortly after application of large volumes of liquid swine manure. Trans. ASAE 44, 495–503. Cools, D., Merckx, R., Vlassak, K., and Verhaegen, J. (2001). Survival of E. coli and Enterococcus spp. derived from pig slurry in soils of diVerent texture. Appl. Soil Ecol. 17, 53–62. Crabill, C., Donald, R., Snelling, J., Foust, R., and Southam, G. (1999). The impact of sediment fecal coliform reservoirs on seasonal water quality in Oak Creek, Arizona. Water Res. 33, 2163–2171. Craig, D. L., Fallowfield, H. J., and Cromar, N. J. (2002). Comparison of decay rates of faecal indicator organisms in recreational coastal water and sediment. Water Sci. Tech. Water Supply 2, 131–138. Crump, B. C., Baross, J. A., and Simenstad, C. A. (1998). Dominance of particle-attached bacteria in the Columbia River estuary, USA. Aquatic Microb. Ecol. 14, 7–18. Culley, J. L. B., and Phillips, P. A. (1982). Bacteriological quality of surface and subsurface runoV from manured sandy clay loam soil. J. Environ. Qual. 11, 155–158. Dai, X., and Boll, J. (2003). Evaluation of attachment of Cryptosporidium parvum and Giardia lamblia to soil particles. J. Environ. Qual. 32, 296–304. Davenport, C. V., Sparrow, E. B., and Gorden, R. C. (1976). Fecal indicator bacteria persistence under natural conditions in an ice covered river. Appl. Environ. Microbiol. 32, 527–536.

172

D. M. OLIVER ET AL.

Davies, C. M., Long, J. A. H., Donald, M., and Ashbolt, N. J. (1995). Survival of fecal microorganisms in marine and fresh-water sediments. Appl. Environ. Microbiol. 61, 1888–1896. DeGuise, K. A., Wolfe, M. L., and Dillaha III, T. A. (1999). ‘‘A Foundation for Modeling Bacterial Contamination of Surface Waters due to Nonpoint Sources’’. ASAE Annual International Meeting. Toronto, Canada. Drozd, C., and Schwartzbrod, J. (1996). Hydrophobic and electrostatic cell surface properties of Cryptosporidium parvum. Appl. Environ. Microbiol. 62, 1227–1232. Economic Research Service/U.S. Department of Agriculture (ERS/USDA) (2003). ‘‘Economics of Foodborne Disease: Salmonella’’. http://www.ers.usda.gov/briefing/FoodborneDisease/ Salmonella. Ehlers, W. (1975). Observations on earthworm channels and infiltration on tilled and untilled loess soil. Soil Sci. 119, 242–249. England, L. S., Lee, H., and Trevors, J. T. (1993). Bacterial survival in soil—eVect of clays and protozoa. Soil Biol. Biochem. 25, 525–531. Entry, J. A., Hubbard, R. K., Thies, J. E., and Fuhrmann, J. J. (2000). The influence of vegetation in riparian filterstrips on coliform bacteria: II. Survival in soils. J. Environ. Qual. 29, 1215–1224. Faust, M. A. (1982). Relationship between land-use practices and fecal bacteria in soils. J. Environ. Qual. 11, 141–146. Fayer, R., Trout, J. M., and Jenkins, M. C. (1998). Infectivity of Cryptosporidium parvum oocysts stored in water at environmental temperatures. J. Parasitol. 84, 1165–1169. Fenlon, D. R., Ogden, I. D., Vinten, A., and Svoboda, I. (2000). The fate of Escherichia coli and E. coli O157 in cattle slurry after application to land. J. Appl. Microbiol. 88, 149S–156S. Fleisher, J. M., and McFadden, R. T. (1980). Obtaining precise estimates in coliform enumeration. Water Res. 14, 477–483. Flint, K. P. (1987). The long-term survival of Escherichia coli in river water. J. Appl. Bacteriol. 63, 261–270. Fontes, D. E., Mills, A. L., Hornberger, G. M., and Herman, J. S. (1991). Physical and chemical factors influencing transport of microorganisms through porous-media. Appl. Environ. Microbiol. 57, 2473–2481. Fricker, E. J., and Fricker, C. R. (1996). Use of two presence/absence systems for the detection of E. coli and coliforms from water. Water Res. 30, 2226–2228. Fukushima, H., Hoshina, K., and Gomyoda, M. (1999). Long-term survival of Shiga toxinproducing Escherichia coli O26, O113, and O157 in bovine feces. Appl. Environ. Microbiol. 65, 5177–5181. Gagliardi, J. V., and Karns, J. S. (2000). Leaching of Escherichia coli 0157:H7 in diverse soils under various agricultural management practices. Appl. Environ. Microbiol. 66, 877–883. Gameson, A., and Gould, D. (1975). EVects of solar radiation on the mortality of some terrestrial bacteria in seawater. In ‘‘Discharge of Sewage from Sea Outfalls’’ (A. Gameson, Ed.), pp. 209–217. Pergamon Press, London. Gammack, B. M., Paterson, E., Kemp, J. S., Cresser, M. S., and Killham, K. (1992). Factors aVecting movement of micro-organisms in soils. In ‘‘Soil Biochemistry’’ (G. Stotzky and L. M. Bolla, Eds.), 7, pp. 263–305. Marcel Dekker, Inc, New York. Gannon, J. T., Mingelgrin, U., Alexander, M., and Wagenet, R. J. (1991). Bacterial transport through homogeneous soil. Soil Biol. Biochem. 23, 1155–1160. Geissler, K., Manafi, M., Amoros, I., and Alonso, J. L. (2000). Quantitative determination of total coliforms and Escherichia coli in marine waters with chromogenic and fluorogenic media. J. Appl. Microbiol. 88, 280–285. George, I., Petit, M., Theate, C., and Servais, P. (2001). Use of rapid enzymatic assays to study the distribution of faecal coliforms in the Seine river (France). Water Sci. Technol. 43, 77–80.


173

Gerba, C. P. (1996). Pathogens in the environment. In ‘‘Pollution Science’’ (I. L. Pepper, C. P. Gerba, and M. L. Brusseau, Eds.), pp. 279–294. Academic Press, New York. Gerba, C. P., Wallis, C., and Melnick, J. L. (1975). Fate of wastewater bacteria and viruses in soil. J. Irrig. Drain. Div. 101, 157–174. Green, B. L., Clausen, E., and Litsky, W. (1975). Comparison of the new millipore HC with conventional membrane filters for the enumeration of fecal coliform bacteria. Appl. Microbiol. 30, 697–699. GriYths, B. S., and Young, I. M. (1994). The eVects of soil-structure on protozoa in a clay-loam soil. Eur. J. Soil Sci. 45, 285–292. Grimes, D. J. (1975). Release of sediment bound fecal coliforms by dredging. Appl. Microbiol. 29, 109–111. Hallaran, G., and Sumner, J. (2001). Prevalence of E. coli O157 in dairy cows presented for slaughter in Victoria. Aust. Vet. J. 79, 707–708. Harter, T., Wagner, S., and Atwill, E. R. (2000). Colloid transport and filtration of Cryptosporidium parvum in sandy soils and aquifer sediments. Environ. Sci. Technol. 34, 62–70. Harvey, R. W. (1997). Microorganisms as tracers in groundwater injection and recovery experiments: A review. FEMS Microbiol. Rev. 20, 461–472. Hedges, A. J. (2002). Estimating the precision of serial dilutions and viable bacterial counts. Intl. J. Food Microbiol. 76, 207–214. Heijnen, C. E., and Vanveen, J. A. (1991). A determination of protective microhabitats for bacteria introduced into soil. FEMS Microbiol. Ecol. 85, 73–80. Heinonen-Tanski, H., Niskanen, E. M., Salmela, P., and Lanki, E. (1998). Salmonella in animal slurry can be destroyed by aeration at low temperatures. J. Appl. Microbiol. 85, 277–281. Heinonen-Tanski, H., and Uusi-Kamppa, J. (2001). RunoV of faecal microorganisms and nutrients from perennial grass ley after application of slurry and mineral fertiliser. Water Sci. Technol. 43, 143–146. Hekman, W. E., Heijnen, C. E., Burgers, S., Vanveen, J. A., and Vanelsas, J. D. (1995). Transport of bacterial inoculants through intact cores of 2 diVerent soils as aVected by water percolation and the presence of wheat plants. FEMS Microbiol. Ecol. 16, 143–157. Herbert, R. A. (1990). Methods for enumerating microorganisms and determining biomass in natural environments. Meth. Microbiol. 22, 1–39. Hoar, B. R., Atwill, E. R., Elmi, C., Utterback, W. W., and Edmondson, A. J. (1999). Comparison of fecal samples collected per rectum and oV the ground for estimation of environmental contamination attributable to beef cattle. Am. J. Vet. Res. 60, 1352–1356. Hoar, B. R., Atwill, E. R., and Farver, T. B. (2000). Estimating maximum possible environmental loading amounts of Cryptosporidium parvum attributable to adult beef cattle. Quant. Microbiol. 2, 21–36. Hooda, P. S., Edwards, A. C., Anderson, H. A., and Miller, A. (2000). A review of water quality concerns in livestock farming areas. Sci. Total Environ. 250, 143–167. Hornberger, G. M., Mills, A. L., and Herman, J. S. (1992). Bacterial transport in porousmedia—evaluation of a model using laboratory observations. Water Resour. Res. 28, 915–923. Huston, C. L., Wittum, T. E., and Love, B. C. (2002). Persistent fecal Salmonella shedding in five dairy herds. J. Am. Vet. Med. Assoc. 220, 650–655. Huysman, F., and Verstraete, W. (1993). Water-facilitated transport of bacteria in unsaturated soil columns—influence of cell-surface hydrophobicity and soil properties. Soil Biol. Biochem. 25, 83–90. Jacobs, N. J., Zeigler, W. L., Reed, F. C., Stukel, T. A., and Rice, E. W. (1986). Comparison of membrane filter, multiple-fermentation-tube, and presence-absence techniques for detecting total coliforms in small community water systems. Appl. Environ. Microbiol. 51, 1007–1012.

174

D. M. OLIVER ET AL.

Jacobsen, O. H., Moldrup, P., Larsen, C., Konnerup, L., and Petersen, L. W. (1997). Particle transport in macropores of undisturbed soil columns. J. Hydrol. 196, 185–203. Jamieson, R. C., Gordon, R. J., Sharples, K. E., Stratton, G. W., and Madani, A. (2002). Movement and persistence of faecal bacteria in agricultural soils and subsurface drainage water: A review. Can. Biosys. Eng. 44, 1.1–1.9. Jenkins, M. B., Bowman, D. D., Fogarty, E. A., and Ghiorse, W. C. (2002). Cryptosporidium parvum oocyst inactivation in three soil types at various temperatures and water potentials. Soil Biol. Biochem. 34, 1101–1109. Jiang, X., Morgan, J., and Doyle, M. P. (2002). Fate of Escherichia coli 0157:H7 in manure amended soil. Appl. Environ. Microbiol. 68, 2605–2609. Jin, Y., and Flury, M. (2002). Fate and transport of viruses in porous media. Adv. Agron. 77, 39–101. Joachim, A., Krull, T., Schwarzkopf, J., and Daugschies, A. (2003). Prevalence and control of bovine cryptosporidiosis in German dairy herds. Vet. Parasitol. 112, 277–288. Joergensen, R. G., Kuntzel, H., Scheu, S., and Seitz, D. (1998). Movement of faecal indicator organisms in earthworm channels under a loamy arable and grassland soil. Appl. Soil Ecol. 8, 1–10. Joergensen, R. G., and Seitz, D. (1998). Depth transfer of fecal indicator organisms in an arable clayey soil. Zeitschrift Pflanzenernahrung Bodenkunde 161, 309–314. Johnson, W. P., Blue, K. A., Logan, B. E., and Arnold, R. G. (1995). Modeling bacterial detachment during transport through porous-media as a residence-time-dependent process. Water Resour. Res. 31, 2649–2658. Jones, D. L. (1999). Potential health risks associated with the persistence of Escherichia coli O157 in agricultural environments. Soil Use Manage. 15, 76–83. Jones, D. L., Campbell, G., and Kaspar, C. W. (2002). Human enteric pathogens. In ‘‘Agriculture, Hydrology and Water Quality’’ (P. M. Haygarth and S. C. Jarvis, Eds.), pp. 133–153. CAB International, Wallingford and New York. Jones, K. (2001). Campylobacters in water, sewage and the environment. J. Appl. Microbiol. 90, 1–12. Ketley, J. M. (1997). Pathogenesis of enteric infection by Campylobacter. Microbiology–UK 143, 5–21. Kim, S. B., and Corapcioglu, M. Y. (2002). Contaminant transport in dual-porosity media with dissolved organic matter and bacteria present as mobile colloids. J. Contamin. Hydrol. 59, 267–289. Klein, D. A., and Casida, L. E. (1967). Escherichia coli die-out from natural soil as related to nutrient availability and the indigenous microflora. Can. J. Microbiol. 13, 1461–1470. Korhonen, L. K., and Martikainen, P. J. (1991). Survival of Escherichia coli and Campylobacter jejuni in untreated and filtered lake water. J. Appl. Bacteriol. 71, 379–382. Kovacs, F., and Tamasi, G. (1979). Survival times of bacteria in liquid manure. Acta Vet. Academ. Hung. 27, 47–54. Kramer, M. H., Herwaldt, B. L., Craun, G. F., Calderon, R. L., and Juranek, D. D. (1996). Waterborne disease: 1993 and 1994. J. Am. Water Works Assoc. 88, 66–80. Kretzschmar, R., Borkovec, M., Grolimund, D., and Elimelech, M. (1999). Mobile subsurface colloids and their role in contaminant transport. Adv. Agron. 66, 121–193. Kudva, I. T., Blanch, K., and Hovde, C. J. (1998). Analysis of Escherichia coli 0157:H7 survival in ovine or bovine manure and manure slurry. Appl. Environ. Microbiol. 64, 3166–3174. Lahti, E., Ruoho, I., Rantala, L., Hanninen, M. L., and Honkanen-Buzalski, T. (2003). Longitudinal study of Escherichia coli O157 in a cattle finishing unit. Appl. Environ. Microbiol. 69, 554–561. Lechevallier, M. W., Schulz, W., and Lee, R. G. (1991). Bacterial nutrients in drinking-water. Appl. Environ. Microbiol. 57, 857–862.


175

Lee, K. E. (1985). ‘‘Earthworms, Their Ecology and Relationships with Soils and Land Use’’. Academic Press, New York. LeJeune, J. T., Besser, T. E., Merrill, N. L., Rice, D. H., and Hancock, D. D. (2001). Livestock drinking water microbiology and the factors influencing the quality of drinking water oVered to cattle. J. Dairy Sci. 84, 1856–1862. Lin, S. D. (1976). Evaluation of millipore HA and HC membrane filters for the enumeration of indicator bacteria. Appl. Environ. Microbiol. 32, 300–302. Ling, T. Y., Achberger, E. C., Drapcho, C. M., and Bengtson, R. L. (2002). Quantifying adsorption of an indicator bacteria in a soil-water system. Trans. ASAE 45, 669–674. Locking, M. E., O’Brien, S. J., Reilly, W. J., Wright, E. M., Campbell, D. M., Coia, J. E., Browning, L. M., and Ramsay, C. N. (2001). Risk factors for sporadic cases of Escherichia coli O157 infection: The importance of contact with animal excreta. Epidemiol. Infect. 127, 215–220. Lund, E., and Nissen, B. (1983). The survival of enteroviruses in aerated and unaerated cattle and pig slurry. Agricult. Wastes 7, 221–233. Macleod, F. A., Lappin-Scott, H. M., and Costerton, J. W. (1988). Plugging of a model rock system by using starved bacteria. Appl. Environ. Microbiol. 54, 1365–1372. Madigan, M. T., Martinko, J. M., and Parker, J. (2003). ‘‘Brock Biology of Microorganisms’’, 10th ed. Prentice Hall, New Jersey. Maki, R. P., and Hicks, R. E. (2002). Salmonella typhimurium survival and viability is unaltered by suspended particles in freshwater. J. Environ. Qual. 31, 1702–1709. Massa, S., Brocchi, G. F., Peri, G., Altieri, C., and Mammina, C. (2001). Evaluation of recovery methods to detect faecal streptococci in polluted waters. Lett. Appl. Microbiol. 32, 298–302. Massa, S., Grandi, M., Poda, G., Cesarondi, D., Cangamella, F., and Trovatelli, L. D. (1989). Coliform detection from river waters: Comparison between mPN and MF techniques. Water Air Soil Pollution 43, 135–145. Maule, A. (1999). Environmental aspects of E. coli 0157. Intl. Food Hygiene 9, 21–23. Maule, A. (2000). Survival of verocytotoxigenic Escherichia coli 0157 in soil, water and on surfaces. J. Appl. Microbiol. Symp. Suppl. 88, 71S–78S. Mawdsley, J. L., Bardgett, R. D., Merry, R. J., Pain, B. F., and Theodorou, M. K. (1995). Pathogens in livestock waste, their potential for movement through soil and environmental pollution. Appl. Soil Ecol. 2, 1–15. Mawdsley, J. L., Brooks, A. E., Merry, R. J., and Pain, B. F. (1996). Use of a novel soil tilting table apparatus to demonstrate the horizontal and vertical movement of the protozoan pathogen Cryptosporidium parvum in soil. Biol. Fertil. Soils 23, 215–220. Maxey, R. B. (1970). Non-lethal injury and limitations of recovery of coliform organisms on selective media. J. Milk Food Technol. 33, 445–448. McCaulou, D. R., Bales, R. C., and Arnold, R. G. (1995). EVect of temperature-controlled motility on transport of bacteria and microspheres through saturated sediment. Water Resour. Res. 31, 271–280. McDonald, A., and Kay, D. (1981). Enteric bacterial concentrations in reservoir feeder streams—baseflow characteristics and response to hydrograph events. Water Res. 15, 961–968. McDowell-Boyer, L. M., Hunt, J. R., and Sitar, N. (1986). Particle transport through porous media. Water Resour. Res. 22, 1901–1921. McEvoy, J. M., Doherty, A. M., Sheridan, J. J., Blair, I. S., and McDowell, D. A. (2003). The prevalence of Salmonella spp. in bovine faecal, rumen and carcass samples at a commercial abattoir. J. Appl. Microbiol. 94, 693–700. McGechan, M. B. (2002). Transport of particulate and colloid-sorbed contaminants through soil, part 2: Trapping processes and soil pore geometry. Biosys. Eng. 83, 387–395. McGechan, M. B., and Lewis, D. R. (2002). Transport of particulate and colloid-sorbed contaminants through soil, part 1: General principles. Biosys. Eng. 83, 255–273.

176

D. M. OLIVER ET AL.

McGee, P., Bolton, D. J., Sheridan, J. J., Earley, B., Kelly, G., and Leonard, N. (2002). Survival of Escherichia coli O157:H7 in farm water: Its role as a vector in the transmission of the organism within herds. J. Appl. Microbiol. 93, 706–713. McGee, P., Bolton, D. J., Sheridan, J. J., Earley, B., and Leonard, N. (2001). The survival of Escherichia coli O157: H7 in slurry from cattle fed diVerent diets. Lett. Appl. Microbiol. 32, 152–155. McGuirk, S. M., and Peek, S. (2003). Salmonellosis in cattle: A review. American Association of Bovine Practitioners 36th Annual Conference, September 15–17, 2003. Columbus, OH. http://www.vetmed.wisc.edu/dms/fapm/forms/7health/salmorev.pdf. McLeod, M., Schipper, L. A., and Taylor, M. D. (1998). Preferential flow in a well drained and a poorly drained soil under diVerent overhead irrigation regimes. Soil Use Manage. 14, 96–100. Medema, G. J., Schets, F. M., Teunis, P. F. M., and Havelaar, A. H. (1998). Sedimentation of free and attached Cryptosporidium oocysts and Giardia cysts in water. Appl. Environ. Microbiol. 64, 4460–4466. Miettinen, I. T., Zacheus, O., von BonsdorV, C. H., and Vartiainen, T. (2001). Waterborne epidemics in Finland in 1998–1999. Water Sci. Technol. 43, 67–71. Mills, A. L., and Saiers, J. E. (1993). Particle-associated transport of pollutants in subsurface environments. In ‘‘Particulate Matter and Aquatic Contaminants’’ (S. S. Rao, Ed.), pp. 105–126. CRC Press, Inc, Florida. Mitscherlich, E., and Marth, E. H. (1984). ‘‘Microbial Survival in the Environment’’. SpringerVerlag, Berlin. Mofidi, A. A., Meyer, E. A., Wallis, P. M., Chou, C. I., Meyer, B. P., Ramalingam, S., and CoVey, B. M. (2002). The eVect of UV light on the inactivation of Giardia lamblia and Giardia muris cysts as determined by animal infectivity assay (P-2951-01). Water Res. 36, 2098–2108. Morita, S., Namikoshi, A., Hirata, T., Oguma, K., Katayama, H., Ohgaki, S., Motoyama, N., and Fujiwara, M. (2002). EYcacy of UV irradiation in inactivating Cryptosporidium parvum oocysts. Appl. Environ. Microbiol. 68, 5387–5393. Mubiru, D. N., Coyne, M. S., and Grove, J. H. (2000). Mortality of Escherichia coli O157:H7 in two soils with diVerent physical and chemical properties. J. Environ. Qual. 29, 1821–1825. Nagels, J. W., Davies-Colley, R. J., Donnison, A. M., and Muirhead, R. W. (2002). Faecal contamination over flood events in a pastoral agricultural stream in New Zealand. Water Sci. Technol. 45, 45–52. Nicholson, F. A., Hutchinson, M. L., Smith, K. A., Keevil, C. W., Chambers, B. J., and Moore, A. (2000). A study on farm manure applications to agricultural land and an assessment of the risks of pathogen transfer into the food chain. A report to The Ministry of Agriculture Fisheries and Food, UK. Nielsen, E. M. (2002). Occurrence and strain diversity of thermophilic Campylobacters in cattle of diVerent age groups in dairy herds. Lett. Appl. Microbiol. 35, 85–89. Nielsen, J. L., Mikkelsen, L. H., and Nielsen, P. H. (2001). In Situ detection of cell surface hydrophobicity of probe-defined bacteria in activated sludge. Water Sci. Technol. 43, 97–103. Nilsson, L., Oliver, D., and Kjelleberg, S. (1991). Resuscitation of Vibrio vulnificus from the viable but non-culturable state. J. Bacteriol. 173, 5054–5059. Obiri-Danso, K., and Jones, K. (2000). Intertidal sediments as reservoirs for hippurate negative Campylobacters, Salmonellae and faecal indicators in three EU recognised bathing waters in North West England. Water Res. 34, 519–527. Ogden, I. D., Hepburn, N. F., MacRae, M., Strachan, N. J. C., Fenlon, D. R., Rusbridge, S. M., and Pennington, T. H. (2002). Long-term survival of Escherichia coli O157 on pasture following an outbreak associated with sheep at a scout camp. Lett. Appl. Microbiol. 34, 100–104.


177

O’Handley, R. M., Cockwill, C., McAllister, T. A., Jelinski, M., Morck, D. W., and Olson, M. E. (1999). Duration of naturally acquired giardiosis and cryptosporidiosis in dairy calves and their association with diarrhea. J. Am. Vet. Med. Assoc. 214, 391–396. Olson, M. E., Goh, J., Phillips, M., Guselle, N., and McAllister, T. A. (1999). Giardia cyst and Cryptosporidium oocyst survival in water, soil, and cattle feces. J. Environ. Qual. 28, 1991–1996. Olson, M. E., Thorlakson, C. L., Deselliers, L., Morck, D. W., and McAllister, T. A. (1997). Giardia and Cryptosporidium in Canadian farm animals. Vet. Parasitol. 68, 375–381. Opperman, M. H., McBain, L., and Wood, M. (1987). Movement of cattle slurry through soil by Eisenia foetida (Savigny). Soil Biol. Biochem. 19, 741–745. Paerl, H. W. (1975). Microbial attachment to particles in marine and freshwater ecosystems. Microb. Ecol. 2, 73–83. Palmateer, G., McLean, D. E., Kutas, W. L., and Meissner, S. M. (1993). Suspended particulate/bacterial interaction in agricultural drains. In ‘‘Particulate Matter and Aquatic Contaminants’’ (S. S. Rao, Ed.), pp. 1–40. CRC Press, Inc., Florida. Pell, A. N. (1997). Manure and microbes: Public and animal health problem? J. Dairy Sci. 80, 2673–2681. Personne, J. C., Poty, F., Vaute, L., and Drogue, C. (1998). Survival, transport and dissemination of Escherichia coli and enterococci in a fissured environment. Study of a flood in a karstic aquifer. J. Appl. Microbiol. 84, 431–438. Postma, J., and Vanveen, J. A. (1990). Habitable pore-space and survival of Rhizobium leguminosarum biovar trifolii introduced into soil. Microb. Ecol. 19, 149–161. Postma, J., Vanveen, J. A., and Walter, S. (1989). Influence of diVerent initial soil-moisture contents on the distribution and population-dynamics of introduced Rhizobium leguminosarum biovar trifolii. Soil Biol. Biochem. 21, 437–442. Quinton, J. N., Tyrrel, S. F., and Ramos, M. C. (2003). The eVect of incorporating slurries on the transport of faecal coliforms in overland flow. Soil Use Manage. 19, 185–186. Rahe, T. M., Hagedorn, C., McCoy, E. L., and Kling, G. F. (1978). Transport of antibioticresistant Escherichia coli through Western Oregon hillslope soils under conditions of saturated flow. J. Environ. Qual. 7, 487–494. Rasmussen, M. A., Cray, W. C., Casey, T. A., and Whipp, S. C. (1993). Rumen contents as a reservoir of enterohemorrhagic Escherichia coli. FEMS Microbiol. Lett. 114, 79–84. Reddy, K. R., Khaleel, R., and Overcash, M. R. (1981). Behavior and transport of microbial pathogens and indicator organisms in soils treated with organic wastes. J. Environ. Qual. 10, 255–266. Reynolds, P. J., Sharma, P., Jenneman, G. E., and McInerney, M. J. (1989). Mechanisms of microbial movement in subsurface materials. Appl. Environ. Microbiol. 55, 2280–2286. Rice, E. W., Allen, M. J., and Edberg, S. C. (1990). EYcacy of beta-glucuronidase assay for identification of Escherichia coli by the defined-substrate technology. Appl. Environ. Microbiol. 56, 1203–1205. Rice, E. W., and Johnson, C. H. (2000). Short communication: Survival of Escherichia coli O157:H7 in dairy cattle drinking water. J. Dairy Sci. 83, 2021–2023. Rice, E. W., Johnson, C. H., Wild, D. K., and Reasoner, D. J. (1992). Survival of Escherichia coli O157:H7 in drinking-water associated with a waterborne disease outbreak of hemorrhagic colitis. Lett. Appl. Microbiol. 15, 38–40. Rodgers, P., Soulsby, C., Hunter, C., and Petry, J. (2003). Spatial and temporal bacterial quality of a lowland agricultural stream in northeast Scotland. Sci. Total Environ. 314–316, 289–302. Rogasik, H., Crawford, J. W., Wendroth, O., Young, I. M., Joschko, M., and Ritz, K. (1999). Discrimination of soil phases by dual energy x-ray tomography. Soil Sci. Soc. Am. J. 63, 741–751.

178

D. M. OLIVER ET AL.

Rollins, D. M., and Colwell, R. R. (1986). Viable but non-culturable stage of Campylobacter jejuni and its role in survival in the natural aquatic environment. Appl. Environ. Microbiol. 52, 531–538. Rompre, A., Servais, P., Baudart, J., de-Roubin, M. R., and Laurent, P. (2002). Detection and enumeration of coliforms in drinking water: Current methods and emerging approaches. J. Microbiolog. Meth. 49, 31–54. Roszak, D. B., and Colwell, R. R. (1987). Survival strategies of bacteria in the naturalenvironment. Microbiolog. Rev. 51, 365–379. Saini, R., Halverson, L. J., and Lorimor, J. C. (2003). Rainfall timing and frequency influence on leaching of Escherichia coli RS2G through soil following manure application. J. Environ. Qual. 32, 1865–1872. Sargeant, J. M., Gillespie, J. R., Oberst, R. D., Phebus, R. K., Hyatt, D. R., Bohra, L. K., and Galland, J. C. (2000). Results of a longitudinal study of the prevalence of Escherichia coli O157 : H7 on cow-calf farms. Am. J. Vet. Res. 61, 1375–1379. Scarce, L. E., Rubenstein, S. H., and Megregian, S. (1964). Survival of indicator bacteria in receiving waters under various conditions. Great Lakes Res. Div. Univ. Mich. 11, 130–139. Schijven, J. F., Hassanizadeh, S. M., and de Bruin, H. A. M. (2002). Column experiments to study nonlinear removal of bacteriophages by passage through saturated dune sand. J. Contamin. Hydrol. 58, 243–259. Scottish Executice and Food Standards Agency Scotland (2001). ‘‘Joint FSA/SE Task Force on E. coli 0157—Final Report’’. http://www.foodstandards.gov.uk/scotland/ecoli.htm. Sharma, S., Sachdeva, P., and Virdi, J. S. (2003). Emerging water-borne pathogens. Appl. Microbiol. Biotechnol. 61, 424–428. Sharpe, A. N., and Michaud, G. L. (1975). Enumeration of high numbers of bacteria using hydrophobic grid-membrane filters. Appl. Microbiol. 30, 519–524. Shere, J. A., Bartlett, K. J., and Kaspar, C. W. (1998). Longitudinal study of Escherichia coli O157 : H7 dissemination on four dairy farms in Wisconsin. Appl. Environ. Microbiol. 64, 1390–1399. Shiaris, M. P., Rex, A. C., Pettibone, G. W., Keay, K., McManus, P., Rex, M. A., Ebersole, J., and Gallagher, E. (1987). Distribution of indicator bacteria and Vibrio-parahaemolyticus in sewage-polluted intertidal sediments. Appl. Environ. Microbiol. 53, 1756–1761. Shipe, E. L., and Cameron, G. M. (1954). A comparison of the membrane filter with the most probable number method for coliform determinations from several waters. Appl. Microbiol. 2, 85–88. Simoni, S. F., Harms, H., Bosma, T. N. P., and Zehnder, A. J. B. (1998). Population heterogeneity aVects transport of bacteria through sand columns at low flow rates. Environ. Sci. Technol. 32, 2100–2105. Sinton, L. W., Hall, C. H., Lynch, P. A., and Davies-Colley, R. J. (2002). Sunlight inactivation of fecal indicator bacteria and bacteriophages from waste stabilization pond eZuent in fresh and saline waters. Appl. Environ. Microbiol. 68, 1122–1131. Sjogren, R. E. (1994). Prolonged survival of an environmental Escherichia coli in laboratory soil microcosms. Water Air Soil Pollution 75, 389–403. Smith, M. S., Thomas, G. W., White, R. E., and Ritonga, D. (1985). Transport of Escherichia coli through intact and disturbed soil columns. J. Environ. Qual. 14, 87–91. Stanley, K. N., and Jones, K. (2003). Cattle and sheep farms as reservoirs of Campylobacter. J. Appl. Microbiol. 94, 104S–113S. Stanley, K. N., Wallace, J. S., Currie, J. E., Diggle, P. J., and Jones, K. (1998). The seasonal variation of thermophilic Campylobacters in beef cattle, dairy cattle and calves. J. Appl. Microbiol. 85, 472–480.


179

Stoddard, C. S., Coyne, M. S., and Grove, J. H. (1998). Fecal bacteria survival and infiltration through a shallow agricultural soil: Timing and tillage eVects. J. Environ. Qual. 27, 1516–1523. Stotzky, G., and Rem, L. T. (1966). Influence of clay minerals on micro-organisms I: Montmorillonite and kaolinite on bacteria. Can. J. Microbiol. 12, 547–563. Straub, T. M., and Chandler, D. P. (2003). Towards a unified system for detecting waterborne pathogens. J. Microbiolog. Meth. 53, 185–197. Sueiro, R. A., Araujo, M., Santos, C. J., Gomez, M. J., and Garrido, M. J. (2001). Evaluation of Coli-ID and MUG Plus media for recovering Escherichia coli and other coliform bacteria from groundwater samples. Water Sci. Technol. 43, 213–216. Tan, Y., Bond, W. J., and M., G. D. (1992). Transport of bacteria during unsteady unsaturated soil water flow. Soil Sci. Soc. Am. J. 56, 1331–1341. Tappeser, B., Jager, M., and Eckelkamp, C. (1998). ‘‘Survival, Persistence, Transfer—An Update on Current Knowledge on GMOs and the Fate of Their Recombinant DNA’’. http://www.biotech-info.net/tappeser_report.pdf. Thelin, R., and GiVord, G. F. (1983). Faecal coliform release patterns from faecal material of cattle. J. Environ. Qual. 12, 57–63. Theron, J., and Cloete, T. E. (2002). Emerging waterborne infections: Contributing factors, agents and detection tools. Crit. Rev. Microbiol. 28, 1–26. Thomas, C., Gibson, H., Hill, D. J., and Mabey, M. (1999a). Campylobacter epidemiology: An aquatic perspective. J. Appl. Microbiol. 85, 168S–177S. Thomas, C., Hill, D. J., and Mabey, M. (1999b). Evaluation of the eVect of temperature and nutrients on the survival of Campylobacter spp. in water microcosms. J. Appl. Microbiol. 86, 1024–1032. Thorpe, I. S., Prosser, J. I., Glover, L. A., and Killham, K. (1996). The role of earthworm Lumbricus terrestuis in the transport of bacterial inocula through soil. Soil Biol. Biochem. 23, 132–139. Thullner, M., Mauclaire, L., Scrhroth, M. H., Kinzelbach, W., and Zeyer, J. (2002). Interaction between water flow and spatial distribution of microbial growth in a two-dimensional flow field in saturated porous media. J. Contamin. Hydrol. 58, 169–189. Tim, U. S., Mostaghimi, S., and Dillaha III, T. A. (1988). ‘‘Modelling the Movement and Persistence of Bacteria and Viruses in Porous Media’’. ASAE Meeting Presentation, Paper No. 88–2627. Tobin, R. S., and Dutka, B. J. (1977). Comparison of the surface structure, metal binding, and fecal coliform recoveries of nine membrane filters. Appl. Environ. Microbiol. 34, 69–79. Trevena, W. B., Willshaw, G. A., Cheasty, T., Domingue, G., and Wray, C. (1999). Zoonotic transmission of vero cytotoxin producing Escherichia coli 0157 infection from farm animals to humans within Cornwall and West Devon, UK. Communic. Dis. Public Health 2, 263–268. Tyrell, S. F., and Quinton, J. N. (2003). Overland flow transport of pathogens from agricultural land receiving faecal wastes. J. Appl. Microbiol. 94, 87S–93S. Unwin, R. J., Pain, B. F., and Whinham, W. N. (1986). The eVect of rate and time of application of nitrogen in cow slurry on grass cut for silage. Agricult. Wastes 15, 253–268. USDA (1997). ‘‘An Update: Escherichia coli 0157:H7 in Humans and Cattle’’. Centers for Epidemiology and Animal Health. Fort Collins, Colorado. Van Donsel, D. J., and Geldreich, E. E. (1971). Relationships of Salmonella to fecal coliforms in bottom sediments. Water Res. 5, 1079–1087. Van Donsel, D. J., Geldreich, E. E., and Clarke, N. A. (1967). Seasonal variations in survival of indicator bacteria in soil and their contribution to storm-water pollution. Appl. Microbiol. 15, 1362–1370. Van Poucke, S. O., and Nelis, H. J. (2000). Rapid detection of fluorescent and chemiluminescent total coliforms and Escherichia coli on membrane filters. J. Microbiolog. Meth. 42, 233–244.

180

D. M. OLIVER ET AL.

Vinten, A. J. A., Lewis, D. R., Fenlon, D. R., Leach, K. A., Howard, R., Svoboda, I., and Ogden, I. (2002). Fate of Escherichia coli and Escherichia coli O157 in soils and drainage water following cattle slurry application at 3 sites in southern Scotland. Soil Use Manage. 18, 223–231. Wang, G. D., and Doyle, M. P. (1998). Survival of enterohemorrhagic Escherichia coli O157:H7 in water. J. Food Protect. 61, 662–667. Wang, G. D., Zhao, T., and Doyle, M. P. (1996). Fate of enterohemorrhagic Escherichia coli O157:H7 in bovine feces. Appl. Environ. Microbiol. 62, 2567–2570. Warnemuende, E. A., and Kanwar, R. S. (2002). EVects of swine manure application on bacterial quality of leachate from intact soil columns. Transact. ASAE 45, 1849–1857. Warnes, S., and Keevil, C. W. (2003). Survival of Cryptosporidium parvum in faecal wastes and salad crops. In ‘‘Cryptosporidium parvum in Food and Water’’ (G. DuVy, Ed.), pp. 15–24. Teagasc, Dublin. Wesley, I. V., Wells, S. J., Harmon, K. M., Green, A., Schroeder-Tucker, L., Glover, M., and Siddique, I. (2000). Fecal shedding of Campylobacter and Arcobacter spp. in dairy cattle. Appl. Environ. Microbiol. 66, 1994–2000. Wilkinson, J., Jenkins, A., Wyer, M., and Kay, D. (1995). Modeling fecal-coliform dynamics in streams and rivers. Water Res. 29, 847–855. Williams, A., Scholefield, D., Dowd, J., Holden, N., and Deeks, L. (2000). Investigating preferential flow in a large intact soil block under pasture. Soil Use Manage. 16, 264–269. Winfield, M. D., and Groisman, E. A. (2003). Role of nonhost environments in the lifestyles of Salmonella and Escherichia coli. Appl. Environ. Microbiol. 69, 3687–3694. Wollum, II, A. G., and Cassel, D. K. (1978). Transport of microorganisms in sand columns. Soil Sci. Soc. Am. J. 42, 72–76. Yao, K. M., Habibian, M. T., and O’Melia, C. R. (1971). Water and waste water filtration: Concepts and applications. Environ. Sci. Technol. 5, 1105–1112. Young, I. M., and Ritz, K. (1998). Can there be a contemporary ecological dimension to soil biology without a habitat? Discussion. Soil Biol. Biochem. 30, 1229–1232. Young, I. M., and Ritz, K. (2000). Tillage, habitat space and function of soil microbes. Soil Tillage Res. 53, 201–213. Zhao, T., Doyle, M. P., Shere, J., and Garber, L. (1995). Prevalence of enterohemorrhagic Escherichia coli 0157-H7 in a survey of dairy herds. Appl. Environ. Microbiol. 61, 1290–1293. Zibilske, L. M., and Weaver, R. W. (1978). EVect of environmental factors on survival of Salmonella typhimurium in soil. J. Environ. Qual. 7, 593–597.

DEVELOPING EXISTING PLANT ROOT SYSTEM ARCHITECTURE MODELS TO MEET FUTURE AGRICULTURAL CHALLENGES L. Wu,1 M. B. McGechan,2 C. A. Watson1 and J. A. Baddeley1 1

Crop and Soil Research Group, SAC, Craibstone Estate, Aberdeen AB21 9YA, United Kingdom 2 Land Economy Research Group, SAC, Bush Estate, Penicuik EH26 0PH, United Kingdom

I. Introduction A. The Future Agronomic Challenge B. Why Model Roots? C. Environment/Root Interactions II. Current Work A. Available Models B. Selected Models III. Model Processes A. General Description B. Branching C. Growth D. Root Architecture E. Data Structure IV. Extending the Scope of Current Models A. Root Mortality B. Interaction with the Environment C. Water Uptake D. Nutrient Uptake E. Photoassimilate Availability and Root Development F. Management V. Structure of an Integrated Model VI. Concluding Remarks Acknowledgments References

Improving our understanding of the relationships between soil conditions and plant growth, both above and below ground, will contribute to the development of cropping systems that are less reliant on mineral fertilizers for crop nutrition. Although many models predicting the flows of nutrients between plants and soil have been developed, few of these deal in detail with 181 Advances in Agronomy, Volume 85 Copyright 2005, Elsevier Inc. All rights reserved. 0065-2113/05 $35.00

182

L. WU ET AL. root architecture and dynamics. In this chapter, we review seven widely cited models of root architecture and development in terms of their ability to improve predictions of plant and soil nutrient flows. We have examined processes related to root system architecture and development, compared mathematical expressions and parameters used in the selected models, and summarized common processes and parameters for simulating root systems. This outcome should benefit researchers and model developers, preventing the need to spend limited resources on repeating the same process. Detailed conclusions include the fact that both inter-branching distance and insertion angle are essential parameters for representing root architecture. Additionally, in a three-dimensional model an extra parameter, radial angle, should be used for determining the location of a branch relative to the root from which it originated. Root growth is simulated by elongation rate and elongation direction, with root component diameter also represented in some models. Almost all the three-dimensional models reviewed calculate the current direction of newly formed root segments using the previous direction of tip extension together with an angle related to geotropism. This review was carried out as the first stage in a research program on integrating root growth models with soil nutrient cycling models. For this purpose, the review suggests that, in order to optimize practical applications of these models in cropping systems, there is a need to integrate a number of additional processes, including root longevity and mortality, environmental responses, and eVects of management such as tillage or the pesticide application regime. The form of root mortality relevant to nutrient cycling in soil is that due to natural senescence of root components. This diVers from catastrophic death of roots due to attack by pathogenic fungi, which has been considered in one existing root model. To achieve the required objectives, there is also a need to strengthen the integration of above-ground plant component dynamics with root system development, particularly in relation to breeding new crop varieties for sustainable ß 2005 Elsevier Inc. agricultural systems.

I. INTRODUCTION A. THE FUTURE AGRONOMIC CHALLENGE Agricultural sustainability is concerned with production of agricultural products over a long period of time. Sustainability ensures that agricultural systems can be operated in such a way that output quantity and quality can be maintained year by year, without degradation of the environment. Global population is anticipated to increase in the decades ahead, although with a declining rate of growth (UN, 2001). A major agricultural challenge will be to achieve a significant increase in agricultural productivity on currently available land, in order to meet the food requirements for this population, but at the same time conserving natural resources. Recent productivity rises

DEVELOPING EXISTING PLANT ROOT SYSTEM

183

have occurred in part due to increase in use of chemicals, new technologies, and mechanization. For example, statistical data for 1961–2001 from the United Nations Food and Agriculture Organization (http://apps.fao.org) show a significant positive relationship between global cereal crop production and nitrogen fertilizer consumption. Although these changes have achieved many positive results in biological, economic, and social terms, there have also been significant costs (Bruinsma, 2003; Norse, 2003; Schaller, 1993). Prominent among these are degradation of soil structure (Jordahl and Karlen, 1993; Pimentel et al., 1995), contamination of water with physical, chemical, and biological pollutants (Carpenter et al., 1998; Foster et al., 1986; Goulding, 2000; Peterson et al., 2001; Smith et al., 1999; Vitousek et al., 1997), and gaseous emissions to the atmosphere (Bobbink et al., 1998; Galloway, 1995; Jenkinson, 2001). It is an ongoing challenge to develop and demonstrate management practices that increase the sustainability of agricultural systems. In Europe, following the Mid-Term Review of the Common Agricultural Policy, it is likely that future agriculture will bifurcate into high-throughput systems producing major food commodities, and more extensive systems primarily focused on the provision of environmental goods. Management practices and the use of agrochemicals will diVer widely under these two scenarios. The second option includes organic production (currently 4% of agricultural land in the United Kingdom [Soil Association, 2003]). Accurate assessment of the long-term consequences of environmental perturbation is very important for the sustainable use of agricultural resources. Long-term agroecosystem experiments at various locations worldwide have attempted to evaluate biological, biogeochemical, and environmental changes associated with agricultural systems over time periods of 100 years or more (Steiner, 1995), e.g., the Broadbalk Experiment at the Rothamsted Experimental Station, UK (van Bergen et al., 1997), the Sanborn Field experiment at the University of Missouri, USA (Upchurch et al., 1985), and the Old Rotation Study at Auburn University, USA (Mitchell and Entry, 1998). Although providing valuable data, these sites cannot represent all environmental, economic, or biological conditions occurring throughout the world. The augmentation of such site-specific, empirical information by process-based knowledge oVers the potential for predicting the sustainability of agricultural systems in a wider range of environments. This includes the diagnosis of problems, prescription of alternative ways of improving agroecosystem performance, and quantification of sustainability in terms of productivity, stability over time (the constancy of production over time), resiliency to disturbance, and equitability of benefits derived from an agroecosystem. An eVective sustainable agriculture system strives to develop a farming strategy that optimizes management practices such that crop productivity is

184

L. WU ET AL.

maintained while adverse environmental impacts are reduced. Field evaluations of nutrient management practices are cumbersome, error prone, complicated, and expensive. It is often more eYcient to study agroecosystem performance using computer models than to experiment with the system itself. Models can simulate and evaluate a range of nutrient management scenarios and aid in evaluation of alternative chemical (in terms of application rate, source, and timing) and field management practices, representing an eYcient and cost-eVective alternative to field evaluations. With the development of information technology and computer hardware, increasing numbers of simulation and other mathematical models have been developed (Grant, 1997; Johnsson et al., 1987; Parton et al., 1987; Williams and Renard, 1985) and applied to the analysis of productivity and sustainability of complex agricultural systems (Jones et al., 1991).

B. WHY MODEL ROOTS? Root systems are central to the acquisition of water and nutrients by plants (Fitter et al., 1991) but are also a major pathway for the input of carbon and nutrients to soil (Persson, 1978; Ruess et al., 1996; Vogt, 1991). Roots have often been described as the ‘‘invisible’’ or ‘‘hidden’’ parts of a plant (Waisel et al., 1996; Weaver, 1926). Despite their obvious importance, much less is known about the dynamics of live roots than about aboveground plant organs. This is because roots grow in soil from which they cannot be extricated or readily observed without destroying both individual roots and the overall architecture of the root system. Plant root systems are complex structures that exist in a spatial and temporal mosaic of resource availability. Attempts have been made to describe the spatial deployment of root systems through the characterization of root architectural parameters by various schemes (Fitter, 1982, 1986; Hackett, 1968). Although valuable, these are spatial descriptions only of roots spread out in two dimensions and at one point in time. In reality, three dynamic root system processes, namely production, extension (growth), and mortality, contribute to the appearance, transformation, and disappearance of root systems in both space and time. Due to the inaccessibility of root systems, special techniques are required to investigate the standing stock, distribution, and turnover. Traditional descriptions of root architecture and characteristics have been made by destructive techniques, such as monolith washing, soil coring, or trench wall methods (Bledsoe et al., 1999; Bo¨hm, 1979; Milchunas et al., 1992). These methods obtain a snapshot of root systems at a specific time. Root systems, however, are plastic and interact dynamically with physical, chemical, and biological factors in the soil that vary in time and space. If a


185

destructive sampling method is used, it is diYcult to track root system developments such as root growth rate and direction, mortality, and branching. Furthermore, it is unable to distinguish a static, unchanging root system from one that is highly dynamic with production balanced by disappearance. Nondestructive, observational techniques using rhizotrons or minirhizotrons provide a nondestructive, in situ method of monitoring the production, growth, and mortality of individual roots over time (Bland and Dugas, 1988; Tierney and Fahey, 2001; van Noordwijk et al., 1985; Watson et al., 2000). Understanding root demography is central to the study of plant growth and development, carbon and nutrient cycling, and water movement within the plant/soil system. The complexity of both biotic and abiotic interactions, combined with stochastic changes in root architecture, makes it diYcult to understand below-ground dynamics on the basis of experimentation alone. Goss and Watson (2003) highlighted the need to refine cropping system models to take account of root dynamics. Models can be used to predict root system architecture in various plant species and to investigate the appropriateness of hypotheses employed in a model by comparing simulated and observed root system morphologies. They can also be used to simulate synchronized development of a root system, and (in conjunction with uptake models) to simulate soil water and/or nutrient uptake behavior. Attempts have been made to simulate the branched, hierarchical nature of plant root systems and plant–environment interactions mathematically (Clausnitzer and Hopmans, 1994; Diggle, 1988a; Dunbabin et al., 2002; Fitter et al., 1991; Hackett and Rose, 1972; Lungley, 1973; Lynch et al., 1997; OzierLafontaine et al., 1999; Page`s et al., 1989). Following an earlier review of the physiological processes of root development by Coutts (1987), Page`s (1999) reviewed some of the underlying principles of processes that must be incorporated into models of root development and architecture. Page`s (1999) concluded that the common basis for the models is that they are phenomenological models that translate and combine morphogenetic rules. Some of the models also consider the eVects of environmental factors on assimilation rate. Experimentation can help refine models, and then simulated results can be used to test and improve the hypotheses upon which the models depend, and in turn add to our understanding of plant processes and functions as well as suggesting knowledge gaps that require further experimentation.

C. ENVIRONMENT / ROOT INTERACTIONS Field and laboratory experiments have demonstrated the complex interactions between the production, growth, and mortality of individual roots and local environmental factors surrounding the plant. Abiotic factors such

186

L. WU ET AL.

as soil temperature (Forbes et al., 1997; Gavito et al., 2001; Vincent and Gregory, 1989; Watson et al., 2000), soil water content (Brissette and Chambers, 1992; Chiatante et al., 1999; Derner et al., 2001; Huck et al., 1983; Joslin et al., 2000), and mechanical resistance from the soil (Bengough and Mullins, 1990; Bingham and Bengough, 2003; Laboski et al., 1998; Misra and Gibbons, 1996) have an important role in root development. Huang et al. (1991) concluded from their experiment with wheat seedlings (Triticum aestivum L.) that the length and dry matter production of both seminal and crown roots increased gradually to a maximum as the temperature increased to 25 8C, but then declined as it rose to 30 8C. Biotic factors, including arbuscular and ecto-mycorrhizal colonization and infection by fungi, some of which are pathogenic (Forbes et al., 1996; Hooker et al., 1992; Niemi et al., 2002), also aVect root architecture, functions, and mortality. Carbon (C) and nitrogen (N) status in plant organs and soil (Bingham et al., 1997; Boukcim et al., 2001) and previous root system architecture also influence root development (Pulgarin et al., 1988). The proportion of CO2 in the atmosphere is currently rising to unprecedented levels, and this will have direct eVects on plants, including their root systems. Elevated atmospheric CO2 may increase root biomass and root length density (Derner et al., 2001; Fitter et al., 1997; Newton et al., 1994), elongation rate (Drennan and Nobel, 1996), and the level of root exudation (Norby et al., 1987), as well as stimulating the branching process (Berntson and Woodward, 1992). King et al. (1997) investigated morphology and tissue quality of seedling root systems of Pinus taeda and Pinus ponderoa aVected by varying levels of CO2, temperature, and nitrogen supply. They found a large increase in root length under elevated CO2, with increasing temperature and nitrogen supply giving further increases in root length.

II. CURRENT WORK A. AVAILABLE MODELS A root system, considered as a collection of sources and sinks, is simulated as a submodel in many larger models that describe either crop– environment relationships or matter transfer (water, carbon, or nutrients) in the soil–plant–atmosphere continuum (SPAC). In such cases, root biomass, as a proportion of plant biomass, root density distribution (in space), and root length density distribution, are all used to control water and/or mineral uptake. A secondary assumption also needs to be made: that the spatial distribution of the roots is homogeneous in the soil layer and the uptake is similar among all roots. Root distribution is assessed in terms of the


187

penetration and proliferation of roots down to the penetrated depth, with consideration of the eVects of one or more environmental factors on root growth (Asseng et al., 1997). There are some models that describe temporal development of root distribution as a diVusion process (Acock and Pachepsky, 1996; de Willigen et al., 2002; Gerwtiz and Page, 1974). For example, Acock and Pachepsky (1996) developed a two-dimensional convective-diVusive root system model in which the proliferation and growth of roots in all directions are considered to result from a diVusion-like gradient, whereas the convection-like propagation of roots downward is perceived to be caused by geotropism. Mmolawa and Or (2000) reviewed some expressions for these parameters as applied to root-zone solute dynamics under drip irrigation. Because root growth diVers in terms of direction, spacing, elongation rate, and functional activity, such assumptions represent an oversimplification (Rengel, 1993). These models are not discussed here, as they ignore root architecture. With the development of computer hardware and software, various root system architecture models have been developed over the last three decades. Pioneering work in the simulation of root systems was carried out by Lungley (1973). Some models purely simulate root static structure (Henderson et al., 1983), root system growth and development in two dimensions (Lungley, 1973; Porter et al., 1986; Rose, 1983), or root architecture in three dimensions (Bernston, 1994; Diggle, 1988a; Fitter et al., 1991; Page`s et al., 1989). Other models involving the root system relate to water uptake (Clausnitzer and Hopmans, 1994; Doussan et al., 1998; Tsutsumi et al., 2002), nutrient uptake (Grant and Robertson, 1997), and uptake-dependent growth (King et al., 2003; Somma et al., 1998). There are diVerent approaches to the description of root systems in the models. The most common is topology of the branching process (Acock and Pachepsky, 1996; Clausnitzer and Hopmans, 1994; Diggle, 1988a; Fitter et al., 1991; Hackett and Rose, 1972; Lungley, 1973; Lynch et al., 1997; Page`s et al., 1989). Roots are classified according to branching order, and each order has its own characteristics in terms of growth rate, life span, and branching ability. Fractal geometry has also been used in connection with root architecture simulation (Ozier-Lafontaine et al., 1999; Shibusawa, 1994). In this method, the network of a root system is described as being selfsimilar or following scale-invariant branching rules. This is achieved by deducing properties of the entire root system from basic rules governing individual bifurcations and the geometry of each segment or branch. A stochastic (as opposed to deterministic) approach has also been practiced for the description of root system architecture and development (Jourdan and Rey, 1997). Stochastic processes (e.g., automata, probability, and graphic models) have been used to simulate the topology of branched structures and root development (growth, mortality, and branching).

188

L. WU ET AL.

Because biological hypotheses are not quantified, such a model is purely descriptive. The current chapter builds on an earlier review (Page`s, 1999) to present an in-depth, systematic review of individual models and their equations. At this level of detail, it becomes clear that published models often use diVerent terminology to describe processes of root growth and development, and a number of diVerent mathematical expressions have been used to describe the same process. It has been found to be helpful, when considering further development of such models, to formalize the process descriptions and to distinguish those that are essential to the main processes of the models. In this chapter, seven existing models are discussed and compared. These models have been developed in various diVerent areas of the world, cover a range of diVerent plant species, and have been frequently cited in the literature. Although some of the reviewed models have submodels to simulate water flow, nutrient transport, and carbohydrate allocation to various plant components, we limit the analysis here to processes directly associated with the root system.

B. SELECTED MODELS The seven models reviewed here are listed under abridged headings (either the model name or the organization at which the model was developed); they will be referred to henceforth using these names.

1.

WAITE Model

The WAITE model is a pioneering two-dimensional numerical dynamic model that was developed at the Waite Agriculture Research Institute (WAITE), Australia (Lungley, 1973). Simulation is based on individual roots, and all parameters are kept constant for the whole simulation period. Branching is restricted to the laterals of first and second order. Computations are performed in discrete time steps (1 day), and each root tip grows individually for the entire duration of the simulation. It is interesting to note that most of the recently published root simulation models largely follow this approach.

2.

ROOTMAP

ROOTMAP is a static morphogenetic three-dimensional model of the growth and structure of fibrous root systems (Diggle, 1988a). Lengths and locations of each root segment and location and age of each root tip and


189

each branch in the system are explicitly calculated. The growth of all emerged root tips is simulated concurrently. In this model, soil conditions and root growth, together with branching responses to those conditions, remain unchanged during the course of a simulation. All growth rates and development times are temperature dependent. Growing roots are tracked by keeping a separate record for each root tip and each branch in the root system. The model has recently been developed to allow simulated root systems to respond to the supply of water and nutrients in the soil environment (Dunbabin et al., 2002).

3.

INRA Model

The INRA model is a three-dimensional architecture model of the maize root system that was developed in France by INRA (Institut National de la Recherche Agronomique) (Page`s et al., 1989). It simulates root architecture in discrete time steps in terms of three basic processes: emergence of new root axes from the shoot, extension, and branching. It takes into account the kinetics of emergence of a specific primary root, with geometric representation of its location. The emergence and location of branches are estimated only by spatial parameters. The model has been further extended to assess the influence of assimilate availability on root growth and architecture (Thaler and Page`s, 1998) and to simulate water uptake by root systems (Doussan et al., 1998).

4.

York Model

The York model is a topological three-dimensional root growth model that was developed at the University of York (Fitter et al., 1991). It takes a ‘‘link’’ as the basic unit of root system classification, simulating the development of root systems using topology, branching angles, diameters, and link lengths. Branching probabilities have been applied to determine branching nodes. The model can estimate root system magnitude (the number of external links), ‘‘altitude’’ (the number of links in the longest single path), and the exploitation eYciency of each root system.

5.

Davis Model

The Davis model is a three-dimensional simultaneous dynamic simulation of root growth, soil water flow, solute transport, and uptake from the University of California, Davis (Clausnitzer and Hopmans, 1994). The

190

L. WU ET AL.

model is linked to three-dimensional transient soil water flow and solute transport submodels based on the finite element method. Root age has an eVect on root water and solute uptake, and influences of nutrient deficiency or excessive nutrient concentration on root growth are included. The model also considers feedback functions for water uptake, as well as shoot production of assimilate required for root growth. When simulating root growth, one of three levels of complexity concerning treatment of transpiration and root water uptake must be selected. Each has diVerent requirements in terms of both input parameters and specification of the form of interaction between root and shoot growth and environmental factors. Somma et al. (1998) expanded this model further by considering solute transport, nutrient uptake, and the interaction between plant growth and nutrient concentration.

6. SimRoot SimRoot, the simulation and visualization of root systems (Davis, 1993; Lynch et al., 1997), focuses on the data structure of root segments produced by a specified growth model. In addition to its function as a simulation model, it can also be considered a platform for visualization of a root system in three dimensions. The parameters for root growth are stored in a data structure consisting of several components, while the output from the model is stored in an ‘‘extensible tree’’ data structure. When operating in an optional ‘‘solid rendering’’ visualization mode to display the root system, the diameter of each root is determined on the basis of its position along the root axis, order of the root, nutrient concentrations, etc. The incorporation of kinematic functions in the model can explicitly treat spatial heterogeneity of physiological processes in the root system. A simple carbon cost function is also incorporated, based on measurements of respiration, C exudation, and biomass deposition along root axes for Phaseolus vulgaris seedlings under laboratory conditions (Nielsen et al., 1994). The model has since been modified to include factors involved in competition among multiple root systems (Rubio et al., 2001).

7.

Frac-Root Model

The Frac-Root model is a static three-dimensional model of a root system based on fractal theory, which is also from INRA, France (Ozier-Lafontaine et al., 1999). The model was based on self-similarity and ‘‘pipe model’’ assumptions, together with observations of topology, branching rules, link length, and diameter, as well as root orientation. The root length, diameter, and angle between proximal roots are input parameters. Additionally, the


191

proportionality factor between total cross-sectional area of a root before and after branching and an allocation parameter for partitioning biomass between the new segments after a branching need to be predefined. Because the model fully considers the fractal character of the root system, the model is time independent, and hence no root elongation rate is considered.

III. A.

MODEL PROCESSES GENERAL DESCRIPTION

All the models describe the dynamics of a root system by considering root architecture and developmental processes. Developmental schemes categorize roots into several types, and the terms used to describe roots in this chapter are defined below. A root is called an axis when it has developed from the seed (seminal axis) or the stem (nodal axis). Roots arising from the axis are designated first-order laterals; those branching from first-order laterals are designated second-order laterals, etc. Although the definition of orders and the number of categories vary from model to model (Table I), the topological structure is common to all models. Axes and the first two orders are taken into account in most of the models reviewed here.

Table I Root System Terminology Used in the Reviewed Modelsa

Model

No.

Axis

WAITE model

1

Axis

ROOTMAP

2

INRA model

3

York model

4

Seminal axis and nodal axis First-order (or primary) axis Axis

Davis model SimRoot

5 6

First-order Primary root

Frac-Root model

7

Proximal root

First-order lateral

Second-order lateral

First-order lateral First-order lateral Second-order axis Primary lateral Second-order Secondary root First-order

Second-order lateral Second-order lateral Third-order axis Second lateral Third-order — Second-order

a In order to facilitate discussion in the review, the varying terms used by diVerent model authors have been replaced by a common set of terminology, as shown in the column headings.

192

L. WU ET AL.

Figure 1 General parameters used to describe root architecture. Each root is made up of an axis and laterals (several orders). Apical non-branch distance, basal non-branch distance, and inter-branch distance are used to specify emergence of higher order laterals.

Laterals emerge from each member of the next lower order in acropetal sequence. The youngest lateral is generally separated from the apex by an apical non-branch distance (called an external link in the York model); the oldest lateral is normally located from the base point by a basal non-branch distance (Fig. 1). The orientation of a newly emerged lateral is controlled by two variables: the insertion angle and the radial angle. The insertion angle is the angle between the mother root and the branch in the plane containing the two roots. The radial angle is the angle between the branch direction and a specified reference direction (analogous to north on a map), in the plane perpendicular to the mother root.

B. BRANCHING The branching process can be split into two parts for ease of simulation: branching position on a root and branching orientation. The former focuses mainly on the position of the new branch and the number of nodes that can


193

Table II Parameters Used in the Models to Describe Branching Position and Branching Orientation

Model no. 1 2 3 4 5 6 7

Length of apical nonbranching zone u u u

Length of the basal nonbranched zone

u

u

Inter-branch distance

Insertion angle

u u u u u u u

u u u u u u u

Radial angle

Total number of xylem poles

u u u

u u

u u

u

be created on an axis or a lateral root. The latter calculates the emergence direction of a new branch. The parameters used in the models and their values (if applicable) relating to branching position and orientation are summarized in Table II.

1.

Branching Position

All models treat the expansion of the branched zone in a strictly acropetal way. The parameter inter-branching distance is unanimously considered in all the models, either as an input parameter or as a parameter to be estimated during simulation. In the Frac-Root model, link length is calculated from experimentally derived relationships between the distance, the diameter, and the order of the segment. Four of the models take the length of the apical non-branching zone as a parameter. Only the INRA model sets the basal non-branch zone of a root as a parameter. The WAITE model, ROOTMAP, and the Davis model simply treat it as an inter-branch distance (Tables II and III). If all three parameters related to the branch position are included in a model, the number of branches on a root (Nb) is expressed as 8 > Ll < ðLanz þ Lbnz Þ þ1 Ll ðLanz þ Lbnz Þ; : Lib ð1Þ or alternatively, if only the distance of apical non-branch zone and interbranch distance are considered (so Lbnz ¼ Lib), then

194

Table III Parameter Values in the Models for Branching Position and Branching Orientationa

Species

1 Small grain cereal

Length of apical non-branching zone (cm) Axis 15.00 First-order lateral 5.00 Second-order lateral — Third-order lateral —

2 Wheat

6.00d 2.80d 0.45d 5.00d

Length of the basal non-branched zone (cm) Axis First-order lateral Second-order lateral Interbranch distance (cm) Axis First-order lateral Second-order lateral Third-order lateral

0.40 2.50 — —

3

5b

Maize

Barley

15.00 2.00 — —

100.0 (hr) 150.0 (hr) — —

6c

7

Caropca bean

Leguminous tree Gliricidia sepium

0.70 0.70 — 0.33 0.33 1.00 100.0

0.70 0.70 — —

0.30 0.30 — —

0.5 (0.6) —

Calculated

L. WU ET AL.

Model no.

Insertion angle (degree) First-order lateral Second-order lateral

Total number of xylem poles Axis First-order lateral Second-order lateral a

90 90 Random

63 63

90 90

90(75) —

Changeable

40e 72e

N/A

Changeable

9 5 —

4(4) — —

Values for the York model are not available. Parameter values selected for dynamic assimilate allocation to shoot and root with transpiration rate dependent on current leaf area. c Based on Ge et al. (2000). Values not in parentheses in the axis row are for the taproot, and those in parentheses are for basal roots that arise from the taproot. d Values are estimated based on unimpeded growth rate at 158C and apical non-branching at 15 8C. e Values are calculated based on total number of xylem poles with Eq. (5). b


Radial angle (degree) First-order lateral Second-order lateral

90 90

195

196

L. WU ET AL.

8 > :

Ll < Lanz Lanz Lib

Ll Lanz ;

ð2Þ

where (in both cases) Ll is the length of a lateral root, Lanz is the length of the apical non-branching zones of a lateral root, Lbnz is the length of the basal non-branching zones of a lateral root, and Lib is the inter-branch distance along the lateral root. The symbol b c is a mathematical function representing the largest integer that is less than or equal to the quotient. Potential nodes where branches might arise have been introduced into the York model. This allows variations in inter-branch distance and the length of the apical non-branch zone during simulation. The actual mean interbranch distances are always greater than the minimum value as set by the user. In contrast to other models, inter-branch distance and apical nonbranch zones are outputs from the model rather than input parameters. The probability of generating branches from a given potential node (derived from Fitter, 1987) is calculated as pðvÞ ¼ v max P

e e

bc v

;

ð3Þ

bc ðiþ1Þ

i¼0

where p(v) is the probability of branch generation, v is the root order of the potential node (v ¼ 0 for the axis), and bc is a branching coeYcient. When bc approaches zero, branching becomes equiprobable at all nodes. The Frac-Root model sets the positions of new branches by the relationship between link length and root diameter. The mean inter-branch distance for a given root order (Lib , cm) is calculated as ¯ þ 21:827; Lib ¼ 6:5136 ln ðdÞ

ð4Þ

where d¯ is the mean root diameter (cm) for a given root order. 2.

Branching Orientation

The number of parameters required to determine branch orientation varies in the models, depending on the number of dimensions considered in a particular case. The WAITE model assumes that branch distribution has radial symmetry in a vertical plane, a specified slope being assigned to each segment produced in a discrete time step. For the other six models, insertion angle (vertical angle or branch angle) has been used. The parameter radial angle (relative to the horizontal, azimuth), is included in the INRA, York, SimRoot, and Frac-Root models. In the Frac-Root model, the angles


197

are chosen randomly, depending on the diameter of the previous link, and a negative radial angle is used to represent the angle for branch orientation (Table III). In some models, an additional parameter, the total number of xylem poles, is used to derive the radial angle. The INRA, York, SimRoot, and Frac-Root models share the same algorithm and use both the radial angle and the total number of xylem poles to determine the branch orientation. The radial angle g in the INRA, York, and SimRoot models is expressed as follows: g¼

2pN ; X

ð5Þ

where X is the total number of xylem poles and N is a randomly chosen integer between 1 and X. Use of a transformation matrix for the angles indicated the direction of root branching in the SimRoot model. The Davis model and ROOTMAP use only the insertion angle, modified from its initial value by a random variation within a pre-defined range. Also in ROOTMAP, the initial value of the insertion angle is always 908.

C. GROWTH To describe the growth of a root system dynamically, the elongation rates of various root types need to be known. The general quantitative expression of growth rate, Va, has the following pattern: Va ¼ Vp fðT; W ; S; UÞ;

ð6Þ

where Vp is potential (maximum) growth rate for various root categories, f(T, W, S, U ) is a response function for soil temperature (T ), soil moisture (W), mechanical resistance (S), and nutrient status of the root (U ). The function could be derived from the combination of individual eVects in various ways. Among the models reviewed, only the Davis model links elongation rates to a range of environmental factors, but ROOTMAP includes responses to soil temperature. Parameters, together with their values, used to describe elongation rates in the models are listed in Table IV. Elongation rates were set for diVerent root types as inputs in the models. In the York model, the growth rate of the primary axis is specified as an input (V0), together with fractions of that value for all higher order roots, in order to be comparable with some published experimental results (May et al., 1965; Schuurman and de Boer, 1970):

198

L. WU ET AL. Table IV Parameter Values for Root Elongation Rate in the Modelsa

Model no. Species Primary growth (cm/d) Axis First-order lateral Second-order lateral Third-order lateral

6b

1

2

3

5

Small grain cereal

Wheat

Maize

Barleyc

Caropca bean

2.00 0.50 0.10

2.00 0.40 0.15 0.05

2.00 change change —

2.40 0.48 0.19 —

1.18 (2.04) 0.38 0.38 0.38

Maximum branch length (cm) Axis First-order lateral Second-order lateral Soil resistance parameter Reference temperature (8C) Temperature of zero growth (8C)

200.0 200.0 200.0 1.0 15 0

a

Values for the York model are not available. Based on Ge et al. (2000). Value not in parentheses in the axis row is for the taproot, and that in parentheses is for a basal root that arises from the taproot. c Parameter values selected for dynamic assimilate allocation to shoot and root with transpiration rate dependent on current leaf area. b

8 V0 > > < 2 Va ðvÞ ¼ > Va ð1Þ > : ðv 1Þ2

ðv ¼ 1Þ ðv 2Þ:

ð7Þ

For the INRA model, the growth rate of primary roots is set as a constant value of 2.0 cm d 1, and the root growth rates for the higher-order roots are expressed as Va ¼ Vp e

ki

;

ð8Þ

where t is the age of the root meristem in days and Vp is the potential elongation rate, which was set at 6.4 and 1.5 for first-order and secondorder laterals, respectively. The constant k was set at 0.8 for both orders. The ROOTMAP model assumes a temperature function previously used by Porter et al. (1986), in terms of a time parameter similar to commonly used soil degree-days (SDD). Elongation rates are adjusted by this time parameter. The time parameter ( ft, d 1) is defined by the following linear relationship, incorporating local actual soil temperature (T, 8C) when it falls


199

within the range between the temperature of zero growth (Tmin, 8C) and a reference temperature (Tref, 8C): fl ¼

Tref Tref

T T min

:

ð9Þ

In the Davis model, the actual root elongation rate (Va, cm h 1) is obtained by multiplying the unimpeded elongation rate (Vp, for a given root age and root order, cm h 1) by three response functions: a soil strength factor (imps), a soil temperature factor (impt), and a soil water solution concentration factor (impc): Va ¼ Vp ipms ipmt ipmc

ð10Þ

and ipms ¼

8 < 0

: 1:0

s smax s smax

s < smax ;

ð11Þ

where smax (MPa) is the soil strength at which growth ceases completely and s is the current soil strength:

impt ¼

8 0 > > > > > > T Tmin > > sin p > > Tmax Tmin >
> > > T Tmax > > sin p > > Tmax Tmin > > > :

T > Tmax or T < Tmin 1 Topt ðTmin þ Tmax Þ 2 and Tmin T Tmax 1 Topt > ðTmin þ Tmax Þ 2 and Tmin T Tmax ;

ð12Þ

where

¼

8 ln0:5 > > > > T > < ln T opt TTmin max

1 Topt ðTmin þ Tmax Þ 2

min

> ln0:5 > > > > T : ln max Topt Tmax Tmin

1 Topt > ðTmin þ Tmax Þ 2

ð13Þ

and Topt (8C) is the optimum temperature at which the temperature function is unity, Tmin (8C) is the lower threshold temperature below which the

200

L. WU ET AL.

response is zero, and Tmax (8C) is the upper threshold temperature above which the response is zero (Fig. 2A):

impc ¼

8 0 > > > > c > > > < c

optl

c < cmin ; c > cmax cmin cmin

> 1 > > > > cmax c > > : c max coptu

cmin c < coptl coptl c < coptu coptu c cmax ;

ð14Þ

where c (mmol) is the current soil water solution concentration, cmin and cmax are minimum and maximum concentrations for plant growth, and coptl and coptu are lower and upper limits of the optimum concentration range for plant growth (Fig. 2B).

Figure 2 Response functions to temperature (A) and nutrient concentration (B) in the Davis model. The temperature response curves are based on the following parameter values: Tmin ¼ 0:08C; Tmax ¼ 40:08C; Topt ¼ 15:0 ½Topt < 1=2ðTmax þ Tmin Þ; 20:0 ½Topt ¼ 1=2 ðTmax þ Tmin Þ; 28:08C ½Topt > 1=2ðTmax þ Tmin )]. The nutrient concentration curve is plotted with cmin ¼ 0:005; cmax ¼ 2:0; coptl ¼ 0:1, and coptu ¼ 1:5 (mmol cm 3).


201

A kinematic approach was adopted from Silk et al. (1986) to calculate the local relative rate of root volume change (dV) along a root axis: dV ¼

@vz 2vz @r þ ; @z r @z

ð15Þ

where r is root radius, z is distance from the root tip, and vz is the longitudinal growth velocity. The first term on the right-hand side represents the local relative elongation rate, and the second term represents the radial and tangential components of the relative growth rate.

D. ROOT ARCHITECTURE 1.

Elongation Direction

In the WAITE model, the root distribution assumes radial symmetry, with roots constrained to grow in short, straight segments, each segment having a specified slope. The slope of each root varies over the time course. In all three-dimensional models, the elongation direction of a growing root tip is based on at least two parameters: the previous elongation direction of the tip over the last time step, and an angle related to geotropism. The INRA model uses an additional, third parameter to represent mechanical constraints, either as a random value or as a user specified value (Table V). In the ROOTMAP model, the growth direction of a root tip is determined stochastically from a deflection angle (f, 8) from the previous direction, and a deflection orientation (u, 8), defined as the angle between the elongation direction and the vertical. These two angles are calculated on the basis of user-defined values of the probability of occurrence (p) and two exponents (indices). f ¼ 360

pI1 d 2

and

1

u ¼ 180 p1 Ig ;

ð16Þ

where Id is the deflection index and Ig is the geopropism index, both with a range of 0–1. Both these indices have to be specified for each branch order. The York model allows the branch angle to decline progressively to a user-defined final value to represent dynamic changes in growth direction. Also, the value can be randomized by specifying a percentage range around the existing value of branch angle by which it can vary, for any growing tip. ! In the INRA model, growth direction (D i ) is computed using three directional components: the initial direction of the root at the previous

202

L. WU ET AL.

Table V Parameters and Their Values to Determine the Direction of Root Growth in Some Modelsa Model no. Species

2

3

Wheat

Maize

Mechanical constraint in soil profile 25 cm depth Below 25 cm

Barleyc

6b Caropca bean

0.02 0.08

Soil strength gradient (cm/MPa) Axis First-order lateral Second-order lateral Deflection index(D) Axis

5

0.2 1.0 2.0

0.3

1.0

First-order lateral Second-order lateral

0.3 0.3

1.0 1.0

Geotropism index (G) Axis First-order lateral Second-order lateral

0.6 0.0 0.0

0.004d 0.05d 0.00d

Random within max. 458

N/A

1.0 0.0 0.0

1.00 (0.80) 0.25 —

a

Values for the York model are not available. The elongation direction in the Frac-Root model is chosen randomly from the known actual angle distribution that depends on the diameter of the previous link. b Based on Ge et al. (2000). The value not in parentheses in the axis row is for the taproot and that in parentheses is for a basal root that arises from the taproot. c Parameter values selected for dynamic assimilate allocation to shoot and root with transpiration rate dependent on current leaf area. d Values for seminal root and roots arising from it.

! ! time step (D i 1 ), a vertical vector representing geotropism ( G ), and a vector ! representing mechanical constraints ( S ). The length of the first vector is set at 1, and the last two are expressed as the product of the elongation during the current time step and weighting factors. ! ! Di ¼ Di

1

! ! þS þG

ð17Þ

The Davis and Frac-Root models have the same procedure as the INRA model for calculating growth direction. However, in the Davis model, a ! limited random deviation D i 1 is applied to give an approximate representation of the space-exploring nature of root system growth (Somma et al., 1997). The Frac-Root model calculates the elongation direction of a new


203

growing tip by choosing an angle at random from their known actual distribution, dependent on the diameter of the previous link, an insertion angle, and a positive radial angle. In the SimRoot model, the direction of a new segment is determined in a separate submodel based on the direction of the previous segment. It can be modified in order to take account of geotropism and a randomness factor.

2.

Dynamics of Root Diameter

In order to determine the influence of root architecture on root functions and the volume of soil available for exploitation of resources, root diameter (or radius) was estimated in some three-dimensional models. In the York model, the radius of a growing tip is predefined by the user; the radius (r, cm) of any segment other than a growing tip is calculated in terms of the link magnitude, m (the number of growing tips derived from the segment being considered) (Fitter, 1987). r ¼ 0:2 þ 0:01m:

ð18Þ

In the Frac-Root model, at a given node that is generating new links, the diameter of a root of the same order (dl, mm) is calculated as dl2 ¼

b 2 d ; a bb

ð19Þ

where b and a are allocation and proportionality factors and dbb (mm) is the diameter of the previous segment (link). The diameter of higher order roots is estimated as dh2 ¼

1 b d2 ; ðn 1Þa bb

ð20Þ

where n is the total number of new segments generated at a given node, and the distribution of this number at any branching event is assumed to follow a uniform distribution. In the SimRoot model, simulation of root radius is built into the root growth model. Its radius (r) is correlated to the length of the root (L) from the root tip (Ge et al., 2000). r ¼ a L 2; where a is the root radius growth coeYcient.

ð21Þ

204

L. WU ET AL.

E. DATA STRUCTURE Data structure is crucial when manipulating large databases in which simulation results are stored to reproduce root architecture topologically and visually. Most of the models in our selection use an ASCII format file to store root system simulation output. Each record represents a root segment, including segment location, segment length, connections, branch order, etc. The SimRoot and ROOTMAP models use special data structures to save simulated results. SimRoot has an optimized data structure (extensible tree structure) to store simulated results. For each node in the data structure, four pointers are topologically defined to point to its ‘‘parent,’’ its ‘‘first-generation children,’’ and its ‘‘right and left siblings.’’ Meanwhile, information on the direction of growth and the length and spatial origin of the segment grown during consecutive time steps is kept for each node. This allows visualization of root architecture and operation of the database to be carried out more rapidly than with other approaches. The ROOTMAP model uses a binary tree data structure to store information about root systems. In this structure, each root tip record and each branch record are treated as nodes, and for each branch record there are four pointers (Diggle, 1988b), as follows: 1. One pointing to the root tip of the same order as itself, 2. One pointing to the root tip that originated at the branch record, if it exists, 3. One pointing to the next, younger branch record in the direction of the root tip of the same order, if that branch record exists, and 4. One pointing to the next branch record in the direction of the root tip of the succeeding order, if it exists.

IV. EXTENDING THE SCOPE OF CURRENT MODELS The models reviewed here describe root architecture as it arises from root growth processes such as extension and branching. Root diameter is also described in some of the models. Although the WAITE model is twodimensional with constant parameter values, its diagrammatic description of root architecture is adopted in almost all models developed subsequently. Inter-branching distance and insertion angle are two essential parameters for reproducing root architecture, whether a model is two- or three-dimensional. In a three-dimensional model, an extra parameter, radial angle, must be known in order to define the location of a branch relative to the root from


205

which it originates. Almost all the reviewed three-dimensional models adopt the same algorithm as the INRA model to calculate the current direction of a newly formed root segment, in relation to the previous elongation direction of the tips and an angle related to geotropism. The models include some variation in treatment of the randomness of this direction. Most reviewed models mimic the morphogenetic program to simulate the topological characteristics of the root systems. This review of the development of existing models shows the need for extending the scope of root modelling to consider a number of additional factors. This will facilitate the use of these models to describe real situations.

A. ROOT MORTALITY Root mortality processes lead to the disappearance of roots from a live root system following an aging phase. The process of root mortality has a very large and important influence on nutrient cycling, especially in perennial plants. It causes dynamic changes in both total root volume and root system architecture. Simultaneously, dead root material is added into an organic matter pool for subsequent decomposition. Unfortunately, the mortality process is ignored or simplified in current versions of nutrient cycling models, perhaps because of the complexity of the senescence process. The reviewed models were generally executed for only quite short periods compared to the life cycle of plants, which may have avoided the need to consider senescence. In this chapter we are concerned with the factors influencing the turnover of the fine root pool, rather than cases of catastrophic root death induced by, for example, pathogenic fungi (Brown and Kulasiri, 1994) or harvesting of cereal crops. Individual fine roots tend to have relatively short lifespans and may account for as much as 33% of global annual net primary productivity (Jackson et al., 1997). Interactions between root mortality and endogenous and exogenous environments are poorly understood. Pregitzer et al. (2000) reviewed responses to temperature of fine roots of trees and concluded that soil warming had the greatest eVect on root production and mortality. An experiment reported by Majdi (2001) indicates that liquid fertilization in a Norway spruce (Picea abies (L.) Karst.) stimulates the process of root mortality significantly. In the Davis model, root maturity is controlled by the parameter maximum root length for a given order. In each time step, it scans the whole root system; if a branch’s length is equal or greater than the correspondent maximum length, the branch will be removed from the system. One model that does incorporate the senescence process is that of Jourdan and Rey (1997), who used survival probability ( ps) to represent the process based on field observations for the oil palm (Elaeis guineenis Jacq.)

206

L. WU ET AL.

root system. The survival probability ps(i) of an elementary length unit (segment) in a given length category i, described by the percentage of the maximum length of a given root type for each morphological root type that is distinguished by development pattern and state of diVerentiation, is expressed as 11=Ni Ni 1 0 ps ðiÞ ¼ 1

B B pm ðiÞ ¼ B1 @

1

fi iP1

j¼1

fj

C C C A

;

ð22Þ

where pm(i) is the probability of dying in length category i, fi is the proportion of dead roots in length category i in relation to the initial population, and Ni is number of elementary length units from the base of the root in length category i.

B. INTERACTION WITH THE ENVIRONMENT The main purpose of the models reviewed here was to reproduce root architecture; few of them consider interaction between root growth, development, and the environment. This makes it diYcult to use them to accurately predict root growth under field conditions. Even under controlled conditions, their use is limited because of simplifying assumptions used in process descriptions and because of the interactions between these processes and the environment. When considering the eVects of environmental factors, the interaction between the roots and the rest of the plant also needs to be considered, since such factors influence both above- and below-ground plant components. Although the Davis model linked elongation rates with environmental factors (soil temperature, moisture, soil strength, and soil solute concentration), there are other factors that may dramatically aVect root growth and development that have not been considered. For example, Aguirrezabal and Tardieu (1996) pointed out that in field-grown sunflower, the elongation of a root branch was related to photosynthetic photon flux density (PPFD) and to the distance from the apex of the branch under study to the base of the taproot. Aguirrezabal et al. (1993) concluded from reviewed papers that carbon nutrition aVects not only the total root biomass and length, but also the number of roots, the individual elongation rate of diVerent branches, and the elongation rate of branches appearing on apical vs basal parts of the taproot. Mycorrhizal colonization is another important biological factor to be considered, as it can alter both root architecture and root longevity (Atkinson et al., 2003; Durall et al., 1994; Espeleta et al., 1999; Hooker et al., 1995). Continued development of root dynamics models should thus include the eVect of mycorrhizal colonization on root


207

growth and development, although this may be challenging because of limited understanding of the processes involved. In their toolkit to simulate root system structure for ecosystem management applications, Brown and Kulasiri (1994) included a representation of the spread of fungal populations along the root system, with options of alternative functions to represent this process to be selected by the user. However, only negative eVects of a fungal population causing death of roots were considered (as discussed earlier), rather than any beneficial eVects of mycorrhizal fungi.

C. WATER UPTAKE While water and nutrient availability are major factors influencing the growth of roots (as discussed in the following) and other plant components, uptake (and in some cases also partitioning among plant components) is generally modelled with the assumption of unlimited availability. Root water uptake has been simulated by two approaches. The microscopic scale approach, first outlined by Gardner (1960), investigates water movement toward an individual root and has been described in relation to simulating root water uptake by Herkelrath et al. (1977) and Aura (1996). It assumes that soil water flows radially through the soil to the plant root from an imaginary thick-walled hollow cylinder of soil, with its outer radius determined by the root density and its inner radius being the surface of the root. In contrast, the macroscopic scale approach ignores the details of water flow patterns toward individual roots. There are two main model groups for a more detailed approach. In one group, root resistances and water potentials inside and at the root–soil water interface are used to calculate the water uptake rate. In the other, plant transpiration is allocated to root uptake, which is a function of root depth and water content. Among the prototype versions of the reviewed models, only the Davis model considers root water uptake and water loss to the atmosphere. Water uptake rate (Wu) at a given time is estimated by the macroscopic scale approach and is thus determined by an extraction function ( fe), a normalized potential root water uptake site distribution ( fnu), and a potential transpiration rate (Tpot): Wu ðx; y; z; tÞ ¼ fe ðx; y; z; tÞ fnu ðx; y; z; tÞ Tpot

ð23Þ

fu ðx; y; z; tÞ ; fnu ðx; y; z; tÞ ¼ P fu ðx; y; z; tÞ

ð24Þ

SD

P where fu is potential root uptake site distribution and fu is the integration of SD the distribution over the complete soil domain.

208

L. WU ET AL.

The extraction function later introduced by Feddes et al. (1978) was used to account for the local influence of soil water potential on the root water uptake rate. In the further extended model (Somma et al., 1998), this expression became one of two options. The second option (which became the default function) used an expression from van Genuchten (1987) that considered the combined eVects of matric and osmotic potential on the uptake rate. The potential root uptake site distribution is constructed by identifying the finite element that surrounds each growing apex, and subsequently setting each of eight nodes to the value of the inverse distance between the apex and the respective node. This function is further modified in the second option. The function value is equal to the inverse distance between the center of the root segment and the respective corner and is proportional to the segment length. To account for root age eVects, the function is multiplied by a weighting factor ( fw), which is a piecewise linear function of root segment age and branching order: f u ðx; y; z; tÞ ¼

Di Ls fw ; 8 P Lt Di

ð25Þ

i¼l

P

where Di is the inverse distance, indicates the summation of the inverse distances of eight nodes, Ls is the root segment length, and Lt is total root segment length over the complete soil domain. An adaptation of the ROOTMAP model to include nutrient and water transport and uptake was described by Dunbabin et al. (2002). In this extended version of the model, root water uptake rate is also estimated according to a macroscopic scale approach: Wu ¼ Tpot fw RLD;

ð26Þ

where RLD is root length density and fw is a weighting factor expressed as a sigmoid curve with soil water potential ( ) ranging from the drainable upper limit (field capacity) to the lower extraction limit (wilting point): fw ¼

1 ; 1 þ b exp ð ku Þ

ð27Þ

where b and ku are parameters.

D. NUTRIENT UPTAKE Modelling root nutrient uptake began with simulations of mass flow and diVusion of nutrients to a uniform cylindrical root surface, as was the case with water flow (Claassen and Barber, 1976; Nye and Marriott, 1969).


209

Mathematical simulation has been attempted according to various approaches, and several reviews have been published (Gregory, 1996; Mmolawa and Or, 2000; Rengel, 1993). Up to now, models have seldom linked root architecture with nutrient uptake. In the Davis model, a finite element grid simulated by summing nodal sink values gives root solute uptake throughout the soil domain: S 0 ¼ d Wu þ ð1

dÞ A;

ð28Þ

where S0 is solute sink value for a given nodal, d is a partition coeYcient, Wu is the water uptake rate for a given node, and A describes the rate of active uptake for that node and is represented by the sum of Michaelis-Menten and linear components: J max þ f Rd ; A¼ ð29Þ Km þ c where Jmax is maximum uptake rate, Km is a Michaelis-Menten constant, f is a first-order rate coeYcient, Rd is the root area density, and c is the nodal solute concentration. The SimRoot model has a general formula to represent a local rate (Ur) for nutrient uptake, CO2 respiration, exudation, and carbohydrate deposition, based on Silk et al. (1986) and Sharp et al. (1990): Ur ¼

dQ dðQ vz Þ dQ dvz dQ þ ¼ þQ ; þ vz dt dz dt dz dz

ð30Þ

where Q is the local cumulative quantity of substance, z is the distance from the root tip, and vz is the longitudinal growth velocity. In the extended ROOTMAP model, there is an approximate representation of root solute uptake by a randomly dispersed root system developing within a finite volume of soil (Baldwin et al., 1973).

E. PHOTOASSIMILATE AVAILABILITY AND ROOT DEVELOPMENT The fact that growth of a root system depends on carbohydrate supply from above-ground plant organs has a profound eVect on root system architecture. In the Davis model, the quantity of assimilate allocated to the root system is limited by a piecewise linear function that determines the root/shoot allocation ratio of new assimilate. At a given growth time step, a tentative segment length is calculated for each growing apex, and the potential requirement for new root assimilates is estimated. If the quantity of assimilate allocated to the root is smaller than the potential requirement, that allocated to all new

210

L. WU ET AL.

segments is scaled down by the same factor to make the actual assimilation equal to that allocated to the root system. If the allocation exceeds the requirement, the extra assimilate is assumed to be exuded by the root. Thaler and Page`s (1998) used a source-sink relationship as an indicator to represent the endogenous environment when they simulated rubber seedling root growth and architecture using the INRA model as a framework. The growth of each root was calculated as a function of its own growth potential and of assimilate availability within the whole plant; the potential elongation rate of a root was then estimated by its apical diameter as an indication of the size of the meristem. The latter was evaluated by a monomolecular function fitted to the upper limit of the observed apical diameter-elongation rate scatter plot: h i br ðdt d0 Þ Vp ¼ Vmax 1 e Vmax ; ð31Þ where Vp is potential elongation rate for a given root (cm d 1), Vmax is maximum elongation rate for all roots (1.70 cm d 1), br is an initial slope of the curve (40 d 1), d0 is the threshold diameter below which the root does not elongate (0.025 cm), and dt is diameter of the root at time t (cm), measured at the distance from the tip corresponding to the meristem level (0.3 cm). The dynamics of root diameter was simulated by successive applications of the function: dt ¼ cdt dt 1 ;

ð32Þ

where dt is the diameter of a given root at time step t, dt 1 is that at the previous time step, and cdi is the decrease/increase rate, which is controlled by the supply:demand ratio of carbon.

F. MANAGEMENT Field management factors, including nutrient application (fertilizer/manure), soil water management (irrigation/drainage), row spacing, cutting or pruning of perennial plants, and grazing in grassland, all aVect plant assimilation and respiration and in turn influence photosynthate availability to root growth and root architecture. Experiments on the relationship between the fine root dynamics of sugar maple (Acer saccharum) and nitrogen availability suggest greater metabolic activity for roots in nitrogen-rich zones, leading to greater carbohydrate allocation to these roots (Burton et al., 2000). Arredondo and Johnson (1998) investigated the influence of cutting on root architecture and morphology of three grass species and found that root branching of a grazing-tolerant species decreased over time, while root branching of a grazing-sensitive species increased over time. Up to now, such management eVects have not been built into a root architecture model.


211

However, it will be important to incorporate these eVects into potential applications for the optimization of resource management. Interactions between roots and above-ground plant components also need to be considered in relation to field management factors and cropping system choices. Agroforestry systems are based on the spatial and temporal complementarity between species that allows resource exploitation. This applies both above- and below-ground, and there is wide acceptance of the value not only of having spatially varied root activity between species, but also of the potential nutrient cycling value of diVerent temporal dynamics. These principles can also be applied to more traditional intercropped agricultural or horticultural situations. Hauggaard-Nielsen and Jensen (2001) concluded that the competitiveness of the pea root system was an important factor in the success of pea–barley intercrops. One application of the model under development is in screening the most likely candidate species for field trials of intercropping systems. Similar principles apply to selecting the most appropriate varieties for the agricultural systems of the future. Plant breeders constantly strive to produce new crop varieties that will improve yield, yield stability, or specific attributes such as disease resistance. New varieties are generally selected under high nutrient conditions, and thus root topology of these varieties may therefore not be optimal in lower nutrient conditions (Siddique et al., 1990). Future farming scenarios across the world will include systems such as organic farming and other lower input forms of agriculture in which nutrient availability may be limited. Plant breeding has generally not focused on root characteristics; however, models may be a way of predicting how new varieties will perform under diVerent farm management scenarios. Research to find markers linked to genes controlling root architectural characteristics, e.g., deep rooting characteristics from wild relatives of lettuce, is now underway (Johnson et al., 2000). Future development of a model would allow the interaction of disease and root system dynamics and architecture on nutrient flows to be investigated. Cook (2001) estimated that 25 to 50% of the root of wheat and barley in the U.S. Pacific Northwest are aVected by disease. Nutrient placement, especially of immobile nutrients such as P, is known to be critical for crop nutrition in plants suVering from root disease (Cook et al., 2000).

V.

STRUCTURE OF AN INTEGRATED MODEL

The integration of dynamic models of above-ground growth, three-dimensional root system demography, and interactions between plant and environment into one single model is a major challenge because of the complexity of the systems discussed in this chapter. Currently, each root elongation rate is

212

L. WU ET AL.

set as an input, taking no account of the source of carbohydrate and proteins. In order to simulate root growth under field conditions, the fraction of photosynthate allocated to the root system must also be known, which requires development of an appropriate submodel. In order to understand the interaction between a plant and the environment, it will be advantageous to develop a model framework to integrate submodels that simulate various plant and environmental components. For example, in the most recent developments to the ROOTMAP model described by Dunbabin et al. (2002), various interacting components of the rooting environment, including nitrate and water flows, dynamic resource allocation, and root architectural development, have been integrated within an object-oriented framework. At the core of this framework is an ‘‘engine’’ that interacts with the relevant components for information exchange. It is also straightforward to plug in additional components or modules to the framework in order to investigate potential mechanisms that control the response of a plant to its environment.

VI. CONCLUDING REMARKS We have shown that a number of existing models adequately describe root architecture arising from the processes of root extension and branching. Procedures for computerized visualization of root architecture and growth processes also exist. It would be appropriate to develop simulation tools to avoid spending time repeatedly rewriting the same processes in each successive model. The expressions describing branching orientation and position, for example, are similar in all the reviewed models. It would be possible to write a library for such processes so that model developers could simply treat these processes as ‘‘plug-ins’’ for their own models. Future work should therefore concentrate on integrating various other relevant processes into such a model, with the ultimate objective of creating an interactively linked root growth and soil nutrient cycling systems model. These additional processes include root longevity and mortality, as well as the influence of environmental and management factors on root growth, extension, branching, and mortality. One existing model has been developed within a framework suitable for adding in further modules representing these additional processes and factors, so use may be made of such a framework for the anticipated developments.

ACKNOWLEDGMENTS SAC receives financial support from The Scottish Executive Environment and Rural AVairs Department.


213

REFERENCES Acock, B., and Pachepsky, Y. A. (1996). Convective-diVusive model of two-dimensional root growth and proliferation. Plant Soil 180, 231–240. Aguirrezabal, L. A. N., Pellerin, S., and Tardieu, F. (1993). Carbon nutrition, root branching and elongation: Can the present state of knowledge allow a predictive approach at a wholeplant level? Environ. Exp. Bot. 33, 121–130. Aguirrezabal, L. A. N., and Tardieu, F. (1996). An architectural analysis of the elongation of field-grown sunflower root systems. Elements for modelling the eVects of temperature and intercepted radiation. J. Exp. Bot. 47, 411–420. Arredondo, J. T., and Johnson, D. A. (1998). Clipping eVects on root architecture and morphology of 3 range grasses. J. Range Manage 51, 207–213. Asseng, S., Richter, C., and Wessolek, G. (1997). Modelling root growth of wheat as the linkage between crop and soil. Plant Soil 190, 267–277. Atkinson, D., Black, K. E., Forbes, P. J., Hooker, J. E., Baddeley, J. A., and Watson, C. A. (2003). The influence of arbuscular mycorrhizal colonization and environment on root development in soil. Eur. J. Soil Sci. 54, 751–757. Aura, E. (1996). Modelling non-uniform soil water uptake by a single plant root. Plant Soil 186, 237–243. Baldwin, J. P., Nye, P. H., and Tinker, P. B. (1973). Uptake of solutes by multiple root systems from soil. III—A model for calculating the solute uptake by a randomly dispersed root system developing in a finite volume of soil. Plant Soil 38, 621–635. Bengough, A. G., and Mullins, C. E. (1990). Mechanical impedance to root growth: A review of experimental techniques and root growth responses. J. Soil Sci. 41, 341–358. Berntson, G. M. (1994). Modelling root architecture: Are there tradeoVs between eYciency and potential resource acquisition? New Phytol. 127, 483–493. Berntson, G. M., and Woodward, F. I. (1992). The root system architecture and development of Senecio vulgaris in elevated CO2 and drought. Funct. Ecol. 6, 324–333. Bingham, I. J., and Bengough, A. G. (2003). Morphological plasticity of wheat and barley roots in response to spatial variation in soil strength. Plant Soil 250, 273–282. Bingham, I. J., Blackwood, J. M., and Stevenson, E. A. (1997). Site, scale and time-course for adjustments in lateral root initiation in wheat following changes in C and N supply. Ann. Bot. 80, 97–106. Bland, W. L., and Dugas, W. A. (1988). Root length density from minirhizotron measurements. Agron. J. 80, 271–275. Bledsoe, C. S., Fahey, T. J., Day, F. P., and Ruess, R. W. (1999). Measurement of static root parameters. Biomass, length, and distribution in the soil profile. In ‘‘Standard Soil Methods for Long-Term Ecological Research’’ (G. P. Robertson, D. C. Coleman, C. S. Bledsoe, and P. Sollins, Eds.), pp. 413–436. Oxford University Press, Oxford, UK. Bobbink, R., Hornung, M., and Roelofs, J. G. M. (1998). The eVects of air-borne nitrogen pollutants on species diversity in natural and semi-natural European vegetation. J. Ecol. 86, 717–738. Bo¨hm, W. (1979). ‘‘Methods of Studying Root Systems’’. Springer-Verlag, Berlin. Boukcim, H., Page`s, L., Plassard, C., and Mousain, D. (2001). Root system architecture and receptivity to mycorrhizal infection in seedlings of Cedrus atlantica as aVected by nitrogen source and concentration. Tree Physiol. 21, 109–115. Brissette, J. C., and Chambers, J. L. (1992). Leaf water status and root system water flux of shortleaf pine (Pinus echinata Mill.) seedlings in relation to new root growth after transplanting. Tree Physiol. 11, 289–303.

214

L. WU ET AL.

Brown, T. N., and Kulasiri, D. (1994). Simulation of Pinus radiata root system structure for ecosystem management applications. Simulation 62, 286–294. Bruinsma, J. (2003). ‘‘World Agriculture: Towards 2015/30, an FAO Perspective’’. FAO, Rome. Burton, A. J., Pregitzer, K. S., and Hendrick, R. L. (2000). Relationships between fine root dynamics and nitrogen availability in Michigan northern hardwood forests. Oecologia 125, 389–399. Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., and Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8, 559–568. Chiatante, D., Iorio, A. D., Maiuro, L., and Scippa, S. G. (1999). EVect of water stress on root meristems in woody and herbaceous plants during the first stage of development. Plant Soil 217, 159–172. Claassen, N., and Barber, S. A. (1976). Simulation model for nutrient uptake from soil by a growing plant system. Agron. J. 68, 961–964. Clausnitzer, V., and Hopmans, J. W. (1994). Simultaneous modeling of transient three-dimensional root growth and soil water flow. Plant Soil 164, 299–314. Cook, R. J. (2001). Management of wheat and barley root diseases in modern farming systems. Australas. Plant Path. 30, 119–126. Cook, R. J., Ownley, B. H., Zhang, H., and Vakoch, D. (2000). Influence of paired-row spacing and fertiliser placement on yield and root diseases of direct-seeded wheat. Crop Sci. 40, 1079–1087. Coutts, M. P. (1987). Developmental processes in tree root systems. Can. J. Forest Res. 17, 761–767. Davis, R. D. (1993). ‘‘Geometric Modeling and Computer Visualization of Botanical Root Systems’’. Master Thesis, The Pennsylvania State University, University Park, PA. Derner, J. D., Polley, H. W., Johnson, H. B., and Tischler, C. R. (2001). Root system response of C4 grass seedlings to CO2 and soil water. Plant Soil 231, 97–104. de Willigen, P., Heinen, M., Mollier, A., and van Noordwijk, M. (2002). Two-dimensional growth of a root system modelled as a diVusion process. I. Analytical solutions. Plant Soil 240, 225–234. Diggle, A. J. (1988a). ROOTMAP—A model in three dimensional coordinates of the growth and structure of fibrous root systems. Plant Soil 105, 169–178. Diggle, A. J. (1988b). In ‘‘ROOTMAP2.1—A Root Growth Simulation Program’’, Miscellaneous Publication No. 6188. Western Australian Department of Agriculture, Australia. Doussan, C., Page`s, L., and Vercambre, G. (1998). Modelling of the hydraulic architecture of root systems: An integrated approach to water absorption-model description. Ann. Bot. 81, 213–223. Drennan, P. M., and Nobel, P. S. (1996). Temperature influences on root growth for Encelia farinosa (Asteraceae), Pleuraphis rigida (Poaceae), and Agave deserti (Agavaceae) under current and doubled CO2 concentrations. Am. J. Bot. 83, 133–139. Dunbabin, V. M., Diggle, A. J., Rengel, Z., and van Hugten, R. (2002). Modelling the interactions between water and nutrient uptake and root growth. Plant Soil 239, 19–38. Durall, D. M., Marshall, J. D., Jones, M. D., Crawford, R., and Trappe, J. M. (1994). Morphological changes and photosynthate allocation in ageing Hebeloma crustuliniforme (Bull.) Quel. and Laccaria bicolor (Maire) Orton mycorrhizas of Pinus ponderosa Dougl. ex. Laws. New Phytol. 127, 719–724. Espeleta, J. F., Eissenstat, D. M., and Graham, J. H. (1999). Citrus root responses to localized drying soil: A new approach to studying mycorrhizal eVects on the roots of mature trees. Plant Soil 206, 1–10.


215

Feddes, R. A., Kowalik, P. J., and Zaradny, H. (1978). ‘‘Simulation of Field Water Use and Crop Yield’’. Simulation Monographs, Pudoc, Wageningen. Fitter, A. H. (1982). Morphometric analysis of root systems: Application of the technique and influence of soil fertility on root system development in two herbaceous species. Plant Cell Environ. 5, 313–322. Fitter, A. H. (1986). The topology and geometry of plant root systems: Influence of watering rate on root system topology in Trifolium pratense. Ann. Bot. 57, 91–101. Fitter, A. H. (1987). An architectural approach to the comparative ecology of plant root systems. New Phytol. 106(Suppl.), 61–77. Fitter, A. H., Graves, J. D., Wolfenden, J., Self, G. K., Brown, T. K., Dogie, D., and Mansfield, T. A. (1997). Root production and turnover and carbon budgets of two contrasting grasslands under ambient and elevated atmospheric carbon dioxide concentrations. New Phytol. 137, 247–255. Fitter, A. H., Stickland, T. R., Harvey, M. L., and Wilson, G. W. (1991). Architectural analysis of plant root systems. 1. Architectural correlates of exploitation eYciency. New Phytol. 118, 375–382. Forbes, P. J., Black, K. E., and Hooker, J. E. (1997). Temperature-induced alteration to root longevity in Lolium perenne. Plant Soil 190, 87–90. Forbes, P. J., Ellison, C. H., and Hooker, J. E. (1996). The impact of arbuscular mycorrhizal fungi and temperature on root system development. Agronomie 16, 617–620. Foster, S. S. D., Bridge, L. R., Geake, A. K., Lawrence, A. R., and Parker, J. M. (1986). ‘‘The Groundwater Nitrate Problem: A Summary of Research on the Impact of Agricultural Land-Use Practices on Groundwater Quality between 1976 and 1985’’. Hydrogeological Report No.86/2. British Geological Survey, Wallingford, UK. Galloway, J. N. (1995). Acid deposition: Perspectives in time and space. Water Air Soil Poll. 85, 15–24. Gardner, W. R. (1960). Dynamic aspects of water availability to plants. Soil Sci. 89, 67–73. Gavito, M. E., Curtis, P. S., Mikkelsen, T. E., and Jakobsen, I. (2001). Interactive eVects of soil temperature, atmospheric carbon dioxide and soil N on root development, biomass and nutrient uptake of winter wheat during vegetative growth. J. Exp. Bot. 52, 1913–1923. Ge, Z., Rubio, G., and Lynch, J. P. (2000). The importance of root gravitropism for inter-root competition and phosphorus acquisition eYciency: Results from a geometric simulation model. Plant Soil 218, 159–171. Gerwitz, A., and Page, E. R. (1974). An empirical mathematical model to describe plant root systems. J. Appl. Ecol. 11, 773–781. Goss, M. J., and Watson, C. A. (2003). The importance of root dynamics in cropping systems research. J. Crop Prod. 8, 127–157. Goulding, K. (2000). Nitrate leaching from arable and horticultural land. Soil Use Manage 16, 145–151. Grant, R. F. (1997). Changes in soil organic matter under diVerent tillage and rotation: Mathematical modeling in ecosys. Soil Sci. Soc. Am. J. 61, 1159–1175. Grant, R. F., and Robertson, J. A. (1997). Phosphorus uptake by root systems: Mathematical modelling in ecosys. Plant Soil 188, 279–297. Gregory, P. J. (1996). Approaches to modelling the uptake of water and nutrients in agroforestry systems. Agroforestry Syst. 31, 51–65. Hackett, C. (1968). A study of the root system of barley. I. EVects of nutrition on two varieties. New Phytol. 67, 287–299. Hackett, C., and Rose, D. A. (1972). A model of the extension and branching of a seminal root of barley, and its use in studying relations between root dimensions. I. The model. Aust. J. Biol. Sci. 25, 669–679.

216

L. WU ET AL.

Hauggaard-Nielsen, H., and Jensen, E. S. (2001). Evaluating pea and barley cultivars for complementarity in intercropping at diVerent levels of soil N availability. Field Crop Res. 72, 185–196. Henderson, R., Ford, E. D., and Renshaw, E. (1983). Morphology of the structural root system of Sitka spruce: 2. Computer simulation of rooting patterns. Forestry 56, 137–153. Herkelrath, W. N., Miller, E. E., and Gardner, W. R. (1977). Water uptake by plant: II. The root contact model. Soil Sci. Soc. Am. J. 41, 1039–1043. Hooker, J. E., Black, K. E., Perry, R. L., and Atkinson, D. (1995). Arbuscular mycorrhizal fungi induced alteration to root longevity of poplar. Plant Soil 172, 327–329. Hooker, J. E., Munro, M., and Atkinson, D. (1992). Vesicular-arbuscular mycorrhizal fungi induced alteration in poplar root system morphology. Plant Soil 145, 207–214. Huang, B. R., Taylor, H. M., and McMichael, B. L. (1991). Growth and development of seminal and crown roots of wheat seedlings as aVected by temperature. Environ. Exp. Bot. 31, 471–477. Huck, M. C., Ishihara, K., Peterson, C. M., and Ushijima, T. (1983). Soybean adaptation to water stress at selected stages of growth. Plant Physiol. 73, 422–427. Jackson, R. B., Mooney, H. A., and Schulze, E. D. (1997). A global budget for the fine root biomass, surface area, and nutrient contents. Proc. Natl. Acad. Sci. USA 94, 7362–7366. Jenkinson, D. S. (2001). The impact of humans on the nitrogen cycle, with focus on temperate arable agriculture. Plant Soil 228, 3–15. Johnson, W. C., Jackson, L. E., Ochoa, O., van Wijk, R., Peleman, J., St Clair, D. A., and Michelmore, R. W. (2000). Lettuce, a shallow-rooted crop, and Lactuta serriola, its wild progenitor, diVer at QTL determining root architecture and deep soil water exploration. Theor. Appl. Genet 101, 1066–1073. Johnson, H., Bergstro¨m, L., Jansson, P. E., and Paustian, K. (1987). Simulated nitrogen dynamics and losses in a layered agricultural soil. Agr. Ecosyst. Environ 18, 333–356. Jones, C. A., Dyke, P. T., Williams, J. R., Kiniry, J. R., Benson, V. W., and Griggs, R. H. (1991). EPIC: An operational model for evaluation of agricultural sustainability. Agr. Syst. 37, 341–350. Jordahl, J. L., and Karlen, D. L. (1993). Comparison of alternative farming systems. III. Soil aggregate stability. Am. J. Alternative Agr. 8, 27–33. Joslin, J. D., Wolfe, M. H., and Hanson, P. J. (2000). EVects of altered water regimes on forest root systems. New Phytol. 147, 117–129. Jourdan, C., and Rey, H. (1997). Modelling and simulation of the architecture and development of the oil-palm (Elaeis guineensis Jacq.). I. The model. Plant Soil 190, 217–233. King, J., Gay, A., Sylvester-Bradley, R., Bingham, I., Foulkes, J., Gregory, P., and Robinson, D. (2003). Modelling cereal root systems for water and nitrogen capture: Towards an economic optimum. Ann. Bot. 91, 383–390. King, J. S., Thomas, R. B., and Strain, B. R. (1997). Morphology and tissue quality of seedling root systems of Pinus taeda and Pinus ponderosa as aVected by varying CO2, temperature, and nitrogen. Plant Soil 195, 107–119. Laboski, C. A. M., Dowdy, R. H., Allmaras, R. R., and Lamb, J. A. (1998). Soil strength and water content influences on corn root distribution in a sandy soil. Plant Soil 203, 239–247. Lungley, D. R. (1973). The growth of root systems—A numerical computer simulation model. Plant Soil 38, 145–159. Lynch, J. P., Nielsen, K. L., Davis, R. D., and Jablokow, A. G. (1997). SimRoot: Modelling and visualization of root systems. Plant Soil 188, 139–151. Majdi, H. (2001). Changes in fine root production and longevity in relation to water and nutrient availability in a Norway spruce stand in northern Sweden. Tree Physiol. 21, 1057–1061.


217

May, L. H., Chapman, F. H., and Aspinall, D. (1965). Quantitative studies of root development. I. The influence of nutrient concentration. Aust. J. Biol. Sci. 18, 25–35. Milchunas, D. G., Lee, C. A., Lauenroth, W. K., and CoYn, D. P. (1992). A comparison of C14, Rb86, and total excavation for determination of root distributions of individual plants. Plant Soil 144, 125–132. Misra, R. K., and Gibbons, A. K. (1996). Growth and morphology of eucalypt seedling-roots, in relation to soil strength arising from compaction. Plant Soil 182, 1–11. Mitchell, C. C., and Entry, J. A. (1998). Soil C, N and crop yields in Alabama’s long-term ‘Old rotation’ cotton experiment. Soil Till. Res. 47, 331–338. Mmolawa, K., and Or, D. (2000). Root zone solute dynamics under drip irrigation: A review. Plant Soil 222, 163–190. Newton, C. D., Clark, H., Bell, C. C., Glasgow, E. M., and Campbell, B. D. (1994). EVects of elevated CO2 and simulated seasonal changes in temperature on the species composition and growth rates of pasture turves. Ann. Bot. 74, 53–59. Nielsen, K. L., Lynch, J. P., Jablokow, A. G., and Curtis, P. S. (1994). Carbon cost of root systems: An architectural approach. Plant Soil 165, 161–169. Niemi, K., Ha¨ggman, H., and Sarjala, T. (2002). EVects of exogenous diamines on the interaction between ectomycorrhizal fungi and adventitious root formation in Scots pine in vitro. Tree Physiol. 22, 373–381. Norby, R. J., O’Neill, E. G., Hood, W. G., and Luxmoore, R. J. (1987). Carbon allocation, root exudation and mycorrhizal colonization of Pinus echinata seedlings grown under CO2 enrichment. Tree Physiol. 3, 203–210. Norse, D. (2003). Fertilizers and world food demand—Implications for environmental stresses. IFA-FAO Agriculture Conference: Global Food Security and the Role of Sustainable Fertilization, 26–28 March 2003 Rome, Italy. http://www.fertilizer.org/ifa/publicat/PDF/ 2003_rome_norse.pdf. Nye, P. H., and Marriott, F. H. C. (1969). A theoretical study of the distribution of substances around roots resulting from simultaneous diVusion and mass flow. Plant Soil 30, 459–472. Ozier-Lafontaine, H., Lecompte, F., and Sillon, J. F. (1999). Fractal analysis of the root architecture of Gliricidia sepium for the spatial prediction of root branching, size and mass: Model development and evaluation in agroforestry. Plant Soil 209, 167–180. Page`s, L. (1999). Root system architecture: From its representation to the study of its elaboration. Agronomie 19, 295–304. Page`s, L., Jordan, M. O., and Picard, D. (1989). A simulation model of the three-dimensional architecture of the maize root system. Plant Soil 119, 147–154. Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S. (1987). Analysis of factors controlling soil organic-matter levels in Great-Plains Grasslands. Soil Sci. Soc. Am. J. 51, 1173–1179. Persson, H. (1978). Root dynamics in a young Scots’s pine in Central Sweden. Oikos 30, 508–519. Peterson, B. J., Wolheim, W. M., Mulholland, P. J., Webster, J. R., Meyer, J. L., Tank, J. L., Marti, E., Bowden, W. B., Valett, H. M., Hershey, A. E., Mcdowell, W. H., Dodds, W. K., Hamilton, S. K., Gregory, S., and Morrall, D. D. (2001). Control of nitrogen export from watersheds by headwater streams. Science 292, 86–90. Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D., Mcnair, M., Crist, S., Shpritz, L., Fitton, L., SaVouri, R., and Blair, R. (1995). Environmental and economic costs of soil erosion and conservation benefits. Science 267, 1117–1123. Porter, J. R., Klepper, B., and Belford, R. K. (1986). A model (WHTROOT) which synchronizes root growth and development with shoot development for winter wheat. Plant Soil 92, 133–145.

218

L. WU ET AL.

Pregitzer, K. S., King, J. S., Burton, A. J., and Brown, S. E. (2000). Responses of tree fine roots to temperature. New Phytol. 147, 105–115. Pulgarin, A., Navascue´s, J., Casero, P. J., and Lloret, P. G. (1988). Branching pattern in onion adventitious roots. Am. J. Bot. 75, 425–432. Rengel, Z. (1993). Mechanistic simulation models of nutrient uptake: A review. Plant Soil 152, 161–173. Rose, D. A. (1983). The description of the growth of root systems. Plant Soil 75, 405–415. Rubio, G., Walk, T., Ge, Z. Y., Yan, X. L., Liao, H., and Lynch, J. P. (2001). Root gravitropism and below-ground competition among neighbouring plants: A modelling approach. Ann. Bot. 88, 929–940. Ruess, R. W., Cleve, K. V., Yarie, J., and Viereck, L. A. (1996). Contributions of fine root production and turnover to the carbon and nitrogen cycling in taiga forests of the Alaskan interior. Can. J. Forest Res. 26, 1326–1336. Schaller, N. (1993). The concept of agricultural sustainability. Agr. Ecosyst. Environ 46, 89–97. Schuurman, J. J., and de Boer, J. J. H. (1970). The developmental pattern of roots and shoots of oats under favourable conditions. Neth. J. Agr. Sci. 18, 168–181. Sharp, R. E., Hsiao, T. C., and Silk, W. K. (1990). Growth of the maize primary root at low water potentials. II. Role of growth and deposition of hexose and potassium in osmotic adjustment. Plant Physiol. 93, 1337–1346. Shibusawa, S. (1994). Modeling the branching growth fractal pattern of the maize root-system. Plant Soil 165, 339–347. Siddique, K. H. M., Belford, R. K., and Tennant, D. (1990). Root:shoot ratios of old and modern, tall and semi-dwarf wheats in a Mediterranean environment. Plant Soil 121, 89–98. Silk, W. K., Hsiao, T. C., Diedenhofen, U., and Matson, C. (1986). Spatial distributions of potassium, solutes, and their deposition rates in the growth zone of the primary corn root. Plant Physiol. 82, 853–858. Smith, V. H., Tilman, G. D., and Nekola, J. C. (1999). Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100, 179–196. Soil Association (2003). ‘‘Soil Association Organic Food and Farming Report 2003’’. Soil Association, Bristol, UK. Somma, F., Clausnitzer, V., and Hopmans, J. W. (1997). ‘‘An Algorithm for Three-Dimensional, Simultaneous Modeling of Root Growth, Transient Soil Water Flow, and Solute Transport and Uptake, V. 2.1’’, Paper No. 100034. Department of Land Air, and Water Resources, University of California, Davis, CA. Somma, F., Hopmans, J. W., and Clausnitzer, V. (1998). Transient three-dimensional modeling of soil water and solute transport with simultaneous root growth, root water and nutrient uptake. Plant Soil 202, 281–293. Steiner, R. A. (1995). Long-term experiments and their choice for the research study. In ‘‘Agricultural Sustainability: Economic, Environmental and Statistical Considerations’’ (V. Barnett, R. Payne, and R. Steiner, Eds.), pp. 15–21. John Wiley & Sons Ltd., West Sussex, UK. Thaler, P., and Page`s, L. (1998). Modelling the influence of assimilate availability on root growth and architecture. Plant Soil 201, 307–320. Tierney, G. L., and Fahey, T. J. (2001). Evaluating minirhizotron estimates of fine root longevity and production in the forest floor of a temperate broadleaf forest. Plant Soil 229, 167–176. Tsutsumi, D., Kosugi, K., and Mizuyama, T. (2002). EVect of hydrotropism on root system development in soybean (Glycine max): Growth experiments and a model simulation. J. Plant Growth Regul. 21, 441–458.


219

United Nations (UN) (2001). ‘‘World Population Prospects, the 2000 Revision—Highlights’’, Document No. ESA/P/WP. 165. The United Nations, New York, NY. Upchurch, W. J., Kinder, R. J., Brown, J. R., and Wagner, G. H. (1985). ‘‘Sanborn Field— Historical Perspective’’. Missouri Agricultural Experiment Station Research Bulletin (1054). Missouri Agricultural Experimental Station, Columbia, MO. van Bergen, P. F., Bull, I. D., Poulton, P. R., and Evershed, R. P. (1997). Organic geochemical studies of soils from the Rothamsted classical experiments. 1. Total lipid extracts, solvent insoluble residues and humic acids from Broadbalk wilderness. Org. Geochem. 26, 117–135. van Genuchten, M. T. (1987). ‘‘A Numerical Model for Water and Solute Movement in and Below the Root Zone’’, Research Report No. 121. U.S. Salinity Lab, ARS USDA, Riverside, CA. van Noordwijk, M., Dejager, A., and Floris, J. (1985). A new dimension to observations in minirhizotrons—A stereoscopic view on root photographs. Plant Soil 86, 447–453. Vincent, C. D., and Gregory, P. J. (1989). EVects of temperature on the development and growth of winter wheat roots. II. Field studies of temperature, nitrogen and irradiance. Plant Soil 119, 99–110. Vitousek, P. M., Aber, J. D., Howarth, R. W., Likens, G. E., Matson, P. A., Schindler, D. W., and Schlesinger, W. H. (1997). Human alteration of the global nitrogen cycle: Sources and consequences. Ecol. Appl. 7, 737–750. Vogt, K. (1991). Carbon budgets of temperate forest ecosystems. Tree Physiol. 9, 69–86. Waisel, Y., Eshel, A., and Kafkafi, U. (1996). ‘‘Plant Roots: The Hidden Half’’. Marcel Dekker, Inc., New York, NY. Watson, C. A., Ross, J. M., Bagnaresi, U., Minotta, G. F., RoY, F., Atkinson, D., Black, K. E., and Hooker, J. E. (2000). Environment-induced modifications to root longevity in Lolium perenne and Trifolium repens. Ann. Bot. 85, 397–401. Weaver, J. E. (1926). ‘‘Root Development of Field Crops’’. McGraw-Hill, New York, NY. Williams, J. R., and Renard, K. G. (1985). Assessments of soil erosion and crop productivity with process models (EPIC). In ‘‘Soil Erosion and Crop Productivity’’ (R. F. Follett and B. A. Stewart, Eds.), pp. 67–103. American Society of Agronomy, Madison, WI.

This Page Intentionally Left Blank

LABILE ORGANIC MATTER FRACTIONS AS CENTRAL COMPONENTS OF THE QUALITY OF AGRICULTURAL SOILS: AN OVERVIEW R. J. Haynes Discipline of Soil Science, School of Applied Environmental Sciences, University of Natal, Pietermaritzburg, Scottsville 3209, South Africa

I. Introduction II. Total Soil Organic Matter A. Attainment of Equilibrium B. EVects of Agricultural Practice III. Particulate Organic Matter A. Method of Quantification B. Nature of the Fraction C. Amounts Present in Soils D. Management-Induced Changes E. Seasonal Fluctuations F. Significance to Soil Quality IV. Dissolved Organic Matter A. Method of Extraction B. Nature of the Fraction C. Biodegradability of DOM D. Adsorbed Organic Matter E. Quantities of DOM F. Management-Induced Changes G. Seasonal Fluctuations H. Significance to Soil Quality V. Extractable Forms of Organic Matter A. Hot Water-Extractable Organic Matter B. Dilute Acid-Hydrolyzable C C. Permanganate-Oxidizable C VI. Potentially Mineralizable C and N A. Method of Quantification B. Nature of the Fraction C. Relationship with Other Pools D. Amounts Present in Soils E. Management-Induced Changes F. Seasonal Flunctuations G. Significance to Soil Quality


222

R. J. HAYNES VII. Synthesis and Conclusions A. Significance of Labile Organic Matter Fractions B. Practical Value of Labile Organic Matter Fractions References

Total soil organic matter content is a key attribute of soil quality since it has far-reaching eVects on soil physical, chemical, and biological properties. However, changes in contents of organic carbon (C) and total nitrogen (N) occur only slowly and do not provide an adequate indication of important short-term changes in soil organic matter quality that may be occurring. Labile organic matter pools can be considered as fine indicators of soil quality that influence soil function in specific ways and that are much more sensitive to changes in soil management practice. Particulate organic matter consists of partially decomposed plant litter, and it acts as a substrate and center for soil microbial activity, a short-term reservoir of nutrients, a food source for soil fauna and loci for formation of water stable macroaggregates. Dissolved (soluble) organic matter consists of organic compounds present in soil solution. This pool acts as a substrate for microbial activity, a primary source of mineralizable N, sulfur (S), and phosphorus (P), and its leaching greatly influences the nutrient and organic matter content and pH of groundwater. Various extractable organic matter fractions have also been suggested to be important, including hot water-extractable and dilute acid-extractable carbohydrates, which are involved in stabilization of soil aggregates, and permanganate-oxidizable C. Measurement of potentially mineralizable C and N represents a bioassay of labile organic matter using the indigenous microbial community to release labile organic fractions of C and N. Mineralizable N is also an important indicator of the capacity of the soil to supply N for crops. It is concluded that individual labile organic matter fractions are sensitive to changes in soil management and have specific eVects on soil function. Together they reflect the diverse but central eVects that organic ß 2005 Elsevier Inc. matter has on soil properties and processes.

I. INTRODUCTION Concerns regarding soil degradation and agricultural sustainability have kindled interest in assessment of soil quality. Soil quality is simply defined as the capacity of a soil to function, encompassing its living and dynamic nature (Karlen et al., 1997). A more specific definition is the capacity of a soil to function within ecosystem boundaries to sustain biological productivity, maintain environmental quality, and promote plant and animal health (Carter et al., 1997; Doran and Parkin, 1994). An assessment usually includes measurement of soil quality indicators that, in some way, influence the function for which the assessment is being made. Such indicators can be divided into chemical (e.g., pH, extractable nutrients, salinity), physical (e.g.,

LABILE ORGANIC MATTER

223

aggregation, bulk density, hydraulic conductivity), and biological (e.g., microbial biomass C, basal respiration, earthworm numbers). Soil organic matter is an extremely important attribute of quality since it influences soil physical, chemical, and biological properties and processes. For example, it is a source of energy and nutrients for soil biota, it is a plant nutrient (N, S, and P) source via mineralization, and it aVects aggregate stability, traYcability, water retention, and hydraulic properties. As a result, soil organic matter content and quality are now regarded as key factors in the evaluation of the sustainability of management practices (Gregorich et al., 1994, 1997a). Changes in total soil organic matter content in response to alterations in soil management practice are diYcult to detect because of the generally high background levels and natural soil variability (Haynes and Beare, 1996). However, soil organic matter is a heterogeneous mixture of materials, ranging from fresh plant and microbial residues to relatively inert humic compounds, with turnover rates measured in millennia (Baldock and Nelson, 2000; Stevenson, 1994). Many attempts have been made to identify labile pools of organic matter that are more sensitive to changes in management or environmental conditions than total soil organic matter content. Examples include C and N held in the microbial biomass and particulate organic matter and in water soluble, easily extractable and potentially mineralizable fractions (Gregorich et al., 1997a; Haynes and Beare, 1996; Janzen et al., 1997; Moore, 1997). The level of our knowledge regarding the significance and applicability of various labile fractions as indicators of soil quality diVers greatly. For example, a number of workers have reviewed, in detail, the significance of microbial biomass C and N levels (Carter et al., 1999; Dalal, 1998; Smith and Paul, 1990; Sparling, 1997). By contrast, the nature and significance of the non-living, labile organic matter pools are much less well understood. For example, past research on soluble C and N has concentrated on forest soils, and their significance to the quality of agricultural soils has only recently been recognized. The significance of particulate organic matter has been recognized for some time (Gregorich and Janzen, 1996), but that of the mineralizable and extractable fractions is less well-known. The objective of this chapter is to discuss the nature and significance and interrelationship between these non-living labile organic matter fractions and their value as indicators of the quality of agricultural soils.

II. TOTAL SOIL ORGANIC MATTER Soil organic matter content is generally measured as organic C and/or total N content. Although the organic fraction of soils typically accounts for a small, but variable, proportion (typically 5–10%) of soil mass, it exerts

224

R. J. HAYNES

far-reaching eVects on soil properties. Indeed, soil organic matter has long been suggested as the single most important indicator of soil productivity (Allison, 1973; Campbell, 1978). This is because organic matter greatly aVects chemical, physical, and biological properties and processes in soils. Several workers have tabulated and discussed these eVects in detail (Baldock and Nelson, 2000; Stevenson, 1994). The main chemical factors aVected are charge characteristics, cation exchange capacity, buVering capacity, formation of soluble and insoluble complexes with metals, and interactions with xenobiotics such as pesticides. Key physical properties that are influenced include aggregate formation and stabilization, water retention, resistance and resilience to compaction, and thermal properties. The most important biological properties of organic matter are its role as a reservoir of metabolizable energy for soil microbial and faunal activity, its eVect in stabilizing enzyme activity, and its value as a source of plant-available N, S, and P via mineralization.

A.

ATTAINMENT OF EQUILIBRIUM

An equilibrium soil organic matter content is attained within a mature natural ecosystem that is dependant upon the interaction of soil-forming factors (i.e., climate, topography, parent material, and time) (Baldock and Nelson, 2000; Haynes, 1986a). At this equilibrium level, the amount of organic C accumulating in the soil is the same as the amount lost via respiration as CO2. In agricultural soils, changes in soil management practice aVect soil organic matter content by (i) altering the annual input of organic matter from above- and below-ground plant litter and (ii) altering the rate at which the decomposer community degrades organic matter and releases organic C to the atmosphere as CO2. Under any particular long-term soil management practice, soil organic matter content reaches a new steady-state level where organic matter accumulation is balanced by losses as CO2. This balance of soil C is shown schematically in Fig. 1. The input of C to the soil occurs mainly as above-ground plant litter, turnover of root material, and exudation of carbonaceous material from roots (Cadisch and Giller, 1997; Paustian et al., 1997). This C originates from atmospheric CO2 that has been photosynthetically fixed and incorporated into organic compounds in plants. Once the organic residues are added to the soil, they are decomposed by the combined actions of soil fauna and microorganisms. During this process, the bulk of the residue C (about 70%) is returned to the atmosphere as CO2 through faunal and microbial respiration (Jenkinson et al., 1991). The remainder of added C, including that incorporated into the microbial biomass, undergoes further


225

REMOVAL

Harvested C NET PRIMARY PRODUCTION

CO2

DECOMPOSITION

Plant C

LITTER DECOMPOSITION

Labile C

Stabilized C

SOIL ORGANIC MATTER

Figure 1 A schematic diagram of the C cycle in agricultural soils. Reprinted from ‘‘Soil Quality for Crop Production and Ecosystem Health’’, 1997, Gregorich et al. (Eds.), pp. 277–291, Janzen et al.: Soil organic matter dynamics and their relationship to soil quality, with permission from Elsevier.

transformations with the eventual formation of relatively recalcitrant humic substances. These materials may be further stabilized by formation of complexes with soil mineral surfaces (Sollins et al., 1996). Soil organic C is shown in Fig. 1 as being composed of two major pools: a labile and a stabilized fraction. This is a convenient division, although, in fact, soil organic matter includes a continuum of materials ranging from highly decomposable to very recalcitrant. The labile fraction consists of material in transition between fresh plant residues and stabilized organic matter. Much of it is plant and microbial tissue in various states of decomposition. It generally is considered to have a short turnover time (less than 10 years) (Janzen et al., 1997). Pools of organic matter that have been identified as part of the labile fraction include particulate organic matter, microbial biomass C, soluble C, potentially mineralizable C, and that extractable with various reagents. Each of these pools defines an aspect of the labile fraction and their significance is discussed in detail in the following sections of this review. Stabilized organic C is composed of organic materials that are highly resistant to microbial decomposition because of their chemical structure and/or their association with soil minerals. It consists mainly of humic substances, which are complex systems of high-molecular-weight organic molecules made up of phenolic polymers produced from the products of

226

R. J. HAYNES

biological degradation of plant and animal residues and the synthetic activity of microorganisms (Baldock and Nelson, 2000; Stevenson, 1994). Humic substances make up 70–80% of the soil organic matter content of most mineral soils. The complex structure of humic substances is largely responsible for their stability, although other factors such as the formation of biologically stable clay–organic matter complexes and physical inaccessibility of organic matter within soil aggregates are also important (Sollins et al., 1996; Stevenson, 1994).

B. EFFECTS OF AGRICULTURAL PRACTICE The most dramatic eVect of agricultural practice occurs when soil under native vegetation is converted to arable agriculture. Typically, organic matter levels decline rapidly in the first 10–20 years and then stabilize at a new equilibrium level after 30–100 years (Fenton et al., 1999; Haynes and Beare, 1996; Paustian et al., 1997). A number of factors contribute to the losses of organic matter, including (i) a much lower allocation of carbonaceous residues to the soil (due to the relatively wide spacing of crop plants, removal of harvested products, and burning or removal of crop residues); (ii) tillage-induced aggregate disruption and exposure of physically protected organic material to microbial action, thus hastening decomposition rates; (iii) more favorable conditions for decomposition (e.g., tillage-induced aeration, irrigation, fertilizer and lime additions); and (iv) greater losses of surface soil by wind and water. It is important to recognize that long-term arable agriculture characteristically results in an increase in bulk density compared with that under native vegetation or pasture (Dominy and Haynes, 2002). It is therefore important to compare organic matter measurements on a volume basis (i.e., kg ha 1 to a stipulated depth) as well as on a mass basis (Gregorich et al., 1994). This also applies to the measurements of labile organic matter fractions discussed below. In some cases, trends in organic matter content with land use calculated on a volume basis can diVer significantly from those presented on a mass basis (Dominy and Haynes, 2002). Factors that increase organic matter inputs, and thus that tend to increase soil organic matter content, under arable agriculture include (i) a decreasing proportion of fallow in rotation, (ii) an increase in the proportion of cereal compared to root crops, (iii) an increasing proportion of perennial crops (forage legumes and grasses) in rotation, (iv) the return of crop residues to the soil rather than burning or removal, (v) fertilizer and irrigation additions that promote increased yields and thus greater organic matter returns, and (vi) additions of organic manures or other organic wastes (Fenton et al., 1999; Janzen et al., 1997, 1998a,b; Johnston, 1986; Paustian et al., 1997). The


227

most common method of attempting to reduce the rate of organic matter decomposition is to create less disturbance to the soil by conversion from conventional to minimum or zero tillage. Although this characteristically results in accumulation of organic matter in the surface 5 cm, the total organic matter in the soil profile often remains unchanged (Haynes and Beare, 1996; Janzen et al., 1998a). Large perturbations to the soil system, such as conversion of native vegetation to arable agriculture, cause large changes in organic C or total N content. These are reflected in sizeable decreases in the size of both the labile and stabilized organic matter fractions, although the decrease is more pronounced, and occurs first, in the labile fractions. Changes in soil management within agricultural systems usually cause more subtle changes in the balance between inputs and losses of soil organic matter and thus in total soil organic matter content. Because of the relatively large quantity of background organic matter already present, changes are diYcult to detect and are usually demonstrated in long-term (e.g., >25 years) experiments (Campbell et al., 1997; Christensen and Johnston, 1997; Janzen, 1995; Johnston, 1986). That is, as already noted, the stabilized fraction makes up the bulk of the soil organic matter, and it has turnover times measured in thousands of years. As a result, its content is largely unaVected by management practices imposed on the soil. By contrast, the labile fraction, with its much greater turnover time, is aVected much more rapidly by management-induced changes in organic matter inputs or losses. Janzen et al. (1998a), for example, measured a progressive decline in soil organic C content between 1910 and 1953 that was more pronounced under wheat fallow than continuous wheat (Fig. 2). Radiocarbon dating of soil organic C showed that the mean residence time increased with time; this was particularly marked in the wheat fallow system (Fig. 2). Thus, the loss of soil organic C occurred largely by depletion of the young, labile fractions so the mean residence time of the remaining soil organic C increased. In contrast, at another site, applications of fertilizer N were shown to induce an increase in organic matter content (due to increased yields and greater organic matter returns), and the mean residence time was decreased (Janzen et al., 1998a). This was due to a disproportionate accumulation of C in young, labile organic C fractions. Similarly, many field experiments have shown that management-induced changes in soil organic matter status occur much more rapidly in the labile pools (e.g., microbial biomass, particulate organic matter, soluble organic matter) than in organic C or total N (Campbell et al., 1999a,b; Graham et al., 2002). Thus, labile pools can be used as early indicators of changes in total organic matter that will become more obvious in the longer term (Gregorich et al., 1994, 1997a). In addition, the labile fraction has a disproportionately large eVect on nutrient-supplying capacity and structural stability of soils (Haynes and Beare, 1996; Janzen et al., 1997). These properties of the labile

228

R. J. HAYNES

Figure 2 Changes in the concentration and mean residence time of organic C in the surface 15 cm of soil at a long-term site in Alberta. Rotation sequence: W, continuous wheat; FWW, fallow–wheat–wheat. ‘‘Soil Processes and the Carbon Cycle’’ by Lal et al. Copyright 1998 by CRC Press LLC. Reproduced with permission of CRC Press LLC in the format Other Book via Copyright Clearance Center.

fraction are the major reason why it has been the subject of much research in recent years.

III.

PARTICULATE ORGANIC MATTER

Particulate organic matter (POM) is a transitory pool of organic matter between fresh plant residues and humified organic matter (Gregorich and Janzen, 1996). It is typically enriched in C and nutrients, and although it


229

represents only a small portion of the soil mass, it is an important attribute of soil quality since its short turnover time makes it an important source of C and nutrients.

A. METHOD OF QUANTIFICATION Particulate organic matter can be separated from soils by two distinct methods resulting in two diVerent terms: light fraction (LF) organic matter and sand-sized fraction (SSF) organic matter. Light fraction is isolated by collection of dispersed soil materials that float on heavy liquids of densities typically between 1.5 and 2.0 g cm 3. Commonly, soils are dispersed in NaI solution having a specific gravity of about 1.7 g cm 3 (Gregorich and Ellert, 1993). The fractionation is based on the fact that the density of soil minerals is typically >2.0 g cm 3, so free organic matter floats on these dense liquids. Sand-sized fraction is defined as organic matter associated with sand-sized organic matter (>20 mm diameter for European and >53 mm diameter for American particle size classification systems). It is isolated by sieving a dispersed soil. Studies regarding aggregate stability have suggested that stable macroaggregates tend to have cores of POM and that aggregates form around particles of decaying plant residues (Golchin et al., 1994, 1998). Thus, two forms of POM may exist in soils: (i) POM that is free and without any significant association with mineral particles and (ii) occluded POM that is buried within soil aggregates and/or strongly associated with mineral particles. In recent times, some workers have diVerentiated these two forms. The extent to which soil aggregates are disrupted and dispersed will determine the relative amounts of free and occluded material that are released. Thus, the free POM is usually extracted by flotation following shaking of soil samples with a heavy liquid (density 1.6–1.8 g cm 3) for 5 or 10 minutes (Besnard et al., 1996; Gregorich et al., 1997b). The occluded fraction is then released either by shaking for 16–18 hours or through sonification, and is then isolated as LF by density fractionation or as SSF by sieving.

B. NATURE OF THE FRACTION Particulate organic matter is composed primarily of plant debris with a recognizable cellular structure, but microscopic examination has revealed that it also contains fungal hyphae, spores, seeds, faunal skeletons, and charcoal (Skjemstad et al., 1990; Spycher et al., 1983). It contains a portion of the soil microbial biomass (involved in decomposing the plant residue) as

230

R. J. HAYNES

well as humified material (produced during the decomposition of the plant residue) (Baldock et al., 1992; Ladd et al., 1977). Although both LF and SSF organic matter contain mostly plant residues, their chemical and biological properties are not identical. For example, Gregorich et al. (1995) measured the decrease in soil organic matter when forest soils were converted to continuous maize for 25 years. By measurement of total organic C and natural 13C abundance, they showed that mineralization of LFC was faster than that of SSFC, with the result that after 25 years, 70% of the C in the LF was derived from maize, compared to only 45% for the SSF. Similarly, Carter et al. (1998) investigated loss of soil organic matter when forest soils were converted to arable agriculture. Whereas there was a 72% decrease in LFC, SSFC was not greatly influenced by arable cultivation. The SSFC generally represents a much higher proportion of soil C than LFC, particularly in agricultural soils (Carter et al., 1998). In a survey of 20 forest and cropped soils, it was found that the SSF contained more organic C and had a lower C:N ratio than LF, suggesting that the former was more decomposed (Gregorich and Janzen, 1996). Using 13C nuclear magnetic resonance (13C NMR) and pyrolysis-field ionization mass spectrometry (Py-FIMS), Gregorich et al. (1996) confirmed that SSF is more decomposed than LF. The mass spectra showed fewer lignin monomers, and dimers, lipids, and alkyl-aromatic compounds were present in the SSF compared with the LF. 13C NMR data indicated that the SSF contained relatively lesser amounts of carbohydrates and aliphatic compounds and had a higher degree of aromaticity than the LF. These diVerences can be attributed to diVerences in the methodology of isolating the two forms of particulate organic matter. In fractions collected on the basis of particle size, humified organic materials bound strongly to large inorganic particles, and organic debris coated with mineral particles will be retained on sieves and included in the SSF (Baldock and Nelson, 2000). Much of the organic matter present as coatings on sand grains may be more decomposed and humified than that which is floated oV the sand fraction as LF organic matter. In addition, LFC is separated from silt- and clay-sized particles as well as that from sand-sized material. Because of these diVerences, several workers have concluded that density fractionation is more eVective than particle size fractionation in separating labile and non-labile organic matter fractions (Dalal and Mayer, 1987; Gregorich and Janzen, 1996). Certainly, the less aromatic nature and more rapid turnover time of LFC compared to SSFC confirms that the LF is a more labile pool.


C.

231

AMOUNTS PRESENT IN SOILS

In agricultural soils, the LF typically contains 20–30% C and 5–20% N and makes up 2–18% of total C and 1–16% of total N contained in the whole soil (Gregorich and Janzen, 1996). The C:N ratio of the LF is normally intermediate between that of whole soil and plant tissue (Gregorich et al., 1997a). As noted previously, SSF organic matter typically accounts for a proportion of total soil organic matter content considerably larger than the LF. In general, SSF makes up 20–45% of total organic C and 13–40% of total N found in the whole soil (Bowman et al., 1999; Cambardella and Elliott, 1992; Carter et al., 1994, 1998; Doran et al., 1998; Franzluebbers and Arshad, 1997; Hussain et al., 1999).

D. MANAGEMENT-INDUCED CHANGES The LF typically accounts for a considerably higher percentage of total organic C in undisturbed soils under native vegetation than in cultivated agricultural ones (Carter et al., 1998; Skjemstad et al., 1986). Conversion of undisturbed sites to arable agriculture typically results in a disproportionate decrease in LF organic matter (Dalal and Chan, 2001; Janzen et al., 1998a). Carter et al. (1998), for instance, showed that in comparison with forested sites, arable cultivation caused a mean decrease in organic C content of 24% but a decrease of 72% for LFC. The explanation for this is that upon conversion to arable agriculture, litter inputs are greatly decreased, and their rate of decomposition is increased by factors such as tillage, irrigation, and fertilizer inputs. Similarly, agricultural practices that aVect the amount of residue input and/or the rate of residue decomposition have a much greater and earlier eVect on LF than on whole-soil organic matter content (Biederbeck et al., 1994; Bremer et al., 1994; Janzen et al., 1992). The greater responsiveness of LF to changes in management compared to total soil organic matter content has included increases due to continuous cropping compared to frequent summer fallow (Janzen et al., 1992), cropping with grasses, legumes, and continuous pastures rather than arable row crops (Angers et al., 1999; Bremer et al., 1994; Carter et al., 1998), conversion from conventional to zero tillage (Alvarez et al., 1998; Bolinder et al., 1999), and addition of fertilizers, thus increasing crop growth and residue inputs in both arable (Gregorich et al., 1997b) and grassland (Nyborg et al., 1999) systems. As illustrated in Fig. 3, Gregorich et al. (1996) showed that the increase in LFC in response to long-term fertilization of maize was much more

232

R. J. HAYNES

Figure 3 Quantities of total organic C and C4 (maize)-derived C in the whole soil and light fraction in the surface 10 cm of fertilized and unfertilized soil following 32 years of maize cropping. Data from Gregorich et al. (1996). Reprinted from ‘‘Soil and Tillage Research’’, Vol. 47, 1998, pp. 181–195, Janzen et al.: Management effects on soil C storage on the Canadian prairies, with permission from Elsevier.

pronounced than that for organic C. Using 13C techniques to discriminate between native soil C and maize-derived C, they also showed that the gain in LFC was predominantly derived from C4 (maize) residues. Similarly, on a long-term sugarcane trash management experiment (Fig. 4), the increase in organic C between unfertilized pre-harvest burnt (BR) and trashed (T) treatments was 1.2-fold while that for LFC was 2.7fold. The lower values for LFC in the fertilized treatment compared to the unfertilized trashed treatment (Fig. 4) illustrate another important point: that the LF is a transient pool, and its size and composition will fluctuate depending on the time of crop residue inputs and their rate of decomposition. Fertilizer applications promoted more rapid decomposition of crop residues (trash) and LF so that at the time of sampling (8 months after trash deposition), LFC was lower in fertilized plots. Although SSFC accounts for a considerably greater proportion of organic C than LFC, there is still a disproportionate loss of SSFC, compared with total organic C content of the whole soil, when undisturbed vegetation is converted to arable agriculture (Cambardella and Elliott, 1992). Similarly, there is a greater increase in SSFC than in organic C when conventionally tilled soils are converted to zero tillage (Franzluebbers and Arshad, 1997; Hussain et al., 1999; Needelman et al., 1999), when continuous cereal cropping replaces the use of a summer fallow (Bowman et al., 1999), and when forage grasses rather than cereals or row crops are grown (Doran et al., 1998; Franzluebbers et al., 2000).


233

Figure 4 EVects of long-term trash management and fertilizer applications under sugarcane on soil organic C, light fraction C, K2SO4-extractable C, and potentially mineralizable C. Grass, undisturbed grassed area; BR, burnt with harvest residues removed; B, burnt with harvest residues left on plots; T, green cane harvested with retention of a trash blanket; FO, unfertilized; and F, fertilized annually with N, P, and K. Means associated with the same letter are not significantly diVerent (P 0.05). Reprinted from ‘‘Soil Biology and Biochemistry’’, Vol. 34, 2002, pp. 93–102, Graham et al.: Soil organic matter content and quality: Effects of fertilizer applications, burning and trash retention on a long-term sugarcane experiment in South Africa, with permission from Elsevier.

E. SEASONAL FLUCTUATIONS Although some workers have noted only minor variations (Bremer et al., 1994), substantial seasonal fluctuations in LF material have been observed by a number of workers (Boone, 1994; Campbell et al., 1999a,b; Conti et al., 1992; Spycher et al., 1983). Campbell et al. (1999a) noted that temporal variability in LFC and LFN was associated with changes in soil moisture,

234

R. J. HAYNES

temperature, rainfall and, in some cases, rhizodeposition of root material during anthesis of wheat. Whenever conditions favored rapid decomposition in situ (e.g., high moisture, temperature, and/or precipitation), Campbell et al. (1999a,b) obtained low values for LF material in subsequent laboratory measurements. Boone (1994) concluded that the seasonality of organic matter inputs to the soil was the main factor aVecting the amounts of LF extracted from soils under maize.

F.

SIGNIFICANCE TO SOIL QUALITY

Particulate organic matter contributes to soil function in a number of ways. First, it is the pathway through which C is returned to the soil from plant litter (either as above-ground residues or from root turnover). It is, therefore, the precursor for formation of other forms of organic matter (e.g., microbial biomass, soluble, nonhumic, and humic) and thus is a key attribute of soil quality. The POM is the major source of cellular C and energy for the heterotrophic microbial biomass. As a result, microflora are concentrated on and around the POM rather than being distributed homogeneously throughout the soil volume (Gregorich and Janzen, 1996). Kanazawa and Filip (1986), for example, reported that 34–42% of bacteria and 33% of fungi in soil were associated with organic particles ( SO24 > NO3 ¼ Cl ) (Kaiser and Zech, 1997; Reemtsma et al., 1999; Styllberg and Magnusson, 1995; Vance and David, 1991). For the above reasons, the use of various salt solutions to extract DOM is likely to influence the amounts present, particularly in soils with an appreciable content of Al and Fe oxides.

240

R. J. HAYNES

E. QUANTITIES OF DOM Dissolved organic C in field-moist soils typically accounts for only 0.05–0.40% SOC in agricultural soils (Campbell et al., 1999a,b; Chantigny et al., 1999; Haynes and Williams, 1999; Lundquist et al., 1999; Sarathchandra et al., 1988). In forest soils, it is often in the range of 0.25–2.0% but can be considerably higher (Boyer and GroVman, 1996; Cook and Allen, 1992a; Smolander et al., 2001). Less is known regarding the proportion of total N present as DON. However, Smolander et al. (2001) found that in Norway spruce stands DON represented 0.15% of total N; under clear cutting, it represented 0.34%. In agricultural soils, Haynes (2000) found it accounted for 0.15–0.19% of total N, while in undisturbed pasture soils it represented 0.15–0.61%, and following cultivation of pastures, 0.22–2.8% (Bhogal et al., 2000).

F. MANAGEMENT-INDUCED CHANGES The responsiveness of DOM to changes in agricultural practice is not well documented at present. Nevertheless, it has been shown to be increased more markedly than organic C or total N by addition of crop residues (Graham et al., 2002; Jensen et al., 1997), replacement of wheatfallow systems by continuous wheat (Campbell et al., 1999a,b), conversion of conventional management to an organic system (Lundquist et al., 1999), conversion of an arable system to pasture (Haynes 1999, 2000), ploughingin a pasture (Murphy et al., 2000), and stock camping by grazing animals (Haynes and Williams, 1999), and Chantigny et al. (1999) found it was decreased by increasing fertilizer N rates. As shown in Fig. 4, Graham et al. (2002) found that the increase in 0.5 M K2SO4-extractable C induced by conversion from preharvest burning (BR) of sugarcane to green cane harvesting with trash retention (T) was greater (1.7-fold) than that for organic C (1.2-fold).

G. SEASONAL FLUCTUATIONS Due to the labile nature of DOC, seasonal fluctuations in its concentration are commonly encountered (McGill et al., 1986; Rolston and Liss, 1989). Under arable systems in Canada, DOC was reported to increase from spring to summer and decrease from summer to autumn (Campbell et al., 1999a,b). Similarly, in an arable system in Denmark, Jensen et al. (1997) found that 0.5 M K2SO4-extractable C and N were higher in spring and summer than in autumn and winter. In arable soils in Britain, Murphy


241

Figure 5 Seasonal changes in water-extractable C in the surface 7.5 cm of soil under a permanent pasture over a 2-year period. Reprinted from ‘‘Biology and Fertility of Soils’’, Vol. 6, 1988, pp. 328–335, Sarathchandra et al.: Seasonal changes and the effects of fertilizer on some chemical, biochemical and microbiological characteristics of high-producing pastoral soil, with permission from Springer-Verlag.

et al. (2000) observed an increase in 2 M KCl-extractable organic N in spring and then a decrease during summer. Because of the shortage of available C in arable soils, DOC probably increases in spring due to deposition of root C by the growing crop. This soluble C is then metabolized by the microbial biomass in late summer, when soil temperature and moisture conditions favor high microbial activity. In C-rich pastoral systems, DOC has been observed to be low in summer and high during late winter (Dormaar et al., 1984; Sarathchandra et al., 1988). The peaks in DOC under a permanent pasture over the late winter–spring period (August–September) are clearly evident in Fig. 5. This increase has been attributed to greatly decreased soil microbial activity and/or death and lysis of microbial cells during the cold conditions. Soluble organic matter decreases during spring because conditions are more favorable for microbial activity; this results in use of DOC by the microbial biomass.

H. SIGNIFICANCE TO SOIL QUALITY Dissolved organic matter is considered the most dynamic C fraction in soils, and a portion of it is a readily available substrate for microbial activity (McGill et al., 1986). It therefore represents a mobile source of energy in soils. It is also a primary source of mineralizable N, S, and P (Haynes, 2000) and so can make an important contribution to nutrient availability and

242

R. J. HAYNES

cycling. It behaves as a reactive component of soil solution and can form soluble complexes with multivalent cations (Stevenson, 1994), thus influencing their bioavailability and/or movement within the soil profile. For example, complexation of monovalent Al in soil solution by soluble organic matter renders it essentially nonphytotoxic (Haynes and Mokolobate, 2001). In addition, DOM can contribute to soil acidity through the presence of low-molecular-weight organic acids (Dijkstra et al., 2001), and its leaching contributes to the nutrient and organic matter status and pH of ground and surface waters (McDowell and Likens, 1988; Moore, 1997; Qualls and Haines, 1991). In many respects, DOM has been found to be more important in its role in the N than the C cycle (Qualls et al., 1991). Indeed, in general, DON (as well as S and P) is more mobile in soils than SOC since it is preferentially concentrated in the lower molecular weight, more mobile humic fractions (Kaiser, 2001; Kaiser and Zech, 1997). Half, or more, of the soluble N in soil solution is in organic form in most forested ecosystems (Bergmann et al., 1999; Casals et al., 1995; Cortina and Romana, 1992; Seely et al., 1998; Smolander et al., 1995). Furthermore, DON is the major form of N exported from most forested watersheds (Qualls et al., 1991; Smolander et al., 2001; Sollins and McCorison, 1981). Smolander et al. (2001) studied concentrations of DON under Norway spruce stands over a 5-year period. As shown in Fig. 6, under the forested plot, organic N in soil solution averaged less than 2 mg L 1 but amounted to 77% of total N in solution. Clear-cutting increased the amount of both mineral and organic N in solution, but the percentage of total N present in organic form still averaged 65%. In the past 5 years, attention has turned to the role of DON in agricultural soils. Bhogal et al. (2000) found that DON accounted for 20–90% of 2 M KCl-extractable N in pastoral soils, with exceptionally high values observed in recently cultivated pastures. Murphy et al. (1999) found that it accounted for 33–60% of total soluble N in arable and grass ley soils, while in 12 arable soils it was observed to make up 40–50% of soluble N (Murphy et al., 2000). In a grassland soil in northern Ireland, DON accounted for up to 55 and 20% of annual N losses via drainage from plots receiving 100 and 500 kg N ha 1 year 1, respectively (Watson et al., 2000), and concentrations of SON in drainage water exceeded the European Community maximum admissible concentration for drinking water (1.0 mg N L 1). Thus, DON seems to be an important pool of N in agricultural soils. The very small size of the DOM pool and its highly labile nature has led some to question the validity of its use as a soil quality indicator (Baldock and Nelson, 2000). That is, it is flux of readily available substrates through the DOM that is important in relation to the size and activity of the microbial biomass and nutrient availability. Because the concentration of readily metabolizable organic compounds in solution is kept low by microbial assimilation and/or mineralization, the size of the pool does not


243

Figure 6 Concentrations of dissolved organic N and percentage of total dissolved N present as organic N in soil solution collected at a depth of 10 cm over a 5-year period after clear-cutting a Norway spruce stand or leaving it under forest. Columns show annual means (SD). Reprinted from ‘‘Biology and Fertility of Soils’’, Vol. 33, 2001, pp. 190–196. Smolander et al.: Dissolved soil organic nitrogen and carbon in a Norway spruce stand and in an adjacent clear-cut, with permission from Springer-Verlag.

necessarily reflect the flux through it. Nonetheless, as discussed above, it has been used successfully as an indicator of changes in soil management, and its role in leaching of N is becoming increasingly recognized.

V.

EXTRACTABLE FORMS OF ORGANIC MATTER

Many diVerent chemical extractants have been used in attempts to extract a labile portion of organic matter from soils. For example, many chemical indices of potentially mineralizable soil N have been proposed (Goh

244

R. J. HAYNES

and Haynes, 1986; Keeney, 1982). These can be divided into three broad groups: (i) weak (hot water, hot 0.01 M CaCl2, hot 1 M or 2 M KCl, 0.01 M NaHCO3), (ii) intermediate (alkaline permanganate, Na2CrO4 plus H3PO4, 1 M NaOH), or (iii) strong (6 N H2SO4, K2Cr2O7-H2SO4) extractants (Goh and Haynes, 1986). Numerous other reagents have been used to extract a labile fraction of organic matter, including NaOH, Na2CO3, Na2P2O7, acetylacetone, acetylaldehyde, acetone (Stevenson, 1994), and chelating resins (Dormaar, 1972). A detailed discussion of the use of various extractants is beyond the scope of this chapter. However, three diVerent extractants that have been used recently to evaluate labile organic matter for soil quality evaluation are discussed in the following sections.

A.

HOT WATER-EXTRACTABLE ORGANIC MATTER

Two diVerent approaches have been employed to extract the hot waterextractable fraction. The first is to extract with boiling water for about 1 hour (Keeney and Bremner, 1966; Leinweber et al., 1995; Redl et al., 1990), and the second is to extract at 80 8C for about 16 hours (Chan and Heenan, 1999; Ghani et al., 2000; Sparling et al., 1998). Hot waterextractable C extracted by either method accounts for about 1–5% of soil organic C (Chan and Heenan, 1999; Leinweber et al., 1995; Sparling et al., 1998). The determination of an easily mineralizable N by extraction with hot water was pioneered by Keeney and Bremmer (1966). Later, Ko¨rschens et al. (1990) suggested that the fertility status of soils could be characterized by determinations of hot water-extractable C and N. Leinweber et al. (1995) used solid state 13C-NMR and pyrolysis-field ionization mass spectrometry to show that hot water-extracted organic matter was largely composed of carbohydrates and N-containing compounds, amino-N species and amides in particular. Ghani et al. (2000) observed that 45–60% of C extractable with hot water was carbohydrates. Several workers have suggested that hot water extracts contain organic substances that are mainly of microbial origin (Redl et al., 1990), and the monosaccharide content of the carbohydrate component confirms this (see below). Hot water-extractable C is usually extracted from air-dried soils, so much of the microbial biomass has been desiccated and the cells lysed. Thus, a substantial portion of the hot water-extractable C and N may originate directly from the microbial biomass (Sparling et al., 1998). It may also originate from root exudates and lysates, organic matter weakly adsorbed to soil minerals, that bound to, or trapped, in humic molecules and that involved in bonding soil aggregates together.


245

The carbohydrate content of hot water extracts has been used as an index of soil quality, particularly in relation to soil aggregation (Haynes and Beare, 1996). Carbohydrates are very important bonding agents for soil aggregates (Degens, 1997; Haynes and Beare, 1996), but measurement of total acid hydrolyzable carbohydrates does not diVerentiate between total carbohydrates and the more specific pool that is involved in aggregation. A promising alternative is to extract an active fraction that is involved in binding aggregates. The hot water-extractable carbohydrate fraction has been suggested as such a pool (Haynes and Swift, 1990). Indeed, a number of workers have observed that the hot water-extractable carbohydrate fraction is more closely related to aggregate stability than total carbohydrates or total organic C of soils (Angers et al., 1993a; Ball et al., 1996; Haynes and Beare, 1997; Haynes and Francis, 1993; Haynes and Swift, 1990; Haynes et al., 1991). This fraction accounts for about 6–13% of the total carbohydrate content of soils (Haynes and Francis, 1993; Haynes et al., 1991; Puget et al., 1999). The monosaccharide content of hot water extracts has been analyzed in order to examine its origin. The [galactose (G) þ mannose (M)]:[arabinose (A) þ xylose (X)] ratio is typically low (2.0) for microbial polysaccharides (Oades, 1984). In bulk soils, the (G þ M):(A þ X) ratio in hot water extracts generally ranges from 1.3 to 1.7 (Ball et al., 1996; Debrosz et al., 2002; Puget et al., 1999), and in rhizosphere soil, Haynes and Francis (1993) and Haynes and Beare (1997) found a ratio of 1.9–2.3. This suggests that the extracts are dominated by mucigel of microbial origin but that plant polysaccharides are also present. Haynes et al. (1991) found that hot water-extractable carbohydrate changed much more rapidly in response to short-term pasture than organic C. As shown in Table I, 4 years of pasture in an arable pasture rotation Table I EVect of Previous Cropping History on Aggregate Stability, Organic C, Hot Water-Extractable Carbohydrate and Biomass C Content of a Soil from the South Island of New Zealand Previous cropping history 18 years pasture 4 years pasturea 1 year pasture 1 year arable 4 years arable 10 years arable a

Aggregate stability (MWD, mm)

Organic C (%)

Hot water extractable carbohydrate (mg C g 1)

Microbial biomass C (mg C g 1)

2.7 2.5 2.0 1.3 1.2 1.0

3.2 2.5 2.4 2.4 2.4 2.0

208 169 152 140 134 127

1018 890 801 738 712 610

The 1-year and 4-years pasture and 1-year and 4-years arable soils come from a cropping rotation of 4 years arable followed by 4 years pasture. (Data from Haynes et al., 1991.)

246

R. J. HAYNES

resulted in a substantial increase in hot water-extractable carbohydrate, microbial biomass C, and aggregate stability, while organic C content did not change significantly. Other workers have also recorded short-term increases in hot water-extractable carbohydrate and microbial biomass C in response to rhizodeposition of organic matter where no changes in organic C were detectable (Haynes and Beare, 1997; Haynes and Francis, 1993).

B. DILUTE ACID-HYDROLYZABLE C A labile fraction of soil C, more specifically, carbohydrate C, has been extracted by dilute acid hydrolysis (0.5 M–2.5 M H2SO4) (Angers and Mehuys, 1989; Angers et al., 1993b; Carter et al., 1994; Chan and Heenan, 1999; Shepherd et al., 2001). Chan and Heenan (1999) found that hydrolysis with 1.5 M H2SO4 released 32–37% of total organic C, while Rovira and Vallejo (2002) found hydrolysis with 5 N H2SO4 extracted between 22 and 45% of total organic C. Dilute acid hydrolysis commonly extracts 5–16 times as much carbohydrate as hot water (Angers et al., 1993a; Puget et al., 1999; Shepherd et al., 2001); it extracts about 65–85% of the total carbohydrate content of soils (Puget et al., 1999). Acid hydrolyzable carbohydrates have been found to change more rapidly in response to changes in management than organic C content. Angers et al. (1993b) found that the ratio of acid hydrolyzable carbohydrate C to total organic C was greater under zero than conventional tillage, while Angers and Mehuys (1989) found that the ratio increased in the order of bare soil < maize < barley < lucerne. Nevertheless, Angers et al. (1993a) observed that aggregate stability was more closely correlated with hot water-extractable than dilute acid hydrolyzable carbohydrates.

C. PERMANGANATE-OXIDIZABLE C Blair et al. (1995a) suggested that a fraction of organic C oxidizable with 333 mM KMnO4 for 1 hour was a useful index of labile soil C. This fraction encompasses all those organic components that can be readily oxidized by KMnO4, including labile humic material and polysaccharides (Conteh et al., 1999). Blair (2000) showed that neither CaCO3 nor charcoal contributes significantly to labile C measured by oxidation. The KMnO4-oxidizable organic C fraction accounts for 5–30% (often after 15–20%) of organic C (Blair, 2000; Blair et al., 1995a, 1998; Conteh et al., 1999; Graham et al., 2002; Whitbread et al., 1998).


247

This oxidizable fraction is usually more sensitive to soil management than total organic C content. It has been shown to be sensitive to conversion from grassland to arable agriculture (Blair et al., 1995a; Lefroy et al., 1993) or conversion from burning to crop residue retention (Blair, 2000; Blair et al., 1998; Conteh et al., 1999). Blair et al. (1998), for example, reported no significant change in organic C in the top 7.5 cm of soil following conversion of sugarcane from burning to trash retention, but there was a significant increase in oxidizable C.

VI. POTENTIALLY MINERALIZABLE C AND N A. METHOD OF QUANTIFICATION Mineralizable C is usually measured by incubating a sample of field-moist soil in a sealed chamber containing an alkali trap. The CO2-C accumulated ¨ hlinger, 1996a; Zimbilske, 1994). in the trap is measured by acid titration (O The incubation period commonly ranges from 10 to 30 days, and the chamber is opened and the trap periodically replaced to allow gas exchange and thus maintenance of aerobic conditions. The CO2 accumulated in the headspace can also be measured using a CO2 analyzer (a gas chromatograph or infrared gas analyzer), and various continuous flow automated methods have been developed to allow simultaneous aeration and periodic gas sam¨ hlinger, 1996b; Zimbilske, 1994). Mineralizable C (and pling (Alef, 1995a; O N) is usually calculated in mg kg 1 soil. Since the CO2 evolved is produced by microbial respiration, it can also be presented as basal respiration rate (mg CO2-C g 1 day 1). Mineralizable N can be measured in closed or open incubation systems. A closed incubation is the same as that described above, and mineralizable C and N can be measured simultaneously. The quantity of exchangeable (2 M KCl-extractable) mineral N (NHþ 4 plus NO3 N) in the soil is measured before and after incubation, and mineralizable N is calculated by the diVerence (Alef, 1995b; Drinkwater et al., 1996). In an open incubation, soil is typically mixed with sand (to maintain porosity and hydraulic conductivity) and incubated in a leaching column for 8–30 weeks (Bundy and Meisinger, 1994; Stanford, 1982). The soil is leached with 0.01 M CaCl2 periodically, and NHþ 4 and NO3 N in leachates is measured. A nutrient solution (minus N) is applied after each leaching, and the soil is then drained to a known tension and reincubated. In recent times, open incubation systems have been used in preference to closed systems since they are thought to simulate the eVect of continual removal of mineralized NHþ 4 and NO3 N by plant uptake. Their disadvantage

248

R. J. HAYNES

is that they are time consuming in comparison with short-term closed incubations. Short-term anaerobic (waterlogged) laboratory incubation systems have also been used to measure mineralizable N (Keeney, 1982), while C and N mineralization can both be measured in field incubations (Anderson, 1982; Raison et al., 1987).

B. NATURE OF THE FRACTION It is important to note here that potentially mineralizable C and N are not analagous measurements. The CO2 evolved during incubation indicates the total metabolic activity of the heterotrophic microorganisms in the soil that are decomposing organic matter, using substrate C as an energy source, and respiring CO2. Potentially mineralizable N is a measure of the net flux of mineral N released from the mineralizable organic fraction in the soil. However, mineralization and immobilization of N occur simultaneously. That is, a portion of the mineral N released during gross N mineralization is assimilated by the soil microflora, and the excess not required by them accumulates in the soil as NHþ and NO3 N. As a result, the magnitude and 4 patterns of potentially mineralizable C and N do not necessarily correspond. For example, if the soil contains an available substrate with a wide C:N ratio, then during its decomposition, and release of CO2, mineral N will be assimilated from the surrounding soil (immobilized) by the decomposer microflora. As a result, there may be no immediate release of mineral N into the soil.

C. RELATIONSHIP WITH OTHER POOLS Potentially mineralizable C and N have been observed to be positively correlated with microbial biomass C and N (Angers et al., 1993b; Campbell et al., 1991, 1997; Franzluebbers et al., 1994; Hassink, 1995; Janzen et al., 1992), light fraction C and N (Barrios et al., 1996; Campbell et al., 1999a,b; Hassink, 1995; Janzen et al., 1992; Wander and Bidard, 2000), and soluble C and N (Campbell et al., 1999a,b). The strong linear relationship between LFC and soil respiration rate (i.e., potentially mineralizable C) for soils from a long-term soil management trial in Saskatchewan is shown in Fig. 7. Such results are not surprising because the light fraction, microbial biomass, and soluble organic matter can all be substrates for mineralization of C and N. In addition, the microbial biomass is the agent for mineralization. In this regard, it is interesting to note that Campbell et al. (1999b) observed a negative relationship between seasonal fluctuations in microbial biomass


249

Figure 7 Relationship between basal soil respiration and light fraction C on a long-term crop rotation experiment in Saskatchewan. Reprinted from ‘‘Soil Science Society of America Journal’’, Vol. 56, 1992, pp. 1799–1806. Janzen et al.: Light-fraction organic matter in soils from long-term crop rotations, with permission from the Soil Science Society of America.

C and potentially mineralizable C. They suggested that conditions in the field that were optimal for development of a large microbial population also favored greater in situ mineralization. As a result, there is less substrate left in the soil to be mineralized in a subsequent laboratory incubation. Often, light fraction C is more closely correlated with potentially mineralizable C than light fraction N is with potentially mineralizable N (Campbell et al., 1997, 1999b; Janzen et al., 1992). This is thought to be the case because the wide C:N ratio of the light fraction can induce temporary N immobilization (Janzen et al., 1992; Whalen et al., 2000). On a long-term crop rotation experiment, Biederbeck et al. (1994) found that a multiple regression including microbial biomass C and light fraction C accounted for 98% of the variability in potentially mineralizable C while at another similar site these two parameters accounted for 82% of variability (Campbell et al., 1997). The positive correlations of (i) light fraction organic matter, (ii) microbial biomass, and (iii) soluble organic matter with potentially mineralizable C and N in soils do not necessarily mean that these three fractions contribute most of the mineralizable C and N in soils. For example, in most studies, light fraction N has been shown to contribute considerably less to net mineralization than the remaining humified heavy fraction (Boone, 1994; Whalen et al., 2000; Yakovchenko et al., 1998). This is because the light fraction comprises a relatively small proportion (e.g., 1–16%) of total soil nitrogen, and only a small proportion of that (1–5%) is readily mineralizable (Barrios et al., 1996; Imhof et al., 1996; Yakovchenko et al., 1998). Boone (1994) calculated that the light fraction contributed between 2 and 13% of net soil N mineralization.

250

R. J. HAYNES

The salient points here are that these labile pools are dynamic and that it is the flux of C and nutrients flowing through them that is important rather than the amount held in them at any one time. The size of these pools may, however, be indicative of the flux through them. Nitrogen present in the humic component in easily degradable forms may well have recently passed through the labile light fraction, soluble, and microbial biomass pools. Recently immobilized N is normally more readily mineralized than the bulk of native soil organic N, and immobilized N becomes increasingly recalcitrant with time as it becomes more strongly incorporated into complex humic molecules (Haynes, 1986b).

D.

AMOUNTS PRESENT IN SOILS

The quantities of mineralizable C and N measured are dependent on many factors, including temperature, moisture content, aeration, sample pretreatment (particularly air-drying), and the duration and measurement interval of incubation (Goh and Haynes, 1986; Keeney, 1982). Due to the above reasons, and the lack of accepted standard conditions and duration of incubation, it is often not possible to compare absolute quantities of potentially mineralizable C and N reported between studies. However, changes in mineralizable organic matter due to alterations in land management are generally evident regardless of the magnitude of absolute values. As a broad generalization, potentially mineralizable C and N account for between 0.8 and 12% (often 1.5–5.0%) of total organic C and N (Franzluebbers et al., 1996; Gregorich et al., 1994; Hassink, 1994; Haynes, 1999; Sollins et al., 1984; Whalen et al., 2000).

E. MANAGEMENT-INDUCED CHANGES In general, mineralizable C and N show a greater responsiveness to changes in soil management than do organic C or total N (Campbell et al., 1997; Gregorich et al., 1997a). Disproportionately greater increases in mineralizable than in total organic matter have been observed in response to decreases in the amount of fallow in cereal rotations (Biederbeck et al., 1994; Bremer et al., 1994; Campbell et al., 1999a,b), cropping with grasses rather than cereals (Biederbeck et al., 1994; Campbell et al., 1997), conversion from conventional to zero tillage (Carter and Rennie, 1982; Needelman et al., 1999), and long-term fertilization (Campbell et al., 1997). A disproportionate increase in potentially mineralizable C (1.9-fold) compared with organic C (1.2-fold) in response to trash retention rather than preharvest burning of sugarcane is shown in Fig. 4. In general,


251

mineralizable N concentrations are less responsive to soil management than those for C (Biederbeck et al., 1994; Campbell et al., 1997). This is because temporary immobilization of N can occur concomitantly with C mineralization and CO2 evolution. It has been suggested that the amount of C rendered mineralizable following air-drying is a good indicator of labile organic C (Franzluebbers et al., 1996, 2000). Franzluebbers et al. (2000) found that this fraction was positively correlated with soil microbial biomass C, DOC, and POC and was a more sensitive measure than total organic C to the eVects of conversion from conventional to zero tillage, use of forage crops in rotation, and long-term fertilizer applications.

F.

SEASONAL FLUCTUATIONS

Seasonal fluctuations in potentially mineralizable C and N occur in field soils (Bonde and Rosswall, 1987; Boone, 1994; Campbell et al., 1999a,b; Franzluebbers et al., 1995) and can usually be related to rhizodeposition root material during crop growth and/or inputs of litter and crop residues. Fluctuations generally appear less pronounced for mineralizable N than for C (Campbell et al., 1999a,b). In wheat fields in Swift Current, Saskatchewan, Campbell et al. (1999a) found that potentially mineralizable C increased over the growing season (due to inputs of root material) and then declined in autumn as C mineralization in situ increased due to favorable moisture conditions. By contrast, early in the growing season, potentially mineralizable N either remained constant or tended to decrease. This was interpreted as deposition of root material with a wide C:N ratio, causing release of CO2 but concomitant temporary N immobilization.

G. SIGNIFICANCE TO SOIL QUALITY Under field conditions, rates of C and N mineralization are often limited by moisture and temperature restraints. Thus, mineralization of C and N measured in a laboratory incubation under optimum temperature and moisture conditions represents a maximum potential rate. It gives no indication of what proportion of that mineralizable pool will be mineralized during the growing season. It is also important to recognize that sample pretreatment prior to incubation favors mineralization. That is, the soil is normally sieved (53 mm fraction fraction) by dispersion and sieving. The chemical and biological natures of these two fractions diVer in that the SSF makes up a greater proportion of soil organic C and total N than does the LF (Table II). The LF also tends to be less aromatic, and it has a more rapid turnover time than the SSF. Particulate organic matter is a readily decomposable substrate for microorganisms and a short-term resevoir of nutrients. A large part of the

Figure 8 Schematic diagram showing the relationship between various organic matter fractions.

Organic fraction

Typical quantities 1

Organic C ¼ 7–60 g C kg

Particulate organic matter

LF ¼ 2–18% of organic C, 1–16% of total N SSF ¼ 20–45% of organic C and 13–40% of total N

Microbial biomass

1–5% of organic C and 1–6% of total N

Soluble organic matter

About 0.05–0.40% organic C and total N

Extractable organic C and N

Variable amount of organic C (1–40%) depending on the extractant

Potentially mineralizable C and N

About 1–5% of organic C and total N

Nature and significance Sum of organic material (both living and dead) present in soil excluding living plant material. Single most important factor involved in soil productivity. Has massive eVects on chemical, physical, and biological properties and processes in soils. Partially decomposed plant litter isolated by density fractionation (LF) or sieving (SSF). Substrate and center for soil microbial activity, short-term resevoir of nutrients, food source for earthworms, and other soil fauna and focci for formation of water stable aggregates. Organic material associated with cells of living soil microorganisms. Agent for transformation and cycling of organic matter and nutrients, formation and decay of humic material, dynamic source and sink of plant nutrients, and an agent involved in formation and stabilization of aggregates. Water soluble organic compounds present in soil solution, including simple compounds of plant and microbial origin as well as humic material. Available substrate for microbial activity, primary source of mineralizable N, S, and P, its leaching greatly influences nutrient and organic matter status and pH of groundwater. Organic C and N solubilized/hydrolyzed/oxidized by various chemical reagents. The hot water-extractable fraction is dominated by microbial carbohydrates and is believed to be involved in aggregate stabilization. Acid-hydrolyzable carbohydrates are also thought to be involved in aggregation. Permanganate-oxidizable C is a non-specific labile fraction. Quantities of organic C and N released by indigenous soil microflora during a laboratory incubation. Values are the result of an integration of physical, chemical, and microbiological properties of the soil. Indicator of the N fertility of soils and their ability to supply N to crops.

R. J. HAYNES

Total organic C and N

254

Table II Nature, Significance, and Typical Quantities of Selected Organic Matter Fractions Present in Soils


255

microbial community and thus respiratory and enzyme activity in soils is associated with the LF, and it also acts as the center for the formation of water-stable aggregates. While fungal hyphae permeate the decomposing LF organic matter, bacteria live in water films over its surface. The LF is also important as a food source for earthworms and other soil fauna. Increases in POM usually reflect greater organic matter inputs in the form of aboveand/or below-ground plant litter. As such, they can be expected to be translated into a higher soil organic matter content in the longer term. However, if high POM levels are the result of factors that are temporarily reducing decomposition rate, then increases in organic matter content are unlikely. The small, transitory nature of POM means that changes in C supply and/or rate of decomposition induced by changes in soil management practice are generally reflected by earlier, more pronounced changes in particulate than in total soil organic matter content. The microbial biomass mainly consists of bacteria and fungi and makes up about 1–5 and 2–6% of organic C and total N, respectively, in soils. The diverse soil microbial community acts as an agent for the transformation and cycling of organic matter and nutrients and also as a sink (during immobilization) and source (following microbial death) of nutrients. It is important in soil aggregation through the binding and gluing actions of exocellular polysaccharides and enmeshing eVects of fungal hyphae. Because of its high turnover rate, relative to total soil organic matter, the microbial biomass can change rapidly in response to changes in soil chemical and physical properties induced by changes in soil management. It is recognized as a useful, sensitive early indicator of changes in organic matter status induced by changes in soil management. Dissolved organic matter consists of a wide range of organic components including simple organic acids, phenols, carbohydrates, amino sugars, and complex humic molecules. Although it represents a key labile substrate for microbial activity, only about 10–40% of it is readily degradable. This fraction is rich in carbohydrates, while the recalcitrant part consists mainly of relatively resistant soluble humic substances. Dissolved organic matter originates from leaching from plant litter, the products of decomposition of the litter, the synthetic activity of decomposer microflora, hydrolysis of insoluble organic polymers, and desorption of organic matter adsorbed to soil colloids. As well as being an important microbial substrate, it is important in the terrestrial C and N cycles. Its leaching contributes to the nutrient and organic matter status and pH of ground and surface waters. It is the major form of N leached from forests, and it may also account for 50% or more of N leached from agricultural soils. The fact that DOM is extracted with water and sometimes dilute salt extractants makes comparisons of diVerent studies diYcult since the relationship between these various forms of DOM is, at present, unclear.

256

R. J. HAYNES

Several extractable organic matter fractions have also been proposed as important indicators of soil quality, including those extractable with hot water, dilute acid, and permanganate. The hot water-extractable C fraction accounts for 1–5% of soil organic C, and about 50% of this is thought to be present as carbohydrate C. Because it is extracted from air-dried soils, much of it originates from desiccated microbial cells, but it also includes exocellular polysaccharides, root exudates, lysates, and humic material. The hot water-extractable carbohydrate fraction has been used as a sensitive indicator of changes in organic matter status induced by changes in soil management and as a C fraction closely involved in aggregation and aggregate stability. Dilute, acid-hydrolyzable C, or carbohydrate C, has also been used as an indicator of changes in organic matter status and aggregate stability induced by changes in soil management. Generally, it is less well correlated with aggregate stability than the hot water-extractable fraction. Similarly, permanganate-oxidizable C accounts for about 15–20% of organic C and has been used as a relatively sensitive indicator of changes in organic matter status induced by changes in soil management. The measurement of potentially mineralizable C and N is a bioassay of labile organic matter using the indigenous microbial community to release labile fractions of C and N from soil organic matter. It is diYcult to compare mineralizable C and N between studies because of diVerences in moisture content, temperature, and length of incubation period. Mineralizable C indicates the total metabolic activity of the heterotrophic microflora in releasing labile organic C as CO2-C. However, mineralizable N is a measure of the net flux of mineral N released from soil organic N. Nitrogen mineralization and immobilization occur simultaneously, and the quality of organic residues in soils (particularly their C:N ratio) can greatly influence the magnitude of the net release of mineral N. Despite these complications, measurements of potentially mineralizable C and N use the indigenous microflora to release organic C and N under laboratory conditions, where chemical and physical conditions are largely determined by inherent soil properties. Thus, these measurements represent an integration of physical, chemical, and microbiological properties of the soil. Potentially mineralizable N has been used for many years as an indicator of the N fertility of soils and their ability to supply N for crop growth. In addition, it is an indicator of the potential supply of soil nitrate that can be lost to the atmosphere via denitrification or to groundwater via nitrate leaching. Quantities of microbial biomass, light fraction, and water-soluble C and N in soils are commonly positively correlated with levels of mineralizable C and N. This does not, however, necessarily mean that these pools contribute most of the C and N to the potentially mineralizable fraction. Much of this probably originates from easily degradable humified and partially


257

humified organic material. It is important to recognize that pools such as soluble, light fraction, and microbial biomass are dynamic and that it is the rate of transfer of C and N through them that is of equal or greater importance than their size at any one time. Nonetheless, the size of these pools may well be indicative of the flux through them, and as a result, they are valuable indicators of soil quality.

B. PRACTICAL VALUE OF LABILE ORGANIC MATTER FRACTIONS Taken together, labile organic matter fractions reflect the diverse, but central, roles that organic matter have regarding soil properties and processes and thus the ability of the soil to function. This multifunctional role of soil organic matter means that a suite of labile fractions is typically required to provide an overview of major soil functions, including soil structural integrity, nutrient availability and turnover, and soil biological activity. The fact that these labile fractions are transient and highly sensitive means that values are subject to substantial seasonal variability; thus, sampling needs to be carried out at the same time each year. Otherwise, temporal variability may obscure important diVerences. When changes in soil management have resulted in significant alterations in bulk density, comparisons of values for total and labile organic matter on a volume basis are important. In undisturbed natural ecosystems, the quantities of total and labile organic matter present vary greatly from system to system and are a function of soil-forming factors such as climate, topography, parent material, and time. Similarly, under a given agricultural land management practice, the organic matter status attained can diVer appreciably depending upon factors such as climate, clay content, and mineralogy. As a result, threshold levels of total and labile organic matter are not available and, indeed, probably diVer substantially for diVerent soils and their use under diVerent management purposes. Therefore, it is important that a comparison be made to a reference or baseline soil that has remained unaVected by agricultural management (i.e., a sample taken from under undisturbed native vegetation). In addition, investigation of the rate of change in values for labile organic matter fractions over time is generally more meaningful than considering absolute values. Labile organic matter fractions can be and are being used to monitor changes in soil quality. However, it is important that a mechanistic view of agricultural soil management is maintained. Because diVerent fractions reflect diVerent key functions of organic matter, their use is extremely valuable in investigating how various agricultural management strategies influence the biological, chemical, and physical properties of soils and ultimately the sustainability of such strategies. With a mechanistic understanding of

258

R. J. HAYNES

how soil management practices aVect soil properties and processes, new and innovative management strategies can be devised that will improve agricultural sustainability.

REFERENCES Addiscott, T. M., and Powlson, D. S. (1992). Partitioning losses of nitrogen fertilizer between leaching and denitrification. J. Agric. Sci. Camb. 118, 101–107. Aiken, G., and Leenheer, J. (1993). Isolation and chemical characterization of dissolved and colloidal organic matter. Chem. Ecol. 8, 135–151. Alef, K. (1995a). Soil respiration. In ‘‘Methods in Applied Soil Microbiology and Biochemistry’’ (K. Alef and P. Nannipieri, Eds.), pp. 214–219. Academic Press, London. Alef, K. (1995b). Nitrogen mineralization in Soils. In ‘‘Methods of Soil Microbiology and Biochemistry’’ (K. Alef and P. Nannipieri, Eds.), pp. 234–238. Academic Press, London. Allison, F. E. (1973). ‘‘Soil Organic Matter and its Role in Crop Production’’. Elsevier, Amsterdam. Alvarez, C. R., Alvarez, R., Grigera, M. S., and Lavado, R. S. (1998). Associations between organic matter fractions and the active soil microbial biomass. Soil Biol. Biochem. 30, 767–773. Anderson, J. P. E. (1982). Soil respiration. In ‘‘Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties’’ (A. L. Page, R. H. Miller, and D. R. Keeney, Eds.), pp. 831–871. American Society of Agronomy, Madison, WI. Angers, D. A., Bissonnette, N., Le´ge`re, A., and Samson, N. (1993b). Microbial and biochemical changes induced by rotation and tillage in a soil under barley production. Can. J. Soil Sci. 73, 39–50. Angers, D. A., Edwards, L. M., Sanderson, J. B., and Bissonnette, N. (1999). Soil organic matter quality and aggregate stability under eight potato cropping sequences in a fine sandy loam of Prince Edward Island. Can. J. Soil Sci. 79, 411–417. Angers, D. A., and Mehuys, G. R. (1989). EVects of cropping on carbohydrate content and water-stable aggregation of a clay soil. Can. J. Soil Sci. 69, 373–380. Angers, D. A., Samson, N., and Le´ge`re, A. (1993a). Early changes in water-stable aggregation induced by rotation and tillage in a soil under barley production. Can. J. Soil Sci. 73, 51–59. Appel, T. (1998). Non-biomass soil organic N—The substrate for N mineralization flushes following soil drying-rewetting and for organic N rendered CaCl2-extractable upon drying. Soil Biol. Biochem. 30, 1445–1456. Appel, T., and Mengel, K. (1990). Importance of organic nitrogen fractions in sandy soils, obtained by electro-ultrafiltration of CaCl2 extraction, for nitrogen mineralization and nitrogen uptake of rape. Biol. Fertil. Soils 10, 97–101. Appel, T., and Mengel, K. (1992). Nitrogen uptake of cereals grown on sandy soils as related to nitrogen fertilizer application and soil nitrogen fractions obtained by electroultrafiltration (EUF) and CaCl2 extraction. Eur. J. Agron 1, 1–9. Appel, T., and Mengel, K. (1993). Nitrogen fractions in sandy soils in relation to plant nitrogen uptake and organic matter incorporation. Soil Biol. Biochem. 25, 685–691. Baldock, J. A., and Nelson, P. N. (2000). Soil organic matter. In ‘‘Handbook of Soil Science’’ (M. E. Sumner, Ed.), pp. B25–B84. CRC Press, Boca Raton, FL.


259

Baldock, J. A., Oades, J. M., Waters, A. G., Peng, X., Vassallo, A. M., and Wilson, M. A. (1992). Aspects of the chemical structure of soil organic materials as revealed by solid-state 13 C NMR spectroscopy. Biogeochemistry 16, 1–42. Ball, B. C., Cheshire, M. V., Robertson, E. A. G., and Hunter, E. A. (1996). Carbohydrate composition in relation to structural stability, compactibility and plasticity of two soils in a long-term experiment. Soil Tillage Res. 39, 143–160. Barrios, E., Buresh, R. J., and Sprent, J. I. (1996). Nitrogen mineralization in density fractions of soil organic matter from maize and legume cropping systems. Soil Biol. Biochem. 28, 1459–1465. Bergmann, C., Fischer, T., and Hu¨ttl, R. F. (1999). Significance of litter- and humus-layer quality for rates and forms of N cycling in moder- to rawhumus—moder profiles under Scots pine (Pinus sylvestris L.). Plant Soil 213, 11–21. Besnard, E., Chenu, C., Balesdent, J., Puget, P., and Arrouays, D. (1996). Fate of particulate organic matter in soil aggregates during cultivation. Eur. J. Soil Sci. 47, 495–503. Bhogal, A., Murphy, D. V., Fortune, S., Shepherd, M. A., Hatch, D. J., Jarvis, S. C., Gaunt, J. L., and Goulding, K. W. T. (2000). Distribution of nitrogen pools in the soil profile of undisturbed and reseeded grasslands. Biol. Fertil. Soil 30, 356–362. Biederbeck, V. O., Janzen, H. H., Campbell, C. A., and Zentner, R. P. (1994). Labile soil organic matter as influenced by cropping practices in an arid environment. Soil Biol. Biochem. 26, 1647–1656. Blair, G. T., Chapman, L., Whitbread, A. M., Ball-Coelho, B., Larsen, P., and Tiessen, H. (1998). Soil carbon changes resulting from sugarcane trash management at two locations in Queensland, Australia, and in North-East Brazil. Aust. J. Soil Res. 36, 873–881. Blair, G. J., Lefroy, R. D. B., and Lisle, L. (1995a). Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems. Aust. J. Soil Res. 46, 1459–1466. Blair, J. M., Parmelee, R. W., and Lavelle, P. (1995b). Influences of earthworms on biogeochemistry. In ‘‘Earthworm Ecology and Biogeography in North America’’ (P. F. Hendrix, Ed.), pp. 127–158. Lewis, Boca Raton, FL. Blair, N. (2000). Impact of cultivation and sugar-cane green trash management on carbon fractions and aggregate stability for a Chromic Luvisol in Queensland, Australia. Soil Till. Res. 55, 183–191. Boissier, J. M., and Fontvieille, D. (1993). Biodegradable dissolved organic carbon in seepage waters from two forest soils. Soil Biol. Biochem. 25, 1257–1261. Bolan, N. S., Baskaran, S., and Thiagarajan, S. (1996). An evaluation of the methods of measurement of dissolved organic carbon in soils, manures, sludges, and stream water. Commun. Soil. Sci. Plant Anal. 27, 2723–2737. Bolinder, M. A., Angers, D. A., Gregorich, E. G., and Carter, M. R. (1999). The response of soil quality indicators to conservation management. Can. J. Soil Sci. 79, 37–45. Bonde, T. A., and Rosswall, T. (1987). Seasonal variation of potentially mineralizable nitrogen in four cropping systems. Soil Sci. Soc. Am. J. 51, 1508–1514. Boone, R. D. (1994). Light-fraction soil organic matter: Origin and contribution to net nitrogen mineralization. Soil Biol. Biochem. 26, 1459–1468. Bowman, R. A., Vigil, M. F., Nielsen, D. C., and Anderson, R. L. (1999). Soil organic matter changes in intensively cropped dryland systems. Soil Sci. Soc. Am. J. 63, 186–191. Boyer, J. N., and GroVman, P. M. (1996). Bioavailability of water extractable organic carbon fractions in forest and agricultural soil profiles. Soil Biol. Biochem. 28, 783–790. Bremer, E., Janzen, H. H., and Johnston, A. M. (1994). Sensitivity of total light fraction and mineralizable organic matter to management practices in a Lethbridge soil. Can. J. Soil Sci. 74, 131–138.

260

R. J. HAYNES

Brooks, P. D., McKnight, D. M., and Bencala, K. E. (1999). The relationship between soil heterotrophic activity, soil dissolved organic carbon (DOC), leachate, and catchment-scale DOC export in headwater catchments. Water Resour. Res. 35, 1895–1902. Bundy, L. G., and Meisinger, J. J. (1994). Nitrogen availability indices. In ‘‘Methods of Soil Analysis Part 2’’ (R. W. Weaver, S. Angle, P. Bottemly, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, Eds.), pp. 951–984. Soil Science Society of America, Madison, WI. Burford, J. R., and Bremner, J. M. (1975). Relationships between denitrification capacities of soils and total water-soluble and readily decomposable soil organic matter. Soil Biol. Biochem. 7, 389–394. Cadisch, G., and Giller, K. E. (1997). ‘‘Driven by Nature: Plant Litter Quality and Decomposition’’. CAB, Wallingford, UK. Cambardella, C. A., and Elliott, E. T. (1992). Particulate soil organic matter changes across a grassland cultivation sequence. Soil Sci. Soc. Am. J. 56, 777–783. Campbell, C. A. (1978). Soil organic carbon nitrogen and fertility. In ‘‘Soil Organic Matter’’ (M. Schnitzer and S. U. Khan, Eds.), pp. 173–271. Elsevier, New York. Campbell, C. A., Biederbeck, V. O., Zentner, R. P., and Lafond, G. P. (1991). EVects of crop rotations and cultural practices on soil organic matter, microbial biomass and respiration in a thin Black Chernozem. Can. J. Soil Sci. 71, 363–376. Campbell, C. A., Janzen, H. H., and Juma, N. G. (1997). Case studies of soil quality in the Canadian prairies: Long-term field experiments. In ‘‘Soil Quality for Crop Production and Ecosystem Health’’ (E. G. Gregorich and M. R. Cater, Eds.), pp. 351–397. Elsevier, Amsterdam. Campbell, C. A., Biederbeck, V. O., Wen, G., Zentner, R. P., Schoenau, J., and Hahn, D. (1999a). Seasonal trends in selected soil biochemical attributes: EVects of crop rotation in the semiarid prairie. Can. J. Soil Sci. 79, 73–84. Campbell, C. A., Lafond, G. P., Biederbeck, O., Wen, G., Schoenau, J., and Hahn, D. (1999b). Seasonal trends in soil biochemical attributes: EVects of crop management on a Black Chernozem. Can. J. Soil Sci. 79, 85–97. Carter, M. R., and Rennie, D. A. (1982). Changes in soil quality under zero tillage farming systems: Distribution of microbial biomass and mineralizable C and N potentials. Can. J. Soil Sci. 62, 587–597. Carter, M. R., Angers, D. A., and Kunelius, H. T. (1994). Soil structural form and stability, and organic matter under cool-season perennial grasses. Soil Sci. Soc. Am. J. 58, 1194–1199. Carter, M. R., Gregorich, E. G., Anderson, D. W., Doran, J. W., Janzen, H. H., and Pierce, F. J. (1997). Concepts of soil quality and their significance. In ‘‘Soil Quality for Crop Production and Ecosystem Health’’ (E. G. Gregorich and M. R. Carter, Eds.), pp. 1–19. Elsevier, Amsterdam. Carter, M. R., Gregorich, E. G., Angers, D. A., Beare, M. H., Sparling, G. P., Wardle, D. A., and Voroney, R. P. (1999). Interpretation of microbial biomass measurements for soil quality assessment in humid temperature regions. Can. J. Soil Sci. 79, 507–520. Carter, M. R., Gregorich, E. G., Angers, D. A., Donald, R. G., and Bolinder, M. A. (1998). Organic C and N storage, and organic C fractions, in adjacent cultivated and forested soils of eastern Canada. Soil Till. Res. 47, 253–261. Casals, P., Romanya, J., Cortina, J., Fons, J., Bode, M., and Vallejo, V. R. (1995). Nitrogen supply rate in Scots pine (Pinus sylvestris L.) forests of contrasting slope aspect. Plant Soil 169, 67–73. Chan, K. Y., and Heenan, D. P. (1999). Microbial-induced soil aggregate stability under diVerent crop rotations. Biol. Fertil. Soils 30, 29–32.


261

Chantigny, M. H., Angers, D. A., Pre´vost, D., Simard, R. R., and Chalifour, F. P. (1999). Dynamics of soluble organic C and C mineralization in cultivated soils with varying N fertilization. Soil Biol. Biochem. 31, 543–550. Christensen, B. T., and Johnston, A. E. (1997). Soil organic matter and soil quality—Lessons learned from long-term experiments at Askov and Rothamsted. In ‘‘Soil Quality for Crop Production and Ecosystem Health’’ (E. G. Gregorich and M. R. Carter, Eds.), pp. 399–430. Elsevier, Amsterdam. Conteh, A., Blair, G. T., Lefroy, R. D. B., and Whitbread, A. M. (1999). Labile organic carbon determined by permanganate oxidation and its relationships to other measurements of soil organic carbon. Humic Subst. Environ. J. 1, 3–15. Conti, M. E., Palma, R. M., Arrigo, N., and Giardino, E. (1992). Seasonal variations of the light organic fractions in soils under diVerent agricultural management systems. Commun. Soil. Sci. Plant Anal. 23, 1693–1704. Cook, B. C., and Allan, D. L. (1992a). Dissolved organic carbon in old field soils: Compositional changes during the biodegradation of soil organic matter. Soil Biol. Biochem. 24, 595–600. Cook, B. D., and Allan, D. L. (1992b). Dissolved organic carbon in old field soils: Total amounts as a measure of available resources for soil mineralization. Soil Biol. Biochem. 24, 585–594. Cortina, J., and Romana, J. (1992). Nutrients transfer from forest floor to mineral soil in a Pinus radiata D.Don stand. In ‘‘Responses of Forest Ecosystems to Environmental Changes’’ (A. Teller, P. Mathy, and J. N. R. JeVers, Eds.), pp. 767–768. Elsevier, London. Curry, J. P. (1998). Factors aVecting earthworm abundance in Soils. In ‘‘Earthworm Ecology’’ (C. A. Edwards, Ed.), pp. 37–64. St. Lucie Press, Boca Raton, FL. Dalal, R. C. (1998). Soil microbial biomass—What do the numbers really mean? Aust. J. Exp. Agric. 38, 649–665. Dalal, R. C., and Chan, K. Y. (2001). Soil organic matter in rainfed cropping systems of the Australian cereal belt. Aust. J. Soil. Res. 39, 435–464. Dalal, R. C., and Mayer, R. J. (1987). Long-term infertility of soils under continuous cultivation and cereal cropping in Southern Queensland. VI. Loss of total nitrogen from diVerent particle-size and density fractions. Aust. J. Soil Res. 25, 83–93. Davidson, E. A., Galloway, L. F., and Strand, M. K. (1987). Assessing available carbon: Comparison of techniques across selected forest soils. Commun. Soil Sci. Plant Anal. 18, 45–64. Debrosz, K., Vognsen, L., and Labouriau, R. (2002). Carbohydrates in hot water extracts of soil aggregates as influenced by long-term management. Commun. Soil Sci. Plant Anal. 33, 623–634. Degens, B. P. (1997). Macro-aggregation of soils by biological bonding and binding mechanisms and the factors aVecting these: A review. Aust. J. Soil Res. 35, 431–460. Dijkstra, F. A., Geibe, C., Holmstrom, S., Lundstro¨m, U. S., and van Breemen, N. (2001). The eVect of organic acids on base cation leaching from the forest floor under six North American tree species. Eur. J. Soil Sci. 52, 205–214. Dominy, C. S., and Haynes, R. J. (2002). Influence of agricultural land management on organic matter content, microbial activity and aggregate stability in the profiles of two Oxisols. Biol. Fertil. Soils 36, 298–305. Doran, J. W., and Parkin, T. B. (1994). Defining and assessing soil quality. In ‘‘Defining Soil Quality for a Sustainable Environment’’ (J. W. Doran, D. C. Coleman, D. F. Bezedick, and B. A. Stewart, Eds.), pp. 3–21. Soil Science Society of America, Madison, WI. Doran, J. W., Elliott, E. T., and Paustian, K. (1998). Soil microbial activity, nitrogen cycling, and long-term changes in organic carbon pools as related to fallow tillage management. Soil Till Res. 49, 3–18.

262

R. J. HAYNES

Dormaar, J. F. (1972). Chemical properties of organic matter extracted from a number of Ah horizons by a number of methods. J. Soil. Sci. 52, 67–77. Dormaar, J. F., Johnston, A., and Smoliak, S. (1984). Seasonal changes in carbon content, and dehydrogenase, phosphatase, and urease activities in mixed prairie and fescue grassland Ah horizons. J. Range Manage 37, 31–35. Drinkwater, L. E., Cambardella, C. A., Reeder, J. D., and Rice, C. W. (1996). Potentially mineralizable nitrogen as an indicator of biologically active soil nitrogen. In ‘‘Methods of Assessing Soil Quality’’ (J. W. Doran and A. J. Jones, Eds.), pp. 217–229. Soil Science Society of America, Madison, WI. Edwards, C. A., and Bohlen, P. J. (1996). ‘‘Biology and Ecology of Earthworms’’. Chapman & Hall, London. Fenton, T. E., Brown, J. R., and Mausbach, M. J. (1999). EVects of long-term cropping on organic matter content of soils: Implications for soil quality. In ‘‘Soil Quality and Soil Erosion’’ (R. Lal, Ed.), pp. 95–124. CRC Press, Boca Raton, FL. Franzluebbers, A. J., and Arshad, M. A. (1997). Particulate organic carbon content and potential mineralization as aVected by tillage and texture. Soil Sci. Soc. Am. J. 61, 1382–1386. Franzluebbers, A. J., Haney, R. L., Hons, F. M., and Zuberer, D. A. (1996). Determination of microbial biomass and nitrogen mineralization following rewetting of dried soil. Soil Sci. Soc. Am. J. 60, 1133–1139. Franzluebbers, A. J., Hons, F. M., and Zuberer, D. A. (1994). Seasonal changes in soil microbial biomass and mineralizable C and N in wheat management systems. Soil Biol. Biochem. 26, 1469–1475. Franzluebbers, A. J., Hons, F. M., and Zuberer, D. A. (1995). Tillage and crop eVects on seasonal soil carbon and nitrogen dynamics. Soil. Sci. Soc. Am. J. 59, 1618–1624. Franzluebbers, A. J., Stuedemann, J. A., Schomberg, H. H., and Wilkinson, S. R. (2000). Soil organic C and N pools under long-term pasture management in the Southern Piedmont USA. Soil Biol. Biochem. 32, 469–478. Ghani, A., Dexter, M., Sarathchandra, U., Perrott, K. W., and Singleton, P. (2000). Assessment of extractable hot-water carbon as an indicator of soil quality on soils under long-term pastoral, cropping, market gardening and native vegetation. Proc. Aust. NZ. Joint Soils Conf. 2000 2, 119–120. Goh, K. M., and Haynes, R. J. (1986). Nitrogen and agronomic practice. In ‘‘Mineral Nitrogen in the Plant-Soil System’’ (R. J. Haynes, Ed.), pp. 379–468. Academic Press, Orlando, FL. Golchin, A., Baldock, J. A., and Oades, J. M. (1998). A model linking organic matter decomposition, chemistry, and aggregate dynamics. In ‘‘Soil Processes and the Carbon Cycle’’ (R. Lal, J. M. Kimble, R. F. Follett, and B. A. Stewart, Eds.), pp. 245–266. CRC Press, Boca Raton, FL. Golchin, A., Oades, J. M., Skjemstad, J. O., and Clark, P. (1994). Soil structure and carbon cycling. Aust. J. Soil. Res. 32, 1043–1068. Graham, M. H., Haynes, R. J., and Meyer, J. H. (2002). Soil organic matter content and quality: EVects of fertilizer applications, burning and trash retention on a long-term sugarcane experiment in South Africa. Soil Biol. Biochem. 34, 93–102. Gregorich, E. G., and Ellert, B. H. (1993). Light fraction and macro-organic matter in mineral Soils. In ‘‘Soil Sampling and Methods of Analysis’’ (M. R. Carter, Ed.), pp. 397–407. Canadian Society of Soil Science, Lewis, Boca Raton, FL. Gregorich, E. G., and Janzen, H. H. (1996). Storage of soil carbon in the light fraction and macro-organic matter. In ‘‘Structure and Soil Organic Matter Storage in Agricultural Soils’’ (M. R. Carter and B. A. Steward, Eds.), pp. 167–190. CRC Press, Boca Raton, FL.


263

Gregorich, E. G., Carter, M. R., Angers, D. A., Monreal, C. M., and Ellert, B. H. (1994). Towards a minimum data set to assess soil organic matter quality in agricultural soils. Can. J. Soil Sci. 74, 367–385. Gregorich, E. G., Carter, M. R., Doran, J. W., Pankhurst, C. E., and Dwyer, L. M. (1997a). Biological attributes of soil quality. In ‘‘Soil Quality for Crop Production and Ecosystem Health’’ (E. G. Gregorich and M. R. Carter, Eds.), pp. 81–113. Elsevier, Amsterdam. Gregorich, E. G., Drury, C. F., Ellert, B. H., and Liang, B. C. (1997b). Fertilization eVects on physically protected light fraction organic matter. Soil Sci. Soc. Am. J. 61, 482–484. Gregorich, E. G., Ellert, B. H., and Monreal, C. M. (1995). Turnover of soil organic matter and storage of corn residue carbon estimated from natural 13C abundance. Can. J. Soil Sci. 75, 161–167. Gregorich, E. G., Liang, B. C., Drury, C. F., Mackenzie, A. F., and McGill, W. B. (2000). Elucidation of the source and turnover of water soluble and microbial biomass carbon in agricultural soils. Soil Biol. Biochem. 32, 581–587. Gregorich, E. G., Monreal, C. M., Schnitzer, M., and Schulten, H. R. (1996). Transformation of plant residues into soil organic matter: Chemical characterization of plant tissue, isolated soil fractions, and whole soils. Soil Sci. 161, 680–693. Groot, J. J. R., and Houba, V. J. G. (1995). A comparison of diVerent indices for nitrogen mineralization. Biol. Fertil. Soils 19, 1–9. Guggenberger, G., and Zech, W. (1994). Dissolved organic carbon in forest floor leachates: Simple degradation products or humic substances? Sci. Total Env. 152, 37–47. Haney, R. L., Franzluebbers, A. J., Hons, F. M., and Zuberer, D. A. (1999). Soil C extracted with water or K2SO4: pH eVect on determination of microbial biomass. Can. J. Soil Sci. 79, 529–533. Hassink, J. (1994). EVects of soil texture and grassland management on soil organic C and N and rates of C and N mineralization. Soil Biol. Biochem. 26, 1221–1231. Hassink, J. (1995). Density fractions of soil macro-organic matter and microbial biomass as predictors of C and N mineralization. Soil Biol. Biochem. 27, 1099–1108. Haynes, R. J. (1986a). Origin, distribution, and cycling of nitrogen in terrestrial ecosystems. In ‘‘Mineral Nitrogen in the Plant-Soil System’’ (R. J. Haynes, Ed.), pp. 1–51. Academic Press, Orlando, FL. Haynes, R. J. (1986b). The decomposition process: mineralization, immobilization, humus formation, and degradation. In ‘‘Mineral Nitrogen in the Plant-Soil System’’ (R. J. Haynes, Ed.), pp. 52–126. Academic Press, Orlando, FL. Haynes, R. J. (1999). Labile organic matter fractions and aggregate stability under short-term, grass-based leys. Soil Biol. Biochem. 31, 1821–1830. Haynes, R. J. (2000). Labile organic matter as an indicator of organic matter quality in arable and pastoral soils in New Zealand. Soil Biol. Biochem. 32, 211–219. Haynes, R. J., and Beare, M. H. (1996). Aggregation and organic matter storage in mesothermal, humid Soils. In ‘‘Structure and Organic Matter Storage in Agricultural Soils’’ (M. R. Carter and B. A. Stewart, Eds.), pp. 213–262. CRC Press, Boca Raton, FL. Haynes, R. J., and Beare, M. H. (1997). Influence of six crop species on aggregate stability and some labile organic matter fractions. Soil Biol. Biochem. 29, 1647–1653. Haynes, R. J., and Francis, G. S. (1993). Changes in microbial biomass C, soil carbohydrate composition and aggregate stability induced by growth of selected crop and forage species under field conditions. J. Soil Sci. 44, 665–675. Haynes, R. J., and Mokolobate, M. S. (2001). Amelioration of AI toxicity and P deficiency in acid soils by additions of organic residues: A critical review of the phenomenon and the mechanisms involved. Nutr. Cycling Agroecosys. 59, 47–63.

264

R. J. HAYNES

Haynes, R. J., and Swift, R. S. (1990). Stability of soil aggregates in relation to organic constituents and soil water content. J. Soil Sci. 41, 73–83. Haynes, R. J., and Swift, R. S. (1991). Concentrations of extractable Cu, Zn, Fe and Mn in a group of soils as influenced by air- and oven-drying and rewetting. Geoderma 49, 319–333. Haynes, R. J., and Williams, P. H. (1999). Influence of stock camping behaviour on the soil microbiological and biochemical properties of grazed pastoral soils. Biol. Fertil. Soils 28, 253–258. Haynes, R. J., Swift, R. S., and Stephen, R. C. (1991). Influence of mixed cropping rotations (pasture-arable) on organic matter content, water stable aggregation and clod porosity in a group of soils. Soil Tillage Res. 19, 77–87. Herbert, B. E., and Bertsch, P. M. (1995). Characterization of dissolved and colloidal organic matter in soil solutions: A review. In ‘‘Carbon Forms and Functions in Forest Soils’’ (J. M. Kelley and W. W. McFee, Eds.), pp. 63–88. Soil Science Society of America, Madison, WI. Huang, Y., Eglington, G., van der Hage, E. R. E., Boon, J. J., Bol, R., and Ineson, P. (1998). Dissolved organic matter and its parent organic matter in grass upland soil horizons studied by analytical pyrolysis techniques. Eur. J. Soil Sci. 49, 1–15. Hussain, I., Olson, K. R., and Ebelhar, S. A. (1999). Long-term tillage eVects on soil chemical properties and organic matter fractions. Soil Sci. Soc. Am. J. 63, 1335–1341. Imhof, H., Ehaliotis, C., Giller, K., Miranda, C., Pereira, J. M., Urquiaga, S., Boddey, B., and Cadisch, G. (1996). Light fraction mineralization potentials of humid tropical Soils. In ‘‘Progress in Nitrogen Cycling Studies’’ (O. Van Cleemput, G. Hofman, and A. Vermoesen, Eds.), pp. 63–67. Kluwer, Dordrecht. Jandl, R., and Sollins, P. (1997). Water extractable carbon in relation to belowground carbon cycle. Biol. Fertil. Soils 25, 196–201. Janzen, H. H. (1995). The role of long-term sites in agroecological research: A case study. Can. J. Soil Sci. 75, 123–133. Janzen, H. H., Campbell, C. A., Brandt, S. A., Lafond, G. P., and Townley-Smith, L. (1992). Light-fraction organic matter in soils from long-term crop rotations. Soil Sci. Soc. Am. J. 56, 1799–1806. Janzen, H. H., Campbell, C. A., Ellert, B. H., and Bremer, E. (1997). Soil organic matter dynamics and their relationship to soil quality. In ‘‘Soil Quality for Crop Production and Ecosystem Health’’ (E. G. Gregorich and M. R. Carter, Eds.), pp. 277–291. Elsevier, Amsterdam. Janzen, H. H., Campbell, C. A., Gregorich, E. G., and Ellert, B. H. (1998b). Soil carbon dynamics in Canadian agroecosystems. In ‘‘Soil Processes and the Carbon Cycle’’ (R. Lal, J. M. Kimble, R. F. Follett, and B. A. Stewart, Eds.), pp. 57–80. CRC Press, Boca Raton, FL. Janzen, H. H., Campbell, C. A., Izaurralde, R. C., Ellert, B. H., Juma, N., McGill, W. B., and Zentner, R. P. (1998a). Management eVects on soil C storage on the Canadian prairies. Soil Till. Res. 47, 181–195. Jardine, P. M., Weber, N. L., and McCarthy, J. F. (1989). Mechanisms of dissolved organic carbon adsorption on soil. Soil Sci. Soc. Am. J. 53, 1378–1385. Jenkinson, D. S., Adams, D. E., and Wild, A. (1991). Model estimates of CO2 emissions from soil in response to global warming. Nature 351, 304–306. Jensen, L. S., Mueller, T., Magid, J., and Nielsen, N. E. (1997). Temporal variation of C and N mineralization, microbial biomass and extractable organic pools in soil after oilseed rape straw incorporation in the field. Soil Biol. Biochem. 29, 1043–1055. Johnston, A. E. (1986). Soil organic matter eVects on soils and crops. Soil Use Manage. 2, 97–105. Jones, D. L., and Brassington, D. S. (1998). Sorption of organic acids in acid soils and its implications in the rhizosphere. Eur. J. Soil Sci. 49, 447–455.


265

Kaiser, K. (2001). Dissolved organic phosphorus and sulphur as influenced by sorptive interactions with mineral subsoil horizons. Eur. J. Soil Sci. 52, 489–493. Kaiser, K., and Zech, W. (1997). Competitive sorption of dissolved organic matter fractions to soils and related mineral phases. Soil Sci. Soc. Am. J. 61, 64–69. Kaiser, K., Guggenberger, G., and Zech, W. (1996). Sorption of DOM and DOM fractions to forest soils. Geoderma 74, 281–303. Kanazawa, S., and Filip, Z. (1986). Distribution of microorganisms, total biomass, and enzyme activities in diVerent particles of brown soil. Microb. Ecol. 12, 205–215. Karlen, D. L., Mausbach, M. J., Doran, J. W., Cline, R. G., Harris, R. F., and Schuman, G. E. (1997). Soil quality: A concept, definition, and framework for evaluation. Soil Sci. Soc. Am. J. 61, 4–10. Keeney, D. R. (1982). Nitrogen-availability indices. In ‘‘Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties’’ (A. L. Page, R. H. Miller, and D. R. Keeney, Eds.), pp. 711–733. American Society of Agronomy, Madison, WI. Keeney, D. R., and Bremner, J. M. (1966). Comparison and evaluation of laboratory methods of obtaining an index of soil nitrogen availability. Agron. J. 58, 498–503. Keeney, D. R., and Nelson, D. W. (1982). Nitrogen-inorganic forms. In ‘‘Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties’’ (A. L. Page, R. H. Miller, and D. R. Keeney, Eds.), pp. 643–698. American Society of Agronomy, Madison, WI. Ko¨rschens, M., Schulz, E., and Behm, R. (1990). Der Statische Versuch Lauchsta¨dt. In ‘‘Dauerfeldversuche’’ (M. Korschens, Ed.), pp. 7–23. Akademie der Landwirtschaftswissen-Schaften, Berlin. Ladd, J. N., Amato, M., and Parsons, J. W. (1977). Studies of nitrogen immobilization and mineralization in calcareous soils. 111. Concentration and distribution of nitrogen derived from soil biomass. In ‘‘Soil Organic Matter Studies’’, Vol. 1, pp. 301–311. International Atomic Energy Agency, Vienna. Lavelle, P. (1988). Earthworm activities and the soil system. Biol. Fertil. Soils 6, 237–251. Lavelle, P., Pashanasi, B., Charpentier, F., Gilot, C., Rossi, J.-P., Derouard, L., Andre, J., Ponge, J.-F., and Bernier, N. (1998). Large-scale eVects of earthworms on soil organic matter and nutrient dynamics. In ‘‘Earthworm Ecology’’ (C. A. Edwards, Ed.), pp. 103–121. St. Lucie Press, Boca Raton, FL. Lee, K. E. (1985). ‘‘Earthworms: Their Ecology and Relationships with Soils and Land Use’’. Academic Press, New York, NY. Leenheer, J. A. (1981). Comprehensive approach to preparative isolation and fractionation of dissolved organic carbon from natural waters and waste waters. Environ. Sci. Technol. 15, 578–587. Lefroy, R. D., Blair, G. T., and Strong, W. M. (1993). Changes in soil organic matter with cropping as measured by organic carbon fractions and 13C natural isotope abundance. Plant Soil 155, 399–402. Leinweber, P., Schulten, H. R., and Ko¨rschens, M. (1995). Hot water extracted organic matter: Chemical composition and temporal variations in a long-term field experiment. Biol. Fertil. Soils 20, 17–23. Lundquist, E. J., Jackson, L. E., Scow, K. M., and Hsu, C. (1999). Changes in microbial biomass and community composition, and soil carbon and nitrogen pools after incorporation of rye into three California agriculture soils. Soil Biol. Biochem. 31, 221–236. MacDonald, A. J., Powlson, D. S., Poulton, P. R., and Jenkinson, D. S. (1989). Unused fertilizer nitrogen in arable soils—Its contribution to nitrate leaching. J. Sci. Food. Agric. 46, 407–419. McDowell, W. H., and Likens, G. E. (1988). Origin, composition, and flux of dissolved organic carbon in the Hubbard Brook Valley. Ecol. Mono. 58, 177–195.

266

R. J. HAYNES

McGill, W. B., Cannon, K. R., Robertson, J. A., and Cook, F. D. (1986). Dynamics of soil microbial biomass and water-soluble organic C in Breton L after 50 years of cropping to rotations. Can. J. Soil Sci. 66, 1–19. McNeill, A. M., Sparling, G. P., Murphy, D. V., Braunberger, P., and Fillery, I. R. P. (1998). Changes in extractable and microbial C, N, and P in a western Australian wheatbelt soil following simulated summer rainfall. Aust. J. Soil Res. 36, 841–854. Moore, T. R. (1997). Dissolved organic carbon: sources, sinks, and fluxes and role in the soil carbon cycle. In ‘‘Soil Processes and the Carbon Cycle’’ (R. Lal, J. M. Kimble, R. F. Follett, and B. A. Stewart, Eds.), pp. 281–292. CRC Press, Boca Raton, FL. Moore, T. R., de Souza, W., and Koprivnjak, J. F. (1992). Controls on the sorption of dissolved organic carbon by soils. Soil Sci. 154, 120–129. Murphy, D. V., Fortune, S., Wakefield, J. A., Stockdale, E. A., Poulton, P. R., Webster, C. P., Wilmer, W. S., Goulding, K. W. T., and Gaunt, J. L. (1999). Assessing the importance of soluble organic nitrogen in agricultural Soils. In ‘‘Managing Risks of Nitrates to Humans and the Environment’’ (J. R. Wilson, Ed.), pp. 65–86. Royal Society of Chemistry, Cambridge, UK. Murphy, D. V., Macdonald, A. J., Stockdale, E. A., Goulding, K. W. T., Fortune, S., Gaunt, J. L., Poulton, P. R., Wakefield, J. A., Webster, C. P., and Wilmer, W. S. (2000). Soluble organic nitrogen in agricultural soils. Biol. Fertil. Soils 30, 374–387. Needelman, B. A., Wander, M. M., Bollero, G. A., Boast, C. W., Sims, G. K., and Bullock, D. G. (1999). Interaction of tillage and soil texture: Biologically active soil organic matter in Illinois. Soil Sci. Soc. Am. J. 63, 1326–1334. Nunan, N., Morgan, M. A., Brennan, D., and Herlihy, M. (2001). Organic matter extracted with 0.01 M CaCl2 or with 0.01 M NaHCO3 as indices of N mineralization and microbial biomass. Biol. Fertil. Soils 34, 433–440. Nyborg, M., Malhi, S. S., Solberg, E. D., and Izaurralde, R. C. (1999). Carbon storage and light fraction C in a grassland dark grey Chernozem soil as influenced by N and S fertilization. Can. J. Soil Sci. 79, 317–320. Oades, J. M. (1984). Soil organic matter and structural stability mechanisms and implications for management. Plant Soil 76, 319–337. ¨ hlinger, R. (1996a). Soil respiration by titration. In ‘‘Methods in Soil Biology’’ (F. Schinner, R. O ¨ hlinger, E. Kandeler, and R. Margesin, Eds.), pp. 95–98. Springer-Verlag, Berlin. O ¨ hlinger, R. (1996b). Soil respiration by infrared gas analysis. In ‘‘Methods in Soil Biology’’ O ¨ hlinger, E. Kandeler, and R. Margesin, Eds.), pp. 98–102. Springer(F. Schinner, R. O Verlag, Berlin. Paustian, K., Collins, H. P., and Paul, E. A. (1997). Management controls on soil carbon. In ‘‘Soil Organic Matter in Temperate Agroecosystems’’ (E. A. Paul, K. Paustian, E. T. Elliot, and C. V. Cole, Eds.), pp. 15–49. CRC Press, Boca Raton, FL. Puget, P., Angers, D. A., and Chenu, C. (1999). Nature of carbohydrates associated with waterstable aggregates of two cultivated soils. Soil Biol. Biochem. 31, 55–63. Puget, P., Chenu, C., and Balesdent, J. (1995). Total and young organic matter distribution in aggregates of silty cultivated soils. Eur. J. Soil. Sci. 46, 449–459. Qualls, R. G., and Haines, B. L. (1991). Geochemistry of dissolved organic nutrients in water percolating through a forest ecosystem. Soil Sci. Soc. Am. J. 55, 1112–1123. Qualls, R. G., and Haines, B. L. (1992). Biodegradability of dissolved organic matter in forest throughfall, soil solution, and stream water. Soil Sci. Soc. Am. J. 56, 578–586. Qualls, R. G., Haines, B. L., and Swank, W. T. (1991). Fluxes of dissolved organic nutrients and humic substances in a deciduous forest. Ecology 72, 254–266. Raison, R. J., Connell, M. J., and Khanna, P. K. (1987). Methodology for studying fluxes of soil mineral-N in situ. Soil Biol. Biochem. 19, 521–530.


267

Redl, G., Hu¨bner, C., and Wurst, F. (1990). Changes in hot water soil extracts brought about by nitrogen immobilization and mineralization processes during incubation of amended soils. Biol. Fertil. Soils 10, 45–49. Reemtsma, T., Bredow, A., and Gehring, M. (1999). The nature and kinetics of organic matter release from soil by salt solutions. Eur. J. Soil. Sci. 50, 53–64. Rolston, D. E., and Liss, H. J. (1989). Spatial and temporal variability of water-soluble organic carbon in a cropped field. Hilgardia 57, 1–19. Rovira, P., and Vallejo, V. R. (2002). Labile and recalcitrant pools of carbon and nitrogen in organic matter decomposing at diVerent depths in soil: An acid hydrolysis approach. Geoderma 107, 109–141. Sarathchandra, S. U., Perrott, K. W., Boase, M. R., and Waller, J. E. (1988). Seasonal changes and the eVects of fertilizer on some chemical, biochemical and microbiological characteristics of high-producing pastoral soil. Biol. Fertil. Soils 6, 328–335. Seely, B., Lajtha, K., and Salvucci, G. D. (1998). Transformation and retention of nitrogen in a coastal forest ecosystem. Biogeochemistry 42, 325–343. Seto, M., and Yanagiya, K. (1983). Rate of CO2 evolution from soil in relation to temperature and amount of dissolved organic carbon. Jpn. J. Ecol. 33, 199–205. Shepherd, T. G., Saggar, S., Newman, R. H., and Dando, J. L. (2001). Tillage-induced changes to soil structure and organic carbon fractions in New Zealand soils. Aust. J. Soil Res. 39, 465–489. Skjemstad, J. O., Dalal, R. C., and Barron, P. F. (1986). Spectroscopic investigations of cultivation eVects on organic matter of Vertisols. Soil. Sci. Soc. Am. J. 50, 354–359. Skjemstad, J. O., Le Feuvr, R. P., and Prebble, R. E. (1990). Turnover of soil organic matter under pasture as determined by 13C natural abundance. Aust. J. Soil Res. 28, 267–276. Smith, J. L., and Paul, E. A. (1990). The significance of soil microbial biomass estimations. In ‘‘Soil Biochemistry’’ (J. M. Bollag and G. Stotzky, Eds.), Vol. 6, pp. 357–396. Marcel Dekker, New York, NY. Smolander, A., Kitunen, V., and Ma¨lko¨nen, E. (2001). Dissolved soil organic nitrogen and carbon in a Norway spruce stand and in an adjacent clear-cut. Biol. Fertil. Soils 33, 190–196. Smolander, A., Kitunen, V., Priha, O., and Ma¨lko¨nen, E. (1995). Nitrogen transformations in limed nitrogen fertilized soil in Norway spruce stands. Plant Soil 172, 107–115. Sollins, P., and McCorison, F. M. (1981). Nitrogen and carbon solution chemistry of an old growth coniferous forest watershed before and after cutting. Water Resour. Res. 17, 1409–1418. Sollins, P., Homann, P., and Caldwell, B. A. (1996). Stabilization and destabilization of soil organic matter: Mechanisms and controls. Geoderma 74, 65–105. Sollins, P., Spycher, G., and Glassman, C. A. (1984). Net nitrogen mineralization from lightand heavy-fraction forest soil organic matter. Soil Biol. Biochem. 16, 31–37. Sparling, G. P. (1997). Soil microbial biomass, activity and nutrient cycling as indicators of soil health. In ‘‘Biological Indicators of Soil Health’’ (C. E. Pankhurst, B. M. Doube, and V. V. S. R. Gupta, Eds.), pp. 97–119. CAB, Wallingford, UK. Sparling, G., Vojvodic-Vukovic, M., and Schipper, L. A. (1998). Hot-water-soluble C as a simple measure of labile soil organic matter: The relationship with microbial biomass C. Soil Biol. Biochem. 30, 1469–1472. Spycher, G., Sollins, P., and Rose, S. (1983). Carbon and nitrogen in the light fraction of a forest soil: Vertical distribution and seasonal fluctuations. Soil Sci. 135, 79–87. Stanford, G. (1982). Assessment of soil nitrogen availability. In ‘‘Nitrogen in Agricultural Soils’’ (F. J. Stevenson, J. M. Bremner, R. D. Hauck, and D. R. Keeney, Eds.), pp. 651–688. American Society of Agronomy, Madison, WI. Stevenson, F. J. (1994). ‘‘Humus Chemistry: Genesis, Composition, Reactions’’. John Wiley, New York, NY.

268

R. J. HAYNES

Styllberg, U. S., and Magnusson, T. (1995). Cations adsorbed to soil organic matter—A regulatory factor for the release of organic carbon and hydrogen ions from soils to waters. Water Air Soil Poll. 85, 1095–1100. Vance, E. D., Brookes, P. C., and Jenkinson, D. S. (1987). An extraction method for measuring microbial biomass C. Soil Biol. Biochem. 19, 703–707. Vance, G. F., and David, M. B. (1991). Chemical characteristics and acidity of soluble organic substances from a northern hardwood forest floor, central Maine, USA. Geochim. Cosmochim. Acta 55, 3611–3625. Wagai, R., and Sollins, P. (2002). Biodegradation and regeneration of water-soluble carbon in a forest soil: Leaching column study. Biol. Fertil. Soils 35, 18–26. Wander, M. M., and Bidard, M. G. (2000). Tillage practice influences on the physical protection, bioavailability and composition of particulate organic matter. Biol. Fertil. Soils 32, 360–367. Waters, A. G., and Oades, J. M. (1991). Organic matter in water stable aggregates. In ‘‘Advances in Soil Organic Matter Research: The Impact on Agriculture and Environment’’ (W. S. Wilson, Ed.), pp. 163–175. Royal Society of Chemistry, Cambridge, UK. Watson, C. J., Jordan, C., Lennox, S. D., Smith, R. V., and Steen, R. W. J. (2000). Organic nitrogen in drainage water from grassland in Northern Ireland. J. Environ. Qual. 29, 1233–1238. West, A. W., Sparling, G. P., Feltham, C. W., and Reynolds, J. (1992). Microbial activity and survival in soils dried at diVerent rates. Aust. J. Soil Res. 30, 209–222. Whalen, J. K., Bottomley, P. J., and Myrold, D. D. (2000). Carbon and nitrogen from light- and heavy-fraction additions to soil. Soil Biol. Biochem. 32, 1345–1352. Whitbread, A. M., Lefroy, R. D. B., and Blair, G. J. (1998). A survey of the impact of cropping on soil physical and chemical properties in north-western New South Wales. Aust. J. Soil Res. 36, 669–681. Yakovchenko, V. P., Sikora, L. J., and Millner, P. D. (1998). Carbon and nitrogen mineralization of added particulate and macroorganic matter. Soil Biol. Biochem. 30, 2139–2146. Yano, Y., McDowell, W. H., and Aber, J. D. (2000). Biodegradable dissolved organic carbon in forest soil solution and eVects of chronic nitrogen deposition. Soil Biol. Biochem. 32, 1743–1751. Zak, D. R., Grigal, D. F., Gleesan, S., and Tilman, D. (1990). Carbon and nitrogen cycling during old-field succession: Constraints on plant and microbial biomass. Biogeochemistry 11, 111–129. Zimbilske, L. M. (1994). Carbon mineralization. In ‘‘Methods of Soil Analysis, Part 2. Microbiological and Biochemical Properties’’ (R. W. Weaver, S. Angle, P. Bottomly, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, Eds.), pp. 835–863. Soil Science Society of America, Madison, WI.

CROP RESIDUE MANAGEMENT FOR NUTRIENT CYCLING AND IMPROVING SOIL PRODUCTIVITY IN RICE-BASED CROPPING SYSTEMS IN THE TROPICS Yadvinder-Singh,1 Bijay-Singh1 and J. Timsina2 1

Department of Soils, Punjab Agricultural University, Ludhiana 141 004, India 2 CSIRO Land and Water, Griffith NSW 2680, Australia

I. II. III. IV.

V.

VI.

VII. VIII. IX. X.

XI.

Introduction Availability of Crop Residues in Rice-Based Cropping Systems Management Options for Crop Residues Crop Residue Decomposition A. Kinetics of Crop Residue Decomposition B. Factors AVecting Residue Decomposition C. Fallow Period and Crop Residue Management Crop Residue Management EVects on Nutrient Availability in Soils A. Nitrogen B. Phosphorus C. Potassium D. Sulfur E. Micronutrients EVect of Crop Residues on Soil Properties A. Soil Fertility B. Chemical Properties C. Physical Properties D. Biological Properties E. Crop Residues for Reclamation of Salt-AVected Soils Biological Nitrogen Fixation Phytotoxicity Associated with Crop Residue Incorporation into the Soil Weed Control and Herbicide EYciency Emission of Greenhouse Gases A. Methane B. Nitrous Oxide C. Mitigation Strategies Agronomic Responses to Crop Residue Management A. Rice–Wheat Cropping System B. Rice–Rice Cropping System C. Rice–Legume Cropping System D. Other Rice-Based Cropping Systems 269 Advances in Agronomy, Volume 85 Copyright 2005, Elsevier Inc. All rights reserved. 0065-2113/05 $35.00

270

YADVINDER-SINGH ET AL. XII. Summary and Conclusions XIII. Research Needs References

I. INTRODUCTION Rice is the most important crop in Asia, where more than 90% of all rice is grown and consumed (Blake, 1992). About half of the total area planted to rice is irrigated, and it accounts for the three-fourths of global production (Huke and Huke, 1997). In tropical Asia, rice–rice constitutes an important annual crop rotation. In the subtropical Asia, rice and wheat are grown in rotation in more than 13 million hectares in the Indo-Gangetic plains of South Asia (India, Bangladesh, Nepal, Pakistan) and on similar hectarages in the basin of the Yangtze river in China (Timsina and Connor, 2001). In addition to wheat, other crops grown in rotation with rice are barley, oats, maize, sorghum, legumes (mung bean, peanuts, soybean, lentil, chickpea), oilseeds (mustard, rapeseed), potato, sugarcane, and cotton. Nutrient cycling in the soil–plant ecosystem is an essential component of sustainable productive agricultural enterprise. Although during the last three decades fertilization practices have played a dominant role in the rice-based cropping systems, crop residues—the harvest remnants of the previous crop—still play an essential role in the cycling of nutrients. Incorporation of crop residues alters the soil environment, which in turn influences the microbial population and activity in the soil and subsequent nutrient transformations. It is through this chain of events that management of crop residues regulates the eYciency with which fertilizer, water, and other reserves are used in a cropping system. Another feature of rice-based cropping system in the tropics is the inherent conflict between maximizing shortterm production at minimum cost versus providing sustainable health and long-term productivity of the soil. One reason for this conflict is the general below-average economic condition of the farmers practicing rice-based cropping systems. In the tropics, crop residues have, in fact, played a pivotal role in the maintenance of soil resources at acceptable levels because these are the major sources of C inputs. Tropical agricultural ecosystems are distinct from temperate ones in terms of biological degradation of soils, which results in reduction in organic matter content due to decline in the amount of C inputs from biomass (Stewart and Robinson, 1997). Tropical soils vary widely in their properties and are generally poor in native soil fertility and productivity. The removal of crop residues leads to low soil fertility and thereby decreased crop

CROP RESIDUE MANAGEMENT

271

production. Organic materials such as crop residues oVer sustainable and ecologically sound alternatives for meeting the nutrient requirements of crops. In addition to their role as the primary source of C inputs, crop residues, and the way they are managed, have a significant impact on soil physical properties (Boyle et al., 1989). Future increase in food production in the tropics will only be possible through improvement in soil productivity. Increased concern for the environment and increased emphasis on sustaining soil productivity has resulted in major interest in the maintenance and improvement in soil organic matter in recent years. Proper management and utilization of crop residues and other agricultural wastes will constitute an important factor in achieving this objective. With widespread use of combine harvesters, crop residues (mainly rice and wheat) largely remain in the field and must be managed to provide the greatest advantages. The increasing constraints of labor and time under intensive agriculture have led to the adoption of mechanized farming in rice-based cropping systems. For example, under highly intensive rice–wheat cropping system in northwestern India, combine harvesting of rice and wheat fields, which leaves large amounts of crop residues in the fields, is now a common practice. As crop residues interfere with tillage and seeding operation for the next crop, farmers often prefer to burn these residues. In addition to causing environmental pollution, burning results in large losses of organic carbon and plant nutrients. In recent years, the concept of soil quality has been suggested as a tool for assessing the long-term sustainability of agricultural practices at local regional, national, and international levels. Crop residue management is known to aVect either directly or indirectly most of the soil quality indicators—chemical, physical, or biological. It is perceived that soil quality is improved by the adoption of sound crop residue management practices. For example, Karlen et al. (1994) evaluated several soil quality indicators and developed a soil quality index, which had values of 0.45, 0.68, and 0.86 for removal, normal, and double residue treatments, respectively. In comparison with green manures and legume residues, cereal straws are relatively poor with respect to N and P content. Thus, crops sown immediately after the incorporation of residues of cereal crops suVer due to deficiency of plant-available N. Addition of fertilizer N to the decomposing residues only partially oVsets the immobilization process. Therefore, a major problem encountered in the profitable utilization of cereal crop residues is the occurrence of microbial immobilization of soil and fertilizer N (Mary et al., 1996). Suitable manipulations of processes such as nutrient immobilization are an important component of an eYcient crop residue management program. For example, allowing adequate time for decomposition of crop residues before planting the next crop can be beneficial in alleviating adverse eVects due to N immobilization and phytotoxicity.

272

YADVINDER-SINGH ET AL.

According to one estimate, more than 1000 million tonnes of cereal residues are being produced annually in the developing world (FAO, 1999). If crop residues could be better managed, this would directly improve crop yields by increasing soil nutrient availability, decreasing erosion, improving soil structure, and increasing soil water holding capacity. Crop residues can also lead to negative eVects on crop production in the short term because of N immobilization and possible release of phytotoxic compounds. Considerable research has been conducted in the last few decades relating residue management to soil chemical, physical, and biological properties and consequent fertilizer management practices needed for successful crop production. In this chapter, we have tried to use this knowledge to make recommendations and conclusions for crop residue management in rice-based cropping system. We have not attempted to review all available literature; only pertinent data sets have been used to substantiate diVerent conclusions that emerge from the literature. There may be two diVerent systems of crop residue recycling: (1) when residues are applied directly to the soil and (2) when residues are first allowed to decompose and are used as compost. We have focused our attention mainly on the in situ incorporation of crop residues left naturally in the field under rice-based cropping systems. A special situation is created by the planting of green manure crops in a crop rotation and increasing the amount of crop residues at planting time of the next crop. The use of crop residues as green manures has already been reviewed extensively (Buresh and De Datta, 1991; Yadvinder-Singh et al., 1991) and is not included in this chapter. Similarly, depending on the scale considered, although manures, diVerent organic by-products, animal and human wastes, and food processing wastes originate mainly from harvested plants, these are not categorized as crop residues in this chapter. The challenge is to (1) scientifically understand the short- and long-term eVects of diVerent crop residue management on the cycling of C, N, and other nutrients, and (2) develop technologies for crop residue management that are agronomically beneficial, environmentally friendly, and do not add extra costs.

II. AVAILABILITY OF CROP RESIDUES IN RICE-BASED CROPPING SYSTEMS Rice, wheat, corn, soybean, barley, rapeseed, and potato are the major residue-producing crops. Asia is the major producer of crop residues— 52.6% of the world residues production occurs in Asia. Rice, wheat, and corn are the major crops, contributing about 84% of the total production of crop residue in Asia. On a global basis, these seven crops produced 2956


273

Table I Residue Production (103 t) by Rice and DiVerent Crops Grown in Rotation with Rice in the Tropics in 1998a Crop Rice-straw Rice-husk Wheat Barley Sugarcane Cotton Oats Corn

Asia

Africa

South America

World

771,804 154,361 379,788 34,097 53,855 6378 2424 166,205

25,968 5194 27,395 6753 8561 315 342 38,729

24,153 4831 25,539 2141 41,880 69 1604 54,626

844,782 168,956 946,734 208,229 125,227 6801 51,604 604,013

a

Data pertaining to residue production was computed by multiplying grain yield data reported by FAO (1999) with straw:grain ratios reported by Larson et al. (1978) for South America and by Bhardwaj (1995) and Beri and Sidhu (1996) for Asia and Africa.

million tons of residues in 1998; rice residues were around 1000 million tons (FAO, 1999). Production of residues by diVerent crops that can be grown in rotation with rice in diVerent countries is shown in Table I. Rice contributes about 34.3% of the total residue production, which is 1.2 times more than wheat. Reliable quantitative estimates of crop residues in tropical countries are, however, lacking. Below-ground residue production has often been ignored due to the diYculty in measuring it. In India, an attempt has been made to arrive at a figure based on estimates of crop yields and knowledge of the harvest index of diVerent crops. For example, an estimate for India was made by Bhardwaj and Gaur (1985) by assuming that all residues generated were left in the field and that nutrient availability from this component followed mineralization of 50% per cropping cycle. Average yields of irrigated rice will have to increase from 4.9 t ha 1 in 1991 to about 8 t ha 1 in 2025 (Cassman and Pingali, 1995). If rice cropping is intensified at this scale, grain yield and total biomass production will increase by about 60% during the next 30 years. Our rough estimates indicate that the expected increase in biomass production will potentially increase the amount of C remaining in straw and roots by about 90 million t year 1 and that of N by about 1.8 million t year 1 (Doberman and Witt, 2000). This represents an enlarged sink for CO2 but also a greater potential source for CH4 emission. Globally, about 31, 26, and 154% of N, P, and K, respectively, of the fertilizer consumption in 1998 were found in crop residues (FAO, 1999). Residues of seven leading crops in all the continents contained about 18.8 million tons of N, 2.9 million tons of P, and 24.0 million tons of K. An estimate of the quantity of N, P, and K contained annually in

274


Table II Estimates of N, P, and K ( 103 t) in Residue Produced by DiVerent Crops Grown in Rotation with Rice in the Tropics in 1998a Africa and South America

Asia Crop Rice Wheat Barley Sugarcane Cotton Oats Corn

N

P

K

4862.4 1898.9 221.6 226.2 64.4 15.3 781.2

771.8 265.9 30.7 43.1 9.6 3.9 216.1

4600.0 2354.7 235.3 360.8 63.8 40.0 1229.9

N 327.8 308.1 59.9 211.9 4.4 12.3 788.4

P 52.5 37.1 8.4 40.4 0.6 3.1 148.7

World K

N

P

K

446.1 417.6 73.4 338.0 4.2 32.1 1013.1

5345.6 5650.9 1520.9 526.0 69.5 325.1 5393.0

849.5 662.7 220.9 100.2 10.3 82.6 984.8

5321.7 7758.2 2374.1 839.0 68.5 851.5 6824.3

a Estimates of N, P, and K in crop residues were computed by multiplying residue yield data given in Table I with N, P, and K contents in straw reported by Larson et al. (1978) for South America and by Bhardwaj (1995) and Beri and Sidhu (1996) for Asia and Africa.

the residues of major crops grown in rice-based rotations in diVerent continents is presented in Table II. These estimates are based on average nutrient concentrations in crop residues as reported by Larson et al. (1978) for Europe, South and Central America, and Oceania and by Beri and Sidhu (1996) and Bhardwaj (1995) for Asia and Africa. The values reported in Table II do not include nutrients contained in roots. The crop residue N is available to the extent of 41% in Asia followed by 28% in Northcentral America, 15% in Europe, 11% in South America, 4% in Africa, and only 1% in Oceania. In addition to N, P, and K, crop residues also contain substantial amounts of secondary and micronutrients. The fertilizer equivalent value of field residues for nine Indian crops worked out to be 760,000 tons, a sizeable and significant figure (Bhardwaj and Gaur, 1985). In China, straw yield of cereals has been calculated as 621.6 million tons per year, and 20–30% is commonly returned to fields following harvest (Compilatory Committee, 1990).

III.

MANAGEMENT OPTIONS FOR CROP RESIDUES

There exist several options for managing crop residues. These include being removed from the field, left on the soil surface, incorporated into the soil, burned in situ, composted, or used as mulch for succeeding crops. Throughout the tropics there is little recycling of crop residues in the field—these are either harvested for fuel, animal feed, or bedding or are


275

burned in the field. Crop residues removed from the field can also be used as bedding for animals, a substrate for composting, biogas generation or mushroom culture, or as a raw material for industry. Local conditions determine the disposal method. Currently, in China, North Vietnam, India, Bangladesh, and Nepal, complete removal of straw from the field is widespread in areas with hand harvest and great demand for straw as fodder, as fuel, or for industrial purposes, causing large nutrient export from rice fields. Open-field burning of rice straw is predominant in areas with combine harvesting (northern India, Thailand, parts of China) or where manual thrashing is done in the field (Indonesia, Malaysia, Myanmar, Philippines, southern Vietnam). In many parts of the tropics, crop residues are burned in the field due to the ignorance of farmers about their value and lack of proper technology for in situ incorporation of residues (Samra et al., 2003). For example, in the intensive rice–wheat cropping system in the Indo-Gangetic plains of South Asia, crop residues, particularly rice straw, are not used as animal feed and are disposed of by burning. This is a cost-eVective method of straw disposal and helps to reduce pest and disease populations resident in the straw biomass, but it also causes pollution by releasing CO2, N2O, NH3, and particulate, leading to global warming and health concerns (Kirkby, 1999). It also reduces the number and activity of soil microbes. The magnitude of C and nutrient loss during burning is influenced by the quantity of residue burned and the intensity of the fire. Complete burning of rice straw at 470 8C in muZe furnace resulted in 100, 20, 20, and 80% losses of N, P, K, and S, respectively (Sharma and Mishra, 2001). The corresponding losses due to burning of wheat straw were 100, 22, 22, and 75%. The losses of nutrients were less due to incomplete burning of the crop residues in open air under field conditions: 88.6% N, 1.8% P, 17.5% K, and 25.3% S for wheat straw and 89.2% N, 5.5% P, 19.9% K, and 20.5% S for rice straw, as compared to complete burning. No loss of micronutrients was noted during incomplete burning of straw. The temperature of heating was more important than the duration of heating. In the Philippines, Indonesia, and parts of China, heaping of rice straw in the field at threshing sites is common. Heaping the straw in successive quadrants of a field each season is recommended to even out nutrient distribution. The straw decomposes slowly, largely aerobically, and can be easily spread and incorporated into the soil at the beginning of the next season. Not much is known about the rate at which straw in heaps decomposes or about the loss of N via denitrification or loss of N and K through leaching. Because of air pollution concerns and nutrient losses, the burning of residues is now being reconsidered in many regions of the world (Ocio et al., 1991; Miura and Kanno, 1997). However, in double- or triple-cropped rice-based systems with sustained flooding, incorporating straw may reduce

276


yields (Cassman et al., 1995). The crop residues can impede seedbed preparation and contribute to disease and weed problems. There are currently few options for rice straw because of its poor quality for forage, bioconversion, and engineering applications (Jenkins et al., 1997). Rice growers are therefore seeking alternative disposal options, such as incorporation of the straw into the soil. The incorporation of rice residues and continuous flooding has become common in tropical soils through intensification of rice cropping practices (Cassman and Pingali, 1995). In addition to introducing an extra cost, rice straw incorporation in association with flooding likely impacts soil fertility through nutrient and pest interactions (Cassman et al., 1995, 1997; Olk et al., 1996) and environmental quality through greenhouse gas emissions (Bossio et al., 1999; Delwiche and Cicerone, 1993). In rice–wheat cropping systems, too, management of rice straw, rather than wheat straw, is a serious problem, because there is very little turn-around time between rice harvest and wheat sowing. Incorporating the crop residues into the soil and allowing them to decompose returns to the soil almost all the nutrients in the straw. The common practice of burning the residues can have a net short-term beneficial eVect on the N supply to subsequent crops but a deleterious eVect on overall N supply and soil C. In North America and Europe, incorporation of cereal straw is being considered as an alternative to burning because of concern over the adverse environmental impacts of burning (Prasad and Power, 1991). With the advent of direct drilling, there is now much interest in the possibility of direct drilling of wheat into rice stubble—either the full stubble, or after removal or burning of the header tailings. Current research in Punjab, India shows that sowing into stubble using no-till seed drill is impaired by blockages with the loose straw and inadequate closure of the seed slots. Bed planting provides new opportunities and challenges for stubble management in rice–wheat systems, which need to be addressed. Conservation tillage and mulch farming techniques have proven useful in the highly erodible soils of the Loess Plateau of China (Zhiqiang et al., 1999). Keeping in mind both socioeconomic and biophysical factors, there is a need to develop conservation tillage systems for a wide range of ricebased cropping systems, soils, and agroecological environments. Use of crop residues as mulch is important to the development of soil-specific conservation tillage systems in the upland soils of the tropics. DiVerent residue management technologies or strategies need to be developed at a regional level to fit diVerent rice-based cropping systems and to accommodate the management diversity required within a single farming enterprise. Estimates of relative costs of diVerent options must be developed, as the most attractive choices might have significant impacts on environmental quality through their eVects on microbial processes that determine


277

the magnitude of C storage in the soil, methane emission into the atmosphere, and long-term soil fertility. Incorporation of straw in the soil is the management option dealt in detail in this chapter. After the crop is harvested, the straw is spread on the land and incorporated into the soil by disking or plowing.

IV. CROP RESIDUE DECOMPOSITION Decaying of crop residues starts as soon as the residues come into contact with the soil. The process of decomposition is controlled by the interaction of three components: the soil organisms or biological processes, the quality of crop residues, and the physical and chemical environment. The combination of these components determines not only the rate of decomposition of crop residues but also the end product of the decomposition process. The amount of plant materials decomposed in the soils is determined by the loss of dry weight of these plant materials buried in the soil or by the evolution of CO2 from plant materials, either unlabeled or 14C or 13C. Burying of rice straw in soil has been reported to accelerate the decomposition in comparison with placing the straw on the soil surface (Kumar and Goh, 2000). Residues are managed diVerently; e.g., residues can be placed on the surface, mixed into the soil, or confined in mesh bags within the soil. Surface placement or heterogeneous distribution reduces the residue–soil contact as compared with a homogenous distribution. This may aVect the decomposition dynamics. Knowledge of such eVects is important when results from diVerent studies are being compared and is essential when developing and calibrating decomposition models. It is also important when assessing the eVects of tillage practices resulting in diVerent degrees of residue–soil contact, e.g., no-till ploughing and rotovating. The degree of contact between crop residues and the soil matrix, as determined by the method of residue incorporation, aVects decomposition dynamics under both natural and experimental conditions. A dearth of information exists regarding straw decomposition under upland conditions and its eVect on long-term N availability in temperate regions.

A. KINETICS OF CROP RESIDUE DECOMPOSITION Crop residues left in the field after harvest are the raw materials for humus formation and may represent a significant supply of nutrients to subsequent crops. Knowledge about residue decomposition is, therefore,

278


essential for management of agroecosystems. Most of the work on decomposition of crop residues has been carried out in temperate soils (Kumar and Goh, 2000). Soil C content depends on the amount of C that leaves the soil through decomposition, erosion, or leaching. Under normal circumstances, most of the C is lost from the system through decomposition. Kinetic models of decomposition have commonly used some form of the first-order equation (Molina et al., 1983). Although a single rate constant has been used to describe decomposition over the long term in the field or laboratory (Havis and Alberts, 1993; Schomberg et al., 1994b; Kuo et al., 1997), most shortterm laboratory studies have shown that crop residues contain two or more decomposition fractions (Gilmour et al., 1985; Ajwa and Tabatabai, 1994). In cases in which two or more fractions exist, decomposition can be described by a sequence of first-order equations that allow all fractions to decompose at the same time (simultaneous model). The change of C in the soil can be expressed mathematically in one kinetic rate constant of decomposition: Ct ¼ C0 e

k1 t

þ Ca e

k2 t

ð1Þ

;

where Ct is the amount of soil C at time t, C0 is the amount of soil C at time 0, k1 is the decomposition rate constant (day 1) of the total soil C pool before amendment of C added, Ca is the amount of C (crop residue) added, and k2 is the added C. The decomposition process is often viewed as a series of first-order reactions for various C fractions, each with its own size and decomposition rate decomposition rate constant (day 1) (Jenkinson and Rayner, 1977; Parton et al., 1988). The rapid and slow fractions with a characteristic slope and intercept can be mathematically represented as two simultaneous first-order reactions: % decomposed ¼ % rapidð1 ð100

exp½ k1 tÞ þ

% rapidÞð1

exp½ k2 tÞ;

ð2Þ

where % rapid is the amount of crop residue organic C in the rapid fraction, (100 % rapid) is the amount of crop residue organic C in the slow fraction, k1 is the rapid-fraction first-order rate constant, k2 is the slow-fraction first-order rate constant, and t is the elapsed time. The percentage of the crop residue C remaining is 100 minus decomposed in Eq. (2). The rate constant increased with temperature and was significantly lower under flooded conditions (Devevre and Howarth, 2000). The rate of decomposition of rice straw at 25 8C under flooded conditions was as low as the rate of decomposition of rice straw at 5 8C under nonflooded conditions. Under nonflooded conditions at 15 and 25 8C, the model described two pools of decomposable C (C1/k1 and C2/k2). The first pool (C1) in the nonflooded treatment at 25 8C represented 30% of the straw C with a turnover time


279

of 12.5 days; the second pool (C2) represented 36% of the straw C with a turnover time of 100 days. The corresponding values at 15 8C were 24% with a turnover time of 17 days and 53% with a turnover time of 333 days. Under flooded conditions, only one pool of C could be described using the equation. At 25 8C, C mineralized represented 52% of the straw C with a turnover time of 50 days against 46% of the straw C at 15 8C with a turnover time of 100 days. The most apparent reason for only one pool of decomposable C in flooded treatment was that recycling of waste products from fermentative metabolism extended the availability of labile sources of C. From the amount of C mineralized in the flooded treatments, it is evident that the C2 pool (recalcitrant C compounds: cellulose, lignin, and microbial melanins) was partially degraded and contributed to the total C mineralized. The study indicated that the conversion of C2 straw components to C1 components under fermentive conditions most likely increased the C utilization eYciency of the more recalcitrant C2 pool under flooded conditions. The larger biomass, the simultaneously lower total amount of C mineralized, and the higher eYciency of substrate C conversion to microbial biomass (yield factor) in the flooded soil supported these observations (Devevre and Howarth, 2000). These researchers reported values of C1 and C2 of 1086 and 1324 mg C g 1 and k1 and k2 values of 0.08 and 0.01 day 1 under nonflooded conditions, respectively. The C1 and k1 values for flooded soil were 1916 mg C g 1 and 0.02 day 1 incubated at 25 8C. If management changes are desired to achieve an increase in total soil C or soil organic matter (SOM), Eq. (2) allows for two major options: increase C input or reduce decomposition rates. Virtually no studies have explored a reduction in decomposition rates in intensive lowland rice systems, and hence no literature review can be presented. Table III shows that rates of crop residue decomposition depend on residue type, length of decomposition period, and climatic conditions. Cheng and Wen (1998) studied the decomposition of rice straw over a 10-year period in two soils with diVerent mineralogical characteristics in fields under upland and submerged conditions in China. Using the first-order equation for residue decomposition, they calculated annual mineralization rates (k) of 0.127 under upland and 0.106 under submerged conditions in yellow-brown soil (pH 7.7). The corresponding rates in red soil (pH 4.6) were 0.159 and 0.0948. The half-lives of residual C in the two soils were 4.4–5.5 years under upland and 6.5–7.3 years under submerged conditions. The percentage of organic C retained in two soils under upland and submerged conditions was 29.3–31.3% after year 1 and 7.92–11.6% in year 10 in the yellow-brown soil and 33.5–35.2% in year 1 and 7.48–13.0% in year 10 in the red soil. Mineralization of residual organic N followed the same pattern as residual C. More N from plant material was retained in

280

YADVINDER-SINGH ET AL. Table III Decomposition of DiVerent Crop Residues in DiVerent Laboratory and Field Studies

Reference Cookson et al. (1998) Saini (1989) Reddy et al. (1994) Ghidey et al. (1985)

Type of study

Method used and experimental conditions

Field

Litter bag

Field Field

Litter bag Litter bag

Field

Saviozzi et al. (1997)

Field

Kaboneka et al. (1997) Martin et al. (1983)

Laboratory

Litter bag

Laboratory

Yadvinder-Singh Field et al. (2004b)

Litter bag

Crop residue

Duration

Decomposition (%)

Wheat straw Barley straw Rice straw Rice straw

90 days 90 days 197 days 330 days

36 42 73 90

Wheat straw Soybean Sunflower Soybean Sunflower Rape Soybean Wheat straw Rice straw

10 months 10 months 10 months 1 year 1 year 1 year 30 days 30 days 14 days 90 days 1 year 10 days 20 days 40 days 77 days

36 74 61 66 73 75 67 39 14 46 90 23 35 51 61

Rice straw

the yellow-brown soil than in the red soil. Rice straw mineralized more slowly under submerged conditions than under upland conditions. Buyanovsky and Wagner (1997) described the C decomposition from wheat straw using nonlinear regression to fit a two-component exponential model. This relationship could be used to calculate the percentage of the residue C remaining in the soil at any specified time. The first component represents the rate of mineralization of the readily decomposable fraction. This includes the simple sugars, soluble proteins, hemicellulose, and cellulose. The second component represents the mineralization of the resistant products of microorganisms. The half-life of the first component was 18 days and that of second component was 433 days. The humification coeYcient for wheat straw was 0.24. Mishra et al. (2001a) studied the C and N mineralization from wheat straw using the nylon mesh bag technique in a silty loam paddy soil. Wheat straw decomposed at a faster rate initially for 2 weeks, and the rate of decomposition (measured as loss in weight of straw) slowed down thereafter, probably when soluble carbohydrates and proteins were exhausted. Within


281

2 weeks, 30.7 and 25.3% of wheat straw in the bags had decomposed in the wheat straw plus green manure and wheat straw alone plots, respectively. The green manuring accelerated the decomposition of the wheat straw by lowering the C:N ratio of the decomposing material and by stimulating the microbial population to carry out the decomposition. By the end of 22 weeks, 82–86% of the wheat straw was decomposed. The practice of wheat straw incorporation in conjunction with green manure holds promise for improving the soil productivity in rice–wheat cropping systems. The decomposition of wheat straw followed the first-order kinetic model. The decay rate constant (k) for the wheat straw was 0.013 day 1 and the half-life was 60 days. The C loss could account for 48–49% of the weight loss during 22 weeks. The other constituent of weight loss from wheat straw could be soluble components such as K, Cl, and organic substances released as intermediate products during decomposition. Up to 90% of K can be lost from the crop residues within a few weeks after incorporation into the soil. Zhu et al. (1988) reported that about 75% of wheat straw was decomposed in about 150 days under field conditions in China. Using the nylon mesh bag technique, Mishra et al. (2001b) noted three phases in the decomposition of rice straw in a silty loam soil during wheat growing season. The first phase lasted for 5 weeks, during which the rate of decomposition was relatively faster (38% of the rice residue decomposed), followed by the second phase of slow rate of decomposition from the 6th to the 15th weeks (22% of the rice residue decomposed), which may partly be attributed to the prevailing low air temperatures, followed by the third phase of fast rate of decomposition up to 23 weeks (19% of the rice residue decomposed) due to the rising air temperatures. By the end of 23 weeks, 79% of the rice straw was decomposed. The decomposition of rice straw was satisfactorily described by the Douglas and Rickman (1992) model (R2 ¼ 0:97). The computed values of fN and k were 1.356 and 0.00045 CDD 1, respectively, where CDD is cumulative degree days. Witt et al. (1998), using the litter bag technique, reported a 56% decrease in rice crop residues within 56 days after incorporation at IRRI, Philipines. Kanazawa and Yoneyama (1980) observed that the decomposition of rice straw occurred in two phases in a clayey soil under flooded and upland conditions in a laboratory at 30 8C. During the first 2 to 4 months of incubation, the dry weight decreased by half. This was followed by a long period of very gradual weight decrease. After 12 and 24 months, about 70 and 75% of the initial weight of rice straw, respectively, was lost under both flooded and upland conditions. The above decay pattern has two phases: rapid C loss in the first few months and then slow loss in the subsequent long period. The period of rapid decay seems to be almost coincident with the time of high population of bacteria and fungi. This study showed that the fungi are the main agents in the decomposition of organic materials under upland

282


conditions. In contrast, anaerobes are the main agents in the course of residue decomposition under flooded conditions, and the breakdown of cellulose and lignin may be slow under such decomposing systems. Under field conditions, soil moisture conditions may change from time to time, and this may cause the shifting participation of diVerent microorganisms during plant residue decomposition. The rate of decomposition was slightly higher under flooded than under upland conditions, but the pattern of residue decomposition was similar under the two moisture regimes. The residue decomposition trend was closely related to the changes in the microbial population (bacteria, fungi, and actinomycetes) under flooded conditions. On the other hand, under upland conditions, significant correlations were observed between residue weight loss and bacteria or fungi, but not between residue weight and actinomycetes. This suggested that the fresh crop residues added to soil are a good substrate for microbial activities. Though the numbers of microbes were small under flooded compared to upland conditions, aerobic microbes may play some role in the decomposition process of rice residues in the early incubation periods when the O2 concentration in the soil is high. In the early incubation period, addition of rice straw caused an increase in the microbial numbers under both flooded and upland conditions. The decomposing ability of the anaerobes under flooded conditions may be high enough to decompose the plant materials to a similar extent as that of the aerobes under upland conditions. The numbers of microorganisms in upland soil were larger than under flooded conditions, and the addition of rice residues brought about a vigorous increase in the numbers, especially of actinomycetes and fungi.

B. FACTORS AFFECTING RESIDUE DECOMPOSITION In tropical systems, mineralization rates are potentially higher because of high soil temperatures during cropping season, particularly at the time of incorporation of residues. Several reviews have summarized the factors aVecting crop residue decomposition, particularly in temperate climates (Kumar and Goh, 2000; Parr and Papendick, 1978; Prasad and Power, 1991; Smith et al., 1992). We include here only a brief commentary on the pertinent factors of crop residue decomposition that have relevance to ricebased cropping systems in the tropics. Carbon and nitrogen cycling are mainly caused by changes in the frequency, amount, type, and mode of recycling of crop residues; the frequency, length, and intensity of wetting and drying cycles, that is, disturbances that cause severe shifts in microbial activities and also aVect soil physical properties; and the O2 supply to the soil during rice growth, that is, the amount of irrigation water percolating through the soil and the intensity of soil reduction processes.


283

There are three main factors that aVect crop residue decomposition in the soil: (1) crop residue factors, (2) edaphic factors, and (3) management factors. The development of an eVective crop residue management program depends on a thorough understanding of the ways in which these factors influence the decomposition process. It has been recognized that organic residue decomposition and hence soil organic matter dynamics are a direct result of the physiocochemical environments, e.g., aeration (aerobic/ anaerobic, soil structure) and the quality of the resource acting through their regulation of the decomposer community. Crop residue chemical composition plays an important role in determining decomposition rates. Thus, in order to predict the decomposition and nutrient mineralization patterns of plant residues, it is essential to understand their constitution in terms of soluble and resistant fractions. Early in the decomposition process, rapid loss of simple sugars and amino acids may occur within a few hours to a few days, while polysaccharides, proteins, and lipids decompose at much slower rates. Lignin makes up 5 to 30% of crop residue material and is more resistant to decomposition than other plant constituents. Lignin is an important substrate for soil humus formation due to its resistance to decomposition. Janzen and Kucey (1988) found that diVerences in decomposition rates of crop residues were positively correlated with crop N content. There was no significant relationship reported between decomposition rates and C:N ratio, water-soluble C, lignin, hemicellulose, and cellulose content of crop residues. Using a perfusion system, Villegas-Pangga et al. (2000) observed that the CO2 release rates in 30 rice varieties varied; the percentage of C released from straw ranged from 15.4 to 38.4% in 42 days. There was an inverse relationship (R2 ¼ 0.6) between cumulative C release and C:N ratio and a direct relationship between digestible organic matter (DOM) and cumulative C release. A straw quality index (SQI) was developed to describe the decomposition rate of the rice straw as follows: SQI ¼ 2

56:85 þ ð11:68 % NÞ þ ð1:25 % DOMÞ þ ð2:59 % ligninÞ;

R ¼ 0:81:

ð3Þ

These findings suggested that SQI is a practical tool for assaying the quality of the straw materials to predict their usefulness in crop residue management systems. Despite a twofold diVerence between varieties in the amount of C evolved over 20 days, the proportion of nutrient release did not diVer significantly between them. The availability to microbes of C and N contained in crop residues along with lignin content greatly influence decomposition rates and N availability to plants (Vigil and Kissel, 1991). It is generally accepted that residues with low N content or a high C:N ratio decompose more slowly than those with

284


a low C:N ratio or high N content (Magid et al., 1997; Parr and Papendick, 1978). Christensen (1986) found that 44% of wheat straw containing 0.92% N decomposed during the first month but only 7% of the straw containing 0.4% N decomposed during the same period of incubation. Luo and Cheng (1991) found that the number of days required for 50% mass loss of crop residues was significantly correlated with the N content of the residue. Decomposition rates are normally greater for legume residues (low C:N ratio) than those for cereal residues (high C:N ratio) (Ladd and Foster, 1988). Although N content and C:N ratio are useful in predicting residue decomposition rates, they should be used with some caution. Reinertsen et al. (1984) and Stott and Martin (1989) indicated that the C:N ratio of straw was not a good decomposition index. De Haan (1977) found no relationship between percentage of N in added plant residue and the rate of decomposition. Gilmour et al. (1998) observed that initial (0–2 weeks) decomposition was related to crop residues N and C:N ratio, while subsequent decomposition was not related to these factors. Since C:N ratio does not indicate the availability of the C and N to the microorganisms, crop residue decomposition based on available C and N seems to relate more closely to field observations than decomposition based on total C and N contents (Mtambanengwe and Kirchmann, 1995). The concentration of polyphenol is generally greater in mature residues than in green leaves (Fox et al., 1990; Palm and Sanchez, 1991). The rate of plant residue breakdown depends on the relative proportion of these fractions. Hagin and Amberger (1974) estimated the half-life of sugars, hemicellulose, cellulose, and lignin as 0.6, 6.7, 14.0, and 364.5 days, respectively. Other factors such as lignin, hemicellulose, and polyphenol content should also be considered for predicting decomposition of crop residues. Lignin is known to be a recalcitrant fraction and is highly resistant to microbial decomposition (Mellilo et al., 1982). Many workers have found that increasing lignin concentration reduces the decomposition rate and nutrient release from plant residues (Fox et al., 1990; Tian et al., 1992). Saini et al. (1984) reported that the rates of decomposition of stubbles of rice, wheat, and rape were lower than those of their straws due to high ash and lignin contents. Polyphenol concentration in plant tissue also reduces its rate of decomposition by binding to protein and forming complexes resistant to decomposition (Vallis and Jones, 1973). Since polyphenols have diVerent properties with respect to binding N-containing compounds depending upon their molecular weight (Scalbert, 1991), these govern decomposition and N release in some studies but not in others (Vanlauwe et al., 1996). The decomposition rate of plant residues cannot be predicted from a single property of the organic material. When considered simultaneously,


285

these properties can predict the decomposition rate from a wide range of plant residues. In some studies, polyphenol:N and (lignin þ polyphenol): N ratios have been correlated with residue decomposition and nutrient release (Constantinides and Fownes, 1994; Fox et al., 1990; Palm and Sanchez, 1991; Tian et al., 1995). It has been suggested that the polyphenol:N ratio may serve as a short-term index for green manures, while the (lignin þ polyphenol):N ratio could be used for more mature or woody plant materials (Palm, 1995). 1.

Residue Particle Size

The accessibility of plant residues to soil microbes is of primary importance in their rate of decomposition. The particle size of the residue can provide diVerent degrees of accessibility, which in turn aVect residue decomposition rates as well as the mineralization-immobilization process. Generally, small particles decompose faster than large particles because the increased surface area and better distribution in soil will increase the susceptibility to microbial attack (Jensen, 1994). Angers and Recous (1997) studied the eVect of particle size (0.03 to 10 cm) of wheat straw (C:N ¼ 270) on the decomposition in a silt loam soil incubated at 15 8C. Early decomposition (3–17 days) was faster for the small-sized particles (0.06–0.1 cm), followed by the large-sized particle (5 and 10 cm). After 102 days, the very fine particles ( 2. Vigil and Kissel (1995) reported that measured Q10 for N mineralization depended on the C:N ratio of the residue and incubation time, indicating that for predictive purposes a single Q10 value is inadequate for describing the eVect of temperature on crop residue N mineralization. Honeycutt and Potaro (1990) field tested the application of thermal units for predicting N mineralization from crop residues. It was found that thermal units are valid for predicting commencement of net mineralization of N from crop residues, despite the harsh environmental conditions and wide temperature variations to which these residues and soils were subjected. Nitrogen transformations in flooded soils under rice are markedly diVerent from those taking place in upland soils. The diVerence in the behavior of N in upland and submerged soils is due to the diVerence in activity of microorganisms functioning under aerobic and anaerobic conditions. In an incubation experiment, Yoneyama and Yoshida (1977b) found that net mineralization of soil N was depressed by the addition of rice straw, except that the addition of leaf blade under lowland conditions gave more mineral N at later stage than the unamended control (Table V). Under lowland conditions, the amount of N immobilized in soil amended with rice straw was small during the first week but increased substantially after 2 to 3 weeks. Under upland conditions, the immobilized N reached its maximum during the first week, but the amount of N immobilized was smaller than that under lowland conditions. At 30 days of incubation, 26, 20, and 17% of total N under lowland conditions and 14, 7, and 8% under upland conditions were mineralized in leaf blades, stems, and leaf sheaths, respectively. This suggests that mineralization of rice residue N takes place throughout the decomposition of residue even if the net mineralization of N was not observed by the incorporation of residue low in N content. The amount of absorbed soil N in rice residue (influx) and the remaining original rice


301

Table V Mineralization of Rice Residue in Soil under Upland and Lowland Conditions

Rice residue

Incubation time (days)

Total N added (mg kg 1)

2

5

10

30

32 60 16.8

2.8 3.9 0.3

4.5 9.2 1.1

5.3 14.1 1.8

5.3 15.6 3.2

32 60 16.8

0.8 1.0 0.5

1.3 2.1 0.6

1.4 6.6 0.6

2.5 10.4 1.1

Lowland conditions Leaf sheath Leaf blade Stem Upland conditions Leaf sheath Leaf blade Stem

From Yoneyama and Yoshida (1977b).

residue N (outflux) is more vigorous under lowland than under upland conditions. Therefore, rice yields and N uptake will be greater under lowland than under upland conditions. Kanazawa and Yoneyama (1980) observed that mineral N in soil amended with 15N-labeled rice straw under upland conditions remained at low levels compared with untreated control throughout the 24 months of incubation. In flooded soil, the mineral N was lower in straw-treated soil during the first 4 months, and the diVerences were small between unamended and amended soil thereafter. It is generally found that N mineralization is higher under anaerobic conditions than under aerobic conditions (Ono, 1989). According to Liu et al. (1996), higher mineral N levels in rice straw amended soil under anaerobic compared to aerobic conditions possibly occurred because the minimum need of microorganisms for release of ammonium N from crop residues in flooded soil is about 0.5% compared with 1.7% in aerobic systems. Thus, inorganic N is released in larger quantities in anaerobic than in aerated soils, although the release rate may be slower. Mineralization rate of N in crop residues is reduced at low soil water contents. The eVect of soil temperature and water content on N mineralization can be calculated by using a relationship derived by Andren and Paustin (1987). A normalized time (equivalent to Qsum) was calculated as TðnormalizedÞ ¼ tðrealÞ fðTÞ gð*Þ;

ð5Þ

where f(T) is a correction factor due to soil temperature and g(*) is a reduction factor due to soil water potential. The factor f(T) is a multiexponential function of temperature. It is set at 1 at a temperature of 25 8C. The eVect of soil moisture on N mineralization was described as an exponential function of soil water potential. The combined eVect was

302


calculated as the product of the two terms, assuming that there were no interactions between temperature and moisture. This approach enables the comparison of field experiments diVering in climatic conditions to laboratory experiments conducted under constant temperature and moisture. Mary et al. (1996) found that when the normalized days were substituted to real days, the diVerence in the kinetics of net N immobilization and C decomposition in the soils where wheat straw was incorporated under field conditions were not significant between two years (Fig. 4). c. Placement of Crop Residues. Using 15N-labeled crop residues, Smith and Sharpley (1990) found that surface placement of residues reduced N availability as compared to soil incorporation, but the diVerences were only equivalent to 1 to 7 kg N ha 1. Residue placement influences N mineralization through an eVect on the microclimate of the residue. Slower decomposition rates of surface residues may result in greater potential for immobilizing N for longer periods than for incorporated residues. Schomberg et al. (1994b) reported that N immobilization period was longer than 1 year for surface applied wheat and sorghum residues and about 4 months for buried residues. The maximum value for N immobilization was 50% lower for buried residues. Although greater N immobilization may occur with surface residues, subsequent N mineralization can occur within a period that is optimum for crop utilization. Residue incorporation with conventional tillage agroecosystems can be characterized as bacterial-based food webs with fast rates of litter

Figure 4 (A) EVect of wheat straw on net immobilization of soil mineral N versus ‘‘normalized’’ time. (B) Wheat straw C (fraction > 1 mm) remaining in soil in the field experiments versus ‘‘normalized’’ time (Mary et al., 1996).


303

decomposition and nutrient mineralization, while surface residues under no tillage systems support fungal-based food webs that result in slower decomposition and greater nutrient retention (Beare et al., 1996). Placement of residues may play an important role in determining availability of soil N to subsequent crops during the N immobilization-mineralization process. d. Soil Type. Soil texture controls mineralization by (1) influencing aeration/moisture status, (2) aVecting the physical distribution of organic materials and hence potential for degradation, and (3) conferring some degree of ‘‘protection’’ through an association of organic materials with clay particles (Hassink et al., 1993). Becker et al. (1994b) observed that residue N release in clayey soil was approximately twice that of sandy soil. DiVerences in mineralization rates between soils would have an impact on the fertilizer N requirement of the subsequent crop and the potential for N loss due to leaching or denitrification. Using 15N-labeled wheat straw and legume residues, Amato et al. (1987), however, observed that eVect of soil properties and climate on the residual organic 15N was small. Decomposition and mineralization of crop residues, however, are inhibited under strongly acidic conditions. For example, Fu et al. (1987) indicated that N mineralization increased as soil pH increased from 5 to 7. e. Soil and Fertilizer Nitrogen. Cereal residues generally possess low N content and may require addition of exogenous N for decomposition to proceed. From a series of experiments, Yoshida et al. (1973) inferred that N mineralization in soil amended with rice straw increased with increasing 1 soil, but N mineralization NHþ 4 –N concentration up to 300 mg N kg decreased when rates greater than 300 mg N kg 1 were applied. Mary et al. (1996) concluded that immobilization intensity of crop residues expressed per unit of mineralized carbon is reduced and N remineralization is delayed in soils with low mineral N concentrations. Nitrogen availability in soil can therefore strongly modify the mineralization–immobilization kinetics by a feedback eVect. On bare plots, immobilization of mineral N by wheat straw incorporation increased markedly by the addition of mineral N throughout the decomposition. A better prediction of the evolution of mineral N in soil may, therefore, require description and modeling of the respective localization of both organic matter and mineral N in soil aggregates.

3.

Effect of Crop Residues on Utilization of N by Crops

Availability of N from crop residues to subsequent crops is highly dependent on decomposition rate, residue quality, and environmental conditions (Fox et al., 1990). Application of crop residues has been shown to depress

304


the NHþ 4 –N concentration in soil and flood water due to N immobilization and consequential lower N uptake by rice compared with control (Huang and Broadbent, 1988; Nagarajah et al., 1989). Rice is known to take up more organic N than any other crop because (1) it takes up NHþ 4 , amino acids, or relatively high molecules of organic N, preferentially; (2) it has stronger activity in competing with soil microorganisms than the other crops; (3) it secretes organic substrates that support multiplication of microfauna, resulting in rapid decomposition of organic residues; and (4) it has superior Km (Michaelis constant), Vmax (maximum uptake velocity), and Cmin (minimum concentration of a nutrient) for N uptake (Yamagata et al., 1996). Thus, rice is expected to respond to crop residue N better than other crops. Yoneyama and Yoshida (1977a) found that N uptake by rice from residue-amended soil was at its peak during the intermediate stages of growth, and N uptake from the fertilizer was rapid during early growth. They recorded 25% N recovery from straw N by rice plants in 130 days. Although contribution of 5 t straw ha 1 to the current N needs of rice is relatively small, the long-term eVects may be substantial. For example, Tanaka (1974), Chatterjee et al. (1979), and Kosuge and Zulkarnani (1981) reported that continuous application of straw builds up soil organic matter and ensures high N content and uptake and partial substitution of straw N for fertilizer N. Jiang et al. (1998) observed that N utilization by wheat in the presence of wheat straw (4.5 t ha 1) was highest when N was applied in three equal splits at sowing, tillering, and stem elongation. Guirad and Berlier (1971) reported that the reduction in the N uptake from Ca(NO3)2 in the wheat strawamended plots was due to higher losses of NO3–N by denitrification, and from (NH4)2SO4 it was caused by immobilization of N in the soil. Malik et al. (1998) found that incorporation of wheat straw along with green manure enhanced nutrient availability; and synchrony between N release and plant uptake was best achieved in soil receiving straw along with green manure. A temporary lag in N immobilization and mineralization provided a N-conserving mechanism for the system. Broadbent and Nakashima (1965) followed mineralization and plant uptake of N immobilized by application of straw. When N was added with the straw, there were indications that remineralization of immobilized N was faster than mineralization of N in the unamended soil. However, when no N was applied with the straw, the results did not support the synchrony concept. Support for the synchrony concept is found in the results of a field experiment with flooded rice (Amarasiri and Wickramsinghe, 1988) in which rice receiving a 60 kg N ha 1 fertilizer along with straw yielded about the same as that receiving 90 kg N ha 1 as fertilizer alone. This role of straw may be interpreted as one of N recycling in a system where losses from the mineral N pool are potentially large and as such is a type of synchrony.


305

Tanaka and Nishida (1996) observed that wheat straw decreased N uptake by rice and increased the amount of 15N remaining in the soil at 17 days after transplanting. At the booting stage, 6 days before heading, N uptake was higher and the 15N remaining in the soil was lower in the treatments in which wheat straw was applied than in unamended control treatment. It was concluded that decrease in N uptake by wheat straw was caused by N uptake inhibition and not by N deficiency in the early stages of rice growth. In a greenhouse study using 15N-labeled fertilizer, Masayna et al. (1985) found that rice plants recovered 50–69% of applied fertilizer in the unamended soil and 45–53% in the rice straw-incorporated soil. In the second and third crops of rice, recovery of residual N was slightly higher from rice strawamended soil than from unamended soil. Islam et al. (1998) found that large amounts of mineral N pools were lost during the incubation that could not be accounted for by microbial immobilization under field conditions. To the contrary, Xu (1984) reported higher fertilizer utilization eYciency in rice straw-amended soil (75.5 and 82.6%) than that in unamended light clay and sandy loam soils (51.8 and 47.7%). This could be due to the increased N immobilization and decreased losses of N via denitrification in residueamended soil (Craswell, 1978). Available data suggest that 10 to 20% of N freshly supplied through cereal residues with a high C:N ratio (rice and wheat straws) is assimilated by the rice crop, 10 to 20% is lost through various pathways, and 60 to 80% is immobilized or stored in the soil under field conditions (Koyama, 1981).

4.

Losses of N

The presence of crop residues with high C:N ratios may also lead to transformation of fertilizer or soil N into slowly available forms, which may act as slow-release fertilizer and thereby improve N use eYciency. Bird et al. (2001) reported that the total loss of N fertilizer, based on the 15 N isotope balance, was approximately 50% and was largely independent of straw management practice. An increase in total soil microbial biomass in combination with a large amount of added straw could have led to a temporary strong sink for N fertilizer. The ensuing immobilization process could lead to lower N fertilizer losses. Eagle et al. (2001) reported a decrease in fertilizer N use eYciency with a concomitant increase in the plant available soil N following change in straw management from burning to incorporation. Only 1.8 kg ha 1 (3.5%) straw N was directly available to the crop in the year following incorporation, and total N uptake increased by 23 kg N ha 1 5 years after straw incorporation. Huang and Lu (1996) reported that heavy application of rice straw in combination with

306


N fertilizer at a C:N ratio greater than 40 would have a determental eVect on the rice growth. Using 15N–labeled fertilizer, it was observed that total recovery of N was reduced from 40.8% in no straw to 6.1% in straw treatment with a C:N ratio of 40, but the total N loss was decreased from 13.7 to 5.5%. It was concluded from this study that for eYcient management of rice straw and N fertilizer in flooded rice cultivation, it is advisable to incorporate rice straw with a C:N ratio adjusted to 7.0. Narayanasamy and Biswas (1998) reported that application of organic matter along with rock phosphate increased the P eYciency. The suggested reasons were (1) formation of plant-assimilable phosphorus-humic compounds, (2) anion replacement of P ion by humate ion, and (3) coating of sesquoxide particles by humus, which reduces P fixation. Sharma et al. (2001) reported that Mussoorie rock phosphate (MRP) and diammonium phosphate (DAP) proved equally eYcient in increasing grain yield and P uptake of rice when residues of the preceding wheat were incorporated before rice transplanting or rice residue was incorporated before sowing of the preceding wheat (Table VII). Without residue incorporation, MRP (8.1% total P, 12% as citrate soluble) had no significant eVect on grain yield and P uptake of rice. Similarly, MRP and DAP proved equally eYcient in increasing wheat yield on residue-amended plots. Available P in soil did not diVer under no P control and MRP when


311

Table VII EVect of Crop Residue Management and Phosphorus Source on Crop Yields in Rice–Wheat Rotation in India Residue management Rice Removed Incorporated Removed Incorporated LSD ( p ¼ 0.05)

Rice yield

Wheat yield

Wheat

1993

1994

1995

1993–94

1994–95

1995–96

Removed Removed Incorporated Incorporated

4.6 4.9 — — ns

4.3 4.5 4.8 4.7 0.31

4.0 4.5 4.5 4.6 0.18

4.9 4.8 4.9 4.5 ns

4.7 5.0 4.8 4.6 ns

4.4 4.3 4.9 4.8 ns

Control DAP MRP

4.4 4.9 4.9 0.38

4.5 4.8 4.8 0.19

4.3 4.4 4.5 0.15

4.8 4.8 4.8 ns

4.6 4.8 4.8 0.18

4.3 4.7 4.7 0.33

P source Control DAP MRP LSD ( p ¼ 0.05)

DAP, diammonium phosphate; MRP, mussoorie rock phosphate. From Sharma et al. (2001).

Table VIII EVect of Crop Residue Management and Phosphorus Source on Olsen-P in Rice–Wheat Rotation in India P source (kg ha 1) Straw treatment Both straws removed Wheat straw incorporated Rice straw incorporated Both straws incorporated LSD. ( p ¼ 0.05)

Control 14.8 16.4 18.6 19.2

DAP 19.6 18.0 22.0 23.6 Residue management P source ¼ 5.95

MRP 15.6 18.2 21.4 27.6

DAP, diammonium phosphate; MRP, mussoorie rock phosphate. From Sharma et al. (2001).

residues were removed, but the incorporation of crop residues resulted in similar levels of available P in soil under DAP and MRP. The eVect was more pronounced when both the residues were incorporated as compared to incorporation of rice or wheat straw alone (Table VIII). Biswas and Narayanasamy (2002) evaluated composts prepared by mixing rice straw with diVerent sources of rock phosphates collected from within India. Cow dung slurry and Trichoderma viridii, a cellulytic fungus, were inoculated to hasten the composting process. A phosphorus-solubilizing microorganism (Aspergillus awamori) was also introduced 1 month after

312


the start of composting. The study showed that composting enhanced the mobilization of P from rock phosphate as evidenced through increases in water-soluble, citrate-soluble, and organic P fractions. Verma and Mathur (1990) found that incorporation of rice straw along with cellulytic microorganisms and rock phosphate at 15 days before wheat sowing resulted in a significant increase in wheat yield over recommended fertilizer management practices. Tian and Kolawole (1998) reported that application of diVerent crop residues increased the P uptake by Crotolaria ochsolenca from rock phosphate. For eYcient use of P from rock phosphate in the low-fertility soils, it is suggested to apply plant residues with high polyphenol and low lignin contents.

C. POTASSIUM Crop residues contain large quantities of potassium, and their recycling can markedly increase K availability in soils (Chatterjee and Mondal, 1996; Ning and Hu, 1990; Patil et al., 1993; Sarkar et al., 1989). Recycling of crop residues can improve crop yields at low rates of K application and decrease the crop response to the K applications. The role of crop residue recycling in K balance in the rice–wheat cropping system has been dealt with in detail by Bijay-Singh et al. (2003). Yadvinder-Singh et al. (2004b) reported that release of K from rice straw occurred at a fast rate, and within 10 days after incorporation, available soil K contents increased from 50 mg K kg 1 in the untreated control to 66 mg K kg 1 in straw-amended treatments. Tian et al. (1992) reported that most of K in the rice residue was released in less than 41 days. The amount of K released from organic materials in the first month was highly correlated with the water-soluble K (Patil et al., 1993; Sarkar et al., 1989). Potassium is not bound in any organic compound in the plant material, and thus its release does not involve microorganisms. Mishra et al. (2001b) reported that during the decomposition of rice straw, K contents decreased from 1.30 to 0.28%. About 79% of the total K present in rice straw was released within 5 weeks after its incorporation into the soil, and 95.3% of K from straw was mineralized by the end of 23 weeks.

D. SULFUR Sulfur is a critical nutrient for crop growth, and its deficiency is accentuated in soils of the tropics by intensive agricultural practices, less use of organic manures, removal of crop residues, and leaching of SO4 by heavy


313

rains. It is generally accepted that plants assimilate S almost entirely in the form of SO4, which is produced by the mineralization of organic S. Unlike phosphates, sulphates are easily leached. Incorporating crop residues into the soil is one way of reducing S losses by leaching. Mineralization of S in soils is mediated by biological activity. Very limited information is available on S mineralization rates and potentials of wetland soils amended with crop residues. In an incubation study, application of wheat and barley straw to two soils (pH > 7.0) increased the S concentration in equilibrium solution, suggesting that the addition of crop residues to soil would increase available S (Choi and Rossi, 1978). Organic materials with high C:S ratios such as wheat straw and rice husk caused considerable immobilization of S, particularly during the early stages of decomposition (Somani and Saxena, 1975). Addition of inorganic S fertilizers may, therefore, be necessary. Islam and Dick (1998b) observed that addition of wheat straw with a low C:S ratio (100:1) had a significantly higher accumulation of SO4–S than the control or the higher C:S ratio (400:1) wheat straw treatment. The cumulative amount of C mineralized was linearly related to S mineralization. Islam and Dick (1998a) reported that the S mineralization from crop residues followed first-order kinetics and that the amount of SO4 in flooded soils amended with crop residues would depend on the soil type, the nature of the crop residues, and the time of decomposition. Crop residue management is a major determinant of longterm S fertilizer requirements. Singh and Sharma (2000) observed a significant increase in the availability of S in soil with the incorporation of crop residues. The burning of straw or straw removal from rice paddies increases the demand of the cropping system and will lead to increases in S requirements in long term. Whitbread et al. (1999) reported an improvement in the S balance with the incorporation of rice straw over removal. Long-term studies are needed to enable measurement of the eVects of recycling crop residues and the impact of environmental inputs on S dynamics in the soil–plant system.

E. MICRONUTRIENTS A ton each of rice and wheat removes 96, 777, 745, 42, 55, and 4 g ha 1 of Zn, Fe, Mn, Cu, B, and Mo, respectively. The total crop residue production in India stands at 105 million tons, and based on micronutrient contents of the residues, the micronutrient potential associated with crop residues would be about 35.4 thousand tons (Prasad, 1999). About 50 to 80% of Zn, Cu, and Mn taken up by rice and wheat crops can be recycled through residue incorporation (Prasad and Sinha, 1995b). Therefore, recycling of crop residues can help improve the availability of micronutrients in soil.

314


1.

Iron and Manganese

The application of crop residues to flooded soils leads to a reduced redox potential (Eh) and, as a consequence, increases the Fe and Mn concentrations in the soil solution. Katyal (1977) observed that not only did the maximum concentrations of Fe and Mn occur earlier but also their concentrations were significantly higher in flooded soils amended with rice straw compared to control. Yodkeaw and De Datta (1989) also noted that application of rice straw increased Fe2þ and Mn2þ concentrations in soil solution, resulting in increased uptake of Fe and Mn by rice crop. Under controlled Eh and pH conditions, Atta et al. (1996) observed that at an Eh value of 330 mV, soil suspension contained approximately double the amount of water-soluble plus exchangeable Fe as compared with at Eh values of 150 to þ300 mV. Addition of wheat straw to soil suspension decreased the exchangeable Fe fraction at pH 8.0, while it increased the same fraction at both pH 6.0 and 7.0. Exchangeable and water-soluble Mn fractions were reduced due to application of wheat straw at pH 8.0, while the easily decomposable fraction increased at pH 7.0 and 8.0 and decreased at pH 6.0. In a greenhouse experiment, Sharma et al. (1989) measured significantly higher leaching losses of Mn2þ and Fe2þ with increasing rates of rice straw and percolation rate. As much as 111 kg Mn ha 1 and 110 kg Fe ha 1 were lost through leaching in one cropping season.

2.

Zinc

Kang (1988) observed that the availability of Zn in diVerent pools (watersoluble, exchangeable, weakly and tightly complexed to organic matter) was reduced by straw application at soil pH of 8.0. Several other workers (Yoon et al., 1975; Dikshit et al., 1976; Raj and Gupta, 1986; Nagarajah et al., 1989) have also reported that application of rice or wheat straw decreased the Zn concentration in both flooded and upland soils. As a consequence, Zn uptake and dry matter production were reduced compared to that in untreated control. Saviozzi et al. (1997), however, observed no significant eVect of wheat straw (applied at 2% by weight) on the content and distribution of Zn and Cu in soils. Even so, rice straw application has been found to increase the Zn content of rice plants, possibly through its amelioritic eVect on soil pH and ESP. In calcareous soils, application of crop residues decreased the capacity factor due to organic acids converting solid-phase labile Zn to soluble Zn complexes (Prasad and Sinha, 1995a). The diVusion coeYcient of Zn was increased with the addition of crop residues due to the presence of chelating agents released during their decomposition and thereby increasing the


315

concentration of total diVusible Zn. The diVusion coeYcient and Zn uptake by rice in calcareous soils are related linearly.

VI.

EFFECT OF CROP RESIDUES ON SOIL PROPERTIES A. SOIL FERTILITY

Crop residues are an important constituent in nutrient cycling. The straw of most cereal crops contains about 35, 10, and 80% of the total N, P, and K taken up by the crop (Barnard and Kristoferson, 1985). Apart from the straw is plant root material, which in most crops adds a substantial amount of C to the soils. Long-term straw incorporation improves the fertility and productivity of soils (Ponnamperuma, 1984). Soil organic matter has been identified by many workers as a key factor in maintaining soil fertility and crop production. Its maintenance is an essential requirement for increasing and maintaining productivity. In most of Asia, rice straw incorporated into the soil is the main source of SOM in the rice-based cropping systems. Since the maintenance of soil nutrient status is an important aspect of sustainability, the management of crop residues and fertilizer to maintain soil fertility is necessary.

1.

Soil Organic Matter

In tropical soils, SOM plays a major role in soil productivity because it represents the dominant reservoir and source of plant nutrients. It also influences pH, cation exchange capacity, anion exchange capacity, and soil structure. Its level in soil was used as a general indicator of soil productivity. A major factor contributing to the level of SOM is annual input of plant residues. Residue managment impacts on SOM and long-term fertility are becoming more relevant in the context of soil quality in tropical environments. The prominent means of maintaining SOM in irrigated rice-based cropping systems in tropical countries have historically been the incorporation of green manures, animal waste, or crop residues. In recent years, though, the significance of green manures and animal wastes has been dramatically altered by the increased use of mineral N fertilizers and other economic considerations. More recently, crop residues including roots have become a more common source of organic material added to the soil in many countries in the tropics, where the use of combine harvesters is increasing (Flinn and Marciano, 1984). For a given climatic region and soil type, the rate of

316


addition of carbon inputs is an important factor determining the amount of organic matter that can be maintained in the soil. The soils tend to reach equilibrium provided farming techniques and crop residue management practices stay the same over a long enough period. Under conditions of warm temperatures and increased water availability, organic matter accumulation from residues is reduced. Soil organisms use residues as a source of energy and nutrients, thereby releasing CO2, inorganic compounds, and recalcitrant molecules, which contribute to the formation of soil humus. Decomposition of crop residues releases about 55–70% of the C to the atmosphere as CO2, 5–15% is incorporated into microbial biomass, and the remaining C (15–40%) is partially stabilized in soil as new humus (Stott and Martin, 1989). Because the amount of carbon in soils is large and changes rather slowly, the implications of a particular management system on the soil carbon may be apparent only after several years to decades. Numerous calculations have been made of the amount of residues needed to maintain organic matter at a particular level (Paustian et al., 1997). There exist only limited studies on the long-term eVect of crop residue management on organic matter and N content of soils under rice-based cropping systems in tropical and sub-tropical countries (Tables IX and X). In these studies, increases in organic matter content due to crop residue recycling are relatively small compared to those reported from temperate regions (Prasad and Power, 1991). Incorporation of both residues increased organic C and total N compared to removal or burning of straw (Dhiman et al., 2000). When only rice or wheat straw was incorporated, organic C content did not diVer significantly from removal or burning of straw. Rice straw was more eVective in increasing total N content of soil than wheat straw. Raju and Reddy (2000) reported that in rice–rice rotation, incorporation of rice straw to supply 25% of the recommended N fertilizer dose for rainy season crop for 6 years significantly increased organic C content from

Table IX EVect of Straw Management on the Nutrient Status of Mahaas Clay and Grain Yield Averaged for Five Cultivars after the 16th Cropa Straw treatment Removed Burned Incorporated

Organic C (%)

Total N (%)

Olsen P (mg kg 1)

Exchangeable K (mg kg 1)

Grain yield (t ha 1)

1.81b 1.94b 2.17a

0.167b 0.173ab 0.182a

9a 11a 12a

10.5b 12.5a 11.6ab

3.2b 3.4b 4.1a

a In a column, figures followed by a common letter are not significantly diVerent. From Ponnamperuma (1984).


317

0.98% in straw removal treatment to 1.29%. Sharma (2001) reported that organic C content increased from 0.56% in straw removal to 0.66% when both the residues were incorporated for 2 years in rice–wheat rotation. Burning and removal of crop residues were at par for their eVect on organic C content. Yadvinder-Singh et al. (2004b) reported that rice residue incorporation increased organic carbon content of the sandy loam soil more significantly than straw burning or removal after 7 years (Table X). Carbon sequestration derived from changes in soil C content in the soil from rice residue applied at 7.1 t ha 1 annually for 7 years averaged 14.6%. In another long-term study, Yadvinder-Singh et al. (2004a) reported that wheat straw incorporation in rice increased organic C content from 0.40% in straw removal treatment to 0.53% in straw incorporation treatment after 12 years of experimentation on a loamy sand soil. The values after 6 years were 0.38 and 0.49%, respectively, suggesting smaller increases in organic C between 6 and 12 years than during 0–6 years. Carbon sequestration derived from changes in soil C content in the soil from wheat straw incorporation for 12 years represented 10% of the added carbon. The rate of increase in organic C with straw incorporation is generally smaller in coarse-textured soils than in fine-textured soils. For example, Verma and Bhagat (1992) and Dhiman et al. (2000) observed marked increases in organic C in sandy clay loam soils with residue incorporation after 4–5 years. Naklang et al. (1999) observed no significant eVect of rice straw incorporation for 3 years on total and labile C content of a sandy soil. In a rice-barley rotation under dryland conditions in northern India, Kushwaha et al. (2000) observed a significant increase (28%) in soil organic carbon and 33% increase in total N with the incorporation of crop residues compared to their removal after one annual cycle. It was suggested that for soil fertility enhancement in dryland agroecosystems, postharvest retention of crop residues (20–40% aboveground biomass) of previous crop and its incorporation in soil through minimum tillage in the succeeding crop should be followed. Application of rice straw at 10 t ha 1 to an upland sandy soil caused a net increase in soil C by 0.31 t ha 1 over no rice straw treatment (Ono, 1989). The increase in C represented 8% of the C applied in rice straw. At higher rates of straw addition, the net increase in soil C was increased but the percent C increase did not change significantly. The soil C buildup in the soil was significantly positively correlated with %N and negatively correlated with C:N ratio. Using data from a 24-year long-term experiment at IRRI, Los Banos, Alberto et al. (1996) showed that straw incorporation improved organic C, total N, available P, and exchangeable K above that of the burned straw and no straw treatments. There was an average increase of 0.4 t ha 1 in rice yield with straw incorporation, while burning the straw resulted in

318

Table X EVect of Crop Residue Management on Organic Carbon and Total N Content of Soil

Reference and country

Type of crop residue and soil

Beri et al. (1995), India

Rice straw in wheat and wheat straw in rice; sandy loam

10

Sharma et al. (1987), India

Rice straw in wheat and wheat straw in rice, silty clay loam Rice straw in rice–rice rotation; clayey

6

IRRI (1986), Philippines

12

Liu and Shen (1992), China

Milk vetch green manure or milk vetch þ rice straw in rice–rice rotation

9

Zia et al. (1992), Pakistan

Rice straw in rice in rice–wheat rotation; loam Wheat straw, green manure and wheat straw þ green manure in rice in rice–wheat rotation; loamy sand

3

Yadvinder-Singh et al. (2004a), India

12

Residue management

Organic C (%)

Total N(%)

Removed Burned Incorporated Removed Incorporated Removed Burned Incorporated Removed Green manure Green manure þ rice straw Removed Incorporated Straw removed Straw incorporated

0.38 0.43 0.47 1.15 1.31 1.67 1.74 1.90 1.91 2.06 2.21

0.051 0.055 0.056 0.144 0.159 0.173 0.179 0.191 0.176 0.190 0.194

0.53 0.63 0.41 0.53

— — — —

0.59

—

Wheat straw þ green manure


Duration of study (years)

Dhiman et al. (2000), India

Ponnamperuma (1984), Philippines

Rice straw in rice in rice–rice rotation; clayey

Kumar et al. (2000), India

Prasad et al. (1999), India

Verma and Bhagat (1992), India

3

Removed Burned Incorporated Removed Incorporated

0.51 0.51 0.86 0.36 0.61

0.062 0.063 0.084 — —

2

Removed Incorporated

0.53 0.61

— —

2


0.68 0.84

— —

5


1.09 1.24

— —

7

Removed Burned Incorporated Removed Burned Incorporated

0.38 0.39 0.50 — — —

— — — 0.181 0.183 0.202

19 crops


Yadvinder-Singh et al. (2004a), India

Rice straw in wheat and wheat straw in rice in rice–wheat rotation; clay loam Mustard straw in rice in rice– mustard rotation, acidic sandy clay loam Wheat straw in rice in rice– wheat rotation; sandy clay loam Rice straw in wheat in rice– wheat rotation; sandy clay loam Rice straw in wheat in rice– wheat rotation; silty clay loam Rice straw in wheat in rice– wheat rotation; sandy loam

319

320


negligible improvements. This reported increase occurred over a 14-year period and highlights the time frame over which SOM increases occur. A small increase in SOM associated with improved residue management demonstrates how diYcult it is to improve SOM levels and, consequently, nutrient levels in coarse-textured soils of the tropics. Incorporation of rice straw at 5 t ha 1 year 1 for 12 years showed only a small increase in organic C and total N content of soil with 2% initial organic C level (IRRI, 1986). Straw removal or burning caused a decline in organic matter content during the first 3 years of the study, while the straw incorporation maintained the original level. Field experiments on a rice–wheat cropping system in India showed that incorporation of crop residues as compared to burning or removal increased organic carbon and total N contents (Table X). Adiningsih (1984) reported that incorporation of rice straw into the soil for 4 years increased the soil organic matter content from 2.4 to 3.9% and total N content from 0.25 to 0.33% over straw removal in Indonesia. In China, Liu and Weng (1991) found that returning rice straw to rice fields for 2 years usually increased soil organic matter content by 0.03 to 0.05%. From a long-term field experiment in Japan, Gotoh et al. (1984) estimated that 13 to 25% of the organic matter returned to soil through rice straw was incorporated into the soil organic matter in a slowly permeable grey lowland soil. In a 3-year study on a barleyearly rice-late rice cropping sequence in China, He and Liu (1992) reported that addition of organic materials (green manure, crop residues, and FYM) resulted in a mean increase (average of six experiments) of 0.053% organic C compared to loss of 0.04% under inorganic fertilizer treatment. They calculated that supply of 3.2 to 4.6 t ha 1 (mean of 3.8 t ha 1) of crop residues ha 1 year 1 would be needed to maintain the soil health and to improve productivity. In a long-term study on a rice–wheat cropping system in northwestern India, the incorporation of crop residues along with green manure in rice increased soil organic carbon and total N contents as compared to straw removal, but the increase was almost similar to that when crop residues were applied alone. These data suggested little eVect of green manure on soil organic matter content in semi-arid climates, particularly in coarse-textured soils (Table IX). In a long-term study (1981–1990) in China, Liu and Shen (1992) studied the eVect of milk vetch green manure in early rice and milk vetch plus rice straw in late rice in a rice–rice cropping system. The increases in organic matter and total N concentrations in soil were in the decreasing order: green manure plus rice straw, green manure, and inorganic fertilizers. Further, mixed application of green manure and crop residues improved the quality of soil organic matter (Table IX). Vityakon et al. (2000) reported that application of rice straw at 10 t ha 1 increased


321

organic C content of upland soil by 0.31 t ha 1 year 1 over no straw in loam soil in Thailand. Naklang et al. (1999) used the two indices to calculate a carbon management index (CMI). They measured two fractions of organic carbon in soil. The more labile fraction (CL) was measured by oxidation with 333 mM KMnO4, and the nonlabile C (CNL) plus the C not oxidized by 333 mM KMnO4, (i.e., CT-CL). The total C (CT) was measured by combustion. On the basis of changes in CT between a reference site and the cropped site, a carbon pool index (CPI) was calculated: CPI ¼ CTcropped =CTreference

ð6Þ

On the basis of changes in the proportion of CL in the soil (labiality ¼ L ¼ CL/CNL), a labile index (4) was determined. CMI ¼ CPI LI 100

ð7Þ

Incorporation of leaf litters increased the CMI from 9 in 1992 (initial) to about 20 after 3 years in 1996 and CMI in no-litter treatment increased to 13. Straw incorporation did not significantly aVect the CT (4.44 versus 4.11 mg g 1) and CL (0.78 versus 0.79) compared to straw removal treatments. The measurement of CL is a more sensitive indicator of SOM dynamics. Total C measurement is still required to estimate bulk soil C change; however, CL more accurately and quickly detects the impact of management on soil C. Calculation of the CMI takes into account the change in CT pool size and its lability and gives a more definitive picture of soil C dynamics than when only a single parameter is used. The studies on soil organic matter dynamics suggest that soil texture, C inputs, and climatic conditions are the primary factors controlling stabilization of soil C. Simulation models allow us to account for such interacting factors and thus can be profitably used to understand the dynamics of soil organic matter in crop residue-amended soils on a long-term basis. Most of these models predictions have not been tested using observed data, and there is a need to revalidate these models for rice-based cropping systems. There is no single fixed quantity of SOM that can be considered as optimal for all soils. All other factors held constant, an increase of 1% in SOM content will have greater eVects for a sandy soil than for a clay-loam soil on the overall productivity level. Benefits of increased SOM will also depend on land use. For example, improved physical properties in clay soils might be more useful for upland crops than lowland rice, as the common practice of puddling rice soils is intended to destroy soil structure. Soil organic matter levels tend to be stable or increase under irrigated rice double cropping (Cheng, 1984; Nambiar, 1994; Witt et al., 2000). Organic matter content is generally lower in rice-upland crop rotations

322


such as rice–wheat or rice-maize (Cheng, 1984; Nambiar, 1994; Witt et al., 2000). The reduced soil C sequestration in the rice-upland rotation resulted primarily from an increased amount of microbially mediated C mineralization compared to the C mineralization rate in the rice–rice system (Witt et al., 2000). Carbon sequestration with continuous rice cropping would also be favored by the accumulation of phenolic end products that appears to occur when crop residues decompose under anoxic conditions in lowland rice systems (Olk et al., 1996). When crop residues are not regularly incorporated in the lowland-upland crop rotations, the amounts of labile SOM can decrease to the point of reducing the continuous supply of available N through mineralization–immobilization turnover (Stevenson and Kelley, 1985), which could lead to lower grain yield. In the light textured soils, nutrients and soluble C compounds may move down the profile, thus resulting in very slow, or no, long-term increase in soil fertility when residues are added (Naklang et al., 1999). Management of crop residues might also carry longer term impacts on the chemical nature of SOM. The eVects of crop management on SOM quantity in lowland rice soils have received more attention than have their eVects on SOM quality. Few or no studies have examined the eVects of agronomic practices on the quantity or quality of SOM and nutrient supply in intensive continuous rice or rice–wheat rotations. The quantity of SOM is not the sole factor that should be considered when devising management practices to optimize the agronomic benefits of SOM. A higher quantity of SOM does not automatically lead to a higher quality of SOM. If most SOM-bound nutrients are in SOM fractions that have low turnover rates, that is, high residence times, their roles in nutrient supply will remain marginal. If the soil in question is a sandy soil, for example, and if the crop obtains the bulk of its nutrients through decomposition of the various SOM pools rather than through the exchange of nutrients present on CEC complexes, the nutrient supply power of the soil will remain low. Ultimately, it remains the quality rather than the quantity of SOM that will lead to improved soil quality, and hence a more sustainable cropping system, in particular for those agrosystems that are prone to land degradation. It remains a diYcult task to identify and quantify the intrinsic quality of an SOM pool in terms of nutrient supply power, microbial activity, or physical or chemical indices. Labile SOM pools are key suppliers of nutrients to the crop, whereas other SOM pools are more recalcitrant in nature and will provide fewer nutrients, but their chemical and physical properties provide stability to the soil. The relative sizes of the labile versus more recalcitrant pools that make up total SOM might have pronounced eVects on the indigenous nutrient supply and perhaps even yield (Biederbeck et al.,


323

1984; McGill et al., 1986), illustrating the complexities of managing SOM quality. The chemical nature of humic acid fractions changed with an increasing number of annual irrigated rice crops (Olk et al., 1996). The increasingly phenolic nature of the humic acid was speculated to be a contributing factor to an apparent decline in soil N supply and grain yield in continuously cropped lowland rice soils, as phenols are known to stabilize nitrogenous compounds under controlled conditions (Haider et al., 1965). The eVect of rotating upland crops with rice on SOM quality indicated that the phelonic nature of labile SOM extracted from rice–wheat soils is more similar to that of labile SOM from lowland rice–rice soils than that from upland rice soils (Olk et al., 2000). Again, the agronomic significance of this finding is not clear. Bird et al. (2002) examined the five soil organic matter fractions from soil samples obtained after 4 to 6 years of rice residue management treatments using 15N-labeled urea. After 4 years of straw management treatments, soil incorporation of straw increased mobile humic acid (MHA) and light fraction (LF) carbon and N compared with burned straw. Immobilization of fertilizer N peaked in all soil organic matter fractions after one growing season (120 days) and was greater in the MHA over the 2-year study. Nitrogen fertilizer sequestration was in MHA and LF and was greatest with straw incorporation compared with straw burned. Turnover of immobilized 15N fertilizer was fastest in the labile MHA and MFA (mobile fulvic acid) fractions (7–9 years half-life) compared with a half-life of the moderately resistant MAHA (metal-associated humic acid) fraction (53 years) and most stable humic (HUM) fraction (153 years). The MHA and LF fractions represented the primary active sink and source of sequestered N, aVecting both short- and long-term soil fertility. A study by Devevre and Howarth (2000) suggested that it is not primarily the accumulation of degradation byproducts that may sequester N in SOM, as suggested by Olk et al. (1996). The larger and sustained microbial biomass found under flooded compared to aerobic conditions may act to immobilize more N and make it less available for plant uptake. The composition and dynamics of SOM are generally the same in temperate and tropical soils, except that turnover rates in tropical soils usually are higher than in colder climates. Therefore, many results from temperate soils can be used to explain SOM dynamics and control in tropical soils. The main transformations occurring during residue decomposition and humification are the loss of polysaccharides and phenolic moieties, modification of lignin structures, and enrichment in recalcitrant, non-lignin aromatic structures (Zech et al., 1997). The rates of these transformations are controlled primarily by climatic factors and only to a lesser extent by chemical factors such as pH, C:N ratio, or litter quality. Soil organic matter stabilization by

324


interaction with minerals probably is more important in tropical than temperate soils because of the more favorable climatic conditions for decomposition of organic matter. The protective eVect of minerals is most pronounced for labile constituents such as polysaccharides or proteins. Studies on the contribution of labile fraction to SOM dynamics in tropical ecosystems are very scarce. Crop residue management can aVect N immobilization and stabilization processes important to eYcient utilization of N from fertilizers, crop residues, and soil organic matter. Bird et al. (2002) reported that a consistently larger soil microbial biomass N and C pool was observed when straw was incorporated than when it was burned. Because soil microbial biomass is a prime source of available N for the crop, the incorporation of straw led to an increase in the crop-available soil N. Although total soil N content had not changed after 5 years of straw incorporation or burning, a significant increase had taken place in the more labile soil N pools (humic substances). The more labile soil N pools remain key sources of readily available N for crop utilization. 2. Total N About 70% of the rice lands in south and south-east Asia contain 2 mm

1–2 mm

0.5–1 mm

0.1–0.5 mm

Mean weight diameter (mm)

Residue removed Residue incorporated Green manure (GM) Crop residue þ GM

9.8 11.7 11.1 17.1

10.0 15.0 15.5 11.1

5.6 5.5 6.1 6.9

11.3 11.3 12.0 9.1

1.42 1.56 1.58 1.68

Bulk density (Mg m 3)

0–10 cm

10–20 cm

1.59 1.49 1.51 1.48

1.72 1.72 1.71 1.68

From Meelu et al. (1994).

content in soil was low, burying of straw had a more favorable eVect on the stability of aggregates, especially of crumbs 3–5 mm in diameter, than in soil with 27% clay content. Likewise, Verma and Singh (1974) observed that wheat straw caused a marked influence on soil aggregation in four diVerent soils varying in texture. Maximum aggregation occurred in the sandy loam, with minimum aggregation in alkali soil. Application of rice straw to alkali

332


clayey soil significantly increased water stable aggregates >0.25 mm. Total organic C also increased, which resulted in a marked increase of macropores as well as the aggregate size in the 2.0–0.84 mm size fractions (El Samanoudy et al., 1993). In a friable self-mulching clay of the vertisol group, 34 years of either stubble burning or incorporation had, however, little eVect on soil structure (Dexter et al., 1982). The nature of plant material also plays an important role in the development of soil structure. For example, Dhoot et al. (1974) recorded the highest percentage of water-stable aggregates in pearl millet-amended soil followed by rice straw or wheat straw and sesbania green manure.

2.

Porosity

In a long-term field study in China, rice straw incorporation increased the porosity and formation of large micro-aggregates and decreased the bulk density of paddy soils (Li et al., 1986; Xu and Yao, 1988). Rice straw and rape straw were more eVective in increasing porosity of soils than sesbania green manure or pig manure (Li et al., 1986). Bellakki et al. (1998) and Bhagat et al. (2003) noted a significant increase in the porosity of finetextured soils after the application of rice straw and lantana residues. He and Liu (1992) observed that in rice straw-amended soil, porosity (>200 mm) increased quickly after drying, which is favorable for land preparation and sowing of upland crop in time after rice harvest. Beaton et al. (1992) reported that addition of rice straw (6 t ha 1) over a 68-year period compared to inorganic fertilizers reduced the volume weight and increased the porosity of paddy soils in Japan.

3.

Hydraulic Conductivity and Infiltration Rate

Crop residues aVect hydraulic conductivity and infiltration by modifying soil structure, proportion of macropores, and aggregate stability. Marked increases in hydraulic conductivity and infiltration have been reported in treatments where crop residues were retained on the surface or incorporated by conventional tillage over the treatments where residues were either burned or removed (Murphy et al., 1993; Valzano et al., 1997). In a 6-year rice–wheat cropping system on a clay loam soil in India, Sharma et al. (1987) noted increased cumulative infiltration of 7.39 cm h 1 under residue incorporation over 5.70 cm h 1 under residue removal. Similarly, in long-term experiments on rice–wheat cropping system, incorporation of both rice and wheat straw, as compared to their burning or removal, increased both


333

Figure 6 EVect of crop residue and green manure application on infiltration characteristics of a loamy sand soil (Meelu et al., 1994).

infiltration rate and cumulative infiltration in sandy loam soils (Singh et al., 1996; Walia et al., 1995). In another 5-year study on a rice–wheat cropping system on a loamy sand soil, Meelu et al. (1994) observed increased rates of infiltration on soil amended with green manure and crop residues (Fig. 6). A mixed application of green manure and crop residues was more eVective in increasing infiltration compared to their separate applications. On an alkali clayey soil application, rice straw significantly increased hydraulic conductivity and total and quick drainage pores (El Samanoudy et al., 1993). In a long-term rice–rice cropping system on a vertisol, Bellakki et al. (1998) also noticed a significant increase in hydraulic conductivity of soil from incorporation of rice straw (Table XIII).

4.

Bulk Density, Compaction, and Penetration Resistance

In general, incorporation of crop residues into the paddy soils reduced bulk density, penetration resistance, and compaction of soils under both rice–rice and rice–wheat cropping systems (Bellakki et al., 1998; Meelu et al., 1994; Singh et al., 1996; Walia et al., 1995). Xie et al. (1987) also reported that continuous return of rice straw to a paddy field for 7 years resulted in a soil bulk density decrease of 0.17 Mg m 3. In another long-term field experiment over 25 years, incorporation of crop residues improved the porosity and decreased penetration resistance of a gleyed soil (Roppongi

334


et al., 1993). Likewise, combined application of cereal crop residues and green manure has proved to be more eYcient in reducing bulk density, penetration resistance, and crusting of surface soil layers over their separate applications (Liu and Shen, 1992; Meelu et al., 1994; Verma and Singh, 1974). Bhushan and Sharma (2002) reported that with the application of lantana residues to a silty loam soil continuously for 10 years in rice–wheat rotation, clods GM > rice husk > wheat straw > control. The increased availability of nutrients resulted in improved yields and nutrient uptake by rice. Marked decreases in pH, exchangeable sodium percentage, and electrical conductivity of salt-aVected soils amended with crop residues have also been reported by many other workers (Abdul-Wahid et al., 1998; Hussain et al., 1996; Illayas et al., 1997; More, 1994). In a lysimeter study using calcareous sandy loam soil under a rice– wheat–maize fodder system, Sekhon and Bajwa (1993) reported that irrigation with sodic water caused precipitation of Ca and increased the accumulation of Na in the soil and adversely aVected the crop yields. Incorporation of rice straw decreased the precipitation of Ca and carbonates increased the removal of Na in drainage water, decreased pH and electrical


339

conductivity of the soil, and improved crop yields. The release of organic acids during decomposition of residues possibly mobilized the soil Ca. The quantity of gypsum required for controlling the harmful eVect of sodic irrigation water on soil properties can be considerably reduced in the presence of crop residues. Incorporation of wheat straw into a saline soil at 7 t ha 1 for 3 years improved soil physical properties such as bulk density, pore volume, and soil water retention and improved soil productivity (Wang et al., 1988). Improvement in soil physical properties (bulk density, porosity, and hydraulic conductivity) due to addition of crop residues was also reported by Hussain et al. (1996). Thus, recycling of crop residues on salt-aVected soils is likely to have greater benefits than on normal soils (Swarup, 1992; Abdul-Wahid et al., 1998).

VII. BIOLOGICAL NITROGEN FIXATION Naturally occurring heterotrophic and phototrophic bacteria use the straw either directly by the use of hemicellulose and simple carbohydrates or indirectly following the decomposition of cellulose by decomposer microorganisms. Asymbiotic N2-fixing bacteria can use crop residues for energy through the use of some hemicellulose components (Halsall et al., 1985) or products of straw decomposition (Roper and Halsall, 1986). The heterotrophic diazotrophs depend on carbon for energy. Since most N2-fixing bacteria are unable to use cellulose directly as a substrate for N2 fixation, cellulose must be degraded to simpler intermediates before being used by diazotrophs. Adachi et al. (1989) showed the existence of linkage between anaerobic cellulytic bacteria and anaerobic N2-fixing bacteria during the decomposition of straw. The role of crop residues in biological N2 fixation by heterotrophic and phototrophic bacteria has been reviewed in detail by Roper and Ladha (1995). Anaerobic conditions and a decrease in inorganic N content of soil following incorporation of straw favor N2 fixation by heterotrophic and phototrophic bacteria in waterlogged soils (Yoneyama et al., 1977). Under laboratory conditions, a wide range of values of N2 fixation (0.8 to 7.07 mg N fixed per g of straw in 14 to 56 days) have been obtained due to diVerences in the form and amount of straw, time of incubation, and methods used for quantification (Roper and Watanabe, 1986). Only a few quantitative data on the amount of N2 fixed or N gained following straw application in greenhouse or field conditions are available. Enhanced N2 fixation in flooded soils amended with straw has been reported by Rice and Paul (1972) and Charyulu and Rao (1981). Rao

340


(1980) estimated that N2 fixation in 30 days in flooded soil amended with chopped straw at 5 or 10 t ha 1 and planted to rice was two to four times that of the unamended control. Based on a per hectare furrow slice of 0.7 106 kg dry soil ha 1, extrapolation of the values of 15N incorporation in straw-amended soil in a 30-day experiment indicates N2 fixation of about 7 kg ha 1 in the unamended soil and 25 kg N ha 1 in straw-amended soil. Santiago-Ventura et al. (1986) measured twice the N gain following straw incorporation equivalent to 10 t ha 1 after the three consecutive rice crops compared with control pots; N gain ranged from 2 to 4 mg N fixed g 1 straw added. Nugroho and Kwatsuka (1992b) found maximum rates of N2 fixation as stimulated by rice straw amendment to be as high as 220 mg g 1 1 day 1 when the level of NHþ soil. 4 –N in the soil was below 7.8 mg N kg 1 High levels of NH4–N (98–298 mg kg soil) inhibited the initial N2 fixation activity. When denitrification occurred at high rates, N2 fixation was suppressed and vice versa. Yoo et al. (1990) reported that surface application of rice straw increased the pH of the floodwater to an optimum level for the growth of N2-fixing microorganisms, and thereby increased the N2 fixation by phototrophic bacteria and blue-green algae. In aerobic soils, intese microbial activity during the decomposition of crop residues results in the development of anaerobic and microaerobic microsites in soils, including surface soils (Hill et al., 1990). These sites can support N2 fixation by a wide range of free-living, diazotrophic bacteria, including anaerobic bacteria. In situ measurements of N2 fixation associated with wheat straw indicated amounts fixed (based on the acetylene reduction technique) ranging from 1 kg N ha 1 in 31 days to 12.3 kg N ha 1 in 22 days (Roper, 1983). The amount of wheat straw added to soil ranged from 4.3 to 7.2 t ha 1 under conditions where moisture was not limiting (i.e., field capacity). In a laboratory incubation study, Saha et al. (1995) observed that berseem (Trifolium alexandrinum) and rice straw significantly increased aerobic nonsymbiotic N2-fixing bacteria, phosphate-solubilizing bacteria, and S-oxidizing microorganisms, resulting in greater availability of N, P, and S in the soil. Crop residue-associated N2 fixation is modified by mineral N, temperature, moisture, oxygen concentration, soil characteristics, and straw management techniques (Roper and Ladha, 1995). In fact, straw decomposition is also directly aVected by these factors. In a field experiment, Roper (1983) observed a positive correlation (r ¼ þ0.98) between nitrogenase activity and wheat straw decomposition. As already discussed, the N2 fixation rates in straw-amended soils are higher under waterlogged conditions than under upland conditions (Rao, 1976). Roper et al. (1994) found that nitrogenase activity under field conditions was the highest with straw incorporation and the activity decreased in the order straw incorporation > straw mulched > no tillage. The depth of straw incorporation into soil also aVected the


341

nitrogenase activity. Straw mixed lightly with the soil near the surface produced significantly higher nitrogenase activity than soil in which straw was incorporated throughout the plough layer (Roper et al., 1989). Kanungo et al. (1997) recorded higher nitrogenase activity in the top 1–2 cm soil layer after the placement of organic residues, while residue placement in 2–6 cm layers significantly reduced nitrogenase activity, irrespective of soil type. The high nitrogenase activity in the topsoil was associated with larger populations of Azospirillum, Azotobacter, and anaerobic N2 fixers and favorable redox potential supporting growth of N2 fixers.

VIII. PHYTOTOXICITY ASSOCIATED WITH CROP RESIDUE INCORPORATION INTO THE SOIL The adverse eVects of substances originating from decomposing crop residues have long been considered as a cause of poor growth and yield of many crops (Patrick et al., 1963). Since breakdown of cellulose occurs readily, many of the adverse eVects of residues occur within a relatively short time after the incorporation of residues and the sowing of the following crop. Warmer climates further accelerate the breakdown of crop residues. Thus, incorporation of crop residues can have adverse eVects on subsequent crops other than rice if anaerobic conditions develop (Cannell and Lynch, 1984). However, anaerobic decomposition of crop residues with no-tillage may have adverse eVects on seedling establishment of rice. Lynch (1977) reported that under certain conditions, substances toxic to cereal seedlings are produced by cereal residues that decay near the seedlings. These findings assume greater importance when crops are grown immediately after cereals and with minimal cultivation. When seed drills operate in soils where crop residues are placed on the soil surface or are only shallowly incorporated, seed and residue can be placed in close contact, particularly in fine-textured soils. Wet conditions that lead to anaerobic decomposition of the residues can adversely aVect seedling growth (Elliott et al., 1978; Kimbler, 1973). Kimbler (1973) reported that the degree of inhibition of growth of wheat by wheat straw depended on the length of decomposition period and was greatest when the period was only 2–6 days. Surface retention leads to slow decomposition, and incorporation is recommended as soon as possible. Phytotoxic substances (e.g., phenolic acid and acetic acid) are produced from degrading crop residues preferentially under anaerobic soil conditions (at least in localized zones) and seldom accumulate in aerobic soil because of rapid metabolization by microorganisms. Gaur and Pareek (1974), however, detected a larger number of phenolic and aliphatic acids under aerobic than under anaerobic conditions. In a laboratory incubation study, the

342


addition of rice or wheat straw produced large amounts of acetic acid under anaerobic conditions 4 to 8 days after the incorporation of straw (Bhat, 1991). Tanaka et al. (1990) reported that straw incorporation resulted in accumulation of reducing substances and various aliphatic aromatic acids in soil, which can inhibit rice root growth. Low temperature and acidity further favor the production and persistence of fatty acids (Cho and Ponnamperuma, 1971). At temperatures over 30 8C, these acids disappear within 2–3 weeks of straw incorporation. The organic acids are phytotoxic in the millimolar concentration range and can cause significant crop losses, which can be between 13 and 29% in heavy clay soils when seed is direct drilled in the presence of wheat straw in winter (Graham et al., 1986). Studies on homogenous slurries of a soil in a chemostat showed that the formation of organic acids from plant residues is primarily linked to Eh; the critical Eh being about zero (Lynch and Gunn, 1978). Goodlass and Smith (1978) observed that evolution of C2H4 from soils under anaerobic conditions was stimulated by amending soils with barley or wheat straw. Temporary anaerobic conditions resulted in large increases in C3 and C4 hydrocarbons. The association between degradation products and C2H4 suggests that both may be implicated when root growth is adversely aVected by the anaerobic decomposition of plant residues. Wu et al. (1997) observed that application of rice straw increased the level of reducing substances in soil at 20 days after application and reduced rice plant weight at 30 and 70 days after planting. In a greenhouse study, Sharma et al. (1989) found that total water soluble organic acids extracted from the root zone of rice plants (100 mm soil depth) increased with increasing amounts of rice straw (Fig. 7), but the acid production decreased with increasing rate of percolation. Highest acid concentration (364 m mol L 1) was obtained with the addition of 20 t rice straw ha 1 and a percolation rate of 15 mm day 1. The organic acids formed at 2 weeks after transplanting did not persist in soil solution; rather, they disappeared rapidly and the rice yields were same under all the treatments. The toxic eVects of aliphatic acids on rice growth have been widely studied. Most investigations have been of short term and on young plants. Nevertheless, in several instances, quite low concentrations of acetic acid, propionic acid, and butyric acid have killed rice seedlings (Rao and Mikkelsen, 1977). The injury caused by monobasic aliphatic acids depends on the type of acid present and its concentration. The inhibitory eVect on rice seedlings generally increases with increasing molecular weight, increasing with order formic, acetic, propionic, and butyric acid (Chandrasekaran and Yoshida, 1973). Tanaka et al. (1990) observed that rice root elongation was markedly inhibited by the solution extracted from flooded soil with incorporated wheat straw; the extract contained aliphatic and phenolic acids under acidic conditions. Huang and Lu (1996) reported that


343

Figure 7 Temporal changes in volatile organic acid concentrations in the soil solution collected at 100 mm soil depth as aVected by added rice straw (mean of three percolation rates) (Sharma et al., 1989).

pre-flooding after rice straw incorporation for 2 weeks is suYcient for oVsetting any adverse eVect due to phytotoxicity and N deficiency in rice. Wallace and Whitehead (1980) have reported that volatile fatty acids are more toxic than nonvolatile aliphatic acids between 0.5 and 1.0 mM concentrations and that the organic acids produced at one site do not diVuse very far onto the soil. Therefore, the establishing crop roots must not come into close contact with decomposing residues. Adverse eVects of decomposing residues on crops under aerobic conditions have been widely reported (Bhowmik and Doll, 1982). Phenolic acids such as ferulic, p-coumaric, and p-hydroxybenzaldehyde released from living or dead tissues of variety of plant species caused adverse eVects on the growth of crops (Nelson, 1996). Elliott et al. (1981) could not demonstrate the phytotoxicity to winter wheat on plots when wheat straw was mixed into the soil. N immobilization during straw decomposition rather than phytotoxicity appeared to be the primary factor adversely aVecting yield because yield decline was largely overcome by high rate of N application. Chung (2001) identified p-hydroxy benzoic acid (6.34–6.87 mg kg 1), p-coumaric acid (0.34 mg g 1) and ferulic acid (0.05 mg g 1) during the decomposition of rice straw. The nature and composition of allelopathic compounds depended on the type of crop residue or variety. P-hydroxy benzoic acid

344


(10 3 M) showed the greatest inhibitory eVect on barnyard grass seed germination, seedling growth, and dry weight. In the rice field, the concentration of phytotoxins varies during the growth period of the crop and is probably greater in the early stages of flooding. It may also vary spatially within the rhizosphere. The rice crop can also show considerable compensatory growth from adverse eVects on early growth after rice straw has been ploughed into the soil (Gotoh and Onikura, 1971). Organic acids accumulated around straw only in the early stages of decomposition, and hence if straw decomposition could be accelerated by any means, the danger period for seedling could be reduced. The concentration of organic acids in flooded soils in the tropics receiving 5–10 t ha 1 of straw is not toxic to rice (Ponnamperuma, 1984). Witt et al. (2000) noted no evidence that late residue incorporation caused phytotoxic eVects as a result of reduced organic compounds or toxins produced during residue decomposition. It is recommended to plough crop residues shortly after harvest is completed, because the decomposition of the straw occurs early after incorporation, the phytotoxicity occurring in the initial period of growth of the rice plant can be alleviated, and stable yields can be obtained.

IX.

WEED CONTROL AND HERBICIDE EFFICIENCY

Weeds are a major problem in the productivity of rice-based cropping systems. Depending upon their type and intensity, 20 to 50% or even greater losses in grain yields of rice and wheat are common due to competition from the weeds (Walia and Brar, 2003). Most studies in weed control in rice and other crops have been confined to evaluating the eVects of herbicide, tillage, water, and their interactions (Bhagat et al., 1999; Gajri et al., 1999). Few studies have been conducted on the dynamics of weed population and herbicide eYciency under residue management in rice-based cropping systems. Such information is needed in weed control strategies for rice-based cropping systems to improve their productivity. Kumar and Goh (2000) reported that crop residues can suppress weeds in many ways, for example, (1) through their physical presence on the soil surface as mulch and by restricting solar radiation reaching below the mulch layer, (2) by direct suppression caused by allelopathy, and (3) by controlling N availability. Burning of residues can help in eVective removal of weed seeds and weeds. The major disadvantage of incorporation of rice straw compared to burning is the increase in weed and possible pest pressure. Roeder et al. (1998) reported that compared with farmers’ traditional burning of crop and weed residues, mulching reduced rice yield by 43% in one out of four comparisons and increased weed biomass by 19–100%.


345

In addition to influencing the weed growth and population, crop residue management and tillage practices also influence the eYciency of soil-applied pre-emergence herbicides (Kumar and Goh, 2000). Because pre-emergence herbicides are applied to the soil, the amount and quality of residues and ash content left behind after residue burning might aVect their activity. Continuous burning or incorporation of residues of both crops over years results in buildup of ash or organic matter in the soil. The eYciency of soil-applied herbicides may decline because of increased absorption capacity of soil. Brar et al. (1998), however, observed that there was no significant eVect of burning or incorporation of crop residues on the eYcacy of butachlor applied to rice and isoproturon applied to wheat in a rice–wheat cropping system. Mt. Pleasant et al. (1992) observed that mulching residues had little eVect on weed control and crop yields were always higher when residues were incorporated in a rice-based cropping system. The reports on the eVect of crop residue management practices on weed growth and herbicide eYciency are not conclusive and need further investigation to improve the productivity of rice-based cropping systems. The eVects of crop residue management on the pests and diseases in rice-based cropping systems in the tropics have not received much attention.

X.

EMISSION OF GREENHOUSE GASES

Methane (CH4) and nitrous oxide (N2O) are important greenhouse gases, N2O being about 300 and CH4 being 15 times more radiatively active than CO2 (mass basis, considering residence time in the atmosphere) (Rodhe, 1990). Flooded rice soils are a major source of atmospheric CH4, contributing about 10% of the total global emissions of CH4 (Mitra et al., 1999; Neue and Sass, 1996; Rennenberg et al., 1992; Sass et al., 1990; Wassmann et al., 1998). Global methane emission from flooded rice fields has been estimated at 20–100 Tg year 1 (Neue, 1993). In comparison, the total agricultural sources of N2O are quite small, ranging from 0.03 to 3.0 Tg N year 1 (IPCC, 1996). Incorporation of organic materials (crop residues, green manures, compost) to regenerate depleted soil resources and promote sustainable food productions in the tropics should significantly increase CH4 emissions. Thus, residue management strategies may create conflicts between the goals of sustainable agriculture and mitigation of greenhouse gases when used in flooded rice-based systems. Soil properties, water management, organic amendment, and temperature have been reported as the major factors controlling the amount of CH4 emitted from rice fields (Sass et al., 1991; Schu¨tz et al., 1989). It has been estimated that CH4 emissions from rice

346


cultivation in India (45 million ha) should not exceed 2.5 t year 1. The main reason for low CH4 emissions from rice fields in India is that the soils have very low organic C or receive very little organic amendments (Jain et al., 2000). The burning of crop residues also contributes to the global CH4 budget. For each ton of crop residue burned, 2.3 kg CH4 is emitted (Grace et al., 2003). In rice–wheat cropping system, 0.14 t year 1 will be emitted, if one-half of the 12 million ha under rice–wheat cropping system is burned.

A. METHANE Organic C from added crop residues, organic manures, soil organic matter, or rice plant roots is the major driving force for CH4 production in rice-based agriculture systems (Wang et al., 1992; Yagi and Minami, 1990). Numerous studies from all over the world have demonstrated that added crop residues, composts, and green manures enhance CH4 fluxes relative to unamended controls (Bossio et al., 1999; Chen et al., 1993; Chidthaisong et al., 1996; Glissmann and Conard, 1999; Neue et al., 1994; Rath et al., 1999; Wassmann et al., 1993). The seasonal emissions from paddy rice with organic additions ranged from 1.1 to 148 g CH4 m 2 and increased methane emissions 1.2- to 32-fold over unamended control soils. Crop residues serve as a substrate for a complex microbial community, including methanogenic microorganisms. Most studies on the microbiological aspect of CH4 production in flooded rice soil have focused on methanogens (Asakawa and Hayano, 1995; Asakawa et al., 1998). In addition to methanogens, the degradation of organic matter to its most reduced status (CH4), however, involves at least two other kinds of nonmethanogens: the zymogenic bacteria and the acetic acid- and hydrogen-producing bacteria. Thus, from the point of view of microbiological ecology, diVerent eVects of various organic fertilizers on CH4 production potential might be closely related to the amount of easily decomposable organic matter. In principle, the degradation pattern in soils with and without amended straw is similar, with acetate, propionate, and H2 as the main intermediates of anaerobic degradation and CH4 being formed from H2/CO2 (11–27%) and acetate (84–89%). However, the early phase of straw degradation diVers, as a large variety of fatty acids accumulate transiently (Glissmann and Conard, 1999). A study by Weber et al. (2001) indicated that the methanogens colonizing rice straw are less diverse than those inhabiting the soil. Polysaccharolytic bacteria in rice soils constitute the first step in the degradation process and eventually produce substrates needed for the production of CH4. Distinct trends of multiple rate patterns for CH4 emission from waterlogged soils have been shown in laboratory and field studies (Hou et al., 2000). The first peak, between 20 and 40 days at 25 8C, probably originated


347

Figure 8 EVect of rice straw application on methane production in a sandy soil (Hou et al., 2000).

from the decomposition of easily decomposable forms of C in the rice straw, such as microbial products and polysaccharides (Watanabe et al., 1995). The second change in rate of CH4 emission observed may have been associated with the decomposition of structural components of the rice straw, such as cellulose and lignin. The eVect of rice straw application on CH4 production potential is shown in Fig. 8. Methane production in the treatment without rice straw supplement occurred at a much lower rate during the whole period of incubation, in which the highest production rate was less than 40 mg CH4 kg 1 soil day 1. After the application of rice straw, the CH4 production rate increased substantially. Both the quantity and the quality of added organic materials influence CH4 emission from soils. Yagi and Minami (1991) showed that while rice straw increased CH4 emission by a factor of 3.3, addition of rice straw compost increased CH4 emission only slightly compared to the application of mineral fertilizers. The extent and variability of observed methane enhancements by organic additions are governed by several factors, the most obvious being quantity. Schu¨tz et al. (1989) established that CH4 emissions from paddy rice progressively increased with increasing rice straw additions from 3 to 12 t ha 1. Straw levels over 12 t ha 1 did not increase CH4 fluxes further. Likewise, Wang et al. (1992) found increasing CH4 flux to be proportional to rice straw input levels. A field study (Yagi and Minami, 1990) also showed that rice straw applied at rates of 6–9 t ha 1 enhanced CH4 emission rates by 1.8–3.5 times. As reported by Sass et al. (1991) and

348


Watanabe et al. (1995), CH4 production was enhanced by the addition of straw in flooded soil only. Watanabe et al. (1995) proposed a simple straw rate response model to predict cumulative CH4 emissions from a known rice straw application to any soil: Y ¼ k½aðEÞ=ð1 þ bðEÞe

cðEÞx

Þ þ Y ð0Þ;

ð8Þ

where Y is the fractional increase in CH4 emission relative to a chemical fertilizer control, and x is the level of incorporated organic matter (t ha 1). Adjustments to the coeYcients a, b, and c were added to account for responses to temperature (E) and diVerences of soil type (k). Such modifications reflect observations that daily and seasonal CH4 fluxes are temperature dependent (Parashar et al., 1991; Schu¨tz et al., 1989; Yagi and Minami, 1991). Incubation studies have shown that large diVerences in CH4 production potential of soils are related to organic C content (Majumdar et al., 1998). The extent and rapidity with which added organic materials are decomposed depend greatly on chemical composition, including C:N ratio, lignin and polyphenol content, and other critical compounds. Yadvinder-Singh and van Cleemput (1998) reported that maximum methane (9980 mg g 1) was emitted from soil amended with sugar beet leaves, and emissions of CH4 from wheat and rice straw were 4953 and 5030 mg g 1 in 40 days in a silty clay soil under flooded conditions. The emissions of CH4 from composted farmyard manure and poultry manure-amended soils were very low. From an incubation experiment in a Chinese flooded rice soil, Hou et al. (2000) reported that organic matter, added as rice straw and organic manure (pig, chicken, and cattle manure), increased CH4 production rate significantly. The results showed that organic manures had diVerent promoting eVects, with pig manure increasing the CH4 production rate most, followed by rice straw, chicken, and cattle manure. The CH4 production potential caused by organic manures was closely related neither to the total C added to the system nor to the C:N ratio of the materials. A significant linear relationship between CH4 production and the logarithm of the number of zymogenic bacteria was found, with an r value of 0.96. This finding suggests that the number of zymogenic bacteria may be used as an index to predict CH4 production potential in flooded rice fields. Bronson et al. (1997a) observed that organic matter additions as rice straw (5.5 t ha 1, dry) or green manure (Sesbania rostrata, 12 t ha 1, wet) stimulated methane flux several-fold. Rice straw resulted in higher CH4 emissions than GM. The GM plots showed highest CH4 fluxes in the first 2 weeks, but thereafter straw–amended emitted the most CH4. Green manure has more easily decomposable C than straw, although more C was added as straw. Sesbania green manure, being easily degradable material,


349

required the lower activation energy by methanogens to use the substrate as C source than wheat straw (Bhat and Beri, 2002). Rice straw applied before the winter fallow period reduced CH4 emission by 11% compared with that obtained from fields to which the same amount of rice straw was applied during field preparation. Surface mulching of straw instead of incorporation into the soil showed 12% less emission. Composts consistently produced lower CH4 emissions than fresh green manures or straws. Aerobic composting reduces readily decomposable carbon to CO2 instead of CH4 (Inoko, 1984) and also modifies the original organic constituents to forms more resistant to subsequent degradation (Watanabe et al., 1995). Consequently, when compost is incorporated into anaerobic soils, less available carbon is present for methanogenesis. However, the agricultural benefit derived from compost is maintained, especially if composts are applied year after year (Inoko, 1984). Thus, composting provides a compatible option for adding organic materials to flooded soils without substantially enhancing methane emissions. Following the same principal, Miura (1995) found that fall rice straw incorporation or winter mulching combined with spring incorporation significantly reduced CH4 emissions during the subsequent summer rice season. Jain et al. (2000) reported that additions of organic manures and crop residues enhanced CH4 emissions from rice fields. There were wide variations in CH4 emissions because of the variety of organic amendments. Rice fields amended with biogas slurry emitted significantly less CH4 than those amended with other organic amendments. They further reported that CH4 emission rates were very low (between 16 and 40 kg CH4 ha 1 season 1) when the field was flooded permanently. Application of organic manure (FYM plus wheat straw) in combination with urea (1:1 N basis) enhanced CH4 emission by 12–20% compared with fields treated with urea only. The site in New Delhi represents one example of very low CH4 emissions from rice fields. Emissions from other sites in northern India may be higher than those in New Delhi, but they are still lower than in other rice growing regions in India. Jain et al. (2000) reported that organic amendment inputs promoted CH4 emissions, but total emission remained less than 25 kg CH4 ha 1. This finding contrasts with results from other network stations with irrigated rice where total emissions generally exceeded 100 kg CH4 ha 1 after manure application (Wassmann et al., 2000a). The low impact of organic manure in the experiment in New Delhi could be related to high percolation rates. Constant inflow of oxygen into the soil and downward discharge of methanogenic substrate resulted in low CH4 production (Inubushi et al., 1992; Yagi et al., 1994). Thus, emissions were very low even when organic matter was applied. In other stations of the network, organic amendments stimulated emissions during the first half of the season (Wassmann et al., 2000b).

350


Ishibashi et al. (2001) studied the eVect of surface application of rice straw in no-till rice on methane emission in three soils during rice growing season. It was found that CH4 emissions from the no-tilled direct-seeded field on the average were 21, 47, and 91% of that from the tilled transplanted field in high-percolating site, low-percolating site, and extremely low-percolating (4.4 mm day 1) site, respectively. Straw incorporation leads to significantly more methane production than burning or removal. Over the long term, however, incorporation may provide benefits through the accumulation of C as soil organic matter.

B. NITROUS OXIDE The biologically mediated reduction processes of nitrification and denitrification are dominant sources of N2O generation in soils (Paul and Clark, 1989). Nitrous oxide is also produced to a much lesser extent by the abiotic process of chemodenitrification (Bremner, 1997). Denitrification processes can terminate with N2O, or, more commonly, N2O is further reduced to N2 gas. Conditions that promote N2O emissions over N2 are high NO3 levels and/or increasing O2 , while increasing organic carbon levels tend to favor N2 production (Firestone, 1982). Nitrous oxide emissions from rice fields occur as a result of nitrification–denitrification during periods of alternating wetting and drying. Emissions are usually small in irrigated rice systems with good water control and small to moderate inputs of fresh organic material (OM) (Bronson et al., 1997a,b). Bronson et al. (1997a) reported that organic amendments, particularly rice straw, helped in reducing N2O emissions. In the flooded rice soil, straw addition possibly stimulates O2 consumption in the aerobic soil layer and in the rhizosphere, resulting in smaller zones in which nitrification can occur. Enhanced immobilization of fertilizer N with straw would result in less NH4 available for nitrification–denitrification. Additionally, the high CH4 concentration in straw-amended soil could inhibit nitrification (McCarty and Bremner, 1991). Methane emissions ranged from 3 to 557 kg CH4 ha 1 with an average of 182 kg CH4 ha 1. Few measurements have been published for N2O emissions from flooded rice soils amended with organic materials. The existing information indicates that N2O emissions from flooded soils with organic additions are similar to or less than soils receiving chemical fertilizers, indicating that organic amendments do not appear to influence N2O emissions very much. Most information on N2O emissions from rice soils focuses on water management and nitrogen fertilizers as controlling variables (Cai et al., 1997, 1999). A trade-oV relationship between CH4 and N2O, i.e., conditions that favor CH4 production suppress N2O and vice versa, is also well


351

recognized (Mosier et al., 1998a). So, while organic amendments seemingly have no impact on N2O emissions from flooded soils, management practices before or after rice may produce a significant eVect. Aulakh et al. (2001) showed that denitrification is a significant N loss process under wetland rice amounting to 33% of the recommended dose of 120 kg N ha 1 on a permeable sandy loam soil. Integrated management of wheat straw (6 t ha 1) and GM (20 t ha 1 supplying 88 kg N ha 1) and 32 kg N ha 1 as urea fertilizer N significantly reduced cumulative gaseous N losses to 51.6 kg N ha 1 as compared to 58.2 kg N ha 1 for 120 kg N ha 1 alone. The gaseous losses under wheat were 0.6–2% of the applied fertilizer N. Interplay between the availability of NO3 and organic C largely controlled denitrification and N2O fluxes in flooded summer-grown rice, whereas temperature and soil aeration status were the primary regulators of the nitrification–denitrification processes and gaseous N losses during winter grown upland wheat. The irrigated rice–wheat system is a significant source of N2O, as it emits around 15 kg N2O–N ha 1 year 1. The quantity of organic additions may also aVect N2O emissions. In one of the few studies looking at the impact of organic materials on N2O, Bronson et al. (1997a) suggested that organic additions to flooded soils stimulated oxygen depletion to the point of inhibiting nitrification and thereby N2O emissions. From this, one could hypothesize that increased oxygen depletion with more organic material and consequently N2O emissions would decline even more. Burning of crop residues also contributes to the global N2O budget. For each ton of crop residue that is burned, 40 g N2O is emitted (Grace et al., 2003).

C.

MITIGATION STRATEGIES

The objective of reducing CH4 emissions must be combined with improvements associated with increased yields and straw recycling; adhering to CH4 emission quotas might increasingly aVect rice production practices. Possible mitigation options for reducing methane emission from rice fields include reduced length of flooding, temporary drainage (Wassmann et al., 2000b), rice cultivar selection, kind and application mode of mineral fertilizers, and soil and crop management strategies to achieve a high acceptance (Mosier et al., 1998a,b; Neue, 1993; Yagi et al., 1994). CH4 emission was reduced significantly by early incorporation of rice straw during the fallow period, adding to the agronomic benefit of this practice. Bronson et al. (1997b) recorded seasonal N2O emissions during a fallow period as high as 172 and 183 mg N m 2, where rice straw and a green manure had been incorporated the previous season, respectively. Such emissions might be considered maximums because assimilation of nitrogen

352


mineralized from organic additions by fallow weed species or upland crops helps to retain N within the system and minimize N2O emissions (Buresh et al., 1993). Given the influence of soil type, climate, and organic additions on CH4 and N2O emissions, comprehensive studies are needed to quantify more thoroughly the trade-oV eVects between CH4 and N2O during an annual cycle within rice-based cropping systems. Water management is an important management factor when trying to minimize CH4 or N2O emissions from rice-based cropping systems. Midseason drainage, which originally was developed in Japan as a means to supply oxygen to rice roots, is also very eVective in reducing seasonal CH4 emissions from rice (Jain et al., 2000; Yagi and Minami, 1990). Despite projected decreases in CH4 emission by such methods, aerobic soil conditions during fallow and upland cropping intervals between rice crops enhance N2O emissions generated by nitrification of mineralized organic N and subsequent denitrification of NO3 when flooding is reestablished (Bronson and Mosier, 1993). Unintentional mid-season drainage is possible in many rice cropping systems of South and Southeast Asia where light textured soils or water distribution and management problems influence the ability of farmers to keep their soils flooded (Jain et al., 2000). Sitespecific adaptations will be required for an optimum eVect, considering rice yields, water consumption, and CH4 emissions. In summary, methane emissions can be reduced significantly by adopting the following mitigation practices: water management through intermittent irrigation or drainage, the use of composted organic manures instead of fresh manure, allowing pre-decomposition of crop residues under aerobic conditions before rice planting, and the selection of suitable cultivars that emit less CH4. It appears that composted organic additions are the best way to meet sustainable agriculture goals while minimizing greenhouse gas emissions from paddy rice. Adding crop residues or green manures in suYcient quantities to increase soil organic matter levels or replenish deficient nutrients for flooded rice exacerbates N2O emissions to unacceptable levels. Of course, it is important to establish that CH4 and N2O emissions arising from the composting process do not exceed emissions during rice cultivation. Direct dry seeding of rice as well as other crops following rice into surface residues will reduce N2O and CH4 emissions. Grace et al. (2003) suggested three feasible, cost-eVective agronomic interventions that would have an immediate eVect by reducing greenhouse gases production in the rice– wheat cropping systems and that will no doubt be applicable to other ricebased cropping systems in the tropics: (1) a reduction in residue burning, (2) a reduction in flood irrigation frequency for rice, and (3) the use of minimum or no tillage for upland crops following rice (e.g., wheat or maize). It was estimated that Adopting these measures would result in total savings in CO2 equivalent emissions of 1680 kg ha 1 year 1.


XI.

353

AGRONOMIC RESPONSES TO CROP RESIDUE MANAGEMENT

Recycling of crop residues is an essential component in achieving sustainability in crop production systems. Since crops respond diVerently to the application of diVerent organic materials to soil, evaluation of crop residues in terms of fertilizer eVect is complicated by the variable nutrient contents of the materials and the host of other eVects (as already discussed) these may have on crops and soils. In some cases, straw incorporation can actually lead to a reduction in crop yields due to release of phytotoxic compounds during decomposition and immobilization of soil and fertilizer N, causing N deficiency in the crop planted immediately after straw incorporation. In many studies in which crop residues proved to be superior to inorganic fertilizers, the eVect may be due only to better supply of nutrients from organic matter. It is, in fact, impossible to monitor all the eVects of organic matter on nutrient availability. Evaluation of the fertilizer eVects of an organic resource (e.g., for N) requires that the material be assessed both at an equal N application and on equal mass (or carbon) application, preferably in each case over a range of application rates. The eVect of residue incorporation on succeeding crops depends on the amount of residues and the time and method of incorporation. Though the long-term eVects of crop residue incorporation are generally expected to be beneficial in terms of increasing soil organic matter content, availability of nutrients, cation exchange capacity, and microbial, the time scale for these improvements is generally long (e.g., >5 years). However, improvements in soil conditions do not always flow to yields. Thus, despite the very large body of literature on the recycling of crop residues, there exists very little information that enables proper evaluation of organic residues for their fertilizer value.

A.

RICE–WHEAT CROPPING SYSTEM 1.

Effect on Crop Yields

Crop residue management as practiced in the rice–wheat cropping system is of three types (1) wheat straw management in rice and its residual eVect in following wheat, (2) rice straw management in wheat and its residual eVect in following rice, and (3) wheat straw management in rice and rice straw management in wheat (cumulative eVect). In several studies, incorporation of wheat straw into the soil had pronounced but variable eVects on the growth and yield of subsequent rice (Table XV). For example, in a field experiment on clay loam soil, rice yields under removal or incorporation of

354


Table XV EVect of Wheat Straw Management on Grain Yield (t ha 1) of Rice and Its Residual EVect on the Grain Yield of the Following Wheat in Rice–Wheat Cropping System in India Wheat straw management in rice

Experimental details Haryana, 3-year study, clay loam soil Punjab, 12-year study, loamy sand soil

Madhya Pradesh, 2-year study West Bengal, acid silty clay loam soil, 2-year study, Wheat straw incorporated 10 days before rice planting Uttar Pradesh, clay loam soil (pH 8.6), wheat straw (10 t ha 1) incorporated 30 days before rice transplanting a

30 kg ha

1

Crop

Straw Straw Straw removed burned incorporated

Straw þ green manure a

Rice

6.97

7.23

7.01

Wheat Rice

4.65 5.74

4.84 —

4.43 5.37

4.52 5.99

Wheat Rice

4.41 2.21

— —

4.32 2.82

4.44 —

Wheat Rice

4.48 3.74

— —

5.59 4.17

— —

Wheat

1.80

—

2.00

—

Rice (a) 100% NPK (b) 50% NPK Wheat

4.10

—

4.45

—

—

4.08

2.29

—

2.72

7.17

Reference Agrawal et al. (1995) YadvinderSingh et al. (2004a) Pandey et al. (1985) Sharma and Mittra (1992)

Rajput (1995)

—

extra fertilizer N.

wheat straw were similar (Agrawal et al., 1995). On a loamy sand soil, incorporation of wheat straw reduced rice yield by 7% (average for 12 years) compared to when it was removed (Yadvinder-Singh et al., 2004a). Incorporation of wheat straw into an acidic clay loam soil significantly increased the grain yield of rice, with significant residual eVect in the succeeding wheat crop (Sharma and Mitra, 1992). Similar observations were also made by Pandey et al. (1985) and Rajput (1995). A beneficial eVect of wheat straw on the grain yield of rice even in the first year of study has been reported by many workers (Alam and Azmi, 1989; Zia et al., 1992).


355

Table XVI EVect of Time of Rice Straw and Fertilizer N (120 kg N ha 1) Management in Wheat and Its Residual EVect on the Following Rice in Rice–Wheat Cropping System in Indiaa Experiment 2: Yadvinder-Singh et al. (2004b)

Experiment 1: Bijay-Singh et al. (2001) Grain yield (t ha 1)

15

15


Treatment

Wheat

Rice

N recovery (%)

Straw removed Straw burned Straw incorporated (40 DBSb) Straw incorporated (20 DBS) Straw incorporated (20 DBS) and 25% N applied at incorporation Straw incorporated (10 DBS)

5.06a 5.11a 4.89a

4.90a 5.13a 4.87a

40.9a 40.8a 36.1ab

31.7b 31.3b 36.6ab

4.94a 5.10a 5.17a

6.19a 6.25a 6.34a

5.00a

4.97a

34.4bc

34.0ab

5.22a

6.29a

4.79a

5.02a

30.4c

45.2a

4.95a

6.33a

—

—

—

—

4.97a

6.29a

a b

N losses (%)

Wheat (1993–2000)

Rice (1994–1999)

In a column, figures followed by a common letter are not significantly different. DBS, days before sowing of wheat.

In a long-term experiment (1984–94) in the Indo-Gangetic plains of India (Rattan et al., 1996), both rice and wheat had a higher yield with inorganic fertilizers than any of the crop residue management treatment in the first year. After 2 to 3 years, the combination of wheat straw and inorganic fertilizers produced yields similar to those with inorganic fertilizers. It was after 3 to 4 years that the combined use of inorganic fertilizers and wheat straw started giving higher yields than inorganic fertilizer treatment. Interestingly, green manuring in conjunction with wheat straw helped to mitigate the adverse eVect of wheat straw in rice (Yadvinder-Singh et al., 2004a). Similarly, Aulakh et al. (2001) reported that compared to application of 120 kg N ha 1 through urea alone, rice production was greater with wheat straw incorporation when an average of 86 kg N ha 1 of a prescribed 120 kg N ha 1 dose was applied as green manure and the balance as urea N. Green manure and incorporation of wheat straw in rice–wheat cropping systems has the potential to increase soil organic matter while maintaining high yields. In a field experiment conducted using 15N-labeled urea, grain yields of wheat and the following rice were not adversely aVected by incorporation of rice straw at least 20 days before sowing of wheat (Table XVI). In another 7-year study, compared with residue removal or residue burning,

356


incorporation of rice residue 10 to 40 days before seeding wheat did not show any adverse eVect on wheat yield (Table XVI). The application of 25% of fertilizer N as starter N at the time of residue incorporation showed some depression in wheat yield in all years compared with no starter N under 20-day incorporation treatment, although the diVerences were not significant. It was suggested that N applied concurrently with straw incorporation gets immobilized and does not remineralize easily. In this study, annual additions of 40–50 kg N ha 1 through rice residue for 7 years did not influence grain yield of wheat, as the recommended split application of 120 kg N ha 1 (one half drilled at sowing and the remaining half top dressed at 21–25 days after sowing) was already applied to all the treatments in wheat. There also exist several other reports showing similar rice and wheat yields under diVerent residual management practices (burning, removal, or incorporation) (Singh et al., 1996; Walia et al., 1995). Kavinandan et al. (1987) reported that incorporation of wheat straw 10 days before rice transplanting and rice straw 3 weeks before wheat sowing gave 0.25 and 0.42 t ha 1 higher yields of rice and wheat, respectively, over incorporation of wheat straw at 3 days before rice transplanting and rice straw at 2 weeks before wheat sowing; the diVerences, however, were not significant. In a field experiment in Faislabad (Pakistan), incorporation of rice straw into the soil produced significantly higher yields of wheat (3.51 t ha 1) compared to when rice straw was removed (2.91 t ha 1) (Salim, 1995). Singh et al. (1996) reported that in Pantnagar (India), incorporation of rice straw 3 weeks before wheat sowing significantly increased wheat yields on a clay loam but not on a sandy loam soil. In the Himachal Pradesh state of India, however, incorporation of rice straw at 30 days before wheat sowing produced significantly lower wheat yields than removal or burning of straw in the first 2 years, remained at par in the third year, and produced a significantly higher yield and N uptake from the fourth crop onward (Table XVII) (Verma and Bhagat, 1992). The causes of lower yields with straw incorporation, particularly in the initial period of study, were immobilization of N and slow decomposition of rice straw at low temperatures during wheat growth. However, with the advancement of time (fourth crop and onward), the previously added rice straw might have decomposed, resulting in significantly higher wheat yield and N uptake under this treatment. Straw mulch increased the wheat yield and N uptake significantly over straw incorporation during the 2 years, which might be due to more favorable soil moisture regime, regulation of soil temperature, control of weeds, and an increase in the microbiological activity. Yield and N uptake of following rice under straw burn treatment did not vary significantly from the straw removal during the entire study period. Straw mulch produced the lower yield and N uptake of rice as compared to other straw management treatments without N application during the first two rice crops but had a


357

Table XVII EVect of Rice Straw Management on Grain Yield (t ha 1) of Wheat and Its Residual EVect on Grain Yield of the Following Rice in Rice–Wheat Cropping System in India Crop residue management Experimental details Himachal Pradesh, data averaged for 4 years, acidic clay loam soil, rice straw chopped and incorporated 4 weeks before wheat sowing Himachal Pradesh, 5year study, acidic clay loam soil, rice straw chopped and incorporated 4 weeks before wheat sowing Punjab, sandy loam soil. Data are reported for the fourth cropping cycle

Crop

Straw removed

Straw burned

Straw incorporated

Wheat

2.76

—

2.79

Rice

2.37

—

2.47

Wheat (1984–87)

2.6

2.6

2.2

Rice Wheat (1987–89) Rice

3.7 2.4

3.6 2.4

3.7 2.4

3.8

3.7

4.0

Wheat

—

4.88

5.18

Rice

—

6.05

6.00

Reference Sharma et al. (1985, 1987)

Verma and Bhagat (1992)

V. Beri and B. S. Sidhu (personal communication)

significant residual eVect from the third crop onward. Straw incorporation in wheat increased rice yield by 38% in the third crop and 45% in the fourth crop over straw removal. Sharma et al. (1985, 1987) observed no significant eVect of straw incorporation on the grain yield of wheat and on the following rice (Table XVII). Pathak and Sarkar (1997) observed that at recommended fertilizer N (120 kg ha 1), rice straw incorporation produced lower rice yields than straw removal. In a long-term field experiment in Ludhiana in northwestern India, Beri et al. (1995) found that incorporation of rice and wheat residues into soil resulted in significantly lower yields than removal or burning of residues (Table XVIII). It was suggested that the depression in rice yield was not due

358


Table XVIII EVect of Wheat Straw Incorporation in Rice and Rice Straw Incorporation in Wheat on Crop Yields in Rice–Wheat Cropping System

Reference and country Beri et al. (1995), India

Brar et al. (1998), India

Sarkar (1997), India

Type of residue and soil type

Duration (years)

Rice straw in wheat and wheat straw in rice, sandy loam Rice straw in wheat and wheat straw in rice, loamy sand Rice straw in wheat and wheat straw in rice, sandy clay loam


Residue management practice

Rice

Wheat

11

Removed Burned Incorporated

5.50a 5.65a 4.63b

4.14a 4.26a 3.97b

3

Removed Burned Incorporated

3.19a 3.66a 3.40a

4.25a 4.41a 4.01a

2

Removed Half residues incorporated Full residues incorporated

4.68b 5.75a

3.50b 4.25a

4.80b

3.75b

Table XIX EVect of Wheat Straw Incorporation in Rice and Rice Straw Incorporation in Wheat on Crop Yields in Rice–Wheat Cropping System in India Grain yield of wheat (t ha 1) Residue management Removed Burned Incorporated LSD ( p ¼ 0.05)

Grain yield of rice (t ha 1)

1993–94

1994–95

1995–96

1996–97

1994

1995

1996

4.28 4.37 3.58 0.56

3.80 4.28 3.65 ns

3.93 3.44 3.75 0.29

4.30 4.47 4.92 0.28

7.02 7.05 7.66 0.30

6.20 6.10 6.74 0.26

7.28 7.23 7.67 0.31

From Dhiman et al. (2000).

to N immobilization. Dhiman et al. (2000) reported that on a clay loam soil, rice yields increased significantly with the incorporation of residues of both rice and wheat as compared to burning or removal (Table XIX), but wheat yields decreased with residue incorporation, particularly in the initial 2 years of the study. In the fourth cropping season, wheat yield was higher in the residue-incorporated treatment than residue burning or removal treatments. The average productivity during the 4-year period was


359

11.5 t ha 1 year 1 when residues of both the crops were incorporated, and it was higher by about 0.61 t ha 1 year 1 than from burning and removal of residues. In a calcareous sandy loam soil in Bihar (India), Prasad and Sinha (1995b) studied the eVect of incorporation of crop residues after chopping (2 cm size), soaking in 2% urea solution, and then inoculating with cellulytic culture (Aspergillus spp.) to hasten the decomposition. At recommended fertilizer levels, incorporation of crop residues compared to removal increased mean yields of wheat and rice by 7.2 and 8.5%, respectively. Prasad et al. (1999) concluded that residues of both rice and wheat can safely be incorporated without any detrimental eVects on the crops of rice and wheat grown immediately after incorporation. Rice straw was incorporated 32–42 days before sowing of wheat, and wheat straw was incorporated 65–76 days before rice planting. Wheat yields were slightly reduced with rice straw incorporation in the first year of study (3.7 versus 4.1 t ha 1). In microplot experiments with early rice-late rice–wheat rotation in China, fertilizer utilization by rice was 82.6 and 47.7% on a sandy loam soil and 75.5 and 51.8% on a light clay soil for rice straw (C:N ¼ 89:1) plus N fertilizer and N fertilizer alone, respectively. The grain yield from the total rotation was also higher under rice straw plus fertilizer N than under fertilizer N alone treatment (Xu, 1984). In a field experiment at Yanco (Australia), Bacon et al. (1989) observed that increasing quantities of rice stubble retained on the soil surface increased soil NO3 –N concentrations by 46% and wheat on these plots had a 37% increase in grain yield and 29% increase in N uptake. Bacon and Cooper (1985) obtained higher yields from wheat direct-drilled into undisturbed rice stubble plots over where stubble was incorporated at sowing. The high yields were due to increased availability of both soil and fertilizer N. Delayed stubble incorporation until wheat sowing caused greater yield depression due to N immobilization than when stubble was incorporated early after rice harvest. It is also possible that the wide range of phytotoxins released during stubble decomposition directly inhibited plant growth.

2.

Fertilizer Management in Straw-Amended Soils

Incorporation of cereal residues into the soil generally causes rapid immobilization of soil and fertilizer N during the early stages of decomposition, resulting in N deficiency in the succeeding crop. Proper management of fertilizer N may lead to reduced rates of N immobilization by crop residues, thus increasing the eYciency of N usage. The improved fertilizer management practices may include optimum method, time, and rate of

360


fertilizer N application, which may diVer from that when residues are removed or burned. One obvious solution to the N immobilization problem would be to place the fertilizer below the C-enriched surface soil layer formed due to surface placement of crop residues (Doran and Smith, 1987). Yadvinder-Singh et al. (1994b) concluded that on soils amended with crop residues, band placement of urea prills and deep placement of large urea granules would lead to significantly lower amounts of fertilizer N immobilization than mixed application of commercial urea granules. The limited contact between fertilizer N and the decomposing microorganisms was the main reason for the low rates of N immobilization with large urea granules. The adverse eVect of N immobilization on crop growth can also be avoided by applying additional fertilizer N or by delaying planting. Another fertilizer management option may be to apply a part N fertilizer at the time of straw incorporation to enhance decomposition of residues or to allow suYcient time for the decomposition of crop residues before the planting of next crop. Thakur and Pandya (1997) reported that preconditioning urea with rice straw and soil in the ratio 1:3:1 (urea:straw:soil) was significantly superior to urea alone in respect of grain yield and N uptake of wheat. a. Fertilizer N Rate. The target of eYcient nutrient management is to maintain stable nutrient cycling in the long term while supplying suYcient nutrients to crops in the short term. From a 3-year study on a rice–wheat cropping system in Uttar Pradesh (India), Misra et al. (1996) reported that total grain yields of rice and wheat increased due to the incorporation of both the straws, with an extra dose of 20 kg N ha 1 applied at straw incorporation over burning and straw incorporation without an extra N dose. Singh and Sharma (2000) showed that application of 20 kg extra fertilizer N as compared to recommended N levels of 120 kg N ha 1 to wheat on straw removal plots gave a significantly higher grain yield and nutrient uptake on straw-amended plots. In another study, the application of 30 kg extra N ha 1 compared to the recommended fertilizer increased the rice yield only slightly (Table XV) (Agrawal et al., 1995). Sharma and Mitra (1992) found that incorporation of rice straw 15–20 days before wheat sowing decreased the grain yield, while incorporation of wheat straw 15–20 days before transplanting increased rice yields. However, application of 15 kg N ha 1 as a starter dose with straw application increased the yields of both rice and wheat crops. From a 2-year study, Brar et al. (2000) reported that application of 40 kg N ha 1 at rice straw incorporation in addition to the recommended N fertilizer dose (120 kg ha 1) in two equal splits at sowing and 3 weeks after sowing produced a significantly higher wheat yield (4.94 versus 5.31 t ha 1) and N uptake (101 versus 116 kg N ha 1) than application of recommended N fertilizer. Application of


361

irrigation at straw incorporation to enhance straw decomposition further increased the wheat yield by 0.2 t ha 1 compared to no irrigation. Narang et al. (1999) reported that wheat responded significantly to the application of 160 kg N ha 1 during the first 2 years of straw incorporation (both rice and wheat) as compared to the recommended N rate of 120 kg N ha 1 when residues are removed. In the third year of the study, a significant response to fertilizer N was observed up to 120 kg N ha 1 in straw-amended wheat plots. Irrespective of the residue load, response of rice to fertilizer N was also observed up to 160 kg N ha 1 in the first year of study, but the grain yield increased significantly up to 120 kg N ha 1 in the second year of study. Results from this study suggested the application of 25–30 kg ha 1 higher fertilizer N doses to rice in wheat on straw-amended fields during initial 1–2 years after residue incorporation compared to the rates recommended for straw removal fields. Later on, recommended fertilizers may be needed to achieve higher yield productivity of rice–wheat systems. Singh and Sharma (2000) reported that incorporation of wheat residue (40–50 days before rice transplanting) with no N or at low N rates resulted in an adverse eVect on crop yields of rice and wheat. When adequate N (180 kg N ha 1) was applied, residue incorporation increased productivity by 0.4–0.7 t ha 1 and nutrient uptake by 40–65 kg ha 1 over removal or burning of residues. Residue incorporation increased eYciency of applied fertilizer N in rice and had a significant residual eVect in following wheat. Thakur and Singh (1987) estimated optimum N rates of 115 and 140 kg ha 1 for rice on fields without and with wheat straw (5 t ha 1) incorporation. Thus, higher fertilizer N may be required for crops grown on soils amended with crop residues to get maximum benefits. Jha et al. (1992) obtained the highest grain yield of rice when rice straw and green manure (mungbean) along with 60 kg N ha 1 was applied in three equal splits compared to rice straw or green manure applied alone. Grain yield of following wheat was also higher when rice straw and green manure were incorporated. In a 2-year field experiment in Modipuram (northwestern India), incorporation of half of the crop residues along with recommended fertilizers consistently produced higher yields of both rice and wheat than incorporation of full residues or removal of residues (Sarkar, 1997). The rice grain yield with half residue was 5.80 t ha 1 and wheat yield was 4.38 t ha 1 compared to 4.70 t ha 1 of rice and 3.71 t ha 1 of wheat for no straw treatment. In a rice–wheat rotation on a calcareous soil, application of crop residues along with FYM gave the highest yield followed by FYM, crop residues, and no amendment (Prasad and Sinha, 1995b). The grain yield recorded with 50% NPK plus FYM plus crop residues was higher than that

362


with the 100% recommended dose of NPK alone, indicating that FYM plus crop residues substituted 50% of NPK in each of wheat and rice production. Malik and Jaiswal (1993) reported that application of 58 kg N ha 1 as urea super granules plus 28 kg N ha 1 as wheat straw produced a significantly higher rice yield than the recommended practice of applying 87 kg N ha 1 as commercial urea granules. The grain yield of the following wheat was not aVected by the previous residue management practices in the rice crop. Under dryland conditions in Uttar Pradesh (India), Singh and Singh (1995) found that incorporating rice straw (10 t ha 1; C:N ¼ 75.5) 3 weeks before planting rice integrated with 50% of recommended NPK fertilizers produced the highest rice yield and improved the profitability of the system. Rajput (1995) found that incorporation of wheat straw (10 t ha 1) resulted in up to 50% savings in the recommended NPK fertilizers (60 kg N þ 13.1 kg P þ 25 kg K ha 1). A higher yield potential of rice was achieved when wheat straw was applied along with recommended NPK fertilizers (Table XV). The residual eVects of wheat straw on the following wheat were also substantial. In another study (Kundu et al., 1994), however, wheat straw applied to rice had little eVect on the grain yield of the succeeding wheat crop. In fact, several other long-term studies also showed that it is not possible to substitute a part of fertilizer N requirement of rice with N added through wheat straw (Table XX). Katyal et al. (1998) reported results from long-term field experiments conducted at five sites in India during 1983–1991. At Kanpur (U.P., India), 50% recommended NPK fertilizers plus 50% N through wheat straw in rice followed by 100% recommended NPK fertilizers in wheat stabilized the yields of rice and wheat. However, at the three other locations it was not possible to substitute a part of fertilizer N (25% N) with crop residues (Table XX). In West Bengal (India), the application of semi-decomposed wheat straw (0.78%N, dry weight basis) at 3 t ha 1 along with 25 or 50% of the recommended NPK fertilizers (80 kg N þ 26 kg P þ 33 kg K ha 1) 30 days before sowing of rainfed rice resulted in the highest yields. In a rice– wheat cropping system, 5 t wheat straw ha 1 was incorporated 3–10 days before rice transplanting and 5 t rice straw ha 1 was incorporated 2–3 weeks before seeding wheat (Kavinandan et al., 1987). Most of the studies on rice straw management were conducted at recommended N rates, and thus it is diYcult to quantify the contribution of rice straw in supplying N to plants in the cropping system due to the fact that the amount of N fertilizer applied exceeds that for optimum yields. b. Time and Method of Fertilizer Application. Sharma and Bali (1998) showed that application of 30 kg N ha 1 at straw incorporation and remaining 90 kg N ha 1 top dressed during wheat growth soil produced

Table XX EVect of Wheat Straw and Fertilizer Management in Rice and Their Residual EVect on Wheat in Rice–Wheat Cropping System in India Crop

Year/site

Uttar Pradesh (Faizabad)

1984–87 1987–93

Madhya Pradesh, 3-year study

Jabalpur Raipur

Punjab (Ludhiana)

8-year study

Uttar Pradesh (Kanpur)

8-year study

Madhya Pradesh (Jabalpur)

8-year study

West Bengal (Kalyani)

8-year study

Rice Wheat

100% NPK 100% NPK

50% NPK þ 50% N as WSa 100% NPK

75% NPK þ 25% N as WS 75% NPK

LSD ( p ¼ 0.05)

Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat

3.9 3.4 4.5 3.4 4.14 2.94 4.65 1.77 6.37 4.55 3.98 4.63 4.72 2.68 3.50 2.65

3.5 3.0 4.3 3.4 3.37 2.75 4.20 1.84 5.25 4.33 3.31 4.48 3.89 2.61 3.48 3.06

3.3 3.1 4.4 3.1 3.27 2.57 4.19 1.78 5.97 3.93 3.54 4.11 4.03 2.08 3.45 2.55

0.28 NS NS 0.29 0.24 0.13 0.18 0.13 — — — — — — — —

Reference Kumar and Yadav (1995)

Dubey et al. (1997)

Katyal et al. (1998) Katyal et al. (1998)


Location

Treatment

Katyal et al. (1998) Katyal et al. (1998)

a

WS, wheat straw.

363

364


a significantly higher yield (2.0 versus 3.12 t ha 1) than that from applying 120 kg N ha 1 in two equal split doses (half drilled at sowing and half top dressed at 1 month after sowing) on a silty clay loam. Incorporation of rice straw reduced the wheat yield over straw removal at recommended N level through its beneficial eVect on residue decomposition. In field experiments using N-labeled urea, application of a part of the recommended N (25%) at the time of straw incorporation (to hasten decomposition of straw) led to large N losses and low wheat yield (Bijay-Singh et al., 2001). Recovery of 15N by wheat was maximum (41.8%) when rice straw was removed or burned and the minimum (30.4%) when 30 kg of 120 kg N ha 1 fertilizer was applied along with straw incorporation at 20 days before wheat sowing (Table XVI). From long-term field experiments (1990–96) in three locations in India, Yadav (1997) reported that when 20 kg N ha 1 was applied at the time of incorporation and the remainder of the recommended N (100 kg or 120 kg N ha 1 for rice and 120 kg N ha 1 for wheat) during the growth period, grain yields of rice and wheat were significantly lower than those obtained with other N scheduling practices included in the study. However, when an extra 20 kg N ha 1 was applied at the time of residue incorporation over and above the recommended N dose at one location (Jammu and Kashmir) or when N levels were enhanced by 20 kg N ha 1 over the recommended rates at the other site (Uttar Pradesh), the crop yields were the highest. On average of six crop cycles, these practices produced an additional 150 and 510 kg grain ha 1 at the first site and 570 and 810 kg grain ha 1 at the second site in rice and wheat crops, respectively. Jiang et al. (1998) recommended the application of 105 kg N ha 1 in three equal splits (sowing, tillering, and stem elongation) on plots amended with wheat straw (3 t ha 1). Split application of fertilizer N increased wheat yield by 43% compared to fertilizer N applied in single or two splits. Bacon and Cooper (1985) found that application of N to wheat at tillering or stem elongation, compared to at sowing, significantly increased soil mineral N content at least until anthesis. Wheat on the stubble-incorporated plots did not respond significantly to N application at sowing or stem elongation, while N application at any time more than doubled wheat productivity on the stubble-retention plots. Delaying N application until tillering significantly increased yields on stubble incorporation, stubble retention and burned plus till plots. While only 70 kg N ha 1 was required for maximum yield at stem elongation, 140 kg N ha 1 was necessary at sowing. It was concluded that stubble and fertilizer management techniques could be manipulated in order to regulate soil mineral N status, which in turn determined plant N uptake and yield of wheat. In Kanto (Japan), with continuous application of rice and wheat straw, rice yields were low during the initial 3–4 years, but the yield of rice increased dramatically by continuous application of rice straw (Roppongi,


365

1987). The adverse eVects were alleviated when the basal application of N was increased.

B. RICE–RICE CROPPING SYSTEM 1.

Effect on Crop Yields

Rice–rice is a dominant cropping system in Bangladesh, China, Philippines, Korea, Japan, Indonesia, and eastern and southern parts of India. Studies with rice straw in rice–rice systems in a number of countries demonstrate widely varying response to straw incorporation. In sharp contrast to rice– wheat cropping systems, the majority of the studies on rice–rice cropping systems show that incorporation of rice residues enhances rice yield and N use eYciency. Ismunadji (1978) from Indonesia reported that incorporation of 10 t rice straw ha 1 increased grain yield of rice to 2.6 t ha 1 from 2.2 t ha 1 in the untreated control. Burning of rice straw produced a rice yield almost similar to that obtained with its incorporation. Experiments in the Philippines showed that straw incorporation for more than 6 years increased the rice yield by 0.4 to 0.7 t ha 1 compared with fields that used to receive chemical fertilizers and where rice straw was either removed or burned (Table XXI) (Ponnamperuma, 1984). On the unfertilized plot, when the soil was a P-deficient acid clay, the extra yield amounted to 23% (Ponnamperuma, 1984). The beneficial eVects of rice straw incorporation were, however, small during the initial 2–6 years of the study. Sharma et al. (1989), however, observed no significant eVect of rice straw incorporation (6 t ha 1) on grain yield of rice in a short-term study in the Philippines. In a long-term study in Japan, Beaton et al. (1992) noted no beneficial eVects of

Table XXI EVect of Long-Term Management of Rice Straw on Crop Yields (t ha 1) in Rice–Rice Cropping System Experiment 1 Straw treatment Removed Incorporated Burned

Experiment 2

2–5 years

6–10 years

Mean

9.7 9.9 9.9

7.1 7.7 7.1

8.3 8.7 8.3

2–6 years

7–10 years

9.0 9.3 —

7.3 8.0 —

Experiment 3

Mean

1966– 1980

8.2 8.7 —

7.53 7.54 —

1981– 1985

1986– 1989

8.03 8.17 —

7.74 8.44 —

Experiment 1 and Experiment 2 from Ponnamperuma (1984); Experiment 3 from Beaton et al. (1993).

366


incorporation of rice straw over straw removal in the initial 15 years of the study (Table XXI), but in the final 4 years (20–23 years), an average increase of 0.70 t ha 1 in rice grain yield was observed with rice straw incorporation over removal. The length of the period allowed for decomposition of crop residues before the sowing/planting of the next crop aVects the agronomic response to applied residues. Houng and Hwa (1975) found that when rice straw was allowed to decompose for 4 or more weeks before sowing, there was no adverse eVect on germination of rice seeds. In many other studies, crop residues were allowed to decompose for 2 or more weeks before rice transplanting to avoid the adverse eVects of phytotoxicity and N immobilization on crop growth (Ali et al., 1995; Lanjewar et al., 1992; Wu et al., 1997). Sharma and Mitra (1990) observed that rice yields were increased significantly when rice straw was applied 30 days before transplanting, and rice straw also exhibited a favorable residual eVect on the yield of the second rice crop. Witt et al. (2000) reported that early residue incorporation improved the congruence between soil N supply and crop N demand by wet season rice, especially during the vegetative stage of crop growth. This has resulted in 13–20% greater rice yields with early (60–63 days before transplanting) compared to late (14–15 days prior to transplanting) residue incorporation in rice–rice systems without applied N or with moderate rates of applied N. From South Korea, Han et al. (1991) reported that the application of 7.5 t rice straw ha 1 along with the recommended dose of fertilizers produced a rice yield of 4.8 t ha 1, which was significantly higher than the 4.3 t ha 1 obtained with the application of recommended fertilizers alone. Similarly, Sistani et al. (1998) from Malawi, Lee et al. (1995) from South Korea, Beye (1977) from Senegal, Ali et al. (1995) from India, and Gotoh et al. (1984) from Japan also observed beneficial eVects of rice straw incorporation on rice yield. From a 6-year field experiment, Finassi (1976) observed that incorporation of rice straw caused a significant increase in rice yield at the highest rate of N (120 kg ha 1) application only. In a field experiment over four cropping seasons in Andhra Pradesh (India), Vamadevan et al. (1975) obtained the highest rice yield and N uptake in all the seasons when the rice straw was incorporated into the soil, but there was a small eVect at high levels of applied N (100 or 150 kg N ha 1). Houng and Lin (1976) and Oh (1979) observed that incorporating rice root residues into the soil increased rice yield on a poor soil rather than on a fertile soil. In a pot study, incorporation of 10 t rice straw ha 1 into 0–6 cm soil layer without fertilizer increased rice grain yield by 21.2%, while application of fertilizer N along with rice straw increased yield by 52.5% (Rao, 1973). The residual eVect of rice straw in the following rice crop was equal to 42% increase in grain yield. The beneficial eVect of rice straw


367

was explained by the increased biological N2 fixation by the free-living microorganisms in the flooded soil amended with rice straw. Adverse as well as no eVects of incorporation of crop residues into soil on rice yield have also been documented. In poorly drained paddy fields, incorporation of rice straw adversely aVects the rice growth due to the presence of strong reducing conditions in the soil (Kuboto, 1984). On such fields, it is recommended to incorporate the rice straw shortly after harvest. Allowing decomposition of the straw over longer period alleviates the injury occurring in the initial growth of the rice. In paddy fields with heavy clay soils, rice yield decreased in the first and second year after straw incorporation; however, after 3–4 years of continuous application, as the amount of soil mineralized N increased and reduction in soil becomes less pronounced, plant growth and the yield increased (Kuboto, 1984). Proper water management, which includes drainage, is also important in such soils. In a lysimeter experiment, Kondo et al. (1980) observed that rice straw tended to decrease the rice yield in the presence of fertilizer N compared with no straw. Corft et al. (1985), however, reported that 6 t rice straw ha 1 along with recommended N, P, K, and S fertilizers showed no eVect on rice yield over N, P, K, and S fertilizers alone.

2.

Integrated Management of Fertilizers and Crop Residues

Crop residues incorporated during fallow periods will cause immobilization of soil N, but net N mineralization occurring during the following cropping season would need to be accounted for when evaluating N requirements of the following crop. To determine the amount of N fertilizer that can be reduced with annual straw incorporation, a N fertilizer response study was conducted by Eagle et al. (2001). As the level of N fertilizer applied increased, grain yield increased when straw was burned or incorporated. However, grain yields when straw was incorporated continuously for 5 years were higher than when straw was burned. These trials indicated that N fertilizer application can be decreased when straw is incorporated, because no yield response was further observed when more than 115 kg of N ha 1 was applied. It was recommended that N rates can be decreased by at least 30 kg N ha 1 after 5 years of straw incorporation. Clearly, an active, labile N pool was formed when straw was incorporated that led to a reduction in fertilizer N dependency for rice. In long-term experiments carried out at four locations in India, Hegde (1996) observed that at three locations it was not possible to substitute a part of N (25% of the recommended N) needs through rice straw without adversely aVecting crop yields (Table XXII). In Kerala, located in deep South of India, however, rice straw incorporation could substitute for 25% of

368


Table XXII EVect of Integrated Management of Inorganic Fertilizers and Rice Straw on Crop Yields (t ha 1) in Rice–Rice Cropping System at Four DiVerent Locations in India Treatment Rainy season 100% NPK 75% NPK 50% NPK 75% NPK þ 25% N as RSe 50% NPK þ 50% N as RS LSD ( p ¼ 0.05)

Rainy season rice a

b

Winter season rice

c

d

a

Winter season Site 1 Site 2 Site 3 Site 4 Site 1 Site 2b Site 3c Site 4d 100% NPK 75% NPK 100% NPK 100% NPK

5.21 4.46 4.40 5.03

3.62 2.93 2.59 3.21

4.72 3.93 3.23 4.07

3.68 3.65 3.64 3.66

5.42 4.71 4.61 5.00

3.86 3.88 3.80 3.47

4.51 3.85 4.73 4.00

2.99 2.90 3.04 2.88

100% NPK

4.73

2.96

3.53

3.68

5.05

3.84

4.79

2.99

0.19

0.04

0.18

0.16

0.21

0.05

0.20

0.12

a Site 1: Kharagpur (West Bengal), data averaged for 5 years, Udic Ustochrepts sandy clay loam soil (pH 5.4). b Site 2: Bhubneswar (Orissa), data averaged for 10 years, Haplustalts sandy loam soil (pH 5.9). c Site 3: Maruteru (Andhra Pradesh), data averaged for 4 years, Chromustrets clayey soil (pH 7.0). d Site 4: Karemane (Kerala), data averaged for 8 years, Typic Tropfluvent sandy loam soil (pH 5.2). e RS, rice straw. From Hegde (1996).

fertilizer N needs of kharif rice. The relatively high temperatures during both kharif and winter seasons in Kerala might have helped for quick decomposition of rice straw. At low-fertility levels (50% N, P, K), application of rice straw significantly increased the grain yield of rainy season rice over no straw treatment at all the four sites. Raju et al. (1987) and Elankumaran and Thengamuthu (1986) also concluded that it is not possible to substitute a part of N through rice straw due to its high C:N ratio, which causes immobilization of soil and fertilizer N. In a field trial in eastern India, Bhattacharyya et al. (1996) recorded N substitution of up to 50% of the recommended fertilizers from the incorporation of 5 t rice straw ha 1 in an acidic red soil. Russo (1974) reported that incorporating 3–6 t chopped rice straw ha 1 with 65 kg N plus 28 kg P ha 1 produced a slightly higher rice yield than that obtained with the application of 120 kg N plus 43.1 kg P plus 110 kg K ha 1. Kamalan et al. (1989) obtained 0.3 t ha 1 of additional rice yield with the application of urea super granules (USG) combined with rice straw over USG alone. In another study in Malawi, Sistani et al. (1998) observed that on rice straw-amended plots, application of urea in briquette form compared with prilled urea significantly increased rice grain yield in two of the three experiments.


369

Raju and Reddy (2000) conducted a 6-year study on a clay loam soil to investigate the eVect of rice residue management on crop yields in rice–rice system. Rice straw equivalent to supply 25 and 50% fertilizer N requirement of the rainy season crop was incorporated before rice transplanting, and the residual eVect of rice straw was studied in the succeeding winter rice. The incorporation of rice straw along with recommended fertilizers proved superior to inorganic fertilizers alone in increasing rice yield, soil organic matter, and available K contents in soil. This study showed that it is possible to reduce the total fertilizer needs of both rainy and winter season rice by 25% without any adverse eVect on system productivity. The N balance was negative in all the treatments, but the P balance was positive. The K balance was positive when 50% of the fertilizer N was applied as rice straw. In a greenhouse experiment, Shen et al. (1993) obtained a higher rice grain yield by adding 10 g wheat straw plus 350 mg urea N kg 1 soil, but the 10 g straw plus 150 mg urea N treatment registered higher fertilizer use eYciency. It was suggested that an adequate N supply to rice plants could be maintained by applying suYcient N fertilizer with straw to have a C:N ratio equal to 20. Huang and Lu (1996) observed no adverse eVect of rice straw on plant growth and total 15N recovery when rice straw was incorporated along with (NH4)2SO4 at a C:N ratio of

Advances in Agronomy, Volume 85

Advances in Agronomy, Volume 60

Advances in Agronomy, Volume 50

Advances in Agronomy, Volume 68

Advances in Agronomy, Volume 54

Advances in Agronomy, Volume 11

Advances in Agronomy, Volume 31

Advances in Agronomy, Volume 33

Advances in Agronomy, Volume 65

Advances in Agronomy, Volume 21

Advances in Agronomy, Volume 64

Advances in Agronomy, Volume 105

Advances in Agronomy, Volume 20

Advances in Agronomy, Volume 100

Advances in Agronomy, Volume 108

Advances in Agronomy, Volume 69

Advances in Agronomy, Volume 83

Advances in Agronomy, Volume 91

Advances in Agronomy, Volume 46

Advances in Agronomy, Volume 111

Advances in Agronomy, Volume 62

Advances in Agronomy, Volume 56

Advances in Agronomy, Volume 28

Advances in Agronomy, Volume 53

Advances in Agronomy, Volume 14

Advances in Agronomy, Volume 26

Advances in Agronomy, Volume 103

Advances in Agronomy, Volume 81

Advances in Agronomy, Volume 40

Advances in Agronomy, Volume 45

Advances in Agronomy, Volume 101

Advances in Agronomy, Volume 85

Advances in Agronomy, Volume 60

Advances in Agronomy, Volume 50

Advances in Agronomy, Volume 68

Advances in Agronomy, Volume 54

Advances in Agronomy, Volume 11

Advances in Agronomy, Volume 31

Advances in Agronomy, Volume 33

Advances in Agronomy, Volume 65

Advances in Agronomy, Volume 21

Advances in Agronomy, Volume 64

Advances in Agronomy, Volume 105

Advances in Agronomy, Volume 20

Advances in Agronomy, Volume 100

Advances in Agronomy, Volume 108

Advances in Agronomy, Volume 69

Advances in Agronomy, Volume 83

Advances in Agronomy, Volume 91

Advances in Agronomy, Volume 46

Advances in Agronomy, Volume 111

Advances in Agronomy, Volume 62

Advances in Agronomy, Volume 56

Advances in Agronomy, Volume 28

Advances in Agronomy, Volume 53

Advances in Agronomy, Volume 14

Advances in Agronomy, Volume 26

Advances in Agronomy, Volume 103

Advances in Agronomy, Volume 81

Advances in Agronomy, Volume 40

Advances in Agronomy, Volume 45

Advances in Agronomy, Volume 101

Recommend Documents