Advances in Agronomy, Volume 74

Agronomy DVANCES I N VOLUME 74 Advisory Board Martin Alexander Ronald Phillips Cornell University University of...

Author: Donald L. Sparks

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Agronomy

DVANCES I N

VOLUME

74

Advisory Board Martin Alexander

Ronald Phillips

Cornell University

University of Minnesota

Kenneth J. Frey

Kate M. Scow

Iowa State University

University of California, Davis

Larry P. Wilding Texas A&M University

Prepared in cooperation with the American Society of Agronomy Monographs Committee Jerry M. Bigham Jerry L. Hatfield David M. Kral Linda S. Lee

Diane E. Stott, Chairman David Miller Matthew J. Morra John E. Rechcigl Donald C. Reicosky

Wayne F. Robarge Dennis E. Rolston Richard Shibles Jeffrey Volenec

Agronomy

DVANCES IN

VOLUME

74

Edited by

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

San Diego San Francisco

New York

Boston

London

Sydney

Tokyo

This book is printed on acid-free paper.

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C 2001 by ACADEMIC PRESS Copyright

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. (222 Rosewood Drive, Danvers, Massachusetts 01923), 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-2001 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. 0065-2113/01 $35.00 Explicit permission from Academic Press is not required to reproduce a maximum of two figures or tables from an Academic Press chapter in another scientific or research publication provided that the material has not been credited to another source and that full credit to the Academic Press chapter is given.

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Academic Press Harcourt Place, 32 Jamestown Road, London NW1 7BY, UK http://www.academicpress.com International Standard Book Number: 0-12-000792-4 PRINTED IN THE UNITED STATES OF AMERICA 01 02 03 04 05 06 SB 9 8 7 6

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

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SOIL QUALITY: CURRENT CONCEPTS AND APPLICATIONS D. L. Karlen, S. S. Andrews, and J. W. Doran I. II. III. IV. V. VI. VII. VIII. IX.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Response to Reservations Regarding the Soil Quality Concept . . . . . . . Evolution of the Soil Quality Concept. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Quality as an Educational Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Quality as an Assessment Tool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indexing Soil Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current Soil Quality Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Soil Quality Research and Outreach Programs. . . . . . . . . . . . . . . . . . Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 5 6 10 12 14 21 26 34 35

FRONTIERS IN METAL SORPTION/PRECIPITATION MECHANISMS ON SOIL MINERAL SURFACES Robert G. Ford, Andreas C. Scheinost, and Donald L. Sparks I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. From Adsorption to Precipitation: An Overview . . . . . . . . . . . . . . . . . . . . . . . III. Macroscopic Evidence for Surface Precipitation: Utility and Pitfalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Approaches to Modeling Surface Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . V. The Role of the Mineral Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Environmental Implications: Mechanisms for Metal Stabilization . . . . VII. Conclusions and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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42 43 46 49 52 54 56 59

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CONTENTS

ORGANIC ACIDS EXUDED FROM ROOTS IN PHOSPHORUS UPTAKE AND ALUMINUM TOLERANCE OF PLANTS IN ACID SOILS Peter J. Hocking I. II. III. IV. V. VI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Organic Acids and Phosphorus Solubilization in the Rhizosphere . . . . Organic Acids and Soil Organic Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aluminum Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetic Engineering Approaches to Increase Organic Acid Exudation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

64 65 68 80 82 86 87 89

ASPECTS OF BAMBOO AGRONOMY Volker Kleinhenz and David J. Midmore I. II. III. IV.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manipulating Growth and Development in Bamboo . . . . . . . . . . . . . . . . . . . Managing the Environment for Bamboo Production . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

100 101 125 139 142

MANAGING WORLD SOILS FOR FOOD SECURITY AND ENVIRONMENTAL QUALITY R. Lal I. II. III. IV. V. VI. VII. VIII.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Historical Development of Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Green Revolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relation Between Extensive Agriculture, Soil Degradation, and the Greenhouse Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges of Soil and Water Management for the 21st Century . . . . . Toward Sustainable Management of Soil Resources . . . . . . . . . . . . . . . . . . . . Respecting “The Dirt” for Feeding 10 Billion and Mitigating the Greenhouse Effect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

157 160 166 168 176 177 183 185 186

CONTENTS

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THE MANAGEMENT OF WHEAT, BARLEY, AND OAT ROOT SYSTEMS S. P. Hoad, G. Russell, M. E. Lucas, and I. J. Bingham I. II. III. IV. V. VI. VII. VIII. IX. X.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Root Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Structure and Root Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water and Nutrient Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biological Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Root : Shoot Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Farming Practices and Rooting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selection of Appropriate Farming Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

195 196 203 206 210 218 220 228 232 235 237

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

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

S. S. ANDREWS (1), USDA-ARS, National Soil Tilth Laboratory, Ames, Iowa 50011 I. J. BINGHAM (193), Scottish Agricultural College, Agronomy Department, Bucksburn, Aberdeen AB21 9YA, United Kingdom J. W. DORAN (1), Soil and Water Research Unit, University of Nebraska–East Campus, Lincoln, Nebraska 68583 ROBERT G. FORD (41), National Risk Management Research Laboratory, United States Environmental Protection Agency, Ada, Oklahoma 74820 S. P. HOAD (193), Scottish Agricultural College, Crop Science Department, Penicuik, Midlothian EH26 0PH, United Kingdom PETER J. HOCKING (63), CSIRO Plant Industry, Canberra, Australian Capital Territory 2601, Australia D. L. KARLEN (1), USDA-ARS, National Soil Tilth Laboratory, Ames, Iowa 50011 VOLKER KLEINHENZ (99), Plant Sciences Group, Primary Industries Research Center, Central Queensland University, North Rockhampton, Queensland 4702, Australia R. LAL (155), School of Natural Resources, The Ohio State University, Columbus, Ohio 43210 M. E. LUCAS (193), Institute of Ecology and Resource Management, University of Edinburgh, Edinburgh EH9 3JG, United Kingdom DAVID J. MIDMORE (99), Plant Sciences Group, Primary Industries Research Center, Central Queensland University, North Rockhampton, Queensland 4702, Australia G. RUSSELL (193), Institute of Ecology and Resource Management, University of Edinburgh, Edinburg EH9 3JG, United Kingdom ANDREAS C. SCHEINOST (41), Institute of Terrestrial Ecology, Swiss Federal Institute of Technology, CH-8952 Schlieren, Switzerland DONALD L. SPARKS (41), Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19717

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Preface Volume 74 contains six excellent cutting-edge reviews. Chapter 1 is an extensive review on soil quality. Topics include a discussion on concerns that have been raised on the soil quality concept, use of soil quality as an educational and assessment tool, indexing soil quality, and current soil quality applications. Chapter 2 covers advances in understanding the formation of metal hydroxide precipitates on soil surfaces and their implications on metal sequestration and soil remediation. Chapter 3 is a timely review on effects of organic acid exudation from roots on phosphorus uptake and aluminum tolerance of plants in acid soils. Advances in genetic engineering to increase organic acid exudation are featured. Chapter 4 discusses bamboo production and management including manipulation of growth and development and environmental aspects of bamboo production. Chapter 5 addresses a significant worldwide issue—management of soils for food security and environmental quality. Discussions on soil degradation and the greenhouse effect, and other challenges of soil and water management, are included. Chapter 6 is a comprehensive review on the management of wheat, barley, and oat root systems. Topics include: root characteristics, soil attributes, soil structure and root growth, water and nutrient availability, biological factors, and farming practices and rooting. I appreciate the excellent contributions of the authors. DONALD L. SPARKS

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SOIL QUALITY: CURRENT CONCEPTS AND APPLICATIONS D. L. Karlen,1 S. S. Andrews,1 and J. W. Doran2 1

USDA-ARS National Soil Tilth Laboratory Ames, Iowa 50011 2 Soil and Water Research Unit University of Nebraska–East Campus Lincoln, Nebraska 68583

I. II. III. IV. V. VI. VII. VIII.

Introduction Response to Reservations Regarding the Soil Quality Concept Evolution of the Soil Quality Concept Soil Quality as an Educational Tool Soil Quality as an Assessment Tool Indexing Soil Quality Current Soil Quality Applications Other Soil Quality Research and Outreach Programs A. U.S. Activities B. International Activities IX. Summary and Conclusions References

Soil quality has evolved as an educational and assessment tool for evaluating relative sustainability of soil resource management practices and guiding land-use decisions. This review discusses the rapid development of the soil quality concept throughout the decade of the 1990s, addresses misconceptions regarding soil quality efforts, and presents examples to illustrate how soil quality research, education, and technology-transfer activities are being used to help solve various soil resource and agroecosystem problems. This review stresses that soil quality assessment reflects biological, chemical, and physical properties, processes, and their interactions within each soil resource unit. By using examples from throughout the United States and around the world, we demonstrate the importance of using soil quality concepts to integrate both inherent and dynamic properties and processes occurring within a living, dynamic medium. We also emphasize that there is no ideal or magic soil quality index value by illustrating a framework for indexing that can be adapted to local conditions. The framework requires identifying critical soil functions, selecting meaningful indicators for those functions, developing appropriate scoring functions to interpret the indicators for various soil resources, and combining the information into values that can be tracked over time to determine

1 Advances in Agronomy, Volume 74 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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D. L. KARLEN ET AL. if the soil resources are being sustained, degraded, or aggraded. This review is intended to provide a reference and background for land managers, resource conservationists, ecologists, soil scientists, and others seeking tools to help ensure that C 2001 Academic Press. land-use decisions and practices are sustainable.

I. INTRODUCTION Soil quality as a specific concept emerged rapidly during the decade of the 1990s, evolving as an outcome of increased emphasis on sustainable land use. A greater awareness of trade-offs associated with increasing world demands for food, feed, and fiber, public demand for environmental protection, and decreasing supplies of nonrenewable energy and mineral resources (Doran et al., 1996) provided the impetus for questioning the sustainability of current soil management decisions (Pesek, 1994). Interest in and adoption of the soil quality concept as a tool for assessing the effects of land-use and soil management decisions on the sustainability of soil, water, and air resources were most rapid among natural resource conservationists, farmers, land managers, ecologists, and various sustainable-agriculture groups throughout the world. As the soil quality concept evolved, several tangible tools for education and assessment were developed. These included soil quality or soil health scorecards, a soil quality test kit, visual assessment procedures, fact sheets, video presentations, and frameworks for indexing soil quality at various scales. The educational tools were science-based, but generally focused on increasing awareness among the general public, policymakers, and landowners or land operators regarding the soil resource being a dynamic and living entity. Assessment tools were developed to facilitate comparisons between soil management systems and to document changes in soil properties and processes occurring over time in response to land-use or soil management decisions. Most soil quality education and assessment tools have been effective in achieving these goals, although some were too site-specific, narrowly focused, or without interpretation guidelines to have general application. Two common features of both the educational and assessment tools are that they encourage land managers to examine biological, chemical, and physical properties and processes occurring within their soil resources and to use that information as a framework for helping to make adaptive soil management decisions. This action, although placed beneath the umbrella of soil quality, was essentially the same as that suggested by Karlen et al. (1990) with regard to redefining soil tilth and seeking ways to improve it.

SOIL QUALITY CONCEPTS AND APPLICATIONS

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With regard to using soil quality assessments as a tool for evaluating sustainability and ecosystem response, it is essential to recognize that (1) spatial and temporal scales are critical, and (2) soil quality depends on both inherent and dynamic properties and processes. The issue of scale is presumably understandable, with two of its primary impacts being to determine which soil quality indicators will provide the most useful measurements and how large the differences must be to have statistical significance or to be mechanistically or functionally meaningful. The difference between inherent and dynamic properties and processes may be somewhat more subtle, but in general, inherent characteristics are those directly associated with the soil forming factors (Jenny, 1941), while dynamic characteristics are those easily affected by human decisions and actions. Furthermore, since both ecosystems and soil resources are dynamic in space and time, uncertainty, surprise, and limits to our knowledge exist. However, to make well-informed and sound policy choices regarding the fate of natural ecosystems, simple identification of ecosystem services, although necessary, is clearly an insufficient step (Daily, 1997). Interpretation of soil biological, chemical, and physical data by scientists familiar with the intricacies of soil resources must be an integral part of ecosystem evaluations. This input is needed to ensure rational interpretation of the numerous trade-offs faced by society and requires the development of a sound understanding of how soil resource ecosystem services function. Support for the soil quality concept has not been universal, especially among some soil scientists who were fearful that the efforts could lead to premature conclusions advocating a value system as an end unto itself. Their concern was that value-based decisions could supplant value-neutral science and thus lead to premature interpretations and assertions of soil quality before the concept had been thoroughly and analytically challenged (Sojka and Upchurch, 1999). This concern has some merit, but we must also realize that to serve the public effectively, scientists must convert data into useful information by helping to interpret it in appropriate and meaningful ways. Two publications that embody the concern about subjectivity inherent in indicator interpretation are entitled “Ecological Indicators for the Nation” (National Research Council, 2000) and “Designing a Report on the State of the Nation’s Ecosystems” (The Heinz Center, 1999). Although both effectively discuss the need for and present a series of potential indicators for assessing ecosystem health or quality (including soil quality), neither chooses to offer interpretation criteria needed for assessing sustainability. Our response to the concerns about interpreting indicators is that all decisions are value-laden and dominated by personal experiences and expectations (Keeney and Raiffa, 1976; Mayhew and Alessi, 1998). Even seemingly objective decisions, such as which grant proposals to fund, are driven by personal social values and preferences (Keeney, 1992). Given that all decisions are biased, who is better qualified to interpret scientific indicators than

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the scientists who developed them? Therefore, the most important step with regard to assessing the sustainability of various ecosystems is to recognize that their management is driven by explicit goals, executed by policies and protocols, and made adaptable by monitoring and research based on our best understanding of ecological interactions and processes necessary to sustain ecosystem composition, structure, and function (Christensen et al., 1996). In response to the concern that soil quality assessment and education efforts may be premature, we counter that waiting for complete knowledge to develop better guidelines and approaches for achieving sustainability of soil, water, and air resources could result in outcomes that are simply too late to reverse undesirable trends. As concluded by the National Research Council (1999), the pathway to sustainability cannot be charted in advance. It will have to be navigated through trial and error and conscious experimentation. The urgent need is to design strategies and institutions that can better integrate incomplete knowledge with experimental action into programs of adaptive management and social learning. While more than enough may be known to guide natural resource policy at the present, further information will permit more efficient allocation of human effort and material resources. This knowledge will also help overcome current resource management approaches that despite political rhetoric or even legislative mandates often focus primarily on maximizing short-term yield and economic gain rather than on long-term sustainability (Christensen et al., 1996). Ideally, this knowledge will help overcome many of the obstacles to solving the disparity in decision making, including (1) inadequate information on the biological diversity of ecosystems, (2) widespread ignorance of the function and dynamics of ecosystems, (3) a lack of understanding regarding the openness and interconnectedness of ecosystems on scales that transcend management boundaries, and (4) a prevailing public perception that the immediate economic and social value of supposedly renewable resources outweighs the risk of future ecosystem damage or the benefits of alternative management approaches. Based on an assumption that soil quality education and assessment tools can contribute significantly to a better understanding of ecosystem sustainability, our goals for this review are to (1) discuss the rapid development of the soil quality concept, (2) address the misconception regarding soil quality efforts being “an end unto itself,” and (3) document how soil quality research, education, and technology-transfer efforts are being used to guide research and help solve 21st century soil resource management and related ecosystem problems. Indeed, advocates and early adopters of the soil quality concept totally agree with Sojka and Upchurch (1999) that “our children and grandchildren of 2030 will not care whether we crafted our definitions or diagnostics well. They will care if they are well fed, whether there are still woods to walk in and streams to splash in—in short, whether or not we helped solve their problems, especially given a 30-year warning.”


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II. RESPONSE TO RESERVATIONS REGARDING THE SOIL QUALITY CONCEPT Efforts to develop and utilize the soil quality concept as a tool for assessing the sustainability of various soil management and land-use practices are deeply rooted in a belief that soil scientists must take a more active role in translating good political intentions regarding productivity and environmental quality into innovative soil management practices that can ensure sustainable production systems in harmony with nature (Bouma, 1997). Developing this type of proactive approach required addressing (1) the patchiness and disciplinary character of many current soil research programs, (2) implications that soil quality has unique characteristics that are dynamic in space and time, and (3) a common perception that “what is good for the environment is bad for business.” The increased need for information and rapid interpretation of soil science knowledge led to an emphasis on evaluating biological, chemical, and physical indicators to understand positive, negative, and interactive effects on soil properties and processes, and relationships among those factors for a variety of land uses and soil management practices. As previously acknowledged, more effort was placed on understanding those effects within the production-agriculture arena than on other land uses simply because of the disciplinary focus of the early adopters. However, efforts by Sims et al. (1997) and others, including a Soil Science Society of America symposium in November 2000, are beginning to address the need to develop soil quality criteria that apply to nonagricultural land management. Furthermore, as illustrated by the basic frameworks developed for indexing soil quality (discussed in Section VI), once additional information is available it can easily be incorporated into new, presumably more appropriate assessments for other land uses. As previously stated, the soil quality indexing process that we have proposed is founded on an understanding that soil resources have both inherent and dynamic characteristics that must be considered. For that reason, single soil quality values were never considered. Rather, a strong emphasis was placed on developing a process or framework that could easily accommodate changes associated with different soils or land uses. There never was nor can there be a single value for rating all soils or land uses. Trends and changes over time provide the only feasible way to project the effects of soil management or land use on the sustainability of a natural resource that is dynamic, living, and ever changing. With regard to the reservation that there is little if any parallel between soil, air, and water quality, we suggest that all three resources have a plethora of definitions based on current or anticipated use. Indeed, water can exist in a pure state whereas soil cannot, but under natural conditions water is not pure. If it were, single-celled organisms would be lysed and the habitat would be unsuitable or of “low quality.” For applications involving environmental and human interactions

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(e.g., allergy ratings, odors, suitability for swimming, fishing, or drinking), air and water quality are defined based on current or intended use. It is that parallel that allows these three entities to be equated in overall sustainability indices. Finally, with regard to perceptions of regional or taxonomic bias in soil quality efforts, the broad adoption of the concept as a tool for assessing sustainability of soil management and land-use practices documents that the process is widely adaptable and can be tailored to a wide variety of soils and land uses. As illustrated in Section VI, use of nonlinear scoring functions to “score” the various indicators easily accommodates differences among soils that are typically caused by their inherent characteristics (e.g., Mollisols in the Midwestern United States will typically have higher soil organic matter levels than Ultisols in the southeastern United States). Furthermore, the relative index of inherent soil quality (Sinclair et al., 1996), criticized by Sojka and Upchurch (1999) as being biased toward U.S. Midwestern Mollisols, is an accurate reflection of the soil resource potential in the absence of human intervention and external input of energy resources (e.g., fossil fuel, water). Lack of correlation between inherent soil quality and economic value of the products produced is fully expected because the high productivity in areas with low inherent quality can only be achieved by creating a dynamic soil quality through external energy inputs and high-value crops. Using soil quality assessment to estimate long-term sustainability thus requires precise measurement, accurate interpretation, and a thorough understanding of inherent and dynamic soil properties and processes developed through educational tools that have been developed by soil scientists using the rigorous principles of edaphology.

III. EVOLUTION OF THE SOIL QUALITY CONCEPT Warkentin and Fletcher (1977) were among the first to suggest developing a soil quality concept. They stressed that multiple land uses must be considered, even when the primary focus is on intensive production agriculture. Furthermore, because of inherent differences among soils, there is no concept relating amounts of soil components to quality, except for general approaches such as specifying a desirable range of soil organic matter for a specific soil, or that between one-third and one-half of the volume of soil should be occupied by solid and the remainder by air or water. With regard to land capability classes, they argued that those designations provide a soil quality concept based upon limitations rather than a concept based upon positive potential. It is the latter assessment, they argued, that was needed for developing intensive agriculture. Warkentin and Fletcher (1977) suggested four criteria for a future concept of soil quality. These included recognizing (1) that soil resources were constantly being


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evaluated for an ever increasing range of uses (e.g., food, feed, or fiber production; recreation; forestry; urban development), (2) that several different stakeholder groups are concerned about soil resources, (3) that priorities and demands of society are changing, and (4) that soil-resource and land-use decisions are made in a human or institutional context. Although Warkentin and Fletcher first wrote and presented these criteria at an intensive agriculture production conference nearly 25 years ago, they pointed out that soil resources were also being called upon for (a) recycling and waste assimilation, (b) food and fiber production, and (c) aesthetics and leisure use. These multiple demands brought with them increased public awareness and an end to the time when only professional agriculturalists concerned themselves with land. However, as soil resources are recognized for more than their location or production value, more segments of society will become concerned and this will lead to changing priorities and demands. They stated that if, for example, soils were used for disposal of toxic waste materials this could create irreversible changes in the soils that would make them unsuitable for their other functions. Decisions regarding the use of soil resources thus become more than a technical consideration and must be dealt with in the context of different cultural and institutional values. The broader concept of soil quality was not introduced in the North American literature until the mid-1980s. Before that time, the primary areas of emphasis with regard to soil resource management were focused on controlling soil erosion and minimizing its effects on productivity (e.g., Pierce et al., 1984). In the mid- to late 1980s, several reports and books brought attention to the increasing degradation of agricultural soil resources and its implications for sustainable agriculture and environmental health. During that same time in Canada, a report by the Senate Standing Committee on Agriculture launched the subject of soil degradation into the sphere of political interest (Gregorich, 1996). Although this and other similar reports (Gregorich et al., 1994) succeeded in sounding an alarm regarding soil resource management, they were typically short on scientific evidence to support their, sometimes dramatic, claims. A positive effect, however, was the development of a strong incentive to focus federal Canadian soil science research on soil quality and, in 1990, the Canadian Soil Quality Evaluation Program (SQEP) was established under the broader National Soil Conservation Program. During this same period, Larson and Pierce (1991) functionally defined soil quality and suggested ways to evaluate how it changes due to soil management practices. They defined soil quality as the capacity to function within the ecosystem boundaries and to interact positively with the environment external to that ecosystem. They were also among the first to propose a quantitative formula for assessing soil quality. Very quickly, soil quality was interpreted as a more sensitive and dynamic way to document a soil’s condition, response to management changes, and resilience to stresses imposed by natural forces or human uses (Arshad and

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Coen, 1992). This new paradigm provided the focus for discussion at the international workshop “Assessment and Monitoring of Soil Quality” sponsored by the Rodale Institute Research Center in Emmaus, Pennsylvania (Haberern, 1992). The consensus among workshop participants was that soil quality should not be limited to soil productivity, but should encompass environmental quality, human and animal health, and food safety and quality. Interest among policymakers, natural resource conservationists, scientists, and farmers increased rapidly after the U.S. National Academy of Sciences stated that there was a definite need for more holistic soil quality research and published the book entitled Soil and Water Quality: An Agenda for Agriculture (National Research Council, 1993). This interest resulted in several symposia and publications (Doran et al., 1994; Doran and Jones, 1996), producing several definitions, identifying critical soil functions, and suggesting applications for which soil quality should be assessed (Doran and Parkin, 1994). In response to the increasing interest in the soil quality concept, L. P. Wilding, 1994 president of the Soil Science Society of America (SSSA), appointed a 14-person committee (S-581) with representatives from all divisions of that Society. The committee’s charge was to define the concept of soil quality, examine its rationale and justification, and identify the soil and plant attributes that would be useful for describing and evaluating soil quality. In the June 1995 issue of Agronomy News, the committee reported that the simplest definition for soil quality is “the capacity (of soil) to function.” An expanded version of the definition defined soil quality as “the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation” (Karlen et al., 1997). The SSSA committee reported they had struggled with several different words such as replacing “capacity” with “fitness.” Because of the interdisciplinary nature of the concept, choice of words became much more difficult than anyone imagined. A similar reaction occurred in response to using soil quality and soil health interchangeably, as was done in the Canadian report entitled “The Health of Our Soils” (Acton and Gregorich, 1995). Such conflict in terminology and communication, as pointed out nearly 20 years earlier with respect to soil quality (Warkentin and Fletcher, 1977) and later with regard to pest management (Mayhew and Alessi, 1998), is very typical for issues that are strongly influenced by personal values, institutional or cultural expectations, and multiple stakeholders. We suspect that similar semantic disagreements may be the basis for ongoing disputes about soil quality. Throughout the remainder of the 1990s, soil quality research and technologytransfer activities in the United States increased rapidly with many different areas of emphasis. Soil quality test kits (Liebig et al., 1996; Sarrantonio et al., 1996), farmer-based scorecards (Romig et al., 1996), and soil resource management programs (Walter et al., 1997) focusing on soil quality were developed. Soil quality indicator evaluations (Karlen et al., 1999a; Liebig and Doran, 1999) and spatial


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extrapolation techniques (Smith et al., 1993) were studied. Doran et al. (1996) examined the broader linkages between soil quality (or soil health) and sustainability, and, finally, various soil quality indexing approaches (Andrews, 1998; Andrews et al., 1999; Hussain et al., 1999; Jaenicke and Lengnick, 1999; Karlen et al., 1998; Karlen et al., 1999b; Wander and Bollero, 1999) were pursued. Another milestone with regard to the evolution of the soil quality concept was the 1994 reorganization of the USDA Soil Conservation Service. The agency was renamed the Natural Resources Conservation Service (NRCS) to better reflect its work with all natural resources and not simply soil conservation. This reorganization also resulted in the creation of the Soil Quality Institute (SQI). Its mission is to “cooperate with partners in the development, acquisition and dissemination of soil quality information and technology to help people conserve and sustain our natural resources and the environment.” By emphasizing outreach and communication through the Internet (http://www.statlab.iastate.edu/survey/SQI/sqihome.shtml) and other communication outlets, the SQI has been successful in achieving its goals. As previously stated, evolution of the soil quality concept in the United States has not been without controversy (Sojka and Upchurch, 1999). A legitimate concern is that to date, assessments have generally focused on crop production and ecological functions as opposed to remediation and environmental reclamation despite the goals and intentions to address multiple soil uses and functions. We suggest that this occurred primarily because the technical disciplines of the people who were among the first to begin examining the concept were primarily soil biology, soil fertility, and plant nutrition. However, the need to develop a consensus on the proper means to assess soil quality from an environmental perspective was clearly identified by Sims et al. (1997). They stressed the need for soil scientists to take a proactive role in framing, from all perspectives, the debate on soil quality and environmental issues. This includes developing new approaches to quantifying environmental risks posed by soils in agricultural and nonagricultural settings. From a global perspective, contaminant levels and their effects have been more central to the soil quality debate in Canada and Europe (Singer and Ewing, 2000). However, a review of many German-language publications suggests that many scientists are continuing to struggle with how to differentiate the soil quality concept from the numerous definitions and attributes associated with the soil fertility phenomena (Patzel et al., 2000) and other traditional terms (e.g., soil tilth or soil condition). Singer and Ewing (2000) stated that contemporary discussions of soil quality increasingly include the environmental cost of production and the potential for reclamation of degraded soils. They also stated reasons for assessing soil quality in an agricultural or managed system may be somewhat different than reasons for assessing soil quality in a natural ecosystem.

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In an agricultural context, soil quality may be managed to maximize production without adverse environmental effect, while in a natural ecosystem, soil quality may be observed as a baseline value or set of values against which future changes in the system may be compared (emphasis in original). The major challenge, however, is that evaluating soil quality requires one or more value judgments and since humans still have much to learn about soil resources and how their management decisions affect the long-term sustainability of those resources, these issues cannot be easily addressed. Once again, this conclusion recognizes the difficulty of dealing with issues that invoke personal values or other culturally conditioned responses (Mayhew and Alessi, 1998) associated with using scientific knowledge to address issues affecting and affected by multiple stakeholders.

IV. SOIL QUALITY AS AN EDUCATIONAL TOOL Evolution of the soil quality concept has had two distinct areas of emphasis— education and assessment—both based soundly on the principles of soil science. The fundamental reason for striving to develop educational materials is that most people neither understand nor appreciate the complexity of soil resources. They are not aware of how soil literally provides the foundation for sustainable land management through processes such as nutrient and water cycling, buffering, decomposition, and recycling. Some agronomists and soil scientists may question the need for developing materials such as the Soil Quality Information Sheets (Muckel and Mausbach, 1996), scorecards (Romig et al., 1996), or Web sites. Others may be concerned that such materials will compromise the science of edaphology (Sojka and Upchurch, 1999). However, we feel strongly that development of sciencebased educational materials by those qualified to interpret soil measurement data is necessary to improve long-term soil resource management. Furthermore, without such educational efforts, we project that the lack of public awareness will continue to result in simple short-term yield or economically based decisions and actions that literally result in treating the soil like “dirt” (Gibbons and Wilson, 1984). The simple lack of knowledge will result in undesirable resource management decisions despite explicit statements or even legislative mandates that sustainability should be a primary goal for resource management agencies (e.g., Christensen et al., 1996; National Research Council, 1999). Another important milestone in developing the educational component of soil quality was the building of an awareness of scale and an understanding that for some applications the focus is on monitoring soil quality while for others it is on understanding the concept (Fig. 1). Without any doubt, the “monitoring” and


Monitoring Soil Quality

Regional or National Scale LEVEL 5

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Action Agencies or Policy Evaluations

Watershed Scale LEVEL 4

Field Scale LEVEL 3

Understanding Soil Quality

Experimental Plot or Treatment Scale LEVEL 2

Research on Soil Properties and Processes

Point or Mechanistic Scale LEVEL 1

Figure 1 Conceptual framework for soil quality evaluations. (Adapted from D. L. Karlen, J. C. Gardner, and M. J. Rosek. A soil quality framework for evaluating the impact of CRP. J. Prod. Agric. 1998; 11:56–60.)

“understanding” designations in Fig. 1 are arbitrary and fuzzy. Our intent is to illustrate that at finer and finer scales, the understanding of soil processes involved becomes more mechanistic and less qualitative or subjective. Although some of the more qualitative soil quality tools have been developed primarily for educational use, these tools still have a firm scientific foundation. Obviously, the simplified procedures may not be as accurate or precise as more specialized techniques, but they were developed as an interpretation or simple method to help land managers and others better understand the same principles of soil science. Another important factor to understand with regard to the materials developed for soil quality assessment is that the process requires at least two actions and input from everyone regardless of the evaluation scale. The first action is to identify the critical goals or functions that soil resources need to provide or perform at the scale(s) of interest. After the critical functions are identified and prioritized based on stakeholder input, appropriate indicators must be selected to measure soil response for each function. To date, we are not aware of any soil management research that has examined changes in practices because of soil quality education. However, this should not imply that there is no need for these materials. Being

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able to make land-use decisions based on a better understanding of soil biological, chemical, and physical properties and processes occurring within the soil would undoubtedly be better for everyone involved than simply ignoring the unintended environmental and social effects of poor soil management or land-use decisions.

V. SOIL QUALITY AS AN ASSESSMENT TOOL A fundamental principle associated with using the soil quality concept as an assessment tool is that any framework or indexing procedure must recognize both inherent and dynamic soil properties and processes. This has been stressed repeatedly since the concept was first suggested (Warkentin and Fletcher, 1977) and throughout its development during the 1990s (Karlen et al., 1997). Soils with differences due to their forming factors have different absolute capabilities and cannot be directly compared in a meaningful manner with respect to soil health or to assessment of human impact on the resources. Such soils can be compared with regard to inherent differences in productivity and with regard to their capacity for a specific land use in the absence of human intervention. In fact, this is the basis for the entire land capability rating system. Inherent differences among soils and differences in land use are two main reasons that stakeholders must specify the type of soil functions that are critical for the assessment or comparison that is being made. The inherent soil differences are also the reason that there can be no single value or expression that describes soil quality for all uses. What can be developed is a consistent framework or indexing procedure that may be tailored for a specific soil and evaluated with regard to specific land uses or soil management decisions. To illustrate this point, the relationship between inherent soil quality characteristics for two soils is shown in Fig. 2. Inherent characteristics are those determined by the basic soil forming factors: parent material, climate, time, topography, and vegetation (Jenny, 1941). These characteristics determine why any two soils (A and B) will always be different. It has also been stressed throughout development of the soil quality concept that inherent differences between two soils cannot be represented by a static centerline (Fig. 2) because all soils are living and dynamic. Rather, a soil’s quality will fluctuate within a general inherent capability class that is determined by the soil forming factors. In contrast to inherent soil quality, dynamic soil quality reflects the changes associated with current or past land use and anthropogenic management decisions. Dynamic soil quality can be measured and used to compare different practices on similar soils or temporal trends on the same soil by developing a consistent framework or assessment tool that identifies positive, negative, or neutral trends (Fig. 3). Such measurements of trend or comparisons over time for different soil management scenarios of similar soils with equivalent inherent soil quality provide


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Figure 2 Conceptualization of inherent soil quality differences between two soils.

the conceptual linkage between soil quality, environmental quality (soil, water, and air quality), and agricultural sustainability (Fig. 4). This conceptual linkage is why soil quality assessment has been suggested as a tool for quantifying the overall effects associated with imposing a specific set of management practices on a specific soil resource. The primary purpose for developing a soil quality index is to simply help landowners and land operators visualize the integrated effects that land-use decisions are having on the soil physical, chemical, and biological properties or processes. This reinforces why soil quality efforts have focused on developing both assessment and educational tools.

Figure 3 Conceptualization of dynamic soil quality trends from time zero (T0). (Adapted from C. A. Seybold, M. J. Mausbach, D. L. Karlen, and H. H. Rogers. Quantification of soil quality. In Soil Processes and the Carbon Cycle (R. Lal, J. M. Kimble, R. F. Follett, and B. A. Stewart, eds.), pp. 387–404. CRC Press, Boca Raton, Florida.)

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AGRICULTURAL SUSTAINABILITY INDEX Environmental Quality Index

Economic Sustainability Index

Social Viability Index

ENVIRONMENTAL QUALITY INDEX Soil Quality Index

Water Quality Index

Air Quality Index

SOIL QUALITY INDEX Physical Factors

Chemical Factors

Biological Factors

Figure 4 Hierarchy of agricultural indices showing soil quality as one of the critical foundations for sustainable land management. (Adapted from S. S. Andrews. Sustainable agriculture alternatives: Ecological and managerial implications of poultry litter management alternatives applied to agronomic soils. Ph.D. dissertation, University of Georgia, Athens.)

VI. INDEXING SOIL QUALITY As the soil quality concept has evolved, indexing projects have been carried out at several different scales and therefore with various degrees of accuracy (Fig. 5). Techniques for user-based indexing have been developed for the various soil quality/soil health scorecards. These are intended for use at the field or paddock scale and are least analytical because they rely primarily on observational data. The visual soil assessment (VSA) procedure developed for New Zealand arable and pasture lands (Shepherd, 2000; Shepherd and Janssen, 2000; Shepherd et al., 2000a; 2000b) is a relatively new example that has been added to the worldwide collection of indexing tools. Within a four-volume paperback publication, persons using the VSA procedure are given guidelines for assessing soil quality under

Predicted Accuracy


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SQ Index Watershed Studies SQ test kit

Natural Resource Inventory

SQ scorecard

Spatial Scale Figure 5

Trade-offs in scale and accuracy associated with soil quality indexing projects.

cropping and pastoral grazing on lowland or hill country and various suggestions that can help them respond if the assessment indicates that soil quality is moderate or poor. Each volume is written in an easy-to-read style with high-quality comparative photographs and a brief text describing each photo and the soil quality indicator being illustrated. The photographs provide the user criteria for assigning a visual score of 0, 1, or 2 for poor, moderate, or good, respectively, for each soil and plant indicator. If warranted, an intermediate rating can also be assigned for samples that appear to fall between the illustrated conditions. A scorecard that includes suggested weighting factors is used to record the data. Inherent site characteristics including land use, soil type, texture, moisture condition, and seasonal weather conditions are also recorded. Two impressive strengths of the VSA procedure are that it utilizes several traditional soil morphology and genesis criteria (e.g., structure, porosity, color, and mottling) as key indicators and that the authors have provided easy-to-understand interpretations regarding what soil property or process is being reflected by each indicator. For example, dry-aggregate size distribution from which the soil structure is assessed shows a nonlinear correlation with oxygen diffusion rate, air permeability, hydraulic conductivity, air-filled porosity, and crop yield, and a linear correlation with bulk density and wet-aggregate stability. Similarly, a soil that has become strongly gleyed with gray matrix soil colors, or that has developed prominent low-chroma mottles, can only have done so under anoxic conditions with extremely low redox potentials. This can only happen if the soil is saturated for a significant part of the year or has become so degraded that the diffusion of oxygen, air, and water is extremely slow and well below the critical limits for plant growth. These examples clearly demonstrate the linkage between a relatively simple educational approach and a solid science base. The VSA approach also incorporates plant or crop condition and landscape evaluations to help interpret the soil quality assessment. The procedure thus provides relevant, credible, and timely information on soil performance that can be used to help establish best management practices and quality assurance programs. As such, it appears to be a useful tool for on-farm self-determination and self-regulation that warrants a more complete examination for its application in other countries.

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Soil quality indices have also been developed to help interpret soil quality testkit data. These are expected to be more accurate (Fig. 5) because semiquantitative measurements are actually made at each site. However, because the time required for data collection limits the number of tests that can actually be completed, testkit indices will generally be meaningful only at the spatial scale represented by the measurement site(s). Indices developed for specific soil quality assessment projects are often the most accurate because they generally rely totally on analytical laboratory data, often measured using replicated experimental treatments or geo-referenced transects and grid cells. As the scale for soil quality assessment increases from fields to farms to watersheds or even to major land resource areas [such as those sampled for the National Resources Inventory (NRI)] accuracy declines (even with laboratory data) simply because of increasing variability in soil resources and management practices at those scales. The issue of scale affects both the sensitivity of assessment and the choice of indicators that are evaluated. Both will differ depending upon the type of soil management questions that are being asked or the purpose for which soil quality is being evaluated. In general, soil quality evaluations at the farm, watershed, county, state, regional, or national scales will be more general and less precise than those made at the point or plot scale (Karlen et al., 1998). Large-area assessments will often rely on fewer laboratory measurements and place greater dependence on databases, simulation models, remote sensing, and use of statistically representative “point” sampling to verify the projections (Seybold et al., 1997). With regard to soil quality assessment or indexing, the most important fact is that since both inherent and dynamic properties and processes are involved, there are no “magic” scores or perfect ratings. Soil quality index scores are always relative, not absolute. To be meaningful and useful, the comparisons must be logical (e.g., temporal changes or comparison of practices on soils having the same inherent soil quality characteristics) and defendable. Once again, this shows the need for a strong science-based educational component and stresses that the most important application for soil quality assessment is to provide tools for landowners, land operators, or decision makers that will help them assess the combined physical, chemical, and biological effects that management practices or land uses are having on the soil resources they are managing. By recognizing the variability in both scale and accuracy with which soil quality indexing is being evaluated, the reader should understand that the process of indexing soil quality is solely intended to be an adaptive management tool for assessing sustainability and accept that it is not an end in itself. The process of assessment or indexing always leads to questions regarding “standards” or known conditions against which comparisons are to be made. Although Warkentin and Fletcher (1977) stated that soil quality cannot be defined like water or air quality (e.g., based on its purity or the absence of contaminants), the emphasis on soil quality assessment in Europe has evolved primarily with regard


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to the absence of contamination by pollutants including heavy metals and xenobiotics. This application for soil quality assessment was of great concern to Sojka and Upchurch (1999), who stated that there simply is no “pure” soil. However, once again, the intent of developing soil quality standards in countries such as Germany and The Netherlands has been to improve land-use decisions, including those associated with industrial and urban waste and by-product disposal (Höper, 2000). Those striving to develop methods for assessing the sustainability of various land uses have recognized and stated that there simply is no single soil quality standard for every soil management situation or potential land use. The emphasis on trends or relative comparisons ultimately led to the simplest definition or interpretation of soil quality, which can be stated as “its capacity to function,” “how a soil is functioning,” or “fitness for use” with regard to whatever plans a landowner or land operator may be considering. Furthermore, for soil quality to be parallel with water and air quality, and therefore suitable for inclusion in an overall agricultural sustainability index (Fig. 4), it was necessary to define it using a use-dependent protocol. Just as there is no pure soil because of the inherent processes of decomposition and nutrient cycling, in natural ecosystems there is no pure water because it would contain no nutrients to support life and thus be an unsuitable habitat. From a usedependent perspective, there are many examples for indexing water quality (e.g., Karr, 1991) that directly parallel those being developed for assessing soil quality. Understanding soil quality indexing as an iterative process rather than a rigid formula is much more important than trying to agree on the specific functions or indicators that should be used, because the latter will vary according to the land use or management practice for which an index is being developed. For all index calculations, it is important to establish criteria or conditions that can be used to guide the evaluations. Three of the most important are (1) establishing ranges for indicator values that (based on inherent conditions) are appropriate for the specific soil or soils that are being evaluated, (2) determining how data collected for each of the indicators should be scored or interpreted (e.g., “more is better,” “less is better,” or as an “optimum”), and (3) determining the relative importance or weight that should be given to each indicator. Typically, for useful and meaningful assessments, values and ranges that are representative of a specific soil or soils functioning at full inherent potential for the specific land use being assessed are the ones chosen for use in an index. Another approach to incorporating inherent and dynamic soil quality indicator information into an assessment of sustainability is to use appropriate mechanistic models to determine the state of a soil. To some extent, this approach begins to more closely resemble what the NRCS and others have traditionally done for planning or for assessing sustainability at the regional or national scales with models such as the erosion productivity impact calculator (EPIC) or the revised universal soil loss equation (RUSLE). Both processes are dependent upon sound,

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science-based measurements and interpretation of both inherent and dynamic soil attributes. Where the processes differ is that for indexing, interpretation and prioritization (weighting) often depend on empirical relationships developed through experience (expert opinion) or statistical procedures such as principal component analysis of large data sets (Andrews et al., 2001), while for mechanistic modeling, interpretation is often accomplished with the aid of good pedotransfer functions. (Larson and Pierce, 1994). When done over time, the mechanistic models can provide estimates of the “state” or condition of the soil resource for any use to which the models have been calibrated. Examining index values over time will not provide state measurements, but they will provide trend lines that can serve as a warning to land managers and others. Yet another important consideration associated with the indexing process is the issue of scale, both spatial and temporal. This issue is important in all methods of index development. Assessments must be made at the scale and with indicators that are most appropriate for the question(s) being asked. Both sensitivity and accuracy of any index will be affected by sampling intensity and heterogeneity of the sampling sites. Practical considerations include the accuracy that is needed, the intended use of the index, and the cost for sample collection and analysis. Spatially, the process of indexing soil quality to assess the sustainability of various land uses or management practices can be tailored for point, plot, field, farm, watershed, regional, national, or even international scales simply by selecting appropriate indicators for which requirements 1 to 3 listed previously can be met (Table I). With regard to temporal variation, tillage effect, soil water changes through rainfall or irrigation, temperature regimes, vegetation, topography or landscape position, and anthropogenic decisions are all factors that need to be considered in selecting indicators and developing the appropriate indexing framework. To illustrate our suggested approach, a typical flowchart outlining the soil quality indexing process is shown in Fig. 6. An example of the linkage between soil functions and indicators that might be used to begin developing a soil quality index is shown in Table II. Although this linkage is probably most applicable to soil quality assessments related to productivity and cropland assessments, they are given simply to illustrate the process and not to exclude other soil functions and indicators that would be more appropriate for evaluating the sustainability of soil resources for other land uses or soil management practices. As stated previously, the process, and not the specific functions or indicators, is the most important concept to understand with regard to development of useful and meaningful soil quality index values. In a similar manner, the process through which the minimum data set (Fig. 6) is selected can also vary widely. Two common methods are use of expert opinion and principal component analysis (PCA). For both methods, however, adjustment for local (producer or land manager) experience and acceptance of the limits of


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Table I Potential Biological, Chemical, and Physical Indicators of Soil Quality, Measurable at Various Scales of Assessment a Biological

Chemical

Physical

Point-scale indicators Microbial biomass Potential N mineralization Particulate organic matter Respiration Earthworms Microbial communities Soil enzymes Fatty acid profiles Mycorrhiza populations

pH Organic C and N Extractable macronutrients Electrical conductivity Micronutrient concentrations Heavy metals CEC and cation ratios Cesium-137 distribution Xenobiotic loadings

Aggregate stability Aggregate size distribution Bulk density Porosity Penetration resistance Water-filled pore space Profile depth Crust formation and strength Infiltration

Field-, farm-, or watershed-scale indicators Crop yield Weed infestations Disease pressure Nutrient deficiencies Growth characteristics

Soil organic matter changes Nutrient loading or mining Heavy metal accumulation Changes in salinity Leaching or runoff losses

Topsoil thickness and color Compaction or ease of tillage Ponding (infiltration) Rill and gully erosion Surface residue cover

Regional-, national-, or international-scale indicators Productivity (yield stability) Species richness, diversity Keystone species and ecosystem engineers Biomass, density, and abundance

Acidification Salinization Water quality changes Air quality changes (dust and chemical transport)

Desertification Loss of vegetative cover Wind and water erosion Siltation of rivers and lakes

a

Adapted from D. L. Karlen , J. C. Gardner, and M. J. Rosek. A soil quality framework for evaluating the impact of CRP. J. Prod. Agric. 1998;11:56–60.

available information as a starting point are important modifiers for the process. Often, it is recognized that acquiring additional data will improve and could substantially change the index values, but since the indexing is not an end in itself, refinement and revision are regarded as acceptable steps within the overall process of assessing sustainability. The expert opinion process functions best when a multidisciplinary team of scientists representing agronomy, ecology, economics, engineering, entomology, pathology, soil science, social science, or any other discipline deemed critical for the assessment being made can be assembled with landowners, land operators, and other interested stakeholders. Collectively, this group identifies the soil quality goals, critical functions, appropriate indicators, scoring methods, and priorities among each factor. The principal component analysis generally relies less on any individual scientist making selections of goals, functions, and indicators. It uses

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Figure 6

A generalized framework for developing soil quality indices.

a statistical technique to identify the indicators that best represent variability in a large existing data set. This technique affords less opportunity for disciplinary bias but does require a robust data set. Mechanistically, the data set must have a sufficient number of observations and variables. Functionally, whatever is measured must have potential value as an indicator (i.e., some relationship to the critical soil functions). After the data are analyzed and mean comparisons are made, only those indicators showing statistically significant differences are included in the PCA. The data are then analyzed using PCA to prioritize and reduce the number of indicators or variables that need to be measured in subsequent samplings. In some cases, PCA loading values have also been used to provide “weighting factors” for the indicators included in the soil quality indices (Andrews et al., 2001).

Table II Conceptual Linkages between Soil Quality Indicators and Critical Functions That Could Be Used to Compute Soil Quality Indices as Shown in Figure 6 Soil functions Biological productivity Regulating and partitioning water Filtering and buffering Storing and cycling nutrients Supporting socioeconomic structures

Soil quality indicators Texture, depth of soil Infiltration and bulk density, water-holding capacity, aggregate stability Soil organic matter, pH Extractable N, P, and K; microbial biomass C and N, potentially mineralizable N Structure, stability, mineralogy


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The final step, regardless of the scale for which an index is being developed, is selection of an appropriate set of reference conditions. These values must account for differences in inherent soil characteristics and, for that reason alone, there can be no single value that determines soil quality. Specific limits or ranges for each indicator must be identified or defined and are used to “score” the indicator data in unitless numbers between 0 and 1. Typical scoring options are to use binary (0 or 1), linear continuum (0 to 10), or nonlinear scoring functions indicating “optimum,” “more is better,” or “less is better” (Karlen et al., 1994a) relationships. After scoring, unitless values for each indicator can be added or multiplied to give a final index value. The choice between mathematical approaches depends upon the use for which the index was computed and whether any single function or indicator is important enough to give the soil a rating of “0” if its value is not within the anticipated or inherent range for that specific soil. The selection of reference values, and construction of an appropriate scoring function, is one step that will certainly benefit from and draw upon the knowledge base of traditional soil morphology and genesis, especially with regard to inherent chemical and physical indicators including effective soil depth (Rhoton and Lindbo, 1997) and other taxonomic criteria (Soil Survey Staff, 1999). For each potential land use and soil, values consistent with those found for a specific soil functioning at full potential for the specific land use need to be identified. This may seem overwhelming to some (Sojka and Upchurch, 1999), but in reality, reference values for managed land will generally be those found in areas that are being used according to current “best management practices” (BMPs). Also, these values can often be found in agronomy, soil science, engineering, or ecology literature where responses of potential indicators to specific practices or soil management goals are reported (e.g., an experiment on a specific soil type that identifies yield response, erosion rates, leaching, or runoff in response to a range of soil organic matter levels). As a process, the indexing simply provides land managers or decision makers a tool that can be used to assess the sustainability of their current practices with respect to the best response that current technologies could deliver for that specific soil resource.

VII. CURRENT SOIL QUALITY APPLICATIONS Efforts to develop the soil quality concept have focused on two primary uses— education and assessment. As a tool for evaluating the sustainability of land-use and soil management decisions, soil quality assessment has been used to: (1) examine temporal sustainability of current land-management practices, (2) identify probable effects of alternative soil management practices, and (3) monitor effects of restoration activities. Once a soil quality assessment is made, however, the primary

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use for the assessment also becomes one of education and guidance for subsequent soil management decisions as part of an adaptive management program. As an educational tool, the soil quality concept has been presented to NRCS and Cooperative Extension personnel, farmers, crop consultants, land-use planners, and many other groups interested in soil resource management within urban, suburban, and rural areas. The primary products have been soil health scorecards, visual assessment procedures, soil quality test kits, and decision support frameworks (including World Wide Web compatible materials). Romig et al. (1995) were the first to report on the development of soil health cards by farmers and researchers for Wisconsin conditions. Subsequently, the NRCS Soil Quality Institute (USDA-NRCS, 1999) and others adapted the health card development process for use in several other states. These tools were developed with the aid of soil scientists in interpreting data and observations and thus provide a qualitative, self-assessment of the farmers’ current soil and crop management practices. NRCS or Cooperative State Extension Service (CSES) personnel often facilitated the process, but in general, the cards were written by farmers for farmers and therefore use farmer-based descriptive terminology. Two of the reasons for developing scorecards were to promote an increased awareness regarding soil resources and to encourage landowners and land operators to “look below ground” when they are evaluating their soil management practices. The development process also helped encourage cooperation and communication among participants (farmers, NRCS, CSES, and others). Although the data collected through this selfevaluation process are often descriptive, qualitative, observational, and based on sensory perception (look, feel, smell, ease of tillage, etc.), professionals contributing to these efforts worked to provide proper science-based interpretations for what land managers were observing. Similarly, scoring is kept relatively simple (good, fair, poor), and generally based on indicators that reflect soil properties such as tilth or soil condition, soil life or biology, water and air relationships, plant vigor, fertility, and ease of tillage. The soil quality test kit was developed to provide semiquantitative data that could be gathered rapidly to assess soil characteristics recognized as basic indicators of soil quality for the depth 0 to 7.6 cm (Doran, 1994). Indicators measured with the test kit include bulk density, infiltration rate, water-holding capacity, electrical conductivity, soil pH, soil nitrate, and soil respiration. The test kit has been evaluated in several locations, and results have been found to compare favorably with laboratory analyses (Liebig et al., 1996) and to provide a good screening for soil quality (Sarrantonio et al., 1996). The soil quality test kit has also been a useful educational tool for nontraditional clients of the NRCS. For example, at the request of New York Soil and Water Conservation District personnel the kit was used to document the effects of foot traffic on soil compaction and other indicators in Central Park (personal communication,


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Lee Norfleet, 2000). Use of the kit for that evaluation also enabled NRCS-SQI personnel to provide urban land managers a more complete understanding of soil health. Currently, the soil quality test kit is available commercially. The NRCS-SQI provides free guidelines for taking measurements as well as background and interpretive information (USDA-NRCS, 1998). Both guides are currently available for downloading from their Web site. In addition, a third section is being prepared to assist with interpretation, synthesis, and indexing of the physical, chemical, and biological indicator information (Andrews, 1999). Developers of the indexing section stress the need for proper interpretation of the indicator data according to management goals and inherent soil properties. This means that there is no single correct indicator or index value because of inherent differences among soils, crops, and climates. To illustrate the interpretation or indexing process, the developers took the major land resource areas (USDA-SCS, 1981) within the United States (Fig. 7) and divided them into resource groups based on climate, inherent soil characteristics, and dominant crops. They then gathered data from several representative research sites and demonstrated how soil quality test-kit data could be scored. Regional land resource interpretations for various soil quality indicators

Figure 7 U.S. land resource regions grouped for soil quality test kit interpretation according to similarities in climate, dominate soil orders, and predominant crops.

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Indicator Scores

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CO2 Respired (kg CO2/ha/d) Figure 8 Conceptual land resource region group differences for “scoring” soil respiration data during development of a soil quality index.

are being tested based on the data from each region. Expected values for indicators were based on literature data and reflected inherent characteristics for soils in the various regions (Fig. 8). As illustrated, the shape for scoring functions developed for the various regions was similar even though both the expected range and peak values were different. These two scoring functions were fine-tuned based on data from long-term trials near Auburn, Alabama, and Akron, Colorado. Once again, this demonstrates that the indexing process (i.e., selecting appropriate indicators for the land use being evaluated and critical values that are appropriate for inherent conditions) is much more important than the specific indicator values which will change due to inherent soil condition or the land use for which a soil quality index is being computed. Soil quality assessment at the point scale (Liebig and Doran, 1999) with subsequent scaling and indexing is also being used to evaluate farming system impacts within field-scale watersheds (Fig. 9) or catchments (unpublished data, D. L. Karlen, 2000). Studies at this site involve direct farmer/researcher partnerships, with farmers providing equipment and land management capabilities and researchers collecting and interpreting the data. Evaluations of soil quality change due to cropping system, landscape position, and tillage practice are nested into these field-scale watershed evaluations. Technologies such as global positioning systems (GPS), geographic information systems (GIS), soil survey, and other site-specific soil management practices are being used. Soil quality assessment end points include crop yield, erosion loss, and water quality (herbicide, nitrate, phosphorus, and sediment) impacts. Indicator data and end point information are integrated through the soil quality assessment process and used as a primary indicator of overall sustainability for the practices being evaluated. Finally, soil quality assessment was also evaluated in a pilot study to determine if such information could be efficiently gathered through the NRI process and used to improve national natural resource evaluation programs. Initial results documented


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Figure 9 Point-scale assessment sites and predominant soil map units within two field-scale watersheds where soil quality response to crop rotation, landscape position, and tillage are being evaluated.

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spatial variability for several potential soil quality indicators and demonstrated the impact that land use (cropland, forest, or pasture) or government programs (conservation reserve programs) can have on many biological, chemical, and physical indicators (Brejda et al., 2000a,b,c).

VIII. OTHER SOIL QUALITY RESEARCH AND OUTREACH PROGRAMS A. U.S. ACTIVITIES Soil quality research and technology-transfer efforts in the United States increased exponentially throughout the 1990s, making it impossible to review all current activities. However, a search of the USDA–Cooperative States Research Education and Extension Service (CSREES) Cooperative Research Information System (CRIS) database showed 1064 entries with the key words “soil quality.” This represented more than 23% of the 5000+ projects that currently list “soil” as a key word in the database. Research interest in soil quality was linked to several other soil management factors, as evidenced by the other key words associated with the various projects. For example, nitrogen, tillage, pesticide, soil management, and phosphorus were included as key words in 225, 135, 133, 101, and 98 entries, respectively. Crop rotation was included as a key word in 53 of the projects, soil water in 46, and manure in 28. Soil quality assessment, soil organic matter, compost, soil aggregation, hormones, and antibiotics were included as key words in 15, 9, 6, 5, 2, and 1 project(s), respectively. Many current projects appeared to be focused on the processes that link soil management practices with changes in soil properties (organic matter, soil water-holding capacity, nutrient cycling, microbial activity) and are thus associated with various soil functions. A similar search of published literature using the AGRICOLA database returned more than 1700 references that included the key words soil quality. Research (from 1998 to 2000) covered a very broad spectrum of topics, ranging from very mechanistic studies that related soil quality to amino acid and enzyme activities in soil organic matter (Senwo and Tabatabai, 1998; Acosta-Martinez and Tabatabai, 2000a,b) to the use of an adapted form of the index published by Karlen et al. (1994a,b), to compare long-term effects of three tillage systems in southern Illinois (Hussain et al., 1999). For all applications of the concept, however, a primary purpose for assessing soil quality appeared to be as a tool for evaluating the sustainability of the various soil management practices. Publications that typify the use of the soil quality concept as a tool for assessing sustainability include an Illinois study where Wander and Bollero (1999) used


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multivariate analysis of indicator data to show that use of no-till practices improved the biological and physical condition of soil (0 to 15 cm) despite an increase in consolidation (bulk density). They also concluded that particulate organic matter (POM) was a promising indicator of soil quality. McCallister and Chien (2000) compared the effects of tillage and crop sequences typically used in the central Great Plains of the United States on the quantity, quality, and depth distribution of soil organic carbon. They concluded that, overall, continuous corn (Zea mays L.) and no-till production practices contributed the greatest amount of crop residue and maintained a soil environment conducive to preserving the resultant organic matter. These changes in both quantity and quality of soil organic matter, as defined by ratios of humic and fulvic acid carbon, were postulated to be among the factors contributing to improved soil quality and productivity. Two of the first publications using soil quality indexing as a tool focused on comparing long-term effects of crop residue management and alternate tillage methods (Karlen et al., 1994a,b). Thompson and Whitney (2000) evaluated data from a 30-year cropping and tillage study on silt-loam soil in central Kansas to determine if well-fertilized no-till systems improved soil quality. Measurements of total soil organic matter showed no significant change in the nearly level soil with minimal soil erosion. Soil pH in the upper 10 cm was significantly reduced as rates of N application increased. The authors interpreted this change as having the potential to reduce herbicide effectiveness and phosphorus availability. Lal (1999a) also used soil quality assessment to quantify effects of three tillage methods after 25 years of continuous corn on two well-drained soils in central Ohio. He found significant differences in bulk density for the depth 0 to 10 cm between the root zone and traffic zone areas, but no significant differences due to no-tillage (NT), minimum tillage (MT), or plow tillage (PT) treatments. Both initial and equilibrium infiltration rates in the root zone were in the order of NT > PT > MT. Trends for soil organic matter content and Bray-P were in the order of NT ≥ MT ≥ PT for the depth increment 0 to 20 cm. Lal (1999a) concluded that 25 years of no-till with continuous corn had no deleterious effect on either soil physical or chemical quality. Thirteen soil quality indicators were measured for samples collected from longterm replicated field experiments and from paired field sites by Islam and Weil (2000) to determine how soil management affected those parameters in the midAtlantic region. They found that conservation management (defined as some combination of reduced tillage, increased crop diversity, more perennial crops, increased crop residue return, increased soil fertility, and/or increased application of organic amendments) consistently and markedly influenced indicator properties by increasing total and active microbial biomass carbon, increasing the ratio of active microbial biomass carbon to total organic carbon, increasing aggregation, and decreasing the rate of basal respiration per unit of microbial biomass carbon (qCO2). They suggested that these changes indicate reduced stress on soil microbial communities compared to that on conventional systems.

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In a study emphasizing indicator selection and development, Franzluebbers et al. (1999) focused on techniques for measuring microbial biomass because of its importance as part of the active soil organic matter pool. They concluded that chloroform fumigation–incubation, without subtraction of a control, is a robust and reliable method for measuring microbial biomass and thus of assessing biological soil quality for a wide range of land management and soil conditions. Soil management effects were also evaluated by Bowman et al. (2000), who used soil quality assessment to determine if sunflower (Helianthus annuus L.) production on central Great Plains soils was having a detrimental effect because of the need for tillage to incorporate herbicide and the associated loss of surface residue. Their data showed 13% lower soil organic carbon content and 26% lower particulate organic matter carbon content at the depth 0 to 5 cm in rotations with sunflower versus those without. Crop residue mass was five times lower and wheat (Triticum aestivum L.) yields 33% lower in rotations with sunflower, but no differences were measured for wind erodible aggregates or texture. They concluded that if sunflower is to be included in central Great Plains crop rotations, efforts should be made to use no-till production practices and 4-year rotations with corn. Sojka and Upchurch (1999) argued that most of the soil quality effort in the United States has focused on productivity issues despite the numerous other functions that soil resources are called upon to perform. To date this is a valid criticism, but the indexing framework discussed in Section VI is sufficiently flexible that this deficiency can be addressed by identifying other critical functions and appropriate indicators for assessment and inclusion in an environmental index. For example, in a regional study, Burkart et al. (1999) identified several soil properties that were significantly related to the occurrence and concentrations of atrazine and nitrate-N in shallow aquifers in the Midwest. Through additional studies and with interpretation using nonlinear scoring functions or other techniques, those indicators may be useful for characterizing the soil with regard to its ability to function as an environmental buffer. Soil quality assessment was also used by Andrews et al. (1999) to develop and demonstrate a bioeconomic decision aid that was designed to help examine effects of poultry litter application on winter squash (Cucurbita maxima) in the southeastern United States. Their management goals included enhanced environmental and soil quality, maximizing waste recycling, and maximizing net revenues. During development of their decision aid, they conducted sensitivity tests that showed strong correlation between nutrient accumulation in soil pools and estimates of potentially available nutrients in fresh and composted litter. From these studies, they concluded that soil quality was a measurable management goal and that decision aids using such assessments could help various stakeholder groups see how their priorities interact. In a similar manner, soil quality assessment was used as a basis for developing an economic model to evaluate optimal cropping systems for the northern Great Plains (Smith et al., 2000). They concluded that modeling soil quality attributes is


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feasible and that attribute model results apply to a wide range of soils. The most profitable production system was found to be direct drilling of continuous spring wheat. This system had low soil erosion and high soil quality attributes, indicating that the benefits of increased soil quality exceed the higher maintenance costs. On-site value of additional soil organic carbon (OC) ranged from $1 to $4 Mg−1 OC ha−1 year−1. As stated previously, the exponential increase in the use of soil quality assessment as a tool for evaluating the sustainability of various management practices precludes reviewing all of the different uses for which the concept is currently being evaluated in the United States. However, we suggest that this brief collection of studies demonstrates that as an educational and assessment tool, the concept of soil quality is being used in many ways and thus having the impact that was desired when the U.S. National Research Academy of Sciences published the book Soil and Water Quality: An Agenda for Agriculture (National Research Council, 1993).

B. INTERNATIONAL ACTIVITIES Internationally, research and technology-transfer activities associated with the concept of soil quality also increased exponentially throughout the 1990s. The terminology used with regard to soil quality, however, is often broader than that used in the United States, including not only soil health, but also land quality (Bouma, 2000), soil condition, and for some soil fertility (Patzel et al., 2000). From the perspective of a scientist in Slovakia, Bujnovsk`y (2000) concluded that creation of favorable conditions for biomass production is only one of several functions that soil fulfills. Therefore, soil quality or soil health was concluded to be an appropriate concept for the polyfunctional approach necessary for soil assessment. Bujnovsk`y also stated that from the view of formation, environmental stability, and subsequently quality of life, the decisive soil functions were biomass production, filtering and accumulation, transformations, sanitation, buffering, biological habitat, and gene reserve. He also distinguished two main goals for soil quality assessment: comparison for potential soil price derivation and monitoring with respect to degradation. In Germany, interest in soil quality has resulted in passage of the German Federal Soil Protection Act (BbodSchG, 1998). The purpose of the act, which went into effect on March 1, 1999, is to protect or restore the functions of the soil (Höper, 2000). It includes precautions against negative impacts on soils, requirements to prevent harmful changes to soils, and provisions for rehabilitation of soil at contaminated sites and of waters contaminated by such sites. The act recognizes three primary soil functions: (1) soil as a basis for life and a habitat for people, animals, plants, and soil organisms; (2) soil as part of natural systems, especially water and nutrient cycles; and (3) soil as a filter and buffer, especially for

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water protection. The act establishes three levels of threshold values for pollutants to prevent harmful changes to soils. The first, use-independent category provides precautionary values for heavy metals and some persistent organic pollutants (e.g., polychlorinated biphenyls, benzopyrene, polycyclic aromatic hydrocarbons). The second level of threshold values is defined as trigger values. If they are exceeded, investigation with respect to the individual case is required to determine whether a harmful soil change or site contamination exists. These use-dependent variables take into consideration the different potential pathways that pollutants can use to move from soils to humans. These pathways are (1) direct contact or uptake (e.g., ingestion of polluted soil by children on playgrounds, (2) soil through useful plants (e.g., pollutant enrichment in the tissues of fodder and food plants), and (3) soil to groundwater (e.g., contamination of groundwater by mobile fractions of soil pollutants). The third level of threshold values is considered to be an “action value.” If these values are exceeded, the presence of a harmful soil change or site contamination is indicated and remediation measures are required, taking into account the relevant soil use. With regard to agricultural use of soils, the German Federal Soil Protection Act considers that the obligation to take precautions is fulfilled by the use of good agricultural practices. This includes (1) tilling the soil in a way that is appropriate for the site and weather conditions, (2) conserving or improving soil structure, (3) avoiding compaction, (4) reducing soil erosion by using site-adapted practices, (5) preserving natural hedges, field shrubs and trees, terraces and field boundaries needed for soil conservation, (6) conserving or increasing soil biological activity by using appropriate crop rotation, and (7) conserving soil humus content by adequate input of organic substances and reduction of tillage intensity. This act does not regulate the use of fertilizers or pesticides, or the application of sewage sludge, composts, or animal manures to soils, although those practices are regulated by other German legislation. Similar to the efforts within the United States, international efforts focusing on soil quality are very diverse in both scope and scale. Studies focusing on techniques for measuring soil organic matter, monitoring earthworms or other soil biota, and evaluating denitrification or redox processes, as well as those examining indicator response to changes in tillage, cropping systems, or land use were found through the literature search. A consistent theme for all studies, however, is that the primary purpose for evaluating soil quality is to enhance communication to land-use planners, politicians, and the public at large regarding the importance of soil resources and to make them more aware of the diverse functions that they provide in both natural and managed ecosystems. Stenberg (1999) reviewed current viewpoints regarding the soil quality concept and suggested frameworks for its assessment and possible methods for the selection and evaluation of indicators. He concluded that microbial biomass, basal respiration, potential N mineralization, potential ammonium oxidation, and potential denitrification should be included in a minimum data set for monitoring agricultural


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soils in Sweden. In another study, Stenberg et al. (1998) examined the variation in several microbiological, chemical, and physical indicators at the field (52 samples from a single field) and regional (26 samples from very diverse sites) scales. Using principal component analysis, they concluded that the two scales had more similarities than dissimilarities regarding variable variation and functional structures. The relationships between variables at the large scale, however, were more blurred than at the field scale because of soil structure, climate, and cropping differences. Rasmussen (1999) reviewed yield and soil quality indicator response to determine the impact of no-till production in Denmark, Finland, Norway, and Sweden. He found that the success of reduced tillage and direct drilling depended on crop species as well as on the soil type and climatic conditions, especially because of the short growing season in the northern part of Scandinavia. One of the most striking indicator effects was the increase in bulk density, which decreased macropore (> 30 to 60 μm) volume and increased the volume of medium pores (30 to 0.2 μm). This change reduced air-filled porosity, air diffusivity, and air permeability, as well as the hydraulic conductivity and sometimes root development. Increased plant residue on or near the soil surface reduced evapotranspiration and increased soil water content in the upper (0 to 10 cm) soil layer. It also resulted in lower soil temperature and more stable soil aggregates, which provided better protection of the soil against erosion. Plant nutrients and soil organic matter were stratified near the soil surface and over time pH declined. Nearly all species of earthworms increased in number under no-till. With regard to environmental impact, leaching of nitrogen generally increased with more intensive cultivation, especially when carried out in autumn. In an indicator development study conducted in Scotland, Ball et al. (1999) concluded that the methane oxidation rate, which is affected by long-term soil structural damage, may be a useful soil quality indicator when measured in conjunction with other parameters. This study used soil quality assessment as part of an overall effort to quantify tillage and weather effects on several greenhouse gases. Tillage system comparisons on several soils ranging in texture from sand to silt loam in the central German state of Hesse were reviewed by Tebrügge and Düring (1999). They found that, in general, bulk density in the upper layer of no-till (NT) soils was increased, resulting in a decrease in macropores and a lower saturated hydraulic conductivity compared with conventional plough (CT) or reduced tillage (RT) treatments. Surface residue cover and aggregate stability were higher with NT management. Accumulation of organic matter and nutrients near the soil surface under NT and RT enhanced biological activities and were considered favorable consequences of not inverting the soil. Increased earthworm activity in NT treatments was associated with a system of continuous macropores that also increased water infiltration rates. Tebrügge and Düring (1999) concluded that if crop rotation, machinery, and plant protection are well adapted, conservation tillage could replace conventional practices in many locations throughout Germany.

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In a Canadian study, Arshad et al. (1999) concluded that NT is a viable management strategy for improving soil quality in the cold, semiarid region of western Canada. Comparisons of CT and NT in that region showed greater water retention under NT than under CT without a major change in bulk density. Differences were attributed to a redistribution of pore-size classes into more small pores and fewer large pores with NT. Soil organic C was greater under NT than under CT near the soil surface. Water-stable aggregation improved under NT, presumably because more soil organic C was sequestered within macroaggregates. Steady-state water infiltration was greater under NT than under CT because of soil structural improvements associated with surface residue accumulation and lack of soil disturbance. In another Canadian study, Bergstrom et al. (2000) compared enzyme activities and organic carbon content along a topographic and textural gradient. They found the effect of tillage on enzyme activity to be influenced by sampling depth and slope position, and they concluded that an assessment of soil conservation practices requires measurements of soil quality indicators at scales inclusive of soil variability comparable to that of typical farms. Masciandaro and Ceccanti (1999) also used enzyme tests to compare soils under different land uses—native, intermittently cultivated, and intensively cultivated within two Mediterranean ecosystems. Soil samples were collected from humid-temperate sites in centralwest Italy and from the semiarid zone of central-west Spain and used to compare the degree of humification and biochemical activity of humus–enzyme complexes. They concluded that even where most of the labile soil organic matter was lost due to cultivation (>86%) a humic pool was preserved. They considered this an indicator of soil quality because it represents the ability of a soil to resist change. Studies in developing countries (Lal, 1999b; Kaihura et al., 1999) have focused on soil quality as a means of addressing erosion and other soil management challenges that must be met to achieve food security with minimal environmental risk— especially considering per capita land area and water resources are rapidly decreasing. The benefits of using farmyard manure and other soil management practices to prevent or restore degraded soils, enhance soil carbon sequestration, decrease risks of surface water eutrophication, and prevent contamination of groundwater are being evaluated in many of the studies. Results show that the farmer’s criteria for distinguishing soil productivity often include crop performance, soil tilth, moisture, color, and the presence or absence of weeds and invertebrates—many of the same indicators included in U.S. farmer-derived soil health cards. The authors of these studies (Murage et al., 2000; Lal, 1999b; Kaihura et al., 1999) frequently emphasize the need to use biological, chemical, and physical indicators of soil quality to assess the sustainability of various management practices. They also stress the need for soil scientists to work closely with those in the basic sciences to address the environmental concerns of agricultural intensification—a good use for an educational tool such as a soil quality index.


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Ericksen and McSweeney (1999) designed a study in Honduras to determine the impact of land use and landform on soil quality attributes that could be measured at the plot or very-fine scale. They measured and scored 14 biological, chemical, and physical attributes that affect the ability of a soil to perform key functions related to supporting plant growth. Analysis of data from 20 sites representing different combinations of landform and land use demonstrated that land use had a greater influence on soil quality than did landform. Soil organic carbon, texture, A horizon thickness, pH, structure, and bulk density accounted for the greatest differences among sites. Using a weighted and additive combination of the scored indicators to evaluate the soil quality functions showed that coffee groves and forest patches were more sustainable than irrigated agriculture in that region. The final soil quality values were sensitive to the weights and attributes chosen for a given function, leading them to recommend further testing and evaluation, preferably including additional attributes (e.g., pore-size distribution, soil respiration rates, above-ground biomass, and rooting depth) that were not included in their experiment. Development of the visual soil assessment (VSA) procedure (Shepherd, 2000) shows strong commitment to the concept of soil quality in the Southern Hemisphere. In other studies, Chan and Hulugalle (1999) evaluated several physical (tensile strength, structural stability, and dispersion) and chemical (pH, electrical conductivity, organic C, and total N) indicators of soil quality in eastern Australia. On-farm sampling sites were located on rainfed, hardsetting red Alfisols and on irrigated, self-mulching Vertisols. Changing land use from native pasture to intensively tilled wheat cultivation with long fallow and stubble burning caused significant soil physical and chemical changes in the Alfisols. Switching from intensive to minimum tillage in cotton-based cropping systems also caused changes in the Vertisols. For the Alfisols, indicator changes showed a significant deterioration in soil quality that was characterized by an increase in hardsetting behavior, increased acidity, and a decrease in organic C, total N, and aggregate stability. Chan and Hulugalle (1999) concluded that this reduction in soil quality was caused by inappropriate tillage practices causing soil inversion and rapid breakdown of organic matter when the previously untilled soils were brought under intensive cultivation. For the Vertisols where intensive tillage was replaced with minimum tillage for cotton (Gossypium spp.), physical indicators (air-filled pores in oven-dried clods, plastic limit, and soil resilience) showed a decrease in quality due to increased compaction. Conversely, chemical indicators (pH, electrical conductivity, exchangeable sodium percentage, and soil organic C) showed an increase in quality. They concluded the latter improvement was due to an increase in soil organic C and a decrease in exchangeable sodium percentage. In another Australian study, Chan and Heenan (1999) documented changes in the quantity and quality of soil organic carbon and their effect on soil aggregate stability as a result of growing different crops in rotation with wheat in New South Wales. After two cropping cycles, they found that total organic carbon in

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the depth 0 to 5 cm was similar (15.1 g kg−1) for all rotations, but there were significant differences in water-stable aggregation [wheat/lupin (Lupinus spp.) = wheat/barley (Hordeum spp.) > wheat/canola (Brassica napus) > wheat/field pea (Pisum sativum)]. Through carbon pool fraction, Chan and Heenan (1999) concluded that the observed differences in aggregate stability were significantly (P < 0.05) related to only microbial biomass carbon. However, because of the labile nature of the microbial biomass, carbon pool differences in aggregate stability for the various rotations were very transient. Good soil management and use of soil quality assessment as an indicator of sustainability have generated substantial research and technology-transfer activities throughout New Zealand because land-based industries are the main generator of export income for that country (Beare et al., 1999). Increasing economic pressure to intensify land use, possibly beyond the margins of what is truly sustainable, has increased farmer demand for more information and better tools for monitoring and improving the sustainability of their soil management practices. In response to this increasing demand, several tools including the VSA (Shepherd, 2000), a soil quality monitoring system (SQMS) developed by Crop & Food Research Ltd. and the Centre for Soil and Environmental Quality (Beare et al., 1999), and several soil quality Web sites have been developed (e.g., http://www.landcare.cri.nz/science/soilquality/). In addition to these technologytransfer activities, soil quality research has focused on many of the same topics as in Australia or the Northern Hemisphere. For example, Aslam et al. (1999) used microbial biomass C, microbial biomass N, microbial biomass P, and earthworm (Apporrectodea caligninosa) populations as biological soil quality indicators to quantify the effects of converting pastureland to cropland through either plowing or no-tillage practices. They concluded that adoption of no-till practices for conversion could protect soils from biological degradation and maintain soil quality compared with plowing. As stated following our brief review of U.S. soil quality literature, the exponential increase in activities during the past decade obviously prevented us from adequately reviewing every publication. However, through those cited we conclude that the soil quality concept has indeed been adopted not as “an end in itself,” but as a science-based assessment and educational tool useful for evaluating the sustainability of various soil management and land-use practices.

IX. SUMMARY AND CONCLUSIONS Our objectives for this review were to respond to reservations raised regarding the concept by tracing the evolution of the soil quality concept, discussing its use as a tool to assess sustainability of soil management and land-use practices,


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illustrating the indexing process, and summarizing current research and technology-transfer activities related to soil quality. One of the most important concepts for readers to understand is that soil quality reflects both inherent and dynamic properties and processes occurring within a living dynamic medium. Soil quality reflects biological, chemical, and physical properties and processes and their interactions within each soil resource. There is no ideal or magic index value. The suggested process for developing soil quality indices—identifying critical functions, selecting appropriate indicators, developing appropriate scoring or interpretation guidelines, and combining the information into index values to determine if the resource is being sustained, degraded, or aggraded—is intended to be flexible and easily modified according to user need. The determining factor for making changes should be the intended use and whatever is required to translate and disseminate soil science knowledge into information that is useful to the broadest array of clients and stakeholders. Our summary of current national and international activities addressing soil quality shows that the topic covers a very broad array of basic and applied issues and is being studied at a continuum of scales. Finally, in response to reservations regarding the concept of soil quality, we hope to have clearly established that the topic is not an end unto itself and that perceptions of an institutional or regional bias are unfounded. As stated repeatedly, the sole purpose for evaluating soil quality is to consider how soil biological, chemical, and physical properties and processes are responding to anthropogenic decisions and actions over time.

REFERENCES Acosta-Martinez, V., and Tabatabai, M. A. (2000a). Arylamidase activity of soils. Soil Sci. Soc. Am. J. 64, 215–221. Acosta-Martinez, V., and Tabatabai, M. A. (2000b). Enzyme activities in a limed agricultural soil. Biol. Fertil. Soils 31, 85–91. Acton, D. F., and Gregorich, L. J. (1995). “The Health of Our Soils—Toward Sustainable Agriculture in Canada”. Centre for Land and Biological Resources Research, Research Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario. Andrews, S. S. (1998). Sustainable agriculture alternatives: Ecological and managerial implications of poultry litter management alternatives applied to agronomic soils. Ph.D. dissertation, University of Georgia, Athens. Andrews, S. S. (1999). Regional scoresheets for interpreting the soil quality test kit. Annual Meeting Abstracts, p. 219. ASA-CSSA-SSSA, Inc., Madison, WI. Andrews, S. S., Lohr, L., and Cabrera, M. L. (1999). A bioeconomic decision model comparing composted and fresh litter for winter squash. Agric. Syst. 61(3), 165–178. Andrews, S. S., Mitchell, J. P., Mancinelli, R., Karlen, D. L., Hartz, T. K., Pettygrove, G. S., Horwath, W. R., Scow, K. M., and Munk, D. S. (2001). On-farm assessment of soil quality in California’s Central Valley. Agron. J. 93, (in press). Arshad, M. A., and Coen, G. M. (1992). Characterization of soil quality: Physical and chemical criteria. Am. J. Altern. Agric. 7, 25–31.

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FRONTIERS IN METAL SORPTION/PRECIPITATION MECHANISMS ON SOIL MINERAL SURFACES Robert G. Ford,1 Andreas C. Scheinost,2 and Donald L. Sparks3 1

National Risk Management Research Laboratory U.S. Environmental Protection Agency Ada, Oklahoma 74820 2 Institute of Terrestrial Ecology Swiss Federal Institute of Technology CH-8952 Schlieren, Switzerland 3 Department of Plant and Soil Sciences University of Delaware Newark, Delaware 19717

I. II. III. IV.

Introduction From Adsorption to Precipitation: An Overview Macroscopic Evidence for Surface Precipitation: Utility and Pitfalls Approaches to Modeling Surface Precipitation A. Surface Enhanced Precipitation B. Surface Coprecipitation C. Surface Polymerization and Precipitation/Coprecipitation V. The Role of the Mineral Surface A. Surfaces Undergoing Weathering (Coprecipitation) B. Stable Surfaces (Surface Enhanced Precipitation) VI. Environmental Implications: Mechanisms for Metal Stabilization A. Aging Influence on Surface Precipitate Stability VII. Conclusions and Future Research A. Rates of Mineral Structural Modifications Near and Far from Equilibrium B. Solubility of Mixed Component Phases C. Characterizing the Structure of Ill-Defined Precipitates D. Spectroscopic Verification of Surface Precipitates in Soil/Sediment Material References


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Spectroscopic studies provide evidence that inorganic contaminants may be incorporated into precipitates at the surface of soil and sediment minerals. Surface precipitates may form via several mechanisms that are dependent on the unique characteristics of the interfacial region between solid and solution. In general, surface complexation models (SCMs) capture most of the salient features of the interfacial region. However, current SCMs fail to capture the dynamics of mineral surfaces, thus limiting their ability to predict the composition and structure of potential surface precipitates. This review outlines the current implementation of surface precipitation models, spectroscopic studies that highlight the need to develop more comprehensive SCMs, and future research directions that will help fill existing knowledge gaps. Successful modeling approaches to describe surface precipitation phenomena are a necessary component for the evaluation of long-term inorganic contaminant transport in soil and sediment systems. C 2001 Academic Press.

I. INTRODUCTION Since the recognition of “adsorption” to the surfaces of soil or sediment minerals as a control on metal solubility in natural systems, there has been an extensive effort to elucidate and predict the mechanisms and rates of this process (e.g., Davis and Kent, 1990; Stumm, 1992; Sparks, 1995; Sparks and Grundl, 1998). Much progress has been made in the development of mechanistic models, also known as surface complexation models (SCMs), that describe the net partitioning of solutes from solution to the surface of solids (Stumm and Morgan, 1996). While the theoretical basis for model development was derived by comparison to coordination reactions in solution, molecular spectroscopic investigations have generally supported the conceptual framework that underpins an SCM (Stumm, 1992). However, results from macroscopic studies that examine both the forward (adsorption) and reverse (desorption) reactions reveal that the assumption of equilibrium inherent to most SCMs is not always supported. The most common observation is a lack of reversibility within a time frame comparable to the rate of metal uptake. In most cases, apparent “irreversible” sorption can be attributed to failure to consider mass transport limitations and/or observation times that capture a metastable state rather than the equilibrium state. An example of the former situation is the diffusion of a sorbate into the sorbent pore structure limiting the transport of the sorbed metal back into solution (Comans, 1987, 1999). An example of the latter situation is the formation of a modified sorption complex that is structurally more stable than the intermediate structure initially formed during partitioning from solution to solid (e.g., Ainsworth et al., 1994; Ford et al., 1997; Scheidegger et al., 1998). It is this last sorption phenomenon that will be reviewed in detail in this chapter.

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II. FROM ADSORPTION TO PRECIPITATION: AN OVERVIEW The exchange of solution ions between the bulk solution and a solid surface involves sorption and desorption processes. Sorption refers to a physicochemical process that leads to net accumulation of ions from solution to the mineral–water interface, whereas desorption refers to the reversal of this process. These terms are general in that a specific reaction mechanism is not identified. The use of these terms is preferred in the absence of molecular information identifying the mode of interaction between an ion (sorbate) and a solid surface (sorbent). For example, measurement of the loss of an ion from the aqueous phase in a soil or sediment suspension indicates net transfer of the ion from solution to the suspended solids. However, insufficient information is provided by this macroscopic measurement to ascertain the mechanism by which the ion is partitioned to the solid surface. This lack of knowledge becomes problematic primarily during prediction (or speculation) of the reversibility of the partitioning process. Ions may partition from bulk solution to the mineral–water interface via physical (electrostatic) and chemical processes. Electrostatic interactions take place when a charged ion enters the field of influence of a solid surface possessing a net charge of opposite sign. An example of this type of interaction is the partitioning of an anion to a positively charged oxide surface (i.e., below the point of zero charge, or PZC). Chemical interactions are driven by the formation of a specific chemical bond with functional groups at the solid surface. An example of this type of interaction is the formation of a chemical bond between a transition metal and a hydroxyl functional group on the surface of an oxide mineral. This type of reaction parallels solution hydroxylation reactions in which the surface hydroxyl substitutes for water within the metal ligand field (Stumm, 1992). An additional type of chemical process involves the formation of a sorption complex that possesses three-dimensional order, which is often referred to as a surface precipitate (Sposito, 1984). Such a complex may be composed of a population of ions derived from solution and /or the solid surface. Surface precipitates may form due to a variety of processes that are directly or indirectly influenced by the solid surface. Mechanisms that may promote formation of surface precipitates have been reviewed in the literature (e.g., James and Healy, 1972; Farley et al., 1985; McBride, 1991; O’Day et al., 1994; Towle et al., 1997). In general, the proposed mechanisms can be grouped into two categories: (1) those that are driven by changes in sorbate properties induced by the sorbent surface, and (2) those that are driven by modification of the solution composition near the interface (with respect to the bulk solution). These mechanisms may act in concert, and the relative importance of each mechanism will depend on the electrostatic properties and stability of the sorbent surface as well as the physicochemical properties of the sorbate.

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Figure 1 Schematic diagram depicting four mechanisms for the formation of a surface precipitate. (A) Increase of ion activities due to a reduction in the dielectric constant of water at the mineral–water interface. (B) Oversaturation within a finite volume of fluid at the mineral–water interface due to ion sorption. (C) Incorporation of a sorption complex within the sorbent structure due to particle growth. This would be considered an epitaxial growth phenomenon if the sorption complex acts as a nucleation site for surface precipitate growth. (D) Formation of a coprecipitate at the mineral–water interface incorporating the sorbate and dissolved sorbent lattice ions. A structural relationship between the sorbent and the coprecipitate is not required.

Several possible scenarios leading to surface precipitation are illustrated in Fig. 1. Frames (A) and (B) illustrate cases in which the sorbent surface is stable within the timescale of the sorption process. In this instance, the sorbent functions in two primary capacities, it presents a population of sorption sites to the incoming sorbate and it may modify the properties of ions in close proximity of the mineral– water interface. Frame (A) illustrates a case in which the activities of ions at the mineral–water interface increase due to changes in the dielectric properties of the solution bathing the solid. Precipitation is induced not because the critical ion population for solid-phase nucleation is exceeded, but, rather, due to increase in the ion activity product above the thermodynamic solubility for precipitation in the bulk solution. An increase in specific ion activities may result from a decrease in the dielectric constant of interfacial water or due to an overall increase in solution ion populations near a charged interface. This is analogous to a “salting-out” effect near the mineral–water interface. Frame (B) illustrates a simpler case in which the


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population of ions exceeds the critical number for nucleation of a precipitate near the mineral–water interface. A relative increase in ion population is a result of the net attractive force (electrostatic or chemical) exerted by the sorbent surface. In either case, the role of the sorbent is to concentrate the sorbate within a smaller effective volume and/or to modify the activity of the sorbate within the interfacial region. Two additional scenarios are shown in Fig. 1 that illustrate a more complicated role for the sorbent surface. Frame (C) illustrates a case in which an initial adsorption complex transforms to structural inclusion within the sorbent structure due to continued particle growth. In this scenario, the sorbent surface is stable with respect to ion release to solution, but growth can occur due to the addition of ions from solution. This may be viewed in a broad sense as an epitaxial growth of a new solid phase. However, growth may be discontinuous resulting in an illdefined phase with respect to composition or structure. As an example, based on spectroscopic evidence Hazemann et al. (1992) have proposed incorporation of iron clusters possessing hematitelike short-range order within the structure of a natural aluminum oxyhydroxide diaspore. It was hypothesized that the adsorbed iron clusters were incorporated into the overall structure during the addition of aluminum growth units from solution. The hematitelike clusters fall short of satisfying the definition of a new solid phase possessing a known composition and structure, but the overall result of the sorption process was incorporation of a sorbate within a precipitate phase. Clearly, this scenario falls outside the traditional definition for surface precipitation, but it is included since it meets the criterion that an ion from solution is incorporated within a solid structure under conditions in which bulk precipitation is typically not anticipated. A likely prerequisite for this process is structural compatibility between the sorbate and structural elements in the sorbent, otherwise continued growth and inclusion would be poisoned. Structural inclusion could occur rapidly or slowly dependent on the level of saturation in the solution with respect to the sorbent solubility and growth. If structural elements at the sorbent surface do not readily exchange with solution ions, it is possible that structural inclusion of the sorbate may be delayed for a significant period of time. This scenario most likely is true for sparingly soluble oxides. Conversely, the probability of structural incorporation of the sorbate increases if the sorbent surface ions are labile and exchange readily with solution. Relatively soluble oxysalts such as phosphates fall within this category (Ma et al., 1993). Until recently, surface processes that may lead to structures with poorly defined structure and composition have received limited attention in the surface complexation literature, though a mathematical framework for their description has been developed (Farley et al., 1985). The final scenario illustrated in Frame (D) is envisioned for a surface that is unstable with respect to the bathing solution. In this case, structural ions are released

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into solution. Thus, the composition within the interfacial region is dependent on the elemental composition of the sorbent as well as the bulk solution. Sorbates at the mineral–water interface may be consumed in a precipitation reaction that is not predicted based on the bulk solution composition alone. In essence, sorbate partitioning is intimately linked to incongruent dissolution of the sorbent. This process has been implicated to explain long-term changes in the reversibility of transition metal sorption to various aluminum oxides and phyllosilicates (e.g., Scheckel et al., 2000; Thompson et al., 2000). In theory, it should be possible to predict the potential for this type of surface precipitation process based on the known composition of the sorbent and bulk solution and knowledge of the rate and extent of sorbent dissolution. However, this is limited by the lack of solubility data for mixed composition phases. Clearly, this type of surface precipitation process would be difficult to confirm without characterization of the composition and structure of the new phase. The a priori estimation of surface precipitation potential at the mineral–water interface is currently limited. Any of the outlined processes could act in concert toward surface precipitate formation, necessitating an understanding of the relevant kinetic and thermodynamic controls, for example, rates of sorbent dissolution and prediction of interfacial ion activities, respectively. While this poses a daunting task for scientists studying interfacial phenomena in complex natural systems, it is critical that a more comprehensive understanding is developed for reliable prediction of contaminant fate in soil and sediment systems. The purpose of this review is to develop a cohesive picture of the current state of knowledge and to point out critical areas for future research. To achieve this, a brief review will be provided of the macroscopically based studies that have suggested the potential for surface precipitation at the mineral–water interface and of the mechanistic models that have been developed to describe this macroscopic data. A current review will be provided of spectroscopic studies that have confirmed these earlier findings. Finally, this chapter will be concluded through illustration of the influence that surface precipitation phenomena may exert on the fate of inorganic contaminants in natural systems.

III. MACROSCOPIC EVIDENCE FOR SURFACE PRECIPITATION: UTILITY AND PITFALLS One of the initial studies to explicitly address surface precipitation during metal sorption to oxide surfaces was carried out by James and Healy (1972). These authors documented experimental evidence and the thermodynamic framework for the formation of metal hydroxide surface precipitates on silicon and titanium oxide surfaces under bulk solution conditions undersaturated with respect to hydroxide


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precipitation. The primary line of evidence for surface precipitate formation was the observed change in the electrophoretic mobility of the sorbent from the mobility expected for an unaltered surface to the mobility of a sorbate hydroxide precipitate. James and Healy (1972) presented evidence that the surface charging behavior observed for silicon and titanium oxide was consistent with formation of a cobalt hydroxide surface precipitate below bulk solution saturation. Additional macroscopic evidence for surface precipitate formation can be derived from sorption isotherm measurements. This line of evidence relies on the assumption that a given mass of sorbent material possesses a fixed population of sorption sites. Sorption should cease and, thus, the sorption isotherm will plateau once the available sites are fully occupied by the sorbate. However, numerous studies have shown that in contrast to expectations, sorption from solution continues beyond apparent saturation such that sorbate surface concentration continuously increases with increasing solution concentration. To illustrate this point, an isotherm that deviates from the anticipated surface saturation behavior is shown in Fig. 2. This isotherm was derived from data published by Katz and Hayes (1995) for cobalt sorption to corundum (␣-Al2O3) at pH 7.6. The surface excess shown by the shaded area between the predicted monolayer adsorption and the total measured sorption is attributed to formation of polynuclear sorbate species

Figure 2 Generalized sorption isotherms depicting monolayer adsorption and multilayer surface precipitation. The shaded region between both isotherms represents the net accumulation of surface polymers and precipitates.

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Figure 3 Hypothetical reaction process illustrating the transformation of an initially precipitated Ni–Al LDH to a phyllosilicatelike phase during aging. The initial step involves the exchange of dissolved silica for nitrate within the LDH interlayer followed by polymerization and condensation of silica onto the octahedral Ni–Al layer. The resultant solid possesses structural features common to 1:1 and 2:1 phyllosilicates.

with eventual formation of a surface precipitate. This macroscopic trend has been observed for several metals on various sorbent materials (e.g., Bleam and McBride, 1986; Dzombak and Morel, 1986). Sposito (1986) has illustrated the limitations of using macroscopic data to interpret sorption mechanism(s). A clear example of the potential for misinterpretation of the sorption mechanism based on macroscopic observations is provided in research presented by Charlet and Manceau (1992). Their research addressed the sorption of chromium to hydrous ferric oxide (HFO), a disordered form of ferrihydrite, via addition to preformed solids and coprecipitation with iron from solution. At first glance, sorption isotherm results, shown in Fig. 3 of Charlet and Manceau (1992), would suggest similar mechanisms with differences in total surface capacity attributable to a greater population of surface sites during coprecipitation. However, spectroscopic evidence revealed clear distinctions between the chromium local structural environment in postsynthesis sorption and coprecipitation samples. In the former situation, chromium was present as adsorbed mononuclear and polynuclear species with ␣-MeOOH and ␥ -MeOOH local structures, respectively. In contrast, chromium in coprecipitation samples existed only in an ␣-MeOOH structural environment prior to eventual precipitation as a ␥ -MeOOH phase at high surface loading. Such differences in


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sorption mechanism have implications for both sorption model development and overall reversibility of the sorption process.

IV. APPROACHES TO MODELING SURFACE PRECIPITATION The development of SCMs to describe surface precipitate formation has, in part, outpaced the application of spectroscopic methods for their identification. The models that have been developed to predict surface precipitation at undersaturated conditions can be categorized into three types: (1) surface precipitation due to modification of the solubility within the sorbent interfacial region, (2) surface precipitation via formation of polymeric sorbate species that act as nucleation sites for precipitate growth, and (3) surface precipitation via formation of a solid solution between sorbate and sorbent species. These three conceptual models require distinct approaches toward construction of the mathematical framework for describing observed sorption phenomena.

A. SURFACE ENHANCED PRECIPITATION James and Healy (1972) proposed a model that evokes precipitation at the sorbent surface for undersaturated conditions via modification of the solubility product near the interface. Precipitation is enhanced near the solid–solution interface due to the excess free energy imparted to ions residing in the interfacial electric field. This derivation is consistent with the conceptual model depicted in Fig. 1A. The excess free energy imparted to interfacial ions is due to lowering of the dielectric constant of water under the influence of the electric field emanating from the charged sorbent surface. James and Healy (1972) observed the formation of Co(OH)2 surface precipitates on TiO2 and SiO2 based on electrophoretic mobility measurements in aqueous suspensions. In general, two particle charge reversals were observed (in addition to that for an unaltered sorbent surface) that were attributed to the onset of surface precipitation, and, ultimately, near complete coverage of the sorbent surface by Co(OH)2. Charge reversal associated with the onset of surface precipitation occurred at approximately pH 7.8 in the presence of TiO2 and SiO2, resulting in a calculated solubility product of logKso = −16.4 for the surface precipitate. This compared to a bulk solubility product reported for Co(OH)2 of logKso = −14.9. These results are compelling evidence for surface enhanced precipitation. However, the inability to identify the structure and composition of the purported Co(OH)2 surface precipitate in these experimental systems is a critical shortcoming for estimation of an interfacial solubility product. James and Healy (1972)

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assumed that a stoichiometric Co(OH)2 was the product of surface precipitation, ruling out the possible formation of a precipitate with a more complex composition, since the PZC of the suspended solids in their experimental systems approached that for a pure Co(OH)2. This assumption may hold in the case of the TiO2 suspension, since it is a sparingly soluble oxide. However, dissolved silica in equilibrium with SiO2 could participate in the formation of a surface precipitate incorporating Co into a more compositionally and structurally complex environment. As discussed later in this chapter, the formation of a Co phyllosilicatelike phase is an alternative end point for surface precipitation in the presence of SiO2. Unfortunately, this possibility cannot be ruled out, since James and Healy (1972) did not evaluate the PZC of other precipitates that could potentially have formed in their experimental system. Spectroscopic studies examining the nature of Co surface precipitates for similar experimental systems will be presented later in this chapter.

B. SURFACE COPRECIPITATION Farley et al. (1985) postulated that the sorption of metals beyond the predicted adsorption site capacity of the sorbent surface is due to the formation of a continuous solid solution. In this model, the apparent excess sorption that occurs below bulk solution saturation of a known sorbate precipitate can be described by assuming the formation of a solid solution with the sorbent. Macroscopic data for the sorption of several metals onto HFO were modeled assuming the incorporation of the sorbate into the HFO structure for surface loadings exceeding monolayer coverage. The formation of an ideal solid solution was assumed between Fe(OH)3 and Me(OH)2, and thus the activity of each solid solution component could be described by its mole fraction within the precipitate. Several studies have suggested that solid solution formation at the surface of soil minerals may be a common sorption process (e.g., Miller et al., 1986; Davis et al., 1987), but there has been limited spectroscopic evidence to support this hypothesis. This modeling approach has also been applied by Wersin et al. (1989) to describe the partitioning of Mn2+ to siderite (FeCO3) under reducing conditions. Electron spin resonance (ESR) spectroscopy was employed to elucidate the transition from an Mn2+ adsorption complex to incorporation as a surface precipitate. The activity of aqueous Mn2+ and Fe2+ in apparent equilibrium with the solid surface was consistent with formation of a nonideal Mn–Fe carbonate solid solution. This was verified by independent determination of the solid phase activity of Mn2+ and Fe2+ through use of the surface coprecipitation model developed by Farley et al. (1985). The calculated solubility of the Mn–Fe carbonate solid solution was greater than the solubility of the pure end-member phases. This result indicates that the solid solution was a metastable phase that would likely de-mix during approach to


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equilibrium. This behavior has been demonstrated for systems in which trace Pb and Cu have been coprecipitated with metastable iron and aluminum (hydr)oxides (Ford et al., 1999a; Martinez and McBride, 2000). In these systems, coprecipitated Pb and Cu became segregated from the bulk structure during transformation to more crystalline iron and aluminum (hydr)oxides. The formation of intermediate (metastable) precipitate precursors is common in sediment environments (Morse and Casey, 1988). In addition, the formation of nonideal solid solutions appears to be common in soil systems (Bohn and Bohn, 1986).

C. SURFACE POLYMERIZATION AND PRECIPITATION/COPRECIPITATION Katz and Hayes (1995) have suggested that formation of a three-dimensional surface precipitate is preceded by formation of polynuclear species at the mineral– water interface. Their implementation of a “continuum model” included formation of adsorbed monomers, adsorbed polymers, and eventual formation of a surface precipitate with three-dimensional order. Formation of a surface precipitate was modeled using two approaches: (1) formation of a solid solution between the sorbate and sorbent components and (2) formation of a pure surface precipitate phase (i.e., not a solid solution) with activity less than one. The purpose of the second modeling approach was to disassociate the activity of the surface precipitate from the sorbent material. Results from X-ray absorption spectroscopy for their experimental systems were used to justify this modeling approach (Hayes and Katz, 1996). Formation of a polynuclear Co surface complex on ␣-Al2O3 at a surface loading occupying >10% of available sorption sites was indicated by the presence of Co within the second shell of the sorbate bonding environment. The number of second-shell Co nearest neighbors increased with increasing surface loading, suggesting the accumulation of a sorption product with three-dimensional order. The optimal structural model for the X-ray absorption fine structure (XAFS) data indicated the presence of Al within the second coordination sphere of Co. It was argued that the bond distance and number for second-shell Al atoms was more consistent with bonding of a polynuclear complex with surface aluminol functional groups. However, comparison of the XAFS data to possible precipitate structures suggests that a Co–Al layered double hydroxide (LDH) formed in their experimental systems (Scheinost and Sparks, 2000). This casts doubt on the consistency between the observed structural environment and the surface complexation model employed to describe the macroscopic sorption data (Hayes and Katz, 1996). This was highlighted by the observation by Katz and Hayes (1995) that both the surface coprecipitation model and the continuum model provided a reasonable description of the macroscopic sorption data covering a wide range of surface loading.

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These observations point out the need to employ well-constrained experimental systems during development and implementation of a surface complexation model. Clearly the conceptual framework implemented in the “continuum model” is appealing and likely represents the most accurate representation of the interfacial region. The formation of polynuclear hydrolysis complexes as precursors to nucleation and precipitation of three-dimensional hydrous oxide precipitates in bulk solutions is well established (e.g., Dousma and de Bruyn, 1976; Flynn, 1984; Schneider, 1984). However, we would argue that formation of (1) surface coprecipitates or (2) surface polymers that grow into a true homogeneous precipitate remain equally likely scenarios dependent on the sorbent properties and stability. Unfortunately, the role of the sorbent surface remains a poorly defined area within the current implementation of surface complexation models. Efforts to model and describe the structure at the mineral–water interfacial region appear to be unnecessarily constrained by the assumption that the sorbent surface is a static system with a fixed population of surface sites. While this is a convenient (and sometimes necessary) assumption to constrain the number of fitting parameters in an SCM, it must be recognized that the interfacial region is a dynamic system. This is clearly evidenced in the way that an equilibrium solubility expression is constructed, for example, Fe3+ + 3H2 O Fe(OH)3(s) + 3H+ . At equilibrium, a balance is attained between the forward and reverse reaction rates (precipitation and dissolution). However, the forward and reverse reactions do not cease to operate at equilibrium (Bard, 1966). Thus, while a mass balance is achieved between the mass of dissolved and precipitated Fe, exchange between solid and solution can still occur to maintain equilibrium. A cation that attaches to the surface could serve as a new sorption site for dissolved Fe and subsequently be incorporated into the near-surface solid structure. Thus, from a modeling perspective it is critical to consider both the processes governing sorbate partitioning to the solid surface and the processes governing the stability of the sorbent surface over the relevant timescale. With increased efforts to quantitatively model long-term contaminant partitioning in natural systems, greater effort must be placed on understanding and describing the stability of the sorbent surface and the influence exerted on sorbate partitioning. This was illustrated for calcareous aquifer sand in which calcite surface recrystallization reactions appeared to play a significant role in the rate and reversibility of Cd sorption (Davis et al., 1987; Fuller and Davis, 1987).

V. THE ROLE OF THE MINERAL SURFACE In the studies reviewed previously, the mineral (or sorbent) surface played a role in directing the sorption end point. As stated earlier, this role is manifested through direct or indirect determination of the composition and structure of the surface


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precipitate. We illustrate through case studies two scenarios in which the mineral surface directs the formation of a precipitate either by contributing components for coprecipitation or by lowering the energy barrier to precipitation.

A. SURFACES UNDERGOING WEATHERING (COPRECIPITATION) Mineral surfaces in soils and sediments may undergo weathering reactions when in contact with a nonequilibrium solution. This is not an uncommon situation since dynamic hydrologic and biogeochemical processes influence soil and sediment pore-water composition. This process is illustrated through a series of experiments that were designed to examine the sorption of Co onto quartz, a common mineral component in soils and sediments (Chisholm-Brause et al., 1990; O’Day et al., 1996). For these studies, the sorption of Co in an aqueous slurry containing quartz was monitored via macroscopic and spectroscopic techniques. Under solution conditions undersaturated with respect to Co(OH)2 precipitation, O’Day et al. (1996) observed the formation of a Co surface precipitate possessing shortrange structure similar to a hydroxidelike solid. O’Day et al. (1996) suggested that the quartz surface was facilitating formation of a surface precipitate based on a mechanism similar to that proposed in Frames A or B in Fig. 1. However, reevaluation of these results suggested that the short-range order of the surface precipitate was more consistent with formation of a phyllosilicatelike phase (Charlet and Manceau, 1994; Manceau et al., 1999). This was supported by thermodynamic calculations that indicated saturation with respect to the solubility of a 1 : 1 or 2 : 1 Co-containing phyllosilicate in the presence of dissolved silica in equilibrium with quartz. This process is more consistent with the coprecipitation mechanism illustrated in Frame D of Fig. 1. Clarification of the governing process in these sorption studies is critical to the modeling approach that is needed to predict Co sorption under the experimental conditions that were employed. These results highlight the need to characterize or speciate components in both the liquid phase and the solid phase.

B. STABLE SURFACES (SURFACE ENHANCED PRECIPITATION) For sparingly soluble minerals, the surface may still enhance the formation of precipitates during metal sorption by increasing the localized concentration of ions at the interface in excess of the bulk solubility of a precipitate and by providing a structural template that lowers the barrier to precipitate nucleation. This process is illustrated through the formation of cobalt hydroxidelike surface precipitates at the surface of the titanium oxide, rutile (O’Day et al., 1996). Aqueous sorption experiments were conducted over a range of pH (5 < pH < 8) at cobalt

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concentrations undersaturated with respect to bulk precipitation of Co(OH)2. Under the conditions of the experiment, rutile solubility is at a minimum (Baes and Mesmer, 1986). Thus, unlike the case for quartz, soluble components such as Ti4+ and its hydrolysis products are expected at a concentration insufficient for coprecipitation with Co near the interface. Again, sorption of Co was monitored via macroscopic and spectroscopic techniques. X-ray absorption spectroscopy was employed to examine the structure of Co surface complexes over a range of surface loading (0.63–9.51 ␮mole m−2). Structure analysis revealed the formation of inner-sphere surface complexes with Co– Ti bond distances consistent with cobalt occupying Ti structural sites at the end of edge-sharing octahedral rows. With increasing surface coverage, Co multinuclear complexes form based on the appearance of a Co–Co bond consistent with edge-sharing Co octahedra. The multinuclear complex grew with additional cobalt sorption, and a new surface complex began to accumulate with Co–Ti bond distances more consistent with the bonding environment of Ti in anatase, a structural polymorph of rutile. These results suggest that the energetics of rutile sorption sites governed the Co bonding structure. The identified Co bonding environments were inconsistent with separate structural phases such as a Co–Ti LDH (de Roy et al., 1992). The structure of sorbed Co was more consistent with a true epitaxial growth phenomenon leading to the continued growth of a TiO2-like polymorphic phase.

VI. ENVIRONMENTAL IMPLICATIONS: MECHANISMS FOR METAL STABILIZATION As suggested at the beginning of this chapter, it is desirable to develop a mechanistic description of the sorption process to facilitate the prediction of metal uptake and to evaluate the long-term stability of the sorption product. Naturally occurring processes that sequester metals to immobile solids within aqueous systems can minimize deleterious effects on the health of the ecosystem. However, without a more comprehensive understanding of the mechanisms and rates of the sorption processes that stabilize hazardous metals, the utility of predictions of the soil or sediment stabilization capacity and of future risk will be limited.

A. AGING INFLUENCE ON SURFACE PRECIPITATE STABILITY This section will review a series of experiments that have been carried out to examine the sorption of nickel to clay minerals. Specifically, sorption of nickel onto


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the clay mineral pyrophyllite over an extended aging period has been documented to demonstrate the types of processes that may lead to long-term stabilization of the partitioned metal. This research effort was initiated based on the observation by Scheidegger et al. (1997) that the Ni surface precipitate that formed at the pyrophyllite surface was structurally consistent with a Ni–Al LDH. Soluble Al was not introduced into the experimental systems, indicating that Al in the surface precipitate was derived from the clay structure. The instability of the clay surface over timescales of days to months was confirmed by the measured release of Si. These results suggested that the composition and structure of the initially formed Ni surface precipitates may evolve through time as the sorbent and sorbate approached equilibrium with the bathing solution. This hypothesis was tested through a series of experiments to evaluate the structure and stability of Ni surface precipitates as a function of aging time. In these experiments, Ni was sorbed to pyrophyllite in aqueous suspensions in which the pH was held constant with time. The extent of Ni sorption and the structure and stability of the sorption product was monitored over a period of a year (Ford et al., 1999b). The uptake of Ni onto the pyrophyllite surface was complete within a month. However, a significant reduction in the reversibility of Ni sorption was observed beyond one month. Characterization of the short-range structure of the Ni sorption product confirmed the formation of a Ni–Al LDH with only minor changes over an aging period of one year (Ford et al., 1999b; Scheinost et al., 1999). However, the thermal stability of the Ni sorption product increased continuously over the aging period. Changes in the thermal stability of the Ni sorption product during aging were consistent with the transition of a Ni–Al LDH containing interlayer nitrate to a Ni–Al phyllosilicate precursor. A hypothetical scheme illustrating the complete transition from a LDH to a phyllosilicate is shown in Fig. 3. The increased stability of the Ni surface precipitate paralleled the observed release of silica from the pyrophyllite structure. Thus, it was hypothesized that the initial Ni–Al LDH was slowly transforming into a phyllosilicatelike phase. This hypothesis was supported by separate studies demonstrating an increase in the stability of a Ni–Al LDH due to incorporation of silica within the precipitate interlayers (Scheckel et al., 2000; Scheckel and Sparks, 2000). Changes in the increased thermal stability of the surface precipitate are illustrated in Fig. 4 for an aged Ni-pyrophyllite system. Thermogravimetric analysis coupled to characterization of evolved gases with a mass spectrometer illustrate the transition from predominantly a Ni–Al LDH that decomposes at approximately 300◦ C to a Ni–Al phyllosilicate precursor that decomposes at approximately 450◦ C. This structural transformation resulted in a dramatic reduction in the reversibility of Ni sorption. The formation of a Ni–Al phyllosilicate precursor under ambient conditions over a year’s time suggests this sorption process may be common in soil and sediment

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Figure 4 Thermal analysis data for (A) young (3 days) and (B) aged (1 year) Ni surface precipitates formed on pyrophyllite at pH 7.5 and 25◦ C (Ford et al., 1999b). The top panel displays the derivative of the weight loss curve (DTG). Peaks in the DTG coincide with weight loss events. The bottom panels show mass spectroscopy detector intensity for H2O and NO in the evolved gases. H2O is derived from desorption and decomposition (dehydroxylation) events. NO is the decomposition product resulting from release of NO3 from the solid. Thermal analyses were carried out with argon as a purge gas.

systems. Current modeling approaches to describe contaminant partitioning fail to capture this type of process, and, thus, may underpredict the stability (or mobility) of inorganic contaminants sorbed to soil or sediment solids.

VII. CONCLUSIONS AND FUTURE RESEARCH This review illustrates that significant progress has been made over the past several years with respect to our conceptualization and ability to monitor sorption reactions at the mineral–water interface. However, there remain technical issues that warrant additional efforts. In the following we have highlighted several areas that appear critical toward our future success in predicting surface-mediated precipitation reactions.


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A. RATES OF MINERAL STRUCTURAL MODIFICATIONS NEAR AND FAR FROM EQUILIBRIUM In this case, “structural modifications” is used in a general sense to include dissolution and structural transformations that result in either near-surface or bulk changes in the sorbent structure/stability. In both instances, knowledge of the impact these processes exert on sorbate solid-phase partitioning is required to predict the long-term stability of surface precipitates. Many of the sorption studies reviewed in this chapter demonstrate the need to understand the stability of the sorbent surface as a function of time and biogeochemical conditions relevant to soil and sediment environments. There has been a concerted effort to understand the dissolution of minerals within the context of surface coordinative reactions. This is evidenced by several reviews addressing the processes controlling the dissolution of oxide and carbonate solid phases (Stumm, 1992; Brady and House, 1996; Walther, 1996). An effort must be made to couple modeling of surface dissolution and precipitation reactions. This will require knowledge of the rates of dissolution under a range of geochemical conditions. In many cases, ion partitioning in nature involves sorption to metastable minerals that possess high surface area. Unless major element recycling occurs rapidly, it can be anticipated that mineral structural transformations will occur that impact ion partitioning. This is illustrated by experimental results cited earlier for hydrous iron oxide systems that document significant changes in the reversibility of ion partitioning due to crystallization reactions within the sorbent phase. At present, there is limited data to predict the rates of metastable mineral transformations in soils and sediments. In addition, surface complexation modeling approaches that couple sorbent structural transformations and ion partitioning are not yet developed.

B. SOLUBILITY OF MIXED COMPONENT PHASES The experimental observation of coprecipitate formation at the mineral–water interface suggests a need for a more comprehensive database of mixed component phase solubilities. Methods for estimating thermodynamic parameters for unknown phases exist and have been successfully employed to predict the solubility of surface precipitates (Tardy and Duplay, 1992; Manceau et al., 1999). These estimated thermodynamic parameters provide a useful guide toward identifying phases that could potentially form. However, they do not supplant the need for solubility studies conducted under conditions identical to those employed to study ion sorption. The solubility of surface precipitates will be strongly influenced by factors such as particle size and structural defects that are dependent on the rate of precipitate formation.

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Modeling the formation of a solid solution either as a precipitate isolated at the mineral–water interface or as a trace constituent incorporated within the bulk sorbent structure requires knowledge of the solid-phase activity of the sorbate. This was illustrated above for the solid-solution model developed by Farley et al. (1985). If the assumption of an ideal solid solution is inappropriate, then the modeled solid-phase activity becomes a fitting parameter. This should be avoided to maintain a more constrained modeling approach. This limitation would benefit from consideration of the research that has been published examining patterns in solid-phase activities for various mineral structures. Additional research is needed to establish element-specific patterns (e.g., linear free energy relationships) in solid-phase activities for a variety of solid-phase structures.

C. CHARACTERIZING THE STRUCTURE OF ILL-DEFINED PRECIPITATES Studies highlight the difficulties in resolving the structure of surface precipitates in systems in which a number of related structures may form (e.g., Manceau et al., 1999; Scheinost et al., 1999; Scheinost and Sparks, 2000). Review of the literature suggests that there is still a great deal of controversy concerning the evaluation of X-ray absorption spectroscopic (XAS) data for surface precipitates incorporating a combination of light and heavy elements. An effort must be made to reach a consensus on the analytical requirements for XAS data collection and interpretation for these poorly defined systems. In addition, while the advent of synchrotron-based spectroscopies has significantly advanced our ability to probe the mineral–water interface under ambient conditions, it should not be considered a panacea. Studies suggest that multiple lines of evidence (both macroscopic and microscopic) may be required to adequately evaluate sorption processes leading to surface precipitation (Junta and Hochella, 1994; Ford et al., 1999b).

D. SPECTROSCOPIC VERIFICATION OF SURFACE PRECIPITATES IN SOIL /SEDIMENT MATERIAL To validate the use of SCMs to predict surface precipitate formation in soils or sediments, spectroscopic analyses must be employed to identify the contaminant solid-phase speciation. Several examples are available in the literature that illustrate this approach for materials contaminated in situ (O’Day et al., 1998; Ostergren et al., 1999; Manceau et al., 2000; O’Day et al., 2000) and for a soil that was contaminated in a laboratory setting under relevant environmental conditions (Roberts et al., 1999). This approach requires characterization of the types and relative abundance of sorbent surfaces (e.g., phyllosilicates, Fe/Al/Mn oxides,


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or organic matter), to help constrain spectroscopic interpretation of the bonding environment of the contaminant. While this level of analysis may be impractical for purposes of an environmental assessment, some general level of effort must be made to justify the use of model predictions as part of a remedial management decision. In general, if characterization of contaminated soils or sediments does not support the formation of a surface precipitate(s) incorporating the contaminant of concern, then inclusion of this sorption phenomenon in a site- specific geochemical model is not justified. In conclusion, advances in our ability to monitor and model sorption phenomena at the mineral–water interface suggest that the formation of surface precipitates is not a rare phenomenon in soil and sediment systems. This realization suggests that a more comprehensive understanding is required before the long-term prediction of contaminant partitioning can be adequately modeled. This knowledge is critical to our understanding of physicochemical processes controlling and potentially limiting the risk posed by inorganic contaminants in the hydrosphere.

ACKNOWLEDGMENTS We gratefully acknowledge Dr. Andrew McGhie (University of Pennsylvania) for TG-MS analyses. Notice: The research described herein was developed by RGF, an employee of the U.S. Environmental Protection Agency (EPA), prior to his employment with EPA. It was conducted independent of EPA employment and has not been subjected to the Agency’s peer and administrative review. Therefore, the conclusions and opinions drawn are solely those of the author and are not necessarily the views of the Agency.

REFERENCES Ainsworth, C. C., Pilon, J. L., Gassman, P. L., and Van Der Sluys, W. G. (1994). Cobalt, cadmium, and lead sorption to hydrous iron oxide: Residence time effect. Soil Sci. Soc. Am. J. 58, 1615–1623. Baes, C. F., and Mesmer, R. E. (1986). “The Hydrolysis of Cations.” Krieger, Malabar, FL . Bard, A. J. (1966). “Chemical Equilibrium.” Harper & Row, New York. Bleam, W. F., and McBride, M. B. (1986). The chemistry of adsorbed Cu(II) and Mn(II) in aqueous titanium dioxide suspensions. J. Colloid Interface Sci. 110, 335–346. Bohn, H. L., and Bohn, R. K. (1986). Solid activity coefficients of soil components. Geoderma 38, 3–18. Brady, P. V., and House, W. A. (1996). Surface-controlled dissolution and growth of minerals. In “Physics and Chemistry of Mineral Surfaces” (P. V. Brady, Ed.), pp. 221–302. CRC Press, Boca Raton, FL. Charlet, L., and Manceau, A. (1992). X-ray absorption spectroscopic study of the sorption of Cr(III) at the oxide–water interface. II. Adsorption, coprecipitation, and surface precipitation on hydrous ferric oxide. J. Colloid Interface Sci. 148, 443–458. Charlet, L., and Manceau, A. (1994). Evidence of the neoformation of clays upon sorption of Co(II) and Ni(II) on silicates. Geochim. Cosmochim. Acta 58, 2577–2582.

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Chisholm-Brause, C. J., O’Day, P. A., Brown, Jr., G. E., and Parks, G. A. (1990). Evidence for multinuclear metal-ion complexes at solid/water interfaces from X-ray absorption spectroscopy. Nature (London) 348, 528–530. Comans, R. N. J. (1987). Adsorption, desorption and isotopic exchange of cadmium on illite: Evidence for complete reversibility. Water Res. 21, 1573–1576. Comans, R. N. J. (1999). Kinetics and reversibility of radiocesium sorption on illite and sediments containing illite. In “Mineral–Water Interfacial Reactions: Kinetics and Mechanisms” (D. L. Sparks and T. J. Grundl, Eds.), pp. 179–201. American Chemical Society, Washington, DC. Davis, J. A., and Kent, D. A. (1990). Surface complexation modeling in aqueous geochemistry. In “Mineral–Water Interface Geochemistry.” (M. F. Hochella, Jr., and A. F. White, Eds.), pp.177– 260. Mineralogical Society of America, Washington, DC. Davis, J. A., Fuller, C. C., and Cook, A. D. (1987). A model for trace metal sorption processes at the calcite surface: Adsorption of Cd2+ and subsequent solid solution formation. Geochim. Cosmochim. Acta 51, 1477–1490. de Roy, A., Forano, C., El Malki, K., and Besse, J. P. (1992). Anionic clays, trends in pillaring chemistry. In “Synthesis of Microporous Materials, Vol. II: Expanded Clays and Other Microporous Solids” (M. L. Occelli and H. E. Robson, Eds.), pp. 108–169. Van Nostrand Reinhold, New York. Dousma, J., and de Bruyn, P. L. (1976). Hydrolysis-precipitation of iron solutions. I. Model for hydrolysis and precipitation from Fe(III) nitrate solutions. J. Colloid Interface Sci. 56, 527–539. Dzombak, D. A., and Morel, F. M. M. (1986). Sorption of cadmium on hydrous ferric oxide at high sorbate/sorbent ratios: Equilibrium, kinetics, and modeling. J. Colloid Interface Sci. 112, 588–598. Farley, K. J., Dzombak, D. A., and Morel, F. M. M. (1985). A surface precipitation model for the sorption of cations on metal oxides. J. Colloid Interface Sci. 106, 226–242. Flynn Jr., C. M. (1984). Hydrolysis of inorganic iron(III) salts. Chem. Rev. 84, 31–41. Ford, R. G., Bertsch, P. M., and Farley, K. J. (1997). Changes in transition and heavy metal partitioning during hydrous iron oxide aging. Environ. Sci. Technol. 31, 2028–2033. Ford, R. G., Kemner, K. M., and Bertsch, P. M. (1999a). Influence of sorbate–sorbent interactions on the crystallization kinetics of nickel- and lead-ferrihydrite coprecipitates. Geochim. Cosmochim. Acta 63, 39–48. Ford, R. G., Scheinost, A. C., Scheckel, K. G., and Sparks, D.L. (1999b). The link between clay mineral weathering and the stabilization of Ni surface precipitates. Environ. Sci. Technol. 33, 3140–3144. Fuller, C. C., and Davis, J. A. (1987). Processes and kinetics of Cd2+ sorption by a calcareous aquifer sand. Geochim. Cosmochim. Acta 51, 1491–1502. Hayes, K. F., and Katz, L. E. (1996). Application of X-ray absorption spectroscopy for surface complexation modeling of metal ion sorption. In “Physics and Chemistry of Mineral Surfaces” (P. V. Brady, Ed.), pp. 147–223. CRC Press, Boca Raton, FL. Hazemann, J. L., Manceau, A., Sainctavit, P., and Malgrange, C. (1992). Structure of the ␣FexAl1−xOOH solid solution. I. Evidence by polarized EXAFS for an epitaxial growth of hematitelike clusters in Fe-diaspore. Phys. Chem. Minerals 19, 25–38. James, R. O., and Healy, T. W. (1972). Adsorption of hydrolyzable metal ions at the oxide–water interface. II. Charge reversal of SiO2 and TiO2 colloids by adsorbed Co(II), La(III), and Th(IV) as model systems. J. Colloid Interface Sci. 40, 53–64. Junta, J. L., and Hochella, Jr., M. F. (1994). Manganese (II) oxidation at mineral surfaces: A microscopic and spectroscopic study. Geochim. Cosmochim. Acta 58, 4985–4999. Katz, L. E., and Hayes, K. F. (1995). Surface complexation modeling. II. Strategy for modeling polymer and precipitation reactions at high surface coverage. J. Colloid Interface Sci. 170, 491–501. Ma, Q. Y., Traina, S. J., Logan, T. J., and Ryan, J. A. (1993). In situ lead immobilization by apatite. Environ. Sci. Technol. 27, 1803–1810. Manceau, A., Schlegel, M., Nagy, K., and Charlet, L. (1999). Evidence for the formation of trioctahedral clay upon sorption of Co2+ on quartz. J. Colloid Interface Sci. 220, 181–197.


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Manceau, A., Lanson, B., Schlegel, M. L., Harge, J. C., Musso, M., Eybert-Berard, L., Hazemann, J. L., Chateigner, D., and Lamble, G. M. (2000). Quantitative Zn speciation in smelter-contaminated soils by EXAFS spectroscopy. Am. J. Sci. 300, 289–343. Martinez, C. E., and McBride, M. B. (2000). Aging of coprecipitated Cu in alumina: Changes in structural location, chemical form, and solubility. Geochim. Cosmochim. Acta 64, 1729–1736. McBride, M. B. (1991). Processes of heavy and transition metal sorption by soil minerals. In “Interactions at the Soil Colloid–Soil Solution Interface” (G. H. Bolt, M. F. DeBoodt, M. H. B. Hayes, and M. B. McBride, Eds.), pp. 149–175. Kluwer Academic, Boston. Miller, J. W., Logan, T. J., and Bigham, J. M. (1986). The adsorption of o-phosphate on alumina: A solid solution model. Soil Sci. Soc. Am. J. 50, 609–616. Morse, J. W., and Casey, W. (1988). Ostwald processes and mineral paragenesis in sediments. Am. J. Sci. 288, 537–560. O’Day, P. A., Brown, Jr. G. E., and Parks, G. A. (1994). X-ray absorption spectroscopy of cobalt(II) multinuclear surface complexes and surface precipitates on kaolinite. J. Colloid Interface Sci. 165, 269–289. O’Day, P. A., Chisholm-Brause, C. J., Towle, S. N., Parks, G. A., and Brown, Jr. G. E. (1996). X-ray absorption spectroscopy of Co(II) sorption complexes on quartz (␣-SiO2) and rutile (TiO2). Geochim. Cosmochim. Acta 60, 2515–2532. O’Day, P. A., Carroll, S. A., and Waychunas, G. A. (1998). Rock–water interactions controlling zinc, cadmium, and lead concentrations in surface waters and sediments, U.S. tristate mining district. 1. Molecular identification using X-ray absorption spectroscopy. Environ. Sci. Technol. 32, 943–955. O’Day, P. A., Carroll, S. A., Randall, S., Martinelli, R. E., Anderson, S. L., Jelinski, J., and Knezovich, J. P. (2000). Metal speciation and bioavailability in contaminated estuary sediments, Alameda Naval Air Station, California. Environ. Sci. Technol. 34, 3665–3673. Ostergren, J. D., Brown Jr., G. E., Parks, G. A., and Tingle, T. N. (1999). Quantitative speciation of lead in selected mine tailings from Leadville, CO. Environ. Sci. Technol. 33, 1627–1636. Roberts, D. R., Scheidegger, A. M., and Sparks, D. L. (1999). Kinetics of mixed Ni–Al precipitate formation on a soil clay fraction. Environ. Sci. Technol. 33, 3749–3754. Scheckel, K. G., and Sparks, D. L. (2000). Kinetics of the formation and dissolution of Ni precipitates in a gibbsite/amorphous silica mixture. J. Colloid Interface Sci. 229, 222–229. Scheckel, K. G., Scheinost, A. C., Ford, R. G., and Sparks, D. L. (2000). Stability of layered Ni hydroxide surface precipitates—A dissolution kinetics study. Geochim. Cosmochim. Acta 64, 2727–2735. Scheidegger, A. M., Lamble, G. M., and Sparks, D. L. (1997). Spectroscopic evidence for the formation of mixed-cation hydroxide phases upon metal sorption on clays and aluminum oxides. J. Colloid Interface Sci. 186, 118–128. Scheidegger, A. M., Strawn, D. G., Lamble, G. M., and Sparks, D. L. (1998). The kinetics of mixed Ni– Al hydroxide formation on clays and aluminum oxides: A time-resolved XAFS study. Geochim. Cosmochim. Acta 62, 2233–2245. Scheinost, A. C., and Sparks, D. L. (2000). Formation of layered single and double metal hydroxide precipitates at the mineral/water interface: A multiple-scattering XAFS analysis. J. Colloid Interface Sci. 223, 167–178. Scheinost, A. C., Ford, R. G., and Sparks, D. L. (1999). The role of Al in the formation of secondary Ni precipitates on pyrophyllite, gibbsite, talc, and amorphous silica: A DRS study. Geochim. Cosmochim. Acta 63, 3193–3203. Schneider, W. (1984). Hydrolysis of iron(III)—Chaotic olation versus nucleation. Comments Inorg. Chem. 3, 205–223. Sparks, D. L. (1995). Kinetics of soil chemical processes. In “Environmental Soil Chemistry,” pp. 159– 185. Academic Press, San Diego.

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Sparks, D. L., and Grundl, T. J. (Eds.), (1998). “Mineral–Water Interfacial Reactions: Kinetics and Mechanisms.” American Chemical Society, Washington, DC. Sposito, G. (1984). “The Surface Chemistry of Soils.” Oxford Univ. Press, London, UK. Sposito, G. (1986). Distinguishing adsorption from surface precipitation. In “Geochemical Processes at Mineral Surfaces” (J. A. Davis and K. F. Hayes, eds.), pp. 217–228. American Chemical Society, Washington, DC. Stumm, W. (1992). “Chemistry of the Solid–Water Interface.” Wiley, New York. Stumm, W., and Morgan, J. J. (1996). The solid–solution interface. In “Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters,” pp. 516–613. Wiley, New York. Tardy, Y., and Duplay, J. (1992). A method of estimating the Gibbs free energies of formation of hydrated and dehydrated clay minerals. Geochim. Cosmochim. Acta 56, 3007–3029. Thompson, H. A., Parks, G. A., and Brown Jr., G. E. (2000). Formation and release of cobalt(II) sorption and precipitation products in aging kaolinite-water slurries. J. Colloid Interface Sci. 222, 241–253. Towle, S. N., Bargar, J. R., Brown, G. E., and Parks, G. A. (1997). Surface precipitation of Co(II)(aq) on Al2O3. J. Colloid Interface Sci. 187, 62–82. Walther, J. V. (1996). Relation between rates of aluminosilicate mineral dissolution, pH, temperature, and surface charge. Am. J. Sci. 296, 693–728. Wersin, P., Charlet, L., Karthein, R., and Stumm, W. (1989). From adsorption to precipitation: Sorption of Mn2+ on FeCO3(s). Geochim. Cosmochim. Acta 53, 2787–2796.

ORGANIC ACIDS EXUDED FROM ROOTS IN PHOSPHORUS UPTAKE AND ALUMINUM TOLERANCE OF PLANTS IN ACID SOILS Peter J. Hocking CSIRO Plant Industry, Canberra, Australian Capital Territory 2601 Australia

I. Introduction II. Phosphorus A. Forms of Phosphorus in Soils B. Phosphorus Uptake by Plants III. Organic Acids and Phosphorus Solubilization in the Rhizosphere A. Organic Acids and Uptake of Phosphorus B. Quantities of Organic Acids Exuded from Roots C. Phosphorus Supply and Control of Organic Acid Exudation D. Plant Access to Different Pools of Soil Phosphorus E. Intercropping and Rotational Benefits of Organic Acid-Secreting Species F. Selection for Increased Organic Acid Production to Improve Phosphorus Uptake IV. Organic Acids and Soil Organic Phosphorus A. Major Forms of Organic Phosphorus in Soils B. Organic Acids and Phosphatases V. Aluminum Tolerance A. General Considerations B. Major Organic Acids Involved in Aluminum Tolerance VI. Genetic Engineering Approaches to Increase Organic Acid Exudation VII. Concluding Remarks References

Acid soils comprise about 30% of the world’s arable land, and aluminum (Al) toxicity and phosphorus (P) fixation to soil minerals are major problems for crop production on these soils. However, applying even moderate rates of P fertilizer and lime to improve acid soils is not economic in many countries. Plants that can access fixed P and are Al tolerant have an important role in sustaining crop production on these soils. It is now clear that organic acids exuded from roots can benefit the P nutrition of plants and protect roots by detoxifying Al in the rhizosphere. Studies with 32 P-labeled soil have shown unequivocally that species which exude organic acids 63 Advances in Agronomy, Volume 74 C 2001 by Academic Press. All rights of reproduction in any form reserved. Copyright 0065-2113/01 $35.00

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PETER J. HOCKING can access fixed inorganic P that is unavailable to other plants. Organic acids appear to be important in enabling plants to access phytate, a form of organic P which is also fixed to soil minerals. Organic acid anions such as citrate, malonate, oxalate, and tartrate are commonly implicated in enhancing access to fixed soil P, and citrate, malate, and oxalate are implicated in enhancing aluminum tolerance. Intercropping of an organic acid-secreting crop with a nonsecreting crop can improve the P nutrition of the nonsecreting species. An organic acid-secreting crop may also improve the P nutrition of a following crop in the rotation. Genetic engineering has the potential to increase organic acid efflux from roots. Most work has tried to increase citrate efflux by overexpressing citrate synthase, but the results are contradictory. Other approaches include overexpressing enzymes to increase substrate supply for citrate synthesis, or antisensing production of enzymes that metabolize citrate. Mechanisms by which P deficiency and Al activate or induce the efflux of specific organic acids from roots need to be identified. Anion channels in the plasma membrane are likely to have a major regulatory role in the transport of organic acids from roots, and genes that encode for these channels will be key C 2001 Academic Press. targets for genetic engineering.

I. INTRODUCTION Soils that are naturally acid or have become acid through agricultural activities comprise about 30% of the arable land on a global scale (Foy et al., 1978; Von Uexkull and Mutert, 1995). Although soil acidity was considered to be a problem largely confined to agricultural soils in the tropics and humid subtropics, it is increasingly becoming a major problem in temperate agricultural systems. For example, in Australia an estimated 33 million ha of agricultural land is classed as highly acidic and another 48 million ha as moderately acidic (AACM, 1995). Many acid soils are naturally low in phosphorus (P) and require applications of P fertilizer to achieve economic yields. In addition, they are usually highly P-fixing (Marschner, 1995), so that much of the applied fertilizer P is locked up in the soil and unavailable to agricultural plants. Acid soils with high P-fixing capacities are the Alfisols, Andosols, Oxisols, and Ultisols (Sanchez and Uehara, 1980). A further problem of acid soils is the solubilization of aluminum (Al) and manganese (Mn) into the Al3+ and Mn2+ forms that can result in poor plant growth. A combination of high P fixation and metal toxicity in acid soils can devastate agricultural plant production. Aluminum is much more detrimental than Mn because it kills root tips and impairs root hair development, resulting in a stunted root system and greatly reduced uptake of water and nutrients from the soil (Delhaize and Ryan, 1995; Kochian, 1995). The management of acid P-fixing agricultural soils involves the application of P fertilizer, liming to raise the soil pH, implementation of practices that reduce rates

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of acidification, and the use of Al-tolerant plants. In many countries, however, the application of even moderate rates of P fertilizer and lime to acid P-fixing soils is uneconomic because of the vast areas involved or the low-input nature of agricultural production. Consequently, plants that can access poorly available soil P and that are Al tolerant have an important role in sustaining agricultural production on acid soils. The rationale for using or developing Al-tolerant agricultural plants is to broaden management options for growers so they can maintain production while implementing liming and other programs to ameliorate soil acidity. Failure to understand this will result in the soil pH in many regions decreasing to a level at which growth and yields of even the most Al-tolerant genotypes are reduced. Plant roots exude a variety of organic compounds including amino acids, organic acids, phenolics, and sugars into the rhizosphere (Delhaize, 1995). Organic acids have at least one carboxyl group and, although this definition embraces a large and diverse group of compounds, only the low molecular weight organic acids such as aconitic, citric, fumaric, malic, malonic, oxalic, piscidic, succinic, and tartaric are relevant to this review. Many of these organic acids are intermediates of the tricarboxylic acid (TCA) cycle, the major respiratory pathway involved in the oxidation of pyruvate, and thus have key roles in cellular metabolism. Since the pKa values of most of the organic acids exuded from roots are below the cytosol pH of 7.2–7.4, these acids would be fully dissociated anions in the cytosol (Jones, 1998; Ryan et al., 2001). Consequently, although the term organic acid is often used in this chapter, it is evident that organic acids are exuded from roots as anions such as citrate3− and malate2−. A number of organic acids such as citric, malic, malonic, oxalic, piscidic, succinic, and tartaric are implicated in rhizospheric processes, including nutrient acquisition and metal detoxification (Jones, 1998). Evidence for a direct role of these organic acids in plant nutrition is available for P, Al, iron (Fe), and Mn. This chapter will concentrate on how organic acid exudation from roots benefits the P nutrition of plants and the Al3+ detoxification of soils, as Fe is normally not limiting in acid soils and Mn toxicity is of minor importance compared to Al toxicity. This chapter also considers the potential of genetic engineering to increase organic acid exudation from roots for improving plant access to P from soils and fertilizers and for greater tolerance of Al3+ in acid soils.

II. PHOSPHORUS A. FORMS OF PHOSPHORUS IN SOILS Phosphorus is chemically reactive in soils and over 170 mineral forms (Holford, 1997), as well as many organic forms, have been identified. Major inorganic

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Figure 1 Outline of the main processes affecting P availability in the rhizosphere and P supply to plant roots. Roots take up inorganic P (Pi) from the soil solution, which is replenished by Pi from the solid phase and by hydrolysis of organic P (Po) from the soil solution and solid phase. Root exudates, particularly organic acids, increase rates of P solubilization, desorption, and mineralization either directly or indirectly via the activity of microorganisms. Mycorrhizal fungi increase the absorbing area of the root system and the volume of soil exploited. (Modified from P. J. Randall, J. E. Hayes, P. J. Hocking, and A. E. Richardson. Root exudates in phosphorus acquisition by plants. In Plant Nutrient Acquisition—New Concepts for Field Professionals (N. Ae, J. Arihara, K. Okada, and A. Srinivasan, Eds.), 2001;50–62. CSIRO: Melbourne, with kind permission from Springer-Verlag Tokyo.)

P fractions in acid soils include phosphate ions adsorbed to Al- and Fe-oxyhydroxides and P precipitated as amorphous and crystalline Al and Fe minerals (Sample et al., 1980) (Fig. 1). The inorganic forms vary in their solubility, but, with time, transform from sparingly soluble into increasingly insoluble crystalline forms such as variscite (Al–P) and strengite (Fe–P) from which the P is unavailable for uptake by plants. The compounds formed depend on the chemistry of a particular soil, and the solubilization–precipitation reactions involving these compounds are strongly pH-dependent. Organic P accounts for 20 to 80% of the P in most soils (McLaughlin et al., 1990), and its accumulation is a result of the immobilization of P in soil organisms and plant residues that are resistant to mineralization (Dalal, 1977). Phytate is an important constituent (see Section IV.A) and can represent up to 50% of the total organic P (Anderson, 1980). Phosphorus plays a major role in agricultural production and P fertilizer is a significant cost for farmers worldwide. Plant-available P is inadequate in most acid soils even though the total amount of P may greatly exceed crop or pasture requirements (Graham, 1984). This is reflected in the gap between concentrations


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of plant-available P and total P. For example, an Oxisol in Australia had a total P concentration of 3.1 g kg−1, but a Bray-1 extractable P concentration of only 1.8 mg kg−1 soil (Hocking et al., 1997). Although P is a major plant nutrient, it is one of the least available from soils (Barber et al., 1963). Concentrations of inorganic P in the soil solution of many acid soils are low, typically 1–5 mmol m−3 or less, and even in fertile agricultural soils seldom exceed 10 mmol m−3 (Bieleski, 1973) (Fig. 1). Consequently, most acid agricultural soils require fertilizer P to provide soluble P close to the roots to meet plant requirements, but the recovery of this P by plants in the season of application is often only 10 to 20% (Sharpley, 1986; McLaughlin et al., 1988). The rest is incorporated into organic forms, adsorbed to charged surfaces, or reacts with cations and precipitates as sparingly soluble inorganic forms. Some of the fertilizer P has a residual effect and is recovered in subsequent seasons as a result of microbial turnover and mineralization, and desorption–solubilization reactions. However, recalcitrant forms of inorganic and organic P continue to accumulate in fertilized agricultural soils, with the rate and form depending on soil type, land use, and fertilizer history (Harrison, 1987). Phosphorus from past applications that is unavailable to agricultural plants represents a major economic loss to growers, so increasing plant access to this “bank” of fixed P is highly desirable.

B. PHOSPHORUS UPTAKE BY PLANTS Only the inorganic P anions (H2PO4− and HPO42−) in the soil solution are directly available for uptake by roots. There is little evidence that plants take up organic forms of soil P, including colloidal P, from the soil solution. Soil organic P must first be mineralized to inorganic P before it can be taken up by plants (Bieleski, 1973; Marschner, 1995). As plants deplete the inorganic P in the rhizosphere solution, it is replenished by desorption of P from charged surfaces, solubilization of P-containing minerals, and the hydrolysis of organic P compounds (Fig. 1). Diffusion is the main process supplying P to roots, with mass flow making only a small contribution (Barber, 1995; Hinsinger, 1998). Rates of diffusion of P in the soil solution are low (∼0.13 mm day−1) and usually insufficient to match rates of uptake by roots (Jungk, 1991). Consequently, P in the rhizosphere soil solution is rapidly depleted, creating a concentration gradient for the diffusion of P toward roots, which in turn increases the rate of desorption and solubilization of P from soil minerals (Hendriks et al., 1981). Organic acids exuded from roots (Fig. 1) influence the flux of P toward roots and hence the rate of replenishment of P in the rhizosphere. The uptake of H2PO4− and HPO42− into roots occurs against a massive concentration gradient as the concentration of inorganic P in the cytosol of root tissue is several orders of magnitude higher (5–20 mol m−3) than that in the soil solution (Bieleski, 1973) (Fig. 2).

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Figure 2 Outline of organic anion efflux from root cells to the rhizosphere. The exudation of organic anions through anion channels rather than diffusion through the plasma membrane is the likely mechanism to account for reported values of efflux from roots of P-deficient plants, especially white lupin. An H+-ATPase proton pump maintains membrane polarization and balances the majority of organic anion exudation. Cation (K+) cotransport balances malate exudation in Al-tolerant wheat (Ryan et al. 1995a) and may also help to balance organic anion exudation from roots of P-deficient plants. Organic anion efflux is likely to be regulated by organic-anion permeable channels in the plasma membrane or root cells and possibly by the synthesis of organic acids. (Modified from P. J. Randall, J. E. Hayes, P. J. Hocking, and A. E. Richardson. Root exudates in phosphorus acquisition by plants. In Plant Nutrient Acquisition—New Concepts for Field Professionals (N. Ae, J. Arihara, K. Okada, and A. Srinivasan, Eds.), 2001;50–62. CSIRO: Melbourne, with kind permission from Springer-Verlag Tokyo.)

III. ORGANIC ACIDS AND PHOSPHORUS SOLUBILIZATION IN THE RHIZOSPHERE A. ORGANIC ACIDS AND UPTAKE OF PHOSPHORUS Gardner et al. (1982a,b, 1983) provided the first evidence linking organic acid exudation from roots to solubilization of poorly available soil P and enhanced P uptake. Specialized proteoid roots (dense bottle-brush-like clusters of rootlets) of white lupin (Lupinus albus) were shown to exude citrate, and it was proposed that citrate improved the P nutrition of the plant by freeing up fixed forms of soil P (Gardner et al., 1983). Since then, there has been increased interest worldwide in the role of organic acids exuded from roots in enhancing nutrient acquisition by plants (Curl and Truelove, 1986; Marschner et al., 1986; Uren and Reisenauer,


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1988; Bar-Yosef, 1991; Jungk, 1991; Darrah, 1993; Jones and Darrah, 1994; Dinkelaker et al., 1995; Hinsinger, 1998; Jones, 1998; Randall et al., 2001; Ryan et al., 2001). Organic acids exuded from roots modify the chemistry of the rhizosphere and thus alter the availability of P compounds. This may occur indirectly through promoting the growth of microorganisms that mineralize organic forms of P (Richardson, 1994) or directly by (1) changing conditions in the soil solution (e.g., pH), thus increasing the dissolution of sparingly soluble P minerals, (2) altering the surface characteristics of soil particles, (3) competing with phosphate ions for adsorption sites, and (4) complexing and chelating cations that are bound to P (Bar-Yosef, 1991; Jones and Darrah, 1994; Lan et al., 1995; Jones, 1998). The importance of each of these mechanisms depends on the soil type and the forms of P that it contains. For example, an increase in exudation of an organic acid such as citrate can increase soil solution P by solubilizing calcium phosphates (such as from rock phosphate fertilizer) due to a decrease in pH in the rhizosphere and by desorption reactions in acid soils where P solubility is controlled by ion-exchange equilibria involving charged clay minerals and organic matter (White, 1980). Although studies have shown that plant roots exude a variety of organic acids, generally only one of these acids dominates the spectrum. Depending on the species, citric, malic, malonic, and oxalic acids are usually the most common organic acids exuded, but varying amounts of aconitic, fumaric, piscidic, succinic, and tartaric acids have also been reported (Table I). For example, of the total organic acids exuded from white lupin proteoid roots, citrate usually comprises 80 to 90%, malate 5 to 10%, and small amounts of aconitate, oxalate, and succinate comprise the remainder (Johnson et al., 1996b; Keerthisinghe et al., 1998; Neumann and Römheld, 1999; Watt and Evans, 1999a). In contrast to these reports, Kamh et al. (1999) found that succinate comprised 52% and citrate only 29% of the organic acids exuded from white lupin proteoid roots. This discrepancy may be related to differences in the genotypes used, plant growth conditions and stage of development of the proteoid roots, or techniques used for collecting the organic acids. The effectiveness of an organic acid to mobilize soil P complexed with metal ions, such as Al and Fe, and to displace P from charged surfaces depends on the number and arrangement of its carboxyl and hydroxyl groups (Bar-Yosef, 1991; Bolan et al., 1994; Staunton and Leprince, 1996). Organic acids desorb P in soils in the order tricarboxylic > dicarboxylic > monocarboxylic acid. Nagarajah et al. (1970) showed that mobilization of fixed soil P was greatest for citric acid followed by oxalic acid. Malic and tartaric acids were moderately effective, and acetic, succinic, and lactic acids were the least effective at mobilizing P. The tricarboxylic acid, citric acid, is particularly effective at binding to metal ions that are important in the P chemistry of acid soils (Hue et al., 1986; Bar-Yosef, 1991; Bolan et al., 1994; Jones, 1998).

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PETER J. HOCKING Table I Organic Acids Exuded from Roots of Selected Crop Species Grown under P Stress

Growth Plant species conditions

Organic acid exuded from roots a (whole root system; μmol g−1 dry wt h−1) Citric

Malic Malonic

White lupin

P-deficient 11,000 solution

Rice

Low-P soil

2,300

Maize

P-deficient soil P-deficient sand P-deficient solution

1,300

6000

650

279

P-deficient solution

209

Alfalfab Pigeon pea

Chickpea

8000

2.3 trace

74

Others

References

Traces of aconitic, Johnson et al. (1996b); fumaric, oxalic, Neumann et al. (1999) and succinic Traces of oxalic, Kirk et al. (1999b) malic, lactic, and fumaric Jones and Darrah (1995) Succinic (90) Lipton et al. (1987) 752

41

Oxalic (195) Piscidic (83) Tartaric (9) Fumaric (40) Succinic (21) Aconitic (5)

Otani et al. (1996)

Ohwaki and Hirata (1992)

a

Assumes dry weight is 7% of fresh weight where conversion is required. (Modified from P. J. Randall, J. E. Hayes, P. J. Hocking, and A. E. Richardson. Root exudates in phosphorus acquisition by plants. In Plant Nutrient Acquisition—New Concepts for Field Professionals (N. Ae, J. Arihara, K. Okada, and A. Srinivasan, Eds.), 2001;50–62. CSIRO: Melbourne, with kind permission from Springer-Verlag Tokyo.) b Alfalfa (Medicago sativa).

Organic acid dissolution of P-containing minerals is also facilitated by the decrease in soil solution pH that is often associated with roots of P-stressed plants (Dinkelaker et al., 1989; Jones, 1998). For example, root exudates containing citrate can release P for plant uptake in calcareous soils where P is precipitated as calcium phosphates (Dinkelaker et al., 1989) or from rock phosphate fertilizer (Jones and Darrah, 1994). However, this is likely to be due to acidification of the rhizosphere by protons extruded with the citrate, resulting in dissolution of the calcium phosphate (see Section III.C). The citrate3− then reacts with Ca2+ to form a calcium citrate precipitate, preventing the reformation of calcium phosphate in the rhizosphere. There appears to be little or no complexation of Al and Fe by citrate, malate, or oxalate at high soil pH (Jones, 1998). There is considerable evidence for the importance of organic acids exuded from roots in the acquisition of soil and fertilizer P by plants. Addition of organic acids, especially citrate and oxalate, to soils can solubilize significant quantities of fixed P (Trainia et al., 1986; Bar-Yosef, 1991) and reduce the sorption of newly applied


71

Figure 3 Effect of adding various amounts of citrate to an Oxisol on the quantities of P subsequently extracted. Levels of 50 to 100 μmol citrate g−1 soil are similar to those found in rhizospheric soil of white lupin proteoid roots. Note that citrate increased the extraction of both fixed soil P and recently applied fertilizer P. (G. Keerthisinghe, unpublished, with kind permission.)

fertilizer P (Bolan et al., 1994; Jones and Darrah, 1994) (Fig. 3). A number of plant species respond to P deficiency by increased organic acid exudation from their roots, particularly citrate. Phosphorus-deficient white lupin exemplifies this response as it has the highest rates of citrate exudation from its roots so far reported for any species in solution culture studies (Table I). In addition, high concentrations of citrate have been detected in the rhizosphere of white lupin proteoid roots (Dinkelaker et al., 1989; Gerke et al., 1994, 1995; Kamh et al., 1999), as well as elevated levels of P solubilized from Al and Fe complexes (Gardner et al., 1983; Gerke et al., 1994). Similarly, exudates from pigeon pea (Cajanus cajan) roots containing malonic and piscidic acids can solubilize P bound to Fe and Al in acid soils (Ae et al., 1991a; Otani et al., 1996), and the P solubilizing activity of the exudates increases with increasing P stress (Subbarao et al., 1997a). Chickpea (Cicer arietinum) roots exude citrate as well as other organic acids (Ohwaki and Hirata, 1992; Ohwaki and Sugahara, 1997), but the species appears to be more effective at accessing P from alkaline than acid soils (Ae et al., 1991b), probably as a result of proton extrusion accompanying the efflux of organic acids (Ohwaki and Sugahara, 1997). Citrate exudation increases from roots of rice (Oryza sativa) grown in P-deficient soil (Kirk et al., 1998, 1999b), and the increase in P uptake by the plants could be accounted for by the formation of soluble metal chelates rather than displacement of phosphate from adsorption sites or changes in rhizosphere pH (Kirk et al., 1999a,b). Radish (Raphanus sativus) is grown on P-fixing acid

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soils in China and exudes predominantly tartaric acid, whereas rapeseed (Brassica napus) is grown on calcareous soils and exudes malic and citric acids in response to P-deficiency stress (Zhang et al., 1997). It was suggested that the exudation of different organic acids by the two species represented adaptations to the different soil types.

B. QUANTITIES OF ORGANIC ACIDS EXUDED FROM ROOTS Attempts have been made to quantify the exudation of organic acids from roots in soil by measuring their accumulation in the rhizosphere. Dinkelaker et al. (1989) estimated that the cumulative citrate exudation from white lupin proteoid roots was equivalent to 23% of the total plant dry weight at maturity. The concentration of citrate in the proteoid root rhizosphere was about 50 μmol g−1 soil (Dinkelaker et al., 1989). Up to 88 μmol citrate g−1 soil has been reported in the rhizosphere of proteoid roots when white lupin was grown in an Oxisol (Gerke et al., 1994). However, the large quantities of organic acids exuded by white lupin do not seem to affect its growth. Plants grown on a range of P concentrations that either promoted or suppressed the growth of proteoid roots had similar dry weights and root to shoot ratios, despite a change in the proportion of proteoid roots from about 5% for plants grown with high-P supplies to 60% of the total root system for low-P plants (Keerthisinghe et al., 1998) (Fig. 4). The constancy in plant dry-matter production and shoot to root ratio, despite the dramatic change in the morphology of the root system, may be partly due to about 30% of the carbon exuded in organic acids coming from fixation of CO2 in proteoid roots (Johnson et al., 1996b). Exudation rates of organic acids are often expressed on a whole root weight (fresh or dry) or on a root length basis after immersing the root system in a collection vessel, but this may grossly underestimate localized rates if exudation is not uniform along the root. Hoffland (1992) attempted to correct for this by using pH indicators to estimate the proportion of the root system exuding citrate and malate in studies with P-deficient rapeseed. Alternatively, a range of other techniques, including removal of rhizosphere soil, resin bags, agar blocks, filter paper strips, and small root chambers have been used to collect organic acids from defined parts of roots (Dinkelaker et al., 1989; Hoffland et al., 1989; Delhaize et al., 1993; Keerthisinghe et al., 1998; Kamh et al., 1999; Neumann et al., 1999; Watt and Evans, 1999a). These techniques are generally used under nonsterile conditions, so there is always the possibility of microbial degradation of the exuded organic acids, as well as the microbial production of organic acids (Jones, 1998). However, in most cases when the collection periods are short (2–3 h), there seems to be little degradation, as judged from recoveries of known amounts of organic acids added to the collection systems (e.g., Keerthisinghe et al., 1998; Kamh et al., 1999; Watt and Evans, 1999a).


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Figure 4 Effect of P concentration in the nutrient solution on plant dry-matter production, shoot to root dry-matter ratios, and the proportion (%) of the total root system composed of proteoid roots. The vertical bar represents the least significant difference (LSD) ( p = 0.05) for total plant dry matter. (Modified from G. Keerthisinghe, P. J. Hocking, P. R. Ryan, and E. Delhaize. Effect of phosphorus supply on the formation and function of proteoid roots of white lupin (Lupinus albans L.). Plant Cell Environment 1998;21:467–478; with kind permission of Blackwell Science Ltd.)

Rates of organic acid exudation vary greatly between different parts of root systems (Table II). In rapeseed, young regions of the root exude more organic acids than older parts (Hoffland et al., 1989). With white lupin, proteoid roots are the major sites of citrate exudation, and only very small amounts are exuded from the other roots (Johnson et al., 1996b; Keerthisinghe et al., 1998; Watt and Evans, 1999a). Citrate exudation rates are low for about the first 3 to 4 days after the appearance of a proteoid root, increase rapidly, and peak over the next 3 to 5 days, followed by a sharp decline (Keerthisinghe et al., 1998; Kamh et al., 1999; Watt and Evans, 1999a,b; Neumann et al., 2000). The highest rates of citrate exudation occur in the recently developed portions of proteoid roots (Keerthisinghe et al., 1998; Watt and Evans, 1999a). Phenolic compounds (mainly isoflavenoids) are also exuded from the proteoid roots and appear to have a stimulatory effect on citrate exudation (Neumann et al., 2000). Although the rate of citrate exudation declines markedly after the peak period, mature proteoid roots are

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PETER J. HOCKING Table II

Effect of P Supply on Efflux of Malate and Citrate from Different Parts of Root Systems of Rapeseed and White Lupina

Plant species Rapeseed

Site of exudation Root tips Older roots

White lupin

Young proteoid roots Lateral roots (nonproteoid)

Organic acid efflux (nmol h−1 m−1 root) P status

Malate

Citrate

P deficient P adequate P deficient P adequate P deficient

44 8 10 1

14 3 7 1 610

P adequate P deficient

150 70

P adequate

20

References Hoffland et al. (1989)

Keerthisinghe et al. (1998)

a Modified from P. J. Randall, J. E. Hayes, P. J. Hocking, and A. E. Richardson. Root exudates in phosphorus acquisition by plants. In Plant Nutrient Acquisition—New Concepts for Field Professionals (N. Ae, J. Arihara, K. Okada, and A. Srinivasan, Eds.), 2001;50–62. CSIRO: Melbourne, with kind permission from Springer-Verlag Tokyo.

metabolically active for another 7–10 days and retain the capacity to take up P (Keerthisinghe et al., 1998; Neumann et al., 2000). Uptake of P per unit root fresh weight is about 50% greater for proteoid roots than for nonproteoid root apexes (Neumann et al., 1999). The active zone of a proteoid root can also take up P, despite the intensive exudation of citrate (Keerthisinghe et al., 1998; Neumann et al., 2000). The life span of a white lupin proteoid root appears to be 3 to 4 weeks, after which it senesces and another proteoid root develops further along the lateral root. This phenomenon may well be a specific adaptation to maximize the exploitation of new areas of soil (Watt and Evans, 1999b; Neumann et al., 2000). Rates of citrate exudation from the active zone of white lupin proteoid roots range from 610 (Table II) to 670 (Neumann et al., 1999) and up to 1400 nmol h−1 m−1 root (Watt and Evans, 1999a). These rates are far greater than those from root tips of P-deficient rapeseed (14 and 44 nmol h−1 m−1 root for citrate and malate, respectively; Table II). For comparison, rates of exudation from Al-tolerant cereal root tips in the presence of Al3+ are 30 nmol citrate h−1 m−1 root tip for maize (Zea mays) (Pellet et al., 1995) and 1300 nmol malate h−1 m−1 root tip for wheat (Triticum aestivum) (Delhaize et al., 1993). Concentrating organic acid exudation along a limited zone of the root is the most effective strategy for intensive P mobilization from the soil per unit of organic acid (Dinkelaker et al., 1995).


75

C. PHOSPHORUS SUPPLY AND CONTROL OF ORGANIC ACID EXUDATION Early studies using solution culture with either no P or unrealistically high P concentrations (0.5–1.0 mol m−3) concluded that proteoid root formation in white lupin was a specific response to P deficiency (Marschner et al., 1986). However, field observations indicate that it is normal to find abundant proteoid roots on white lupin in a range of P-fertilized agricultural soils (Keerthisinghe et al., 1998). Research has shown strong proteoid root development in P-adequate white lupin grown in solutions maintained at 5 mmol P m−3, and that proteoid roots were only suppressed at high P concentrations unlikely to be found in soil solution (Keerthisinghe et al., 1998) (Fig. 4). However, rates of citrate exudation from plants deprived of P are nearly 4-fold higher than those supplied with adequate P (Table II). Although rates of citrate exudation are much lower from nonproteoid roots, there is a similar proportional increase in exudation due to P deficiency (Keerthisinghe et al., 1998). In rapeseed, P stress increases organic acid exudation from root tips by approximately 5-fold (Hoffland et al., 1989) (Table II). Two processes, the synthesis of organic acids within the root cells and their release into the apoplast, appear to control exudation from roots (Fig. 2). For example, a positive correlation was found between organic acid exudation from roots of rapeseed (Hoffland et al., 1992) and white lupin (Johnson et al., 1994, 1996a; Neumann et al., 1999, 2000) and the activities of enzymes involved in organic acid biosynthesis such as phosphoenolpyruvate carboxylase (PEPC) and malate dehydrogenase (MDH). However, the decline in enzyme activity in white lupin proteoid roots occurs some time after the decrease in citrate exudation (Keerthisinghe et al.,1998; Watt and Evans, 1999a), suggesting that high rates of citrate efflux are not directly coupled to enzyme activity associated with organic acid metabolism in the TCA cycle. In addition, internal citrate concentrations and rates of citrate exudation along proteoid roots are not always correlated (Keerthisinghe et al., 1998; Neumann et al., 1999; Watt and Evans; 1999a,b), although other work has shown correlations between internal citrate concentration and the rate of citrate exudation for rapeseed (Hoffland et al., 1992) and white lupin (Kamh et al., 1999). Further research is required to resolve this apparent discrepancy. Transport mechanisms that control the efflux of organic acids from roots have been studied in Al-tolerant wheat, where malate efflux from root tips appears to be controlled by an anion channel (see Section V. B.1). It is also likely that anion channels are involved in the exudation of organic acids from proteoid roots of white lupin and roots of other P-deficient plants. Anion channels are proteins in membranes that allow the transport of specific anions. When these channels open, organic anions such as citrate diffuse down the concentration and electrical potential gradients between the cytoplasm and the apoplast (Fig. 2). This concept is supported by a study of white lupin proteoid roots that showed a

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decrease of approximately 50% in citrate efflux following treatment with anion channel inhibitors (Neumann et al., 1999). Current information indicates that the exudation of organic anions from the roots of P-deficient plants is accompanied by the extrusion of protons (H+), presumably via an H+-ATPase proton pump (Jones, 1998; Neumann et al., 1999, 2000) (Fig. 2). Studies using agar impregnated with pH indicator dyes have shown acidification around proteoid roots of white lupin (Dinkelaker et al., 1989; Neumann et al., 2000) and young roots of a number of other species when P deficient, including rapeseed (Hoffland, 1992). However, K+ is the counter-ion accompanying malate efflux from root tips of Al-tolerant wheat (Ryan et al.,1995a) and may also help as a counter-ion for organic anion exudation under P deficiency. The exudation of organic anions and extrusion of H+ (or K+) would occur through different transporters on the plasmalemma of root cells (Jones, 1998). Further work is needed to understand the regulation of transport mechanisms involved in the efflux of organic acids from roots under low-P conditions.

D. PLANT ACCESS TO DIFFERENT POOLS OF SOIL PHOSPHORUS While it is clear that agricultural plants differ in the amounts of P they obtain from the same soil (McLachlan, 1976; Randall, 1995), it is difficult to know if they simply access the same P pools at different rates, or whether they access different P pools. Studies comparing the ability of pasture (Smith, 1981) and crop (Armstrong et al., 1993) species to obtain P from different soil P pools using the 32P isotopic dilution (L-value) technique (Larsen, 1952) showed that the L-values (a measure of P availability to the plant) were similar for the species in each study. Both studies concluded that the species drew on the same pools of P but at different rates. However, none of the species used in these studies are known to exude organic acids under P-deficiency stress. In contrast, studies including species known to exude organic acids show that plants do vary considerably in their ability to access P from different pools of soil P. For example, chickpea and pigeon pea can take up P from Al-, Ca-, and Fe-bound P that is unavailable to species that do not secrete organic acids (Ae et al., 1991b, 1993). Hedley et al. (1982) showed that rapeseed had a higher L-value than the soil E-value (a measure of labile soil P), indicating that this species could access fixed inorganic P, most likely through the exudation of citrate and malate (Hoffland et al., 1992). Braum and Helmke (1995) and Hocking et al. (1997) used the L-value technique to show that white lupin accessed soil P that was not available to other species. Hocking et al. (1997) grew seven species, including white lupin and pigeon pea, in an Oxisol labeled with 32P orthophosphate. The L-values indicated that the pool of soil P available to white lupin was substantially larger than that available


77

Figure 5 Differences in the capacity of crop species to access poorly available soil P as assessed by a 32P isotope dilution (L-value) technique. The L-value is a measure of the total quantity of plantavailable P: the higher the value, the greater the plant access to soil P. The plants were grown in pot culture for 30 days in a P-fixing Oxisol high in total P but low in available P. Histograms with the same letter do not differ at p = 0.05. (Modified from P. J. Hocking, G. Keerthisinghe, F. W. Smith, and P. J. Randall. Comparison of the ability of different crop species to access poorly-available soil phosphorus. In Plant Nutrition for Sustainable Food Production and Agriculture (T. Ando, K. Fujita, T. Mae, H. Matsumoto, S. Mori, and J. Sekiya, Eds.), 1997;305–308. Kluwer Academic: Dordrect; with kind permission from Kluwer Academic Publishers.)

to the other species (Fig. 5), probably due to high rates of citrate exuded from its proteoid roots. The L-value for pigeon pea, while lower than that of white lupin, was higher than values for non- or low-organic acid-secreting species such as narrowleafed lupin (Lupinus angustifolius), soybean (Glycine max), rapeseed, wheat, and sunflower (Helianthus annus). The L-value of pigeon pea being lower than for white lupin is probably due to malonic and piscidic acids being less effective than citrate in freeing up fixed P and also to its lower rates of organic acid exudation than for white lupin (Otani et al., 1996). Rapeseed was ineffective at obtaining P from poorly available sources in this Oxisol, although the species can apparently solubilize Ca-bound P in rock phosphates (Hoffland, 1992; Hinsinger and Gilkes, 1997) and access fixed P in the inorganic-P pool in soil of about pH 6.0 (Hedley et al., 1982). The inability of rapeseed to access fixed P from the Oxisol may be due to the low rate of organic acid exudation (ca. 3% of the rate from proteoid roots of white lupin; Table II), and consequently the rapid sorption and biodegradation of the organic acids exuded (Jones and Darrah, 1994; Jones et al., 1996; Jones, 1998).

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PETER J. HOCKING Table III Shoot Dry Weight and P Accumulation by Wheat Shoots Grown Either as a Monoculture or in Mixed Culture with White Lupina

Cropping system

Dry matter per shoot (g)

P accumulation (mg per shoot)

P concentrationb (g kg−1 dry wt)

Wheat monoculture Wheat/lupin mixed

0.53a 0.74b

0.82a 1.11b

1.55 1.50

a Plants were grown in pot culture in a low-P acid Luvisol for 54 days. Values followed by the same letter do not differ at p = 0.05. (Modified from Table 3; M. Kamh, W. J. Horst, F. Amer, H. Mostafa, and P. Maier. Mobilization of soil and fertilizer phosphate by cover crops. Plant Soil 1999;211:19– 27; with kind permission from Kluwer Academic Publishers.) b Calculated from data in columns 1 and 2.

E. INTERCROPPING AND ROTATIONAL BENEFITS OF ORGANIC ACID-SECRETING SPECIES Glasshouse experiments in which white lupin was intercropped with wheat on acid soils have shown improved growth and P uptake by the wheat when compared to monocultures of wheat (Gardner and Boundy, 1983; Horst and Waschkies, 1986, 1987; Kamh et al., 1999) (Table III). This was attributed to the roots of wheat intermingling with the root system of white lupin and acquiring some of the P freed up by organic acids exuded from the proteoid roots. Other glasshouse studies using pigeon pea grown in mixed culture with rice (Oryza sativa) (Suzuki et al., 1996) and sorghum (Sorghum bicolor) (Guedes et al., 1996) showed that the rice and sorghum benefited from the P made available by the organic acids exuded from the pigeon pea roots. Field experiments in which sorghum was intercropped with pigeon pea on soils with low available P also indicated that sorghum had better growth and P nutrition than when sorghum was grown alone (Arihara et al., 1991). In some acid soils, the P-mobilizing effect of citrate can be detected after 90 days of incubation, and this was attributed to the formation of stable complexes of citrate with Al- and Fe-oxyhydroxides that prevent the biodegradation of citrate in the soil solution (Gerke, 1992). Other studies have shown almost no biodegradation of citrate when it is bound to Al and Fe (Boudot, 1992; Jones and Edwards, 1998), particularly at high Al and Fe to carbon ratios (Boudot et al., 1989). It has been suggested that the phenolic compounds exuded from proteoid roots of white lupin also inhibit the biodegradation of citrate and other organic acids, thus prolonging their effect on P mobilization (Dinkelaker et al., 1995; Neumann et al., 2000). In addition, P mobilized by citrate from proteoid roots of white lupin may persist in the soil solution for about 10 weeks (Gerke, 1992), although the extent to which this P is available to plants is uncertain.


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Table IV Effect of Previous Crop on Growth and P Accumulation by Wheat Shootsa

Previous crop


P concentration (g kg−1 dry wt)

P accumulationb (mg per shoot)

Wheat White lupin

0.36a 0.69b

1.06a 1.25b

0.38 0.86

a Plants were grown in pot culture in a low-P acid Luvisol for 49 days. Values followed by the same letter do not differ at p = 0.05. (Modified from Table 4; M. Kamh, W. J. Horst, F. Amer, H. Mostafa, and P. Maier. Mobilization of soil and fertilizer phosphate by cover crops. Plant Soil 1999;211:19–27; with kind permission of Kluwer Academic Publishers.) b Calculated from data in columns 1 and 2.

Glasshouse experiments have shown that wheat following white lupin had better growth and P nutrition than wheat after wheat, suggesting a residual P benefit (Horst and Waschkies, 1986; Kamh et al., 1999) (Table IV). Other glasshouse experiments showed that maize (Zea mays) grown in an Alfisol had better growth and P nutrition following chickpea and pigeon pea than following sorghum (Arihara et al., 1991). Additional evidence for a P carryover benefit from an organic acid-secreting crop to a following crop comes from a glasshouse experiment with 32P labeled soil. Wheat following white lupin or pigeon pea not only had better growth and P nutrition, but also lower specific 32P activities than following crops that did not secrete large amounts of organic acids (Table V). The lower specific activities of wheat following white lupin and pigeon pea indicate that it accessed some of the P

Table V Effect of Previous Crop on Growth, P Accumulation, and Specific 32 P Activity of Wheat Shootsa

Previous crop


P accumulation (mg per shoot)

Specific 32P activity (Bq μg−1 shoot P)

White lupin Pigeon pea Wheat Soybean

0.71a 0.67a 0.47b 0.38b

2.70a 3.17a 1.53b 1.88b

2.14a 2.66a 5.85b 4.43b

a

Plants were grown in pot culture in a low-P Oxisol for 42 days. Values followed by the same letter do not differ at p = 0.05 (P. J. Hocking, unpublished).

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PETER J. HOCKING

freed up by the previous crop, although it is unknown whether this P was from the inorganic or organic soil P pools, or both. In addition, it is uncertain for how long this residual P benefit persists under field conditions. However, field experiments on an Alfisol showed that sorghum following chickpea or pigeon pea had better growth and P nutrition than following non–organic acid-secreting crops (Ae et al., 1990; Arihara et al., 1991), suggesting that the residual P benefit of organic acidsecreting crops persists for some time.

F. SELECTION FOR INCREASED ORGANIC ACID PRODUCTION TO IMPROVE PHOSPHORUS UPTAKE While there is evidence that species which exude organic acids differ from other species in their ability to access various pools of soil P, there is little information on intraspecific variation in organic acid exudation from plants in relation to P acquisition. Even for white lupin, there are no data to indicate whether differences exist between genotypes in rates of exudation or the spectrum of organic acids exuded. Experimentally, it is difficult to show for selection purposes consistent differences between genotypes that exude organic acids because exudation rates are altered by plant P status, root age, and the environment. Some work with pigeon pea cultivars did not measure organic acid exudation, but ranked the cultivars on the ability of their root exudates to solubilize soil P bound to Al or Fe (Subbarao et al., 1997a). Phosphorus uptake by these pigeon pea cultivars from Fe–P in vermiculite culture was also measured (Subbarao et al., 1997b). Differences between cultivars were found, and they were reasonably consistent between the two techniques. This approach is promising for the development of screening procedures to select for genotypes with enhanced organic acid exudation.

IV. ORGANIC ACIDS AND SOIL ORGANIC PHOSPHORUS A. MAJOR FORMS OF ORGANIC PHOSPHORUS IN SOILS Organic P is regarded as an important source of P for plants, but it is only accessible to roots after mineralization to release soluble inorganic P (Richardson, 1994). Agricultural soils usually have about 50% of their total P in organic forms (Dalal, 1977; Harrison, 1987; McLaughlin et al., 1990), but many of these forms of P have not been identified. Of the identified forms of organic P, phosphate monoesters, predominantly inositol hexaphosphate and, to a much lesser extent, inositol pentaphosphate are the major constituents (Cosgrove, 1967; Newman and


81

Tate, 1980). Phytate (here defined as derivatives of inositol hexaphosphate) comprises 20–50% of the soil organic P (Dalal, 1977; Anderson, 1980; Hayes et al., 2000b), so it is an important component of total soil P. However, there are conflicting reports about the ability of plants to access P from phytate (Martin, 1970, 1973; Martin and Cartwright, 1971; Helal and Sauerbeck, 1991; Kroehler and Linkins, 1991; Adams and Pate, 1992; Barrett-Lennard et al., 1993; Findenegg and Nelemans, 1993; Hübel and Beck, 1993; Hayes et al., 2000b). The complications in much of this work arise from experiments done under nonsterile conditions, so there are issues of whether microbial or plant phytase enzymes hydrolyzed the phytate. Nonetheless, the consensus of opinion from these studies is that phytate is a much poorer source of P for plants than most other organic P substrates in soils. Phytate is negatively charged and readily undergoes physical and chemical reactions that impede its degradation (Anderson, 1980; McKercher and Anderson, 1989). This is consistent with the accumulation of phytate in agricultural soils, particularly after application of P fertilizer, and its apparent resistance to mineralization (Williams and Anderson, 1968).

B. ORGANIC ACIDS AND PHOSPHATASES The major factors in determining the availability of organic P to plants are the solubility of the particular compound and its susceptibility to plant and microbial phosphatases (Sanyal and De Datta, 1991). Phosphatases are a diverse group of enzymes required for the mineralization of organic P compounds and the release of inorganic P (Bieleski, 1973) (Fig. 1). Phosphatases have been characterized from roots of a number of species (McLachlan and De Marco, 1980; Panara et al., 1990; Ozawa et al., 1995), and one of the initial responses of plants to P-deficiency stress is an increase in root phosphatase activity (Hübel and Beck, 1996; Schachtman et al., 1998; Hunter et al., 1999; Raghothama, 1999). Phytases are a class of phosphatases with a high affinity for phytate (Laboure et al., 1993; Li et al., 1997a), and they are of interest for plant P nutrition because they catalyze the removal of phosphate moieties from phytate. Increased internal phytase activity in roots in response to P deficiency has been shown for a range of species (Asmar, 1997; Hayes et al., 1999), and Li et al. (1997b) reported that the level of phytase secreted into the rhizosphere by roots of tomato (Lycopersicon esculentum) increased under P deficiency. Similarly, extracellular phytase activity has been detected for roots of subterranean clover (Trifolium subterraneum) that were provided with phytate as the P source (Hayes et al., 1999). However, most plants do not appear to secrete sufficient levels of phytase from their roots to acquire P directly from phytate (Hayes et al., 2000b; Richardson et al., 2000). An additional problem is that, like phosphate, much of the phytate in acid soils is adsorbed to and complexed with metal ions or precipitated as salts of phytate

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(phytins) (McKercher and Anderson, 1989; Jones, 1998). Consequently, even if plants secrete phytase into the rhizosphere, it is unlikely to be able to hydrolyze the phytate. However, there is evidence that organic acids exuded from roots can free up some of this adsorbed and complexed phytate, thus exposing it for breakdown by extracellular phytase enzymes (Jones, 1998; Otani and Ae, 1999; Hayes et al., 2000a). For example, Adams and Pate (1992) showed that white lupin grew better than narrow-leafed lupin (a species that does not secrete organic acids under P stress) when provided with phytate as the only source of P in a low-P soil. This was attributed to the solubilization of Fe- and Al-phytate by organic acids exuded from the proteoid roots of white lupin, enabling phosphatases (presumably phytases) to release P from the phytate for plant uptake. In addition, organic acids can mobilize P bound in humic–metal complexes, although the mechanisms appear to be different from those associated with the release of inorganic P (Fox et al., 1990a,b, Gerke, 1992, 1993, 1994). Further studies are required to establish the role of organic acids in improving the ability of agricultural plants to obtain P from organic P compounds.

V. ALUMINUM TOLERANCE A. GENERAL CONSIDERATIONS Plants vary greatly in their tolerance to Al3+ stress, and many agronomically important crops including maize, soybean, and wheat show a wide range of intraspecific tolerance to Al3+ (Ryan et al., 2001). Aluminum tolerance mechanisms can be grouped into those that keep Al3+ out of root cells and those that detoxify Al3+ internally. Initial evidence of a role for organic acids exuded by roots in Al tolerance came from Kitagawa et al. (1986) who showed that differences in the Al3+ tolerance of wheat genotypes were related to their capacity to exude malate. Since then, research has focused on the exudation of organic acids that chelate Al3+ in the rhizosphere and render it nontoxic (Delhaize and Ryan, 1995; Ma, 2000; Ryan et al., 2001). However, the ability of organic acids to chelate Al3+ varies considerably. For example, citric, oxalic, and tartaric acids were the most effective in protecting cotton (Gossypium hirsutum) roots from Al3+ toxicity, whereas malic and malonic acids provided moderate protection, and succinic, lactic, formic, and acetic acids were relatively ineffective (Hue et al., 1986). The effectiveness of organic acids in protecting roots against Al3+ toxicity is related to the number and positions of OH/COOH groups on the main carbon chain. Organic acids that can form stable 5or 6-bond ring structures with Al3+ provide the best protection (Hue et al., 1986). To date, the evidence indicates that complexes of Al3+ with tri- and dicarboxylic


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anions are not taken up by roots (Akeson and Munns, 1989; Shi and Haug, 1990). In this chapter, Al tolerance based on the exudation of the more important organic acids is considered.

B. MAJOR ORGANIC ACIDS INVOLVED IN ALUMINUM TOLERANCE 1. Malate As mentioned earlier, a role for malate in Al3+ tolerance was first reported for wheat by Kitagawa et al. (1986). Subsequently, Christiansen-Weniger et al. (1992) showed that an Al-tolerant wheat cultivar exuded more malate from its roots than a sensitive cultivar. However, the most convincing evidence came from the study of a pair of near-isogenic wheat lines by Delhaize et al. (1993) who showed that Al3+ stimulated up to a 10-fold greater efflux of malate from roots of the Al-tolerant line than from roots of the sensitive line (Fig. 6). The malate was mostly exuded from the terminal 3 mm of the root, which is the part of the root most susceptible to Al toxicity (Ryan et al., 1993, 1995a). Only monomeric Al3+ triggered malate efflux: other Al forms such as Al(OH)3, or P-deficiency stress were unable to elicit

Figure 6 The effect of exposure to 50 mmol m−3 Al3+ on the cumulative amounts of malate exuded from roots of seedlings of two near-isogenic wheat lines differing in Al tolerance. In the absence of Al3+, both lines exuded similar low amounts of malate. Vertical bars denote ± range, n = 2. (From E. Delhaize, P. R. Ryan, and P. J. Randall. Aluminum tolerance in wheat (Tritium aestivum L.). II. Aluminum-stimulated excretion of malic acid from the root apices. Plant Physiology 1993;103:695– 702; with kind permission from Plant Physiology.)

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the response (Ryan et al., 1995a). Malate efflux has since been confirmed for other wheat cultivars differing in Al tolerance (Basu et al., 1994; Pellet et al., 1996, 1997; De Andrade et al., 1997). A strong correlation was found between malate efflux and Al tolerance among 30 wheat genotypes from diverse sources, suggesting that genes encoding for malate efflux account for a large proportion of the Al tolerance found in hexaploid wheat (Ryan et al., 1995b). There is evidence that Al3+ activates preexisting or constitutively expressed mechanisms for the production and transport of malate from roots of Al-tolerant wheat, as there is no appreciable lag phase in malate secretion after exposing roots to Al3+ (Ryan et al., 1995a; Li et al., 2000; Ma et al., 2000) (Fig. 6). Removal of Al3+ from the rooting medium results in a rapid decline in malate exudation to nonstressed levels (Ryan et al., 1995a), indicating very responsive Al3+-sensing and malate-secreting mechanisms. Assays of enzyme activities and internal malate concentrations in near-isogenic Al-tolerant and Al-sensitive wheat lines showed that both had equal capacity for malate synthesis (Ryan et al., 1995a). Consequently, the difference in tolerance to Al3+ seems to be related to differences in the transport of malate out of apical root cells through Al-activated malate-permeable anion channels (Ryan et al., 1995a; Zhang et al., 2001). It is still uncertain whether Al3+ triggers control of the anion channel indirectly through a series of transduction signals or directly at the plasmalemma (Papernik and Kochian, 1997; Ryan et al., 1997). 2. Citrate Citrate is probably the most effective chelator of Al3+ (Hue et al., 1986; Smith et al., 1997), and a role for citrate exudation from roots in Al tolerance was initially proposed after it was found that an Al-tolerant snapbean (Phaseolus vulgaris) cultivar secreted 10-fold more citrate than an Al-sensitive cultivar (Miyasaka et al., 1991). Subsequently, it was shown that an Al-tolerant maize cultivar secreted 10-fold more citrate from its root tips than a sensitive one (Pellet et al., 1995). Citrate efflux from root tips of Al-tolerant maize was confirmed using another pair of cultivars differing in Al tolerance, and it was shown that citrate chelated Al3+ and reduced its accumulation in the root apex of the tolerant cultivar (Jorge and Arruda, 1997). Aluminum-tolerant soybean also secretes citrate in response to Al3+ (Yang et al., 2000), as do the Al-tolerant cereals, rye (Secale cereale) and triticale (rye × wheat) (Li et al., 2000; Ma et al., 2000). Ma et al. (1997a,b) showed that Al3+ resulted in 2.5–3.0 times more citrate exudation from roots of the Al-tolerant shrub Cassia tora than the Al-sensitive C. occidentalis. In all the above-mentioned cases, an appreciable lag phase in the exudation of citrate was evident after exposure of roots to Al3+ (e.g., Fig. 7), unlike the immediate efflux of malate from wheat root tips. This suggests that exposure to Al3+ triggers gene induction or activation (Ma et al., 2000) and, thus, the de novo synthesis of proteins involved in the biosynthesis and/or transport of citrate out of


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Figure 7 Time course of citric acid efflux from Cassia tora roots exposed to 50 mmol m−3 Al3+. Vertical bars denote ± SE, n = 3. (From J. F. Ma, S. J. Zheng, and H. Matsumoto. Specific secretion of citric acid induced by Al stress in Cassia tora L. Plant and Cell Physiology 1997;38:1019–1025; with kind permission from Plant and Cell Physiology.)

roots (Ryan et al., 2001). The only report to date of a rapid citrate efflux from root tips exposed to Al3+ similar to that of malate from wheat is for tobacco (Nicotiana tabacum) (Delhaize et al., 2001). However, it is likely that further research will reveal other cases of rapid efflux of citrate from roots exposed to Al3+. There is also evidence that Al tolerance mechanisms based on the exudation of citrate occur in important agroforestry tree species. Aluminum increased the exudation of citrate from roots of three leguminous trees, and this could not be mimicked by P-deficiency stress (Osawa et al., 1997). The concentrations of citrate exuded were correlated with the degree of Al tolerance among the three species examined. 3. Oxalate The exudation of oxalate from roots is involved in the Al tolerance of buckwheat (Fagopyrum esculentum). Exposure of buckwheat roots to Al3+ elicited exudation of oxalate from root tips, with similar kinetics to the Al-stimulated efflux of malate from wheat roots (Ma et al., 1997c; Zheng et al., 1998). Unlike most other species, buckwheat accumulates high concentrations of Al in leaves and most of this Al is complexed with oxalate (Ma et al., 1997c). Apparently, Al3+ that enters the roots is initially complexed with oxalate. However, the dominant form of Al in the xylem sap is an Al-citrate complex, indicating ligand exchange of Al from oxalate to citrate (Ma and Hiradate, 2000). Another ligand exchange must occur in the shoot that transfers the Al from citrate back to oxalate. The efflux of oxalate

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and the internal complexation of Al by citrate and oxalate are likely to represent two related mechanisms that confer Al tolerance on buckwheat. Oxalate is also secreted from taro (Colocasia antiquorum) in response to Al3+ in the root zone (Ma and Miyasaka, 1998).

VI. GENETIC ENGINEERING APPROACHES TO INCREASE ORGANIC ACID EXUDATION Based on evidence that organic acids improve access to soil P and increase the aluminum tolerance of some plants, there is considerable interest in the use of gene technologies to enhance the exudation of organic acids from plant roots. Overexpression of genes encoding enzymes involved in organic acid biosynthesis in roots is one approach. For example, citrate synthase (CS) has been targeted because it catalyzes the conversion of oxaloacetate (OAA) and acetyl-CoA to citrate in the TCA cycle. Overexpression of a CS gene from a bacterium (Pseudomonas aeruginosa) in tobacco resulted in up to a 10-fold greater internal citrate concentration in roots and a 4-fold greater citrate efflux in the transgenic plants than in control plants (de la Fuente-Mart´ınez et al., 1997). The increased efflux of citrate was associated with enhanced tolerance to Al3+ (de la Fuente-Mart´ınez et al., 1997). It was shown that the enhanced citrate efflux from the transgenic tobacco lines enabled them to access P from calcium phosphate that was unavailable to the control plants (López-Bucio et al., 2000). Indeed, calcium citrate precipitated around the roots of the transgenics, similar to that shown for proteoid roots of white lupin grown in calcareous soil (Dinkelaker et al., 1989), indicating substantial exudation of citrate from the transgenic tobacco. Collectively, these results suggested that this approach could be used as a general method to enhance Al tolerance and increase plant access to poorly available P. However, attempts to repeat the work using the same transgenic tobacco lines as de la Fuente-Mart´ınez et al. (1997), as well as tobacco transgenics expressing the P. aeruginosa CS gene to a 100-fold greater level, have shown neither increased citrate concentrations in roots nor increased citrate efflux, and thus no improvement in Al tolerance (Delhaize et al., 2001). Consequently, it would appear that the tobacco phenotypes associated with the P. aeruginosa CS gene are either sensitive to environmental factors, or that the reported improvements in Al tolerance and P nutrition were due to other factors. In any case, tobacco is comparatively Al-tolerant as exposure of roots to Al3+ stimulates citrate efflux (Delhaize et al., 2001), so it may not be the most suitable species for assessing the effectiveness of such approaches. The overexpression of a plant gene for the mitochondrial form of CS in Arabidopsis thaliana plants has been reported to enhance citrate efflux by 1.6-fold, with associated small improvements in Al tolerance and P acquisition (Koyama et al., 2000). It is possible that overexpression of a plant rather a bacterial CS gene


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may offer more promise to reliably increase citrate efflux from roots. However, it is likely that the increase in organic acid efflux from transgenic plants needs to be considerably greater than the 1.6-fold achieved for A. thaliana to be of agronomic significance. Other approaches aim to increase organic acid exudation by overexpressing PEPC and MDH (Raghothama, 1999). PEPC catalyzes the conversion of phosphoenolpyruvate and CO2 to OAA, and MDH catalyzes the conversion of OAA to malic acid. Increasing PEPC activity and thus the OAA pool could increase the synthesis of organic acids in roots. Decreasing the cytosolic activities of aconitase and isocitrate dehydrogenase (enzymes that convert citrate to isocitrate and isocitrate to α-ketoglutarate, respectively) by antisense techniques are other possibilities for increasing internal citrate concentration and efflux from roots (Kruse et al., 1998). Again, there needs to be a substantial increase in the efflux of organic acids for these approaches to have practical application in agriculture. This could be difficult to achieve as internal feedback mechanisms may down-regulate the activity of key enzymes, or the phenotypes may have poor growth because of metabolic disorders associated with increased concentrations of organic acids (Neumann et al., 2000). In addition, plants may already have more than enough TCA enzyme and substrate capacity to cope with an increase in organic acid synthesis as a result of increased exudation. Plants grown at elevated levels of CO2 usually have improved carbon status and exude increased amounts of carbon-containing compounds from their roots (Paterson et al., 1997). However, there were no differences in either the rate or the composition of organic acids exuded from proteoid roots of white lupin grown at elevated or ambient CO2 (Watt and Evans, 1999a), suggesting that carbon substrates and enzyme activities do not limit organic acid production. Consequently, genes that encode proteins involved in the transport of organic acids from roots to the rhizosphere are probably the key targets for gene manipulation technologies. Organic anion channels are likely to constitute a pivotal step in the mechanism by which organic acid exudation occurs from roots of plants that are either P stressed or exposed to potentially toxic levels of Al3+. Characterizing and cloning genes controlling the exudation of organic acids from roots is of crucial importance toward optimizing the interaction between the biosynthesis and efflux of organic acids. In addition, better promoters that are root specific and respond to P-deficiency or Al3+ stress are required to ensure that cloned genes related to organic acid synthesis and, especially exudation, are expressed only in the root system or in specific regions of roots, rather than in the whole plant.

VII. CONCLUDING REMARKS There is convincing evidence that some plants secrete organic acids either to increase P uptake or to protect their root tips from Al3+ toxicity in acid soils. The

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strongest evidence supports a role for organic anions such as citrate, malonate, oxalate, and tartrate in enhancing P uptake and citrate malate, and oxalate in Al tolerance. However, more work is needed to assess the efficacy of organic acid exudation in improving plant access to fixed P in different soil types and for different forms of P fertilizer. At the agronomic level, the role of plants that exude organic acids needs to be evaluated in the P economy of crop and pasture systems. Further research is needed to identify the mechanisms by which P deficiency or exposure to Al3+ activates or induces the exudation of specific organic acids out of root cells and to clone the genes involved in these processes. The cloning of genes whose products increase the synthesis of organic acids and their transport across the plasma membrane of roots will undoubtedly enhance conventional breeding approaches aimed at improving the P uptake and Al tolerance of agricultural plants on acid soils. Finally, improved P use-efficiency by crops is critical to reduce the consumption of P fertilizer and to enable the withdrawal of some of the previously applied fertilizer P locked up in the soil P “bank.” Based on current consumption of P fertilizer, it is estimated that world reserves of rock phosphates will be exhausted within the next 60–90 years (Runge-Metzger, 1995). While this estimate may be pessimistic, even the most optimistic estimates do not prolong the life expectancy of rock phosphate reserves by much. Currently, many developing countries in the tropics have to contend with very low soil P reserves and highly P-fixing soils, whereas agriculture in large parts of the United States and Europe has to contend with excessive P in the soil that threatens the environment. Consequently, there is increasing pressure to manage the use of P fertilizer in food and livestock production systems on acid soils, to minimize actual and potential adverse environmental effects caused by the leakage of P (Tunney et al., 1997). Crops that can access reserves of P through the exudation of organic acids in highly P-fixing soils in developing countries can help sustain agricultural production, improve P fertilizer use-efficiency, and enable the use of cheaper and more readily available sources of P fertilizer such as rock phosphates. Plants that exude organic acids can also help reduce the consumption of P fertilizer in developed countries and improve the environment by enabling access to the large “bank” of accumulated soil P, thus reducing the P load in runoff. In addition, the use of existing or new genotypes that exude organic acids to protect their roots against Al3+ toxicity can enable growers to maintain production while agronomic practices such as liming to reduce soil acidity are implemented.

ACKNOWLEDGMENTS I thank Drs. E. Delhaize, A. E. Richardson, and P. R. Ryan for their comments and suggestions on earlier drafts of this manuscript. I thank Dr. G. Keerthisinghe for permission to use unpublished information presented in Figure 3.


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ASPECTS OF BAMBOO AGRONOMY Volker Kleinhenz and David J. Midmore Plant Sciences Group Primary Industries Research Center Central Queensland University North Rockhampton, Queensland 4702, Australia

I. Introduction II. Manipulating Growth and Development in Bamboo A. Uptake of Water and Nutrients B. Photosynthesis C. Storage and Translocation of Photosynthates and Nutrients D. Accumulation and Partitioning of Biomass and Nutrients E. Management of Bamboo Growth III. Managing the Environment for Bamboo Production A. Water B. Soil Physical Properties C. Soil Chemical Properties IV. Summary References

Various aspects of the growth and development of bamboo are reviewed, including growth cycles of plant parts, effects of aging on important plant tissues, uptake of water and nutrients, photosynthesis, storage and translocation of photosynthates and nutrients, and accumulation and partitioning of biomass and nutrients. Also discussed are how these aspects can be manipulated with agronomic techniques, such as management of standing-culm density, culm-age structure, leaf area, and leaf-age structure. Selected aspects of how the environment (i.e., water availability), soil physical properties (such as slope, texture, bulk density, moisture-holding capacity, and soil temperature), and soil chemical properties (such as pH, salinity, and nutrient availability) impact on bamboo production are outlined. This review also discusses how the environment can be managed with irrigation, terracing, tillage, covers and mulches, canopy adjustments, and fertilization with optimal amounts of C 2001 Academic Press. nutrients, nutrient ratios, schedules, and forms of fertilizer.


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I. INTRODUCTION Over 70 genera of bamboo with over 1200 species occur in natural forests, semiexploited stands, and intensive plantations, covering an area of more than 14 million ha worldwide (Dransfield and Widjaja, 1995; Fu and Banik, 1995). Eighty percent of the species and area are confined to South and Southeast Asia and mostly to China, India, and Myanmar. China, with the largest bamboo industry worldwide, has a total of about 7 million ha of bamboo forest (Perez et al., 1999). Of these, over 30% is covered by the world’s single most important bamboo species, Phyllostachys pubescens (Li and Xu, 1997). The area covered by bamboo in India has been estimated at between 3 and 10 million ha (Biswas, 1988; Fu and Banik, 1995), and that in Myanmar has been estimated at 2.2 million ha (Fu and Banik, 1995). The range of uses of bamboo for humans is remarkable, with an estimated annual use of 12 kg of bamboo products per capita in Asia (Recht and Wetterwald, 1988; Sastry, 1998). Besides some minor uses such as leaves for medical purposes (Zhang, 1997), fresh edible shoots and culms for timber or as a raw material for pulping are the major products from bamboo. Data on worldwide production of bamboo products are extremely unreliable because they do not appear in the major commodity databases. Probably more than 2 million tons of bamboo shoots are consumed annually (Kleinhenz et al., 2000), with approximately 1.3 million t produced in China (Shi et al., 1997). Figures for timber will be multiple times greater, for example, 3 million t/year in India (Subramanian, 1995), possibly more than 20 million t/year in China, and most likely about 30 million t/year worldwide. Total trade in bamboo products has been estimated at $4.5 billion/year (Sastry, 1998). While supplying products of immediate use to humans, bamboo also serves multiple ecologic functions such as soil and water conservation and erosion control (Fu and Banik, 1995). Due to its great potential for rapid biomass production (Pearson et al., 1994), bamboo is a significant net sink for global CO2 (Jones et al., 1992). Although vast land tracts in South and Southeast Asia are covered with bamboo, and the area planted with bamboo is increasing in China by 51,000 ha/year (Li and Xu, 1997), there is rising concern about acute scarcity of bamboo products in the future (Hsiung, 1988). For example, in India it is projected that at the current level of bamboo productivity for the paper industry and with the growing demand for paper, an additional 30–60 million ha of land would be required by 2015 (Adkoli, 1991). Better resource use (Shende et al., 1998) and, in particular, suitable crop management practices (Yao, 1994; Fu and Banik, 1995; Perez et al., 1999) are given top priority to satisfy the anticipated increasing future demand for bamboo products. This chapter aims to present an overview of the key aspects of the growth and development of bamboo, showing how they can be managed through agronomic

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techniques that enhance output particularly of edible shoots and timber. Emphasis is placed on the manipulation of standing-culm density, culm-age structure, leaf area, and leaf-age structure. Selected features of the impact of the environment (i.e., water availability and soil properties), on bamboo production are outlined, and approaches on how this environment can be managed (e.g., with irrigation, tillage, mulches, and fertilization), are discussed.

II. MANIPULATING GROWTH AND DEVELOPMENT IN BAMBOO Bamboos (Bambusoideae) comprise a subfamily of the grasses (Poaceae). They are evergreen, monocotyledonous (yet woody) plants which produce primary shoots without any later secondary growth. Each shoot has a distal aerial part called the culm, a proximal, ground-level part called the culm neck, and a subterranean part called the rhizome. Culms consist of nodes and internodes—the former with meristematic tissue from where culm sheaths and branches arise (Chua et al., 1996). Young culms with compressed internodes and including a part of the culm neck are harvested for edible shoots, weighing from 0.25 to 5.00 kg each depending on species. Mature culms provide timber that is put to multiple uses. Rhizomes as well as culms consist of nodes and internodes. According to their morphology, bamboos are broadly divided into monopodial (or “running”) bamboos with “leptomorph” rhizome systems, and sympodial (or “clumping”) bamboos with “pachymorph” rhizome systems. The internodes of leptomorph rhizomes are longer than broad, and lateral buds on nodes can produce either new shoots or other rhizomes. The internodes of pachymorph rhizomes are broader than long, and lateral buds on nodes produce only rhizomes (Valade and Dahlan, 1991). In sympodial bamboos, new culms develop from buds on elongated culm necks (pseudo-rhizomes) rather than from buds on rhizomes. These differences in rhizome systems can be regarded as adaptations to the climatic conditions to which bamboos are native: monopodial bamboos are native to temperate climates with cool, wet winters, and sympodial bamboos are native to tropical climates with a pronounced dry season. The tightclumping habit of tropical species supposedly evolved from the leptomorph form of rhizomes and provides less rhizome surface to dehydrate during extended dry seasons (Farrelly, 1984). That bamboo is “one of the fastest growing plants” is attributed to the speed of culm growth. This fast growth phase results from expansion of individual internodes and, depending on species, culms can grow 3- to 30-m long within 3–4 months (Liese and Weiner, 1995; Chua et al., 1996). It is generally agreed that up to a certain age, height and diameter of the annual flush of culms increase (Londoño, 1992). Particular environmental conditions, such as higher temperature (Sun and Yang, 1988; Lan, 1990), greater water availability (Koyama and Uchimura, 1995),

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and higher air humidity (Farrelly, 1984), promote culm growth. Culms which emerge early in the season can fully develop during the warm summer in temperate climates and during the wet season in tropical climates, whereas “late” culms rarely survive into the second growing season due to cool-temperature conditions in temperate climates or to dry soil conditions in tropical climates (Pearson et al., 1994). The culm tissue consists of about 50% parenchyma and 50% vascular bundles. The latter are composed of fibers (40%) and conducting tissue (10%). The conducting tissue consists of the xylem in the form of metaxylem vessels and the phloem in the form of sieve tubes (Liese, 1995). In contrast to culms which are 50-year-old stand of P. pubescens was located in the topmost 30 cm with only thick (i.e., unproductive) roots and dead biomass below that depth. Huang (1986) found a very similar distribution of root and rhizome biomass for the same species. The author speculated that roots might only extend to a maximum of 1 m when the soil A horizon is very deep. Table I presents a summary of effective root zones for several species. Shallow root systems of bamboo are more prone to deficient and fluctuating soil-water conditions than more deeply located root systems of other plants, but they are less likely affected by overwet soil conditions to which they are susceptible (Farrelly, 1984). The topmost soil horizon is typically well aerated, and natural mineralization of nutrients is usually quicker there than in deeper layers. Plant-available ions are effectively and almost immediately absorbed by the dense root system of bamboo plants in this horizon, which explains their function of accumulating and sequestering nutrients (Toky and Ramakrishnan, 1982) and their quick response to fertilization (Li et al., 1998c). Therefore, leaching of nutrients is very low in bamboo stands

Table I Vertical Location of Root and Rhizome Biomass in Bamboo

Bamboo species

Root and rhizome zonea (cm soil depth)

References

In general Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys nidularia Phyllostachys fimbriligula Chusquea sp. Chusquea culeou

0–30 0–30 0–30 0–33 0–40 10–40 30–40 15–35 0–30 20–30

Farrelly (1984) Li et al. (1998c) Wu (1984) Oshima (1931a) Qiu et al. (1992) Huang et al. (1993) An et al. (1995) Cai and Wang (1985) Widmer (1998) Pearson et al. (1994)

a

That is, the soil layer where at least 80% of the total root and rhizome biomass is located.

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(e.g., 0.3–9.2, 0.07–0.3, and 2.8–13.7 kg/ha/year of N, P, and K, respectively) (Toky and Ramakrishnan, 1981; Mailly et al., 1997). There is not much evidence to support the argument that greater soil depth promotes greater productivity in bamboo. In correlation analyses with P. bambusoides, surface soil depth was positively correlated with bamboo growth (Chung and Ramm, 1990). “Growth” (i.e., biomass production), however, is not necessarily equivalent to yield. Zhang et al. (1996) measured greater stand biomass but lower timber yield in P. nidularia when the soil layer was deeper. This is due to the interaction between rhizome age and position of rhizomes down the soil profile: older rhizomes are generally more deeply positioned. Due to the age of their tissues, buds on nodes of older rhizome parts are less effective in producing new shoots/culms. In the sympodial bamboo species C. culeou, development of buds into shoots was restricted to pseudo-rhizomes ≤4 years old (Pearson et al., 1994). For the monopodial species P. pubescens, buds on 3- to 4-year-old rhizomes tended to have the greatest potential for development into new shoots (Zhou et al., 1985). Rhizomes which are positioned deeper in the soil profile tend to produce only shoots of poor quality (Raghubanshi, 1994). Due to the longer time required for shoots to extend to the soil surface, these develop too late in the season to mature adequately during the short remaining growing season (Oshima, 1931b). From an economic perspective, belowground harvesting of shoots developing from older and deeper positioned rhizomes is too labor intensive for viable production of fresh, edible bamboo shoots (Oshima, 1931b). Figure 1a shows the typical variations in growth rates of rhizomes and roots for a monopodial bamboo in a northern-hemisphere temperate climate. New rhizomes develop from buds on nodes of older rhizomes in June and begin branching in August. Maximum rhizome growth occurs from July to September and is succeeded by growth of new roots, which develop from buds on those rhizomes during September–October. Due to low ambient temperatures during January and February and subsequent rapid growth of belowground shoots and aboveground culms until April/May, growth rates of rhizomes and roots remain low during this period. In sympodial bamboo (i.e., the “tropical” group), the drop in growth during January–February either will not be as pronounced or will not occur at all. The efficiency by which roots absorb nutrients (and possibly water) varies with season as well: higher nutrient absorption rates in P. pubescens were measured during the shoot-emergence and subsequent culm-elongation phase (Fu et al., 1994).

B. PHOTOSYNTHESIS In common with other crops, the total photosynthesis of bamboo stands is determined by the leaf area and by the photosynthetic capacity of the canopy. The photosynthetic capacity of an individual leaf depends on its age, its nutritional


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Figure 1 Annual cycle of (a) growth rates of aboveground and belowground plant parts, and (b) canopy photosynthetic rate of a typical monopodial bamboo in a temperate climate. The straight curve in the lower graph (b) indicates the canopy photosynthetic rate for an “alternating” monopodial bamboo in an “off year,” and the dotted curve is for an “alternating” bamboo in an “on year.” Adapted from Dreckmann (1995). See text for details.

status (Section III.C), the age of the culm it is growing on, its position in the canopy, and on climatic conditions such as temperature and photon-flux density which vary with season. The photosynthetic capacity of the bamboo canopy is not only determined by the photosynthetic capacity of leaves as outlined previously, but is also dependent on the life span of leaves which influences the leaf-age structure of its canopy. The life span of leaves is substantially different between monopodial and sympodial bamboos. Understandably, the leaf area index (LAI) of a complete bamboo canopy increases with leaf biomass across bamboo species and ranges from 5 to 12 (Qiu et al., 1992; Huang et al., 1993; Isagi et al., 1993; Fang et al., 1998; Li et al., 1998a). It is generally recognized that photosynthetic activity of leaves steadily declines with age after full expansion. Huang (1986), Huang et al. (1989), and Qiu et al. (1992) noted photosynthesis rates up to three times higher in new (1 year old) leaves. For example, the net photosynthetic rate of new leaves (2 months old) was 2.5 times greater than that of 1- to 2-year-old leaves in P. pubescens (Huang, 1986). This was attributed to greater metabolic activity of tissues (Huang, 1986) and higher nutrient concentrations (Zhou and Wu, 1997) in younger leaves. A similar trend

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was found in other bamboo species; concentrations of N, P, and K in 7–8 years old) stands, transmission of light decreases rapidly from the top of the bamboo canopy toward the bottom (Yang et al., 1988). In studies conducted by Qiu et al. (1992), only 5% of the light present at the top of the canopy penetrated to the leaves at the bottom of the canopy of P. pubescens. Unpublished data from V. Kleinhenz and D. Midmore confirm that the photosynthetic capacity of leaves depends on the age of the culms they are growing on. Photosynthesis was measured in leaves of the same age (youngest, fully expanded) on culms of up to 4 years old. The photosynthetic rate of leaves decreased significantly with the age of culms. This may be closely related to reduction in the supply to leaves of water and nutrients to sustain high photosynthetic rates when culms age and the conductivity of metaxylem vessels decreases. Diurnal changes in air temperature and photon-flux density and seasonal variation in both, especially for bamboo found in temperate zones, have marked effects on leaf photosynthesis. During January and February, the photosynthetic rate of leaves is low due to low air temperature (Huang et al., 1989; Koyama and Uchimura, 1995; Fig. 1b). In studies conducted by Koyama and Uchimura (1995) with P. bambusoides, net photosynthesis increased until air temperature was 27◦ C, but decreased rapidly thereafter. The compensation point at which the respiration rate equals that of photosynthesis was a little below 40◦ C. Net photosynthesis rates (Pn ) reached saturation at 12 ␮mol CO2/m2/s when the photosynthetically active radiation (PAR) was 1200 ␮mol/m2/s. Therefore, Pn in P. bambusoides peaked around midday. It is probable that “critical” levels especially of temperature and perhaps of PAR will be much higher for tropical and typically sympodial bamboo. Another fundamental difference between monopodial and sympodial bamboo is the life span of their leaves. The life span of leaves of monopodial bamboo is no more than 2 years (Hong, 1994; Li et al., 1998c), whereas that of leaves of sympodial bamboo can extend to about 6 years (Pearson et al., 1994; Shanmughavel et al., 1997). The leaf-age structure of canopies also differs between monopodial


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Table II Characteristics of Leaves, Recorded Just before the Shoot Season, on Culms of Different Ages in P. pubescens Culm age (years)

Leaf characteristics

2000 mm/year). Not only general demand for high water availability but also availability of water during specific growth stages may affect bamboo productivity. The detrimental effects of drought but positive effects of irrigation during the shoot and culm growth phase were outlined by Chu and Xu (1988), Li and Zhang (1987), and Lin (1995). Fu and Banik (1995) stated that irrigation was required for intensively managed shoot stands after 10 days without rainfall during the shoot season. Midmore et al. (1998) stressed the need to pay attention to water supply just before and during shoot production. Thanarak (1996), however, suggested that the harvest of D. asper could be brought forward by irrigating stands during the dry preshoot season, whereas Wan (1994) emphasized the need for irrigation during rhizome growth to ensure survival of new culms. Given that temperatures are sufficiently high for sympodial bamboo growth, bamboo could produce new shoots and culms year-round, with no distinct shoot phase if water demand is satisfied by rainfall (Farrelly, 1984) or irrigation. Therefore, when there are no restrictions on water supply, productivity of sympodial bamboo may be maximized with yearround irrigation. Such is evident with a similarly managed species, asparagus. The benefits of constant warm temperatures and water supply are exploited for yearround cultivation of asparagus in several subtropical regions including Thailand (Jayamangkala, 1992), Ecuador (Krarup, 1996) and Chile (Delgado de la Flor and Oordt, 1996). If water supply is limited, irrigation before, during, and after the shoot phase will have greater impact on production increase. While growth of monopodial species is constrained by low temperature in winter, it is unlikely to respond markedly to supplementary irrigation at this time. In subtropical regions, however, increased water availability early in spring can bring forward the growth and appearance of new shoots (Midmore et al., 1998).

B. SOIL PHYSICAL PROPERTIES Due to the immense diversity of soils and complexity of interactions between soil parameters, it is difficult to relate bamboo growth across sites to specific soil


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factors. Bamboo is known to grow in “poor” soils (Sutiyono, 1987) and is therefore used for rehabilitation of degraded land (Desh, 1990; Rao et al., 1999). In natural bamboo forests and in stands under low-input conditions, the nutrient-supplying capacity of soils is usually the most important soil property governing bamboo growth and yield (Section III.C; Chen et al., 1996). Soil physical factors, such as slope of land, texture, bulk density, moisture-holding capacity, and temperature, however, are among the nonchemical properties of soil which influence bamboo productivity. New rhizomes in monopodial bamboo species are located at diminishing soil depths since they are produced from axillary buds on the top of older rhizomes. As the stand ages, rhizomes of older plants may become exposed to the soil surface (Oshima, 1931a). Except under very intensive bamboo cultivation, this does not seem to seriously affect productivity. The fact that monopodial bamboo species such as P. pubescens cover vast areas of partially steeply sloping land in China, and are recommended for soil conservation and erosion control on slope land (Storey, n. d.; Storey, 2000), may substantiate this. In contrast, the pachymorph rhizome of sympodial species is confined to a much smaller soil volume (Farrelly, 1984) and is, therefore, more subject to erosion. Since the culm base of new shoots is always positioned higher than the culm base of shoots of the previous generation, rhizome and roots arise from progressively higher levels and are eventually exposed (Farrelly, 1984; Lin, 1995). Exposure of rhizome buds to sunlight prevents their development into shoots (Chaturvedi, 1988). Therefore, it is not surprising that natural stands of clumping bamboo species are more likely to be found on flatland where they prosper and grow better (Hassan et al., 1988; Fu and Banik, 1995). Virtucio et al. (1994) studied the performance of planted stands of Sphaerobambos philippinensis on hill- and flatland in the Philippines. Although soil quality was better on the flatland site, standing-culm density and culm yield (2400 culms/ha and 4.8 t dry weight/ha) on the hill-land site were much lower than on the flatland site (4400 culms/ha and 18.5 t dry weight/ha). They concluded that this clumping bamboo species has a greater and potentially more sustainable yield potential on flatland. Annual additions of soil or mulch to individual clumps is a countermeasure to prevent exposure of rhizomes but is laborious and prone to erosion on steep slopes. Therefore, out of preference clumping bamboo species should not be planted on sloping land. If they must be planted on sloping land, they should be planted on terraces to overcome their propensity to soil erosion. In line with sympodial bamboo, monopodial bamboos appear to be more productive on flatland: in Taiwan, planting density of such bamboo is lower on sloping land than on flatland since sloping land cannot support high plant densities (Tai, 1985; Leong et al., 1991). Likewise, He and Ye (1987) found negative relationships between increasing slope and culm yield in six locations in China. Zhang et al. (1996) showed that with P. nidularia yields decreased with increasing slope from 10.6 t/ha at 30◦ .

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Among soils of many bamboo-growing sites in China and Korea, soil texture was one of the most important parameters explaining variations in yield of P. pubescens. Culm yield of P. pubescens was negatively correlated with the percentage of 0.4%) in soil did impact negatively on growth. Litter biomass contributes significantly to soil organic matter and supplies bamboos with nutrients in natural stands and cultivated plantations (Section II.D). Nutrient supply from litter is delayed and sustained since the biomass must first be decomposed (Cao et al., 1997; Kleinhenz et al., 1997a). Decomposition of litter and mineralization of nutrients from litter depend on environmental conditions


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(Cao and Luo, 1996). Not surprisingly, Fu et al. (1989) conclude that constant warm temperatures and sufficient moisture and humidity promote activity of microbial decay organisms, their decomposition of litter, and release of nutrients. Litter may be categorized into three fractions (Christanty et al., 1996): (1) intact fraction, (2) fragmented fraction, and (3) decomposed fraction. Within the first year after litter fall, the litter biomass is still intact, after 3 years it is partly decomposed, and after 4 years, fractions of litter biomass were composed of intact, fragmented, and decomposed material. Decomposition in temperate climates is slow with 6 years required for 95% decomposition (Fu et al., 1989), whereas in tropical climates, more than 50% of litter biomass decomposes within 1 year (Tripathi and Singh, 1992). It follows that the litter biomass in a temperate bamboo stand will increase for up to 6 years until equilibrium is reached between additions of litter biomass and their complete decomposition. Therefore, bamboo litter acts as a slow-release reservoir for nutrients and should ideally not be removed or used for other purposes (e.g., as animal fodder). Harvesting and cultivation of bamboo stands remove biomass and cause soil disturbance. The organic root material is transformed into soil organic matter but soil aeration subsequently promotes growth of aerobic soil microorganisms which decompose soil organic matter. This initially ties up (i.e., immobilizes) nutrients during buildup of microbial tissue. The fraction of nutrients in microbial form, however, is extremely labile and as microbial populations decrease, these nutrients are rapidly mineralized. Soil chemical properties in unharvested and harvested bamboo stands were compared by Raghubanshi (1994). He measured a decrease in total N but increases in fractions of microbial and mineralized N in the harvested stand compared with the unharvested stand. Some authors recommend soil cultivation to increase availability of nutrients (e.g., Nonaka, 1989; Lin, 1995). Aeration of soil promotes mineralization of nutrients but Christanty et al. (1997) showed that hoeing in an Indonesian bamboo forest killed approximately 19 t/ha of fine and small roots which, although it improved soil organic matter and presumably availability of nutrients, reduced bamboo vigor considerably. When harvesting and cultivation of bamboo stands continue for longer periods and removed nutrients are not replenished, the nutrient-supplying capacity of soils will decrease (Lou et al., 1997). 2. Nutrient Management In contrast to other soil physical and chemical properties, nutrient availability can be managed comparatively easily through fertilization. It is generally agreed that fertilization can have dramatic impacts on bamboo productivity under “poor” site conditions and under minimal management. Oshima (1931a) said that as with vegetable cultivation, bamboo requires abundant fertilizer, but no studies show that excessive fertilizer application reduced its yield and quality. Hong (1987)

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recognized the particularly beneficial effect of fertilizer application on bamboo production on poor soils in China. Ahmad and Haron (1994) recommended regular fertilization for optimum bamboo growth and performance in Malaysia. Widjaja (1991) described fertilization as the most important management technique in Indonesia. Meanwhile, Qiu et al. (1992) concluded that productivity of bamboo forests under minimal management in China could be significantly increased by fertilizer application. Fertilizers dramatically increased bamboo yield in India (Lakshmana, 1994a) and in Japan (Suzuki and Narita, 1975). As for many other agricultural and horticultural crops, nutrient application rates, ratios between nutrients, schedules of nutrient application, forms of fertilizer, and nutrient placement are equally important considerations in bamboo production. Since bamboo is a perennial crop, however, nutrient management schemes that have been developed for annual crops may not apply to it. Moreover, bamboo is grown for several products, and it is understandable that optimal fertilization will vary with purpose of cultivation. Due to increasing scarcity of resources in the future, there is a need to match efficient fertilizer use to sustained productivity and to sustain favorable soil conditions over the short and long term. a. Nutrient Application Rates and Nutrient Ratios Due to their absolute lower nutrient-absorption capacity, seedlings and young plants require less nutrient than mature plants. Shanmughavel and Francis (1997), Raina et al. (1988), Thanarak (1996), and Totey et al. (1989) have recommended nutrient application rates for seedlings and young plants of B. bambos, B. tulda, D. asper, and D. strictus, respectively. More importantly, Table VIII presents an overview of reported annual nutrient application rates for, and ratios between, N, P, and K for mature bamboo stands. Nutrient applications average 318, 149, and 126 kg/ha/year of N, P, and K, respectively (N:P:K ratio: 2.5:1.2:1.0). Many more nutrients are applied in stands primarily used for edible shoots (523, 226, and 228 kg/ha of N, P, and K, respectively) than in stands for shoot and timber production (315, 97, and 142 kg/ha of N, P, and K, respectively) and timber-only stands (225, 135, and 89 kg/ha of N, P, and K, respectively). Compared with the average nutrient content of total plant biomass, which is 288, 44, and 324 kg/ha of N, P, and K, respectively, with an N:P:K ratio of 7:1:7 (Section II.D), application rates appear excessive since only a proportion of that total biomass is harvested annually. For example, nutrients removed in fresh shoots (on average 16 t/ha/year yield; 5–10% dry weight; 4.0, 0.6, and 4.0% of N, P, and K, respectively) average 49, 7, and 49 kg of N, P, and K per hectare per year, respectively, and those in culms for timber (on average 13 t/ha/year yield; 50% dry weight; 0.6, 0.1, and 1.0% N, P, and K) at 36, 9, and 63 kg of N, P, and K per hectare per year, respectively (Tables IV, V, and VII; V. Kleinhenz and D. Midmore, unpublished data). At its maximum, about 85, 16, and 112 kg of N, P, and K per hectare per year, respectively, are removed when bamboo is grown for shoot and timber which is approximately 30, 37, and 35% of the total content of N, P, and K,

Table VIII Annual Application Rates of Nutrients (N, P, and K), N:P:K Ratios, and Harvested Yield of Several Bamboo Species Cultivated for Different Products Nutrient application rate (kg/ha/year) Bamboo species

N:P:K ratio K

Yield (t/ha/year)

3 1

1 1

10–30 —a

Fu and Banik (1995) Farrelly (1984)

1.1 4.7 4.5 — 1 — 2.5 3.7 — 4.6 1.7 1.0 3 — 2 — 15 3.4

1.0 1.0 1.0 — 1 — 1 1.0 — 1.7 1.0 1.3 6 — 1 — 4 1.0

1.0 1.3 2.0 — 1 — 1.5 1.4 — 1.0 1.2 1.0 1 — 1.5 — 1 2.3

19.3 — 4.5–6 7.9 30 8.2 4.0 and 7.6 1.4 and 15 12.4 6.9 — 16.4 33.6 25 25 4.1 ≈20 —

An et al. (1995) Shen et al. (1993) Liu and Pan (1994) Jeong et al. (1995) Fu et al. (1991a) Yu (1987) Wang et al. (1996) Hong (1994) Shi and Bian (1987) Xiang and Xi (1987) Suzuki and Narita (1975) Hong (1987) Hong (1987) Huang and Wang (1996) Lu and Liu (1984) Wang (1988) Shen et al. (1996) Jin and Chong (1982)

7.0 — 1 1 2 —

1 — 1 1 1 —

7.9 — 1 1 1 —

— 12–15 — — — —

Zhou and Wu (1997) Lin (1995) Thanarak (1996) Lakshmana (1994a) Patil et al. (1994) Thanarak (1996)

Product

N

P

K

N

(In general) (In general)

Shoots or timber Shoots or timber

110

≈225–570 each 110

110

4–5 1

Monopodial species Phyllostachys nidularia Phyllostachys nidularia Phyllostachys praecox Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys pubescens Phyllostachys reticulata

Timber Timber Shoots Shoots Shoots Shoots Shoots and timber Shoots and timber Timber Timber Timber Timber (site 1) Timber (site 2) Timber Timber Timber Shoots Timber

87 373 750 98 300 ≈882 276 310 131 183 200 214 214 276–414 ≈400 460 760 —

78 80 167 — 300 — 110 83 — 66 120 286 429 — — — 210 —

80 100 333 — 300 — 166 118 — 40 140 214 71 — — — 50 —

Sympodial species Bambusa distegia Bambusa oldhamii Dendrocalamus asper Dendrocalamus strictus Dendrocalamus strictus Thyrostachys siamensis

Timber Shoots Shoots and timber Timber Timber Shoots and timber

70 ≈345 ≈370–440 45 200 270

10 — — 45 100 —

79 — — 45 100 —

a

No information provided.

P

References

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respectively, of a bamboo stand. In perennial crops such as bamboo, however, calculation of fertilizer rates cannot be based upon balancing nutrient removal through harvest with nutrient application, as is done in annual crop production. A great part of the nutrients added promotes stand biomass that is not harvested (i.e., roots, rhizomes, and standing culms), indirectly improving yield. This is so for the period before bamboos reach maximum stature and productivity. As such, Toky and Ramakrishnan (1982) showed that D. hamiltonii accumulated soil potassium only for a few years after clear felling. This suggests that addition of nutrients in excess of those removed through harvest is favorable during earlier growth stages but may not be required once plants reach their maximum stature. At that growth stage, application rates should preferably be oriented toward the amount of nutrients removed through harvest with some allowance for leaching and fixation. Data for average fertilizer-use efficiency of applied N, P, and K follow the same pattern as average nutrient content of plants: K > N > P (Table IX). This and the recognized low rates of K application indicate that application of potassium is the most important measure to improve bamboo productivity. Toky and Ramakrishnan (1982), Rao and Ramakrishnan (1989), Tewari et al. (1994), and Shanmughavel and Francis (1997) pointed to the importance of potassium for growth of bamboo. In general, K application rates appear to be too low and because of the high K-use efficiency, increased rates could result in a significant increase in productivity, particularly in timber production. In contrast, general application rates for P appear to be too high and an increase of P rate is unlikely to increase productivity. Current N application rates for the different purposes for which bamboo is grown would appear to be more realistic. Nutrient management must not only satisfy requirements for yield but also for quality of harvested parts. Very little research has been conducted on the Table IX Average Fertilizer-Use Efficiencya of N, P, and K in Bamboo Production for Shoots and Timber Fertilizer-use efficiency (t yield/kg nutrient) Bamboo product Nutrient

Shoots

Timber

Average

N P K

0.03 0.04 0.08

0.17 0.02 0.54

0.13 0.02 0.45

a

Calculated from published data. Sources: An et al. (1995), Fu et al. (1991a), Hong (1987), Hong (1994), Huang and Wang (1996), Jin and Chong (1982), Wang (1988), Wang et al. (1996), Xiang and Xi (1987).


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effects of fertilizer application on the many quality parameters of fresh edible shoots and bamboo timber. There is, however, a general trend that with more N, P, and K applied, total yields of shoots and/or timber are higher due to the greater number of shoots and/or culms harvested, and longer culms, but, their diameter is reduced (Hong and Jiang, 1986; Hong, 1994). Tissues of shoots and culms harvested from plantations abundantly supplied with nutrients, however, are usually “softer,” resulting in lower shoot quality and less durable culms with poorer mechanical properties (Ueda, 1960). For edible shoots, higher N decreases but higher P increases sugar content in P. pubescens (Hong, 1994). For the same species, Zhu et al. (1991) quantified the effects of N, P, and K on shoot quality with the following results: r r r r r

N, P, or K enhance amino acid content N decreases sugar content P increases sugar content N increases hydrolytic acids N and/or P lower free tyrosine of canned shoots

Silicon has a special role in the nutrition of bamboo because it is associated with cell-wall constituents and is present in xylem cell walls and fibers of plants. There is a general agreement that Si should only be applied to bamboo stands for timber production. For production of edible shoots, Si may prevent shoot development and reduce quality by increasing fiber content (Hamada, 1982; Hong, 1994; Fu and Banik, 1995). In contrast, application of Si improves the mechanical properties of bamboo culms with about 16 kg Si removed in 1 t of timber (Nonaka, 1989). Recommendations for Si application to bamboo timber stands range from 0.2 (Xiang and Xi, 1987) to 6 (Farrelly, 1984) and 16 (Lu and Liu, 1984; Nonaka, 1989) kg SiO2/ha/year. b. Scheduling of Nutrient Application As for many other crops, bamboo responds more favorably to split application of nutrients throughout the year than to a single annual dressing (Raina et al., 1988). Whether nutrients are applied as single or multiple dressings, the optimum time of application during the year is of particular interest. These usually coincide with distinct growth phases in bamboo: r r r r

Before the shoot season During the shoot season After the shoot season/before rhizome growth During rhizome growth: During bud formation on rhizomes During shoot growth below ground

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Several studies have systematically tested the benefits of nutrient application during different times of the year on bamboo productivity; however, no single, most important stage was identified. Nutrient application earlier in the season (i.e., shortly before and during the shoot season) may be the most appropriate time of fertilizer application (Shen et al., 1993; Jeong et al., 1995). Hong (1994) concluded that nutrient demand of bamboo is greatest during periods of rapid growth (i.e., of shoots and rhizomes). Therefore, a second dressing during rhizome growth and/or when buds on rhizomes start developing into new shoots was found to be favorable (Hong, 1994; Wang et al., 1996). These recommendations were developed for cultivation of monopodial bamboo in temperate regions. These regions are usually characterized by the occurrence of some rainfall during the nonshoot season, which improves availability of nutrients to plants. Under subtropical conditions, this preshoot season is typically dry and application of fertilizer may be ineffective without irrigation. Mohamed (1995) concluded from his experiments in tropical Malaysia that no nutrient was released from fertilizer granules in the absence of water during the dry season. If mineralized nutrients accumulate in the soil during the dry season, fertilizer application would be unnecessary because negligible leaching of mineralized nutrients would take place (Kleinhenz et al., 1997b). In support of this, Raghubanshi (1994) showed that highest concentrations of available nutrients and of microbial biomass in a bamboo savannah in India were found during the dry season. Table X presents an overview of reported schedules of nutrient application to bamboo. Most authors recommend multiple dressings with at least one focused shortly (1 month) before aboveground emergence of shoots. This dressing may be N based, whereas applications later in the growing season (i.e., during rhizome growth) may be P and K based. For silicon, Huang (1987) recommended splitting applications throughout the growing season. Special production requirements dictate that these general schedules be modified. For example, for production of “early” shoots after the winter or dry season, several authors recommend an additional N dressing. Thanarak (1996), X. Fang et al. (1997), and Li et al. (1998c) recommended applying up to 460 kg N/ha and achieved earlier shoot emergence by up to 20 days for D. asper, P. praecox, and P. pubescens, respectively. To contend with alternation in monopodial bamboo species a long-term biennial fertilization schedule was recommended (Anon., 1986) with nutrients applied only in the “off season” to improve the photosynthetic capacity of the (low) total leaf area of plants (Section II.B). c. Fertilizer Form There is no consensus on the preferable fertilizer form (i.e., inorganic or organic form) or on their short- and long-term effects on bamboo productivity and site (i.e., soil) quality. Effects very similar to those that accompany disturbance of bamboo

Table X Schedules of Fertilizer Application and Fertilizer Form in Bamboo Species Cultivated for Different Products Schedule/growth phase During rhizome growth

Bamboo species (In general) (In general) Bambusa oldhamii Dendrocalamus asper Gigantochloa scortechinii Phyllostachys sp. P. nidularia P. propinqua P. pubescens P. pubescens P. pubescens P. pubescens P. pubescens P. pubescens P. pubescens P. pubescens a

Before shooting

—a — — Shoots and timber Timber Shoots

Inorganic 1 month before Organic Organic and inorganic — 13% inorganic N-based × × × × —

Timber Timber Shoots Shoots Shoots and timber Shoots and timber Shoots and timber Timber Timber Timber

During shooting — — Inorganic Inorganic — —

Before rhizome growth Inorganic ×b — — — 32% NPK

During bud development

During belowground shoot growth Organic

— — —

— — —

Not during this season 13% NPK 42% organic ×c

— — — — ×

— × — — —

Inorganic

—

—

Organic/slow-release inorganic

—

—

—

×c

N-based 1 month before —

Inorganic N — ×

— — —

No information provided. Nonquantified application. Only specified as during rhizome growth.

b c

Product

×

×

c

—

× —

×c

Mohamed (1995) Wan (1994) Shen et al. (1993) Shen et al. (1996) Fu et al. (1991a) Jeong et al. (1995) Hong (1994) Oshima (1931b) Wang et al. (1996)

P and K — —

References Fu and Banik (1995) Farrelly (1984) Lin (1995) Thanarak (1996)

— —

Huang (1987) Fu et al. (1991b) Fu et al. (1994)

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soils through harvest and cultivation appear to accompany continuous (exclusive) use of inorganic fertilizers. This leads to less total N and soil organic matter but pools of readily available inorganic nutrients are greater (Jin and Chong, 1982). Organic fertilizers are slowly decomposed by microorganisms and, therefore, improve organic matter content of bamboo soils. Although response in bamboo plants is slower immediately after application, organic fertilizers exert a longer lasting effect on “fertility” of bamboo soils than inorganic fertilizers (Wang et al., 1985). For intensive production of shoots and timber, a combination of organic and inorganic fertilizers, integrated with other crop management practices, may be the optimum solution. Liu and Pan (1994) reported that through such intensive management, including high application rates of inorganic fertilizers (e.g., 750 kg/ ha N), of a P. praecox forest in China, output was 14.7 times that of the control. As a general practice, for quick response in bamboo, inorganic N-based fertilizers are preferable before and during the shoot season, whereas organic fertilizers are preferably applied during later growth stages (Table X). d. Fertilizer Placement Distribution of minerals takes place through the rhizome system of monopodial bamboo species (Qiu et al., 1986). Translocation to young, growing plant parts, however, decreases with distance from nutrient sources. Therefore, fertilizers should be placed close to younger plant parts, that is, broadcast around young (≤1 year old) rhizomes (without aboveground culms), rather than applied as a spot dressing to older (>1 year old) rhizomes (with aboveground culms). Similarly, Oshima (1931b) recommended applying fertilizers to younger rhizome parts before the shoot season when a quick response is required and to older rhizome parts when a slow but sustained effect is sought. The effects of fertilizer placement hold only for young bamboo stands since differences in bamboo productivity between broadcast and spot dressings diminish with stand age (Shi and Bian, 1987; Hong, 1994), probably because of the increasing density of the rhizome system. For sympodial bamboo, Lin (1995) recommended applying fertilizers as spot dressings around clumps for immediate effects before the shoot season and broadcasting fertilizers away from clumps for long-term effects after the shoot season. e. Fertilization Based on Plant Analysis Determination of fertilizer application rates based on analysis of nutrient concentrations in plant parts is increasingly common in many crops including plantation/tree crops for production of fruits, nuts, and teak. The approach of Diagnosis Recommendation Integrated System (DRIS) is based upon establishing diagnostic norms for nutrients in specific plant parts and how they can be modified by fertilization. Together with other crop management practices, Ma and Zhang (1997) recommended such a system for intensive multipurpose cultivation of bamboo.


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The photosynthetic capacity of leaves largely depends on their chlorophyll content which, in turn, is positively correlated with the concentration of N in their tissues (Section II.B). There is no question that fertilizer application increases nutrient concentration in leaves of bamboo (Zhou and Wu, 1997; Li et al., 1998c). Shi and Bian (1987) showed specifically that N increases chlorophyll a, chlorophyll b, and consequently, total chlorophyll in P. pubescens. Therefore, monitoring and maintaining certain concentrations of nutrients such as N in leaves may be an alternative to fertilization schemes based upon fixed fertilizer rates and application schedules. In studies with B. distegia, Zhou and Wu (1997) found that nutrient concentrations in1-year-old leaves were better correlated with timber yield than those in N > P. Unregulated exploitation of stands is a major reason for degradation of bamboo resources worldwide. Harvesting of very young culms for fiber or timber has jeopardized bamboo growth since ancient times (e.g., in China), but more recently inappropriate harvest has lead to extremely low and extremely high standing-culm densities. If bamboo stands are left undisturbed, biomass production increases until aboveground and belowground competition results in decreasing annual rates of biomass gain. Control of standing-culm density is the most important measure to combat such a decline in productivity. Appropriate densities vary dramatically depending on the product(s) for which bamboo is cultivated. These average 7400, 9100, and 13,300 standing-culms per hectare for shoot-only, shoot and timber, and timber-only bamboo stands, respectively. Variations from these averages are due to species differences (higher density for species with thinner culms), yield (higher density for greater total yields), shoot and culm quality (lower density for thicker shoots and culms), and production sites (higher density to maximize yield in “poorer” sites and lower density to maximize quality in “richer” sites). To sustain not only the productivity of perennial bamboo but also maintain the productivity of its growing sites, management of the soil is of paramount importance. Bamboo will grow under soil conditions which are not suitable for other crops. Therefore, they are widely used for rehabilitation of degraded land. During commercial production, harvest and management conditions result in soil disturbance, which stimulates a chain reaction that reduces total soil content of nutrients and increases nutrients in the labile fraction of soil biomass and finally in the mineralized form (Silgram and Shepherd, 1999). Exclusive application of inorganic fertilizers has the same effect and, therefore, adding organic fertilizers to bamboo is required. Part of this organic fertilizer is self-provided in the form of bamboo litter: on average, about 8 t/ha of biomass, which is approximately 7% of the total biomass of plants including 63, 6, and 42 kg/ha of N, P, and K, respectively, are annually recycled in bamboo stands. Decomposition is slower in temperate climates (up to 6 years) than in tropical climates (2–3 years). Addition of organic mulches may help to maintain soil organic matter, manipulate soil temperature and soil moisture, and reduce solar radiation incidence at the base of bamboo stands. In conclusion, the present understanding of bamboo growth patterns and their management, and of management of the bamboo growth environment, provides

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the technical basis for concerted efforts to raise productivity and the supply of bamboo products across both temperate and tropical climates.

ACKNOWLEDGMENTS We thank the Rural Industries Research and Development Corporation for funding in part; Mr. Grant Zhu, Ms. Kulanthee Santigritsanalerd, and Mr. Saravut Suparatanachatpun for help in translating literature from Chinese and Thai into English; Mr. Durnford Dart of “Bamboo Australia” for supporting our own field studies; and Ms. Katherine Lopez for editing the manuscript.

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MANAGING WORLD SOILS FOR FOOD SECURITY AND ENVIRONMENTAL QUALITY R. Lal School of Natural Resources The Ohio State University Columbus, Ohio 43210

I. Introduction II. Historical Development of Agriculture A. Conversion of Natural to Agricultural Ecosystems B. Plowing and Mechanized Farming C. Terracing and Soil Erosion Control D. Irrigation E. Soil Fertility Management through Manure and Fertilizers F. Soil Quality Improvement III. The Green Revolution IV. Relation Between Extensive Agriculture, Soil Degradation, and the Greenhouse Effect A. Soil Degradation B. Greenhouse Effect V. Challenges of Soil and Water Management for the 21st Century VI. Toward Sustainable Management of Soil Resources A. Soil Restoration for Mitigating the Greenhouse Effect B. Agricultural Intensification VII. Respecting “The Dirt” for Feeding 10 Billion and Mitigating the Greenhouse Effect VIII. Conclusions References

The global population of 6 billion is increasing at the rate of 1.3% or 73 million persons/year. At the present rate of growth, the population will reach 7.5 billion by 2020 and 9.4 billion by 2050. With the projected increase in world population and change in food habits, the future food demand for cereals will increase drastically. Therefore, among principal global concerns of the twenty-first century are: (1) food security due to a rapid increase in the world population, (2) soil degradation


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R. LAL by land misuse and soil mismanagement; and (3) anthropogenic increases in atmospheric greenhouse gases. All of these issues are linked to the sustainability of soil quality especially in relation to the soil organic carbon (SOC) pool and its dynamics. Soil supports terrestrial life through five processes: (1) biomass productivity, (2) restoration and resilience of ecosystems, (3) purification of water, (4) detoxification of pollutants, and (5) cycling of C, N, P, S, and H2O. These processes are affected by the quality and quantity of the SOC pool. However, the SOC pool of agricultural soils has been depleted by 30–40 Mg C/ha (Mg = 1 metric ton, 1 hectare = 2.47 acres) due to cultivation and other degradative processes, which has set in motion the downward spiral of soil degradation. The increase in atmospheric concentration of CO2 is occurring at the rate of 3.4 billion metric tons (Pg)/year. In addition to fossil fuel combustion and cement manufacture, this increase is caused by deforestation, biomass burning, and soil cultivation. Conversion of natural to agricultural ecosystems has resulted in emission of 66–90 Pg of C from world soils. The rate and magnitude of gaseous emissions from world soils are exacerbated by soil degradative processes, that is, decline in soil structure, accelerated erosion, and nutrient imbalance. Until the 1970s, more C was emitted annually from soils and land-use conversion than from fossil fuel combustion. Currently, about 25% of global emissions are from all agricultural activites. Judicious land use and recommended soil management techniques can resequester 60–70% of the historic C loss. Adoption of recommended agricultural practices or agricultural intensification can improve productivity and resequester C lost from the world soils. In the past 50 years, the number of people fed by a single U.S. farmer has increased from 19 to 129 through adoption of recommended agricultural practices. Such practices include use of conservation tillage, growing cover crops, using biosolids and amendments, enhancing soil fertility through judicious use of fertilizers and adopting precision farming, water conservation and improved methods of irrigation, and use of improved varieties. The global potential of soil C sequestration is estimated at 0.9–1.9 Pg/year through desertification control and about 3 Pg/year through restoration of all degraded soils. Soil carbon sequestration is a win–win strategy based on adoption of improved agricultural practices with potential of 40–70 Pg over a 50-year period. This potential is over and above that in rangeland/pasture soils, forest ecosystem, and biofuel production. In addition, it is important that degraded soils and ecosystems be restored. The soil carbon strategy (1) increases agricultural productivity, (2) improves water and air quality, and (3) reduces the rate of enrichment of atmospheric CO2. As the world population continues to grow, available farmland shrinks and the emission of gases into the atmosphere increases. Nonetheless, world soils have the capacity to feed the current and future population provided that soils are used, improved, and restored. As has been the case in the past, those holding neo-Malthusian views will again be proven wrong through adoption of recommended agricultural practices for sustainable management of soil resources. Contrary to the misconception, adoption of recommended agricultural practices is a solution to the environmental issues and also to achieving global food C 2001 Academic Press. security.

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I. INTRODUCTION The world population was 0.3 billion in 1 A.D., 0.31 billion in 1000, 0.40 billion in 1250, 0.50 billion in 1500, 0.79 billion in 1750, 0.98 billion in 1800, 1.26 billion in 1850, 1.65 billion in 1900, 2.52 billion in 1950, and 6.06 billion in 2000 (Anon. 1999). In 1000 years, from year 1000 to 2000, the human race has multiplied 20-fold. The population is currently increasing at the rate of 1.3% or 73 million per year, and it is projected to be 7.5 billion by 2020 and 9.4 billion by 2050 (Table I). Further, the increase in population will be extremely unequal among geographical regions, since 97.5% of the annual increase is occurring in developing countries where soil, water, and other natural resources are already under great stress. For example, the population of the 10 most populous countries in 1999 and 2050, respectively, is estimated at: (1) 1267 million and 1529 million for China; (2) 998 million and 1478 million for India; (3) 276 million and 349 million for the United States; (4) 209 million and 312 million for Indonesia; (5) 152 million and 345 million for Pakistan; (6) 168 million and 244 million for Brazil; (7) 109 million and 244 million for Nigeria; (8) 127 million and 212 million for Bangladesh; (9) 63 million and 169 million for Ethiopia; and (10) 50 million and 160 million for Congo (Fischer and Heilig, 1997). Of the projected 3.7 billion increase in world population between 1995 and 2050, 2 billion will occur in Asia and 1.3 billion in Africa. Providing food and other basic necessities of life to this vast humanity has been and will remain a major challenge. The threat of pending famines has been voiced throughout human history. At the time when world population was about 1 billion, Thomas Malthus (1798) Table I Global Land Usea

Region

Soil degradation (Mha) Total area Arable land Irrigated (Mha) (Mha) land (Mha) Total land Arable land

Population ( × 106) 1995

2050

Africa Latin America and Caribbean North Central America Asia Europe Oceania

2964 2053

174.9 96.0

12.3 9.8

494 307

121 92

719 477

2046 810

1839

260.2

30.6

96

63

297

384

2679 2662 845

499.0 294.1 54.9

187.2 24.8 3.0

747 218 104

206 72 8

3438 728 28

5443 638 46

World

13042

1379.1

267.7

1966

562

5687

9367

a

Demeny, 1990; Musters et al., 2000; Fischer and Heilig, 1997; FAO, 1998; Oldeman, 1994.

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expressed his apprehension about humankind’s ability to grow enough food and warned that “population, when unchecked, increases in geometric ratio. Subsistence increases in arithmetic ratio.” When Malthus wrote, no one imagined that the population would increase to 6 billion in 2000. Neither could he nor his contemporaries foresee the impact of technological advances on agricultural production. Malthusian fears have also been voiced by others even during the last decades of the twentieth century (Myers, 1991; Ehrlich et al., 1993; Brown, 1994a,b,c; Brown et al., 1998). Over and above the additional 5 billion people in the year 2000 compared with 1800, there is also a difference in the very rapid rate of increase in world population (4.5 billion added during the twentieth century and another 3.4 billion to be added by 2050). The threat to food security may be exacerbated by projected risks of global warming (Parry et al., 1999). To make matters worse, the population explosion is associated with an attendant degradation of soil and the threat of potential global warming. This unprecedented increase in human population has stressed the fragile ecosystems. While the population is increasing, the soil resources are dwindling both by degradation and conversion to nonagricultural uses (Table I). Global soil degradation is estimated at 1966 million hectares (Mha) of which 910 Mha are affected by moderate forms of degradation and 305 Mha are affected by strong/extreme forms of degradation (Oldeman and Van Lynden, 1998). Desertification, soil and vegetation degradation in arid and semiarid regions, presumably affects 1016 Mha, and the rate of desertification is 5.8 Mha/year (UNEP, 1992; Mainguet, 1991). The per capita arable land area is declining even without considering the risks of soil degradation. The per capita arable land area in 1995 was 0.23 ha in the world, 0.23 ha in Africa, 0.20 ha in Latin America, 0.89 ha in North America, 0.12 ha in Asia, 0.17 ha in Europe, and 1.8 ha in Oceania. By 2050, even if there is no soil degradation and conversion to nonagricultural uses, the per capita arable land area will decrease to 0.14 ha in the world, 0.08 ha in Africa, 0.11 ha in Latin America, 0.69 ha in North America, 0.07 ha in Asia, 0.19 ha in Europe, and 1.1 ha in Oceania. World grain-harvested area per person declined from 0.23 ha in 1950 to 0.11 ha in 1998. The basic necessities of life (food, feed, fuel, and fiber) will have to be met from low per capita land area and in changing rainfall and moisture regimes (Rosenzweig and Hillel, 1998; Parry et al., 1999). In fact, the effect of soil degradation is closely linked with the threat of global warming because of a drastic increase in the atmospheric concentration of CO2 (Etheridge et al., 1996) and other greenhouse gases (Harvey, 2000). The problem of food insecurity persists despite impressive gains in agricultural production during the second half of the 20th century. The foodinsecure population in the world was 960 million in 1970, 938 million in 1980, 831 million in 1990, 791 million in 1996, and it is projected to be 680 million in 2010 (FAO, 1999a,b). Sub-Saharan Africa (SSA) and South Asia will represent


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Table II The Challenges to Be Addressed during the 21st Century Issue World population Food insecure population Soil degradation Desertification Atmospheric concentration of greenhouse gases. CO2 CH4 N2O Global C emission per person (1950–1995) a Global irrigated area per persona World grain-harvested area per persona Forested area per capitaa a

Current status

Rate of change

6 billion 790 million 1966 Mha 1016 Mha

+1.3 percent/year −1 percent/year +5–10 Mha/year +5.8 Mha/year

370 ppmv 1.74 ppmv 311 ppbv 1.1 Mg/year 0.045 ha 0.11 ha 0.59 ha

+0.5 percent/year +0.75 percent/year +0.25 percent/year +1 percent/year −1.3 percent/year −0.55 percent/year −0.78 percent/year

Brown et al. (1998).

70% of the food-insecure population of the world by 2010. In addition, there is a severe problem of malnutrition (Measham and Chatterjee, 1999), exacerbated by nutritional imbalance in food grown on nutrient-deficient and degraded soils. While the supporters of the Malthusian concept have been proven wrong during the last two centuries, providing food security to all inhabitants of Earth is among the major challenges of the 21st century (Table II). The issue of food security is intimately linked to the issues of soil and environmental degradation. Ironically, “science is proving that humans can live in outer space and at the bottom of the sea. It’s the area in between that is causing all the problems” (E. C. McKenzie).1 The atmosphere is a common resource to which humans emit annually 6.4 Pg C by fossil fuel combustion, 3.4 Pg C in biomass burning, 1.1 Pg of carbon equivalent (CE) of N2O, and 1.8 Pg CE of CH4. The atmosphere is a victim of the “tragedy of the commons.” Therefore, the objectives of this synthesis are to (1) review historical developments in global agricultural technology, (2) discuss merits and limitations of the green revolution, (3) describe the interrelationship between soil degradation and the greenhouse effect, and (4) outline strategies for achieving food security and mitigating the risks of accelerated greenhouse effect through judicious management of soil and water resources. 1

1980 Baker Book House Co. (p. 461).

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II. HISTORICAL DEVELOPMENT OF AGRICULTURE The world population began to increase with the onset of the industrial revolution during the 19th century. Consequently, increase in food demand was met by agricultural expansion through: (A) conversion of natural to agricultural ecosystems, (B) introduction of mechanized farming operations and plowing, (C) terracing and soil erosion control, (D) expansion of irrigated agriculture, (E) use of fertilizers and other amendments to enhance soil fertility, and (F) soil quality improvement through improved cropping/farming systems. In addition, introduction of improved varieties has been an important component of increasing production.

A. CONVERSION OF NATURAL TO AGRICULTURAL ECOSYSTEMS There has been a rapid increase in agricultural land area during the last three centuries. The world cropland area expanded from 265 Mha in 1700 to 537 Mha in 1850, 913 Mha in 1920, 1170 Mha in 1950, and 1500 Mha in 1980 (Myers, 1996; FAO, 1996; Table III). The world cropland area in 1998 was estimated at 1379 Mha. Deforestation has been a major factor in expansion of the cropland area. Deforestation of the temperate and boreal forests occurred between 1800 and 1950, and that of the tropical rainforest (TRF), estimated at about 250 Mha during the last three decades of the 20th century, at an average rate of about 12 Mha/year. The current rate of deforestation of TRF is about 15 Mha/year

Table III Conversion of Natural to Agricultural Ecosystems over Three Centuries since 1700a Land area (Mha) Year

Forestland

Grassland/pastureland

Cropland

1700 1850 1920 1950 1980 2000

6215 5965 5678 5389 5053 3800

6860 6837 6748 6780 6788 —

265 537 913 1170 1500 1379

a

Adapted from Richards, 1990; Myers, 1996; FAO, 1998.


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(Southgate, 1998; Faminow, 1998), and that in the Amazon is about 2 Mha/year. Bruenig (1996) estimated that the cumulative area of all TRF was 1222 Mha in 1850 and 868 Mha in 1985. Between 1960 and 1990, the magnitude of conversion of TRF was 30% in Asia, 18% in Africa, 18% in Latin America and 20% in the world. By 2050, the agricultural land area could double in SSA and West Asia, and increase by 25% in the Asia–Pacific region. In fact, 20% of the area now under TRF may be converted to cropland by 2050. Deforestation of TRF, by the use of heavy earth-moving equipment can cause severe damage to the soil and environment (Lal and Cummings, 1979; Alegre and Cassel, 1986). The widespread problem of accelerated soil erosion and degradation in the tropics may be attributed to the use of inappropriate methods of deforestation and land development (Lal, 1981c, 1996). Deforestation by heavy earth-moving equipment increases soil compaction, increases runoff and soil erosion, decreases water infiltration rate and the available water capacity, and increases the maximum soil temperature to the supraoptimal range. Consequently, crop growth following the mechanized deforestation in the tropics can be adversely affected. Indeed, failures of many large-scale agricultural development projects in the tropics may be attributed to inappropriate methods of deforestation and land development (Wood, 1950).

B. PLOWING AND MECHANIZED FARMING Settled agriculture originated some 10 to 13 millennia ago, mostly in the Tigris, Euphrates, Nile, Indus, and Yangtze river valleys by the so-called hydric civilizations (Hillel, 1991). Between 5000 and 4000 B.C., Sumerian and other civilizations developed simple tools to place and cover seeds in the soil, eradicate weeds, and harvest grains (Troeh et al., 1980). A written record of plow (or ard) is found in Mesopotamia about 3000 B.C. (Hillel, 1998). Archaeological evidence shows the use of animal-driven plows dating back to 2500 B.C. in the Indus Valley. Plowing is indeed the oldest agricultural technique as “Homer and Hesiod and Virgil knew, the plowshare in its reasonable shape.”2 The Old Testament (I Samuel 13:20) states that the Israelites “went down to the Philistines to sharpen every man his share, and his coulter, and his axe, and his mattock” (Kellogg, 1938). Yajur Ved (18:12), the ancient Hindu scripture written between 500 and 1000 B.C., states, “let the plowshare turn up the furrow slice in happiness, air and sun nourishing the earth with water and elements.” These simple tools eventually evolved into the modern plow. Naturally, there was a wide variation in the design of these tools, ranging from a simple digging stick (still used in many areas of shifting cultivation) to a 2

V. Sackville-West, “The Land.”

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paddle-shaped spade that could be pulled by humans or animals. Xenophon, around 400 B.C., recommended spring plowing because “the land is more friable then” (Tisdale and Nelson, 1966). The use of the plow, and eventually its tractorization during the early part of the 20th century, greatly facilitated expansion of agriculture. Since their humble beginnings, 5000–4000 B.C., the tools used to turn over, mix, and pulverize the soil have been drastically altered to suit the soil-specific needs for mechanized farm operations. Soil can now be plowed to a deeper depth in less time and more intensively than ever before. However, plowing is unlike any natural disturbance because nothing in nature repeatedly and regularly turns over the soil to the specified plow depth of 15–20 cm. Therefore, neither plants nor soil organisms have evolved or adapted to this drastic perturbation. Consequently, plowing renders the soil in a state of an unstable equilibrium which exacerbates risks of soil erosion by water and wind, disrupts cycles of water and other elements (C, N, P, S), and alters the habitats of soil fauna and flora. Some have called this drastic perturbation the “rape of the earth” (Jacks, 1939) or plowman’s folly (Faulkner, 1943; Bromefield, 1943). Mechanized harvesting, often using grain carts with a single-axle load of 10–20 Mg, can lead to densification of soil that necessitates even deeper plowing to alleviate soil compaction. Mechanization, thus, has been a necessary evil creating a vicious cycle of plowing, compaction, and more plowing.

C. TERRACING AND SOIL EROSION CONTROL Managing and controlling soil erosion has been a major challenge since the dawn of settled agriculture. Accelerated soil erosion was controlled by introducing an innovative terraced agriculture. Terracing, land forming, and establishing retaining walls of stone or vegetative barriers, have been widely used to minimize risks of soil erosion on sloping lands. Terraced agriculture is a cultural tradition in many ancient civilizations around the world including the Middle East (The Phoenicians), East and Southeast Asia, West Asia (Yemen), and Central and South America. The Incas designed elaborate systems of stonewalled terraces for intensive use of steeplands in the Peruvian Andes (Williams, 1987). In Peru, about 1 Mha of land was terraced, of which about one-third is still in cultivation (Denevan, 1985). Constructing stone terraces in Peru involved some 3 billion m2 of rock wall and 4-million person-years of labor. Impressive as the terraced agriculture looks, its effectiveness in erosion control depends on proper construction and regular maintenance. Faulty construction and lack of maintenance have caused vast areas of eroded, barren, unproductive, and abandoned hillslopes now seen throughout the Andes, the Mediterranean, and other regions around the world.


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Table IV Past and Projected Irrigated Land Areaa Region

1993

2020

World Developed countries Developing countries Sub-Saharan Africa Latin America South Asia India China

253 65 188 4.9 17.1 74.7 50.1 49.9

296 69 227 7.4 18.7 97.8 68.6 53.1

a

In Mha; Scherr, 1999.

D. IRRIGATION Villages excavated in southern Iraq are the first evidence of dry climate agriculture aided by irrigation from 9500 to 8800 B.C. Irrigated agriculture was widely used by 4000 B.C. by the Sumerians, Babylonians, Assyrians, Egyptians, Harappans, and Chinese in the basins of the Tigris, Euphrates, Nile, Indus, and Yangtze rivers (Hillel, 1994). Irrigated agriculture also developed independently in Central America including Central Mexico and coastal Peru. Irrigation has played a major role in the increase in food production during the 19th and 20th centuries. The land area under irrigated agriculture was 8 Mha in 1800, 40 Mha in 1900, 100 Mha in 1950, 185 Mha in 1975, and 255 Mha in 1995 (Framji and Mahajan, 1969; Field, 1990; FAO, 1996; Postel, 1999). The growth in irrigated agriculture was 2 percent/year between 1960 and 1970 and 1 percent/year between 1970 and 2000. The rate of growth in irrigated agriculture is expected to be 0.8 percent/year between 2000 and 2010. A major increase in irrigated agriculture by 2020 is likely to occur in South Asia (Table IV). The proportion of agriculture dependent on irrigation is 100% in Egypt, 89% in Uzbekistan, 80% in Pakistan, 61% in Iraq, 52% in China, and 39% in Iran (Table V). The impact of irrigation on food production is enormous because 17% of the irrigated cropland produces 40% of the world’s food (Postel, 1999).

E. SOIL FERTILITY MANAGEMENT THROUGH MANURE AND FERTILIZERS The importance of soil fertility management has long been recognized for obtaining high yields on a continual basis. In Mesopotamia, writings dating back

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R. LAL Table V World Land Area under Irrigated Agriculture in 1995a

Country India China United States Pakistan Iran Mexico Russia Thailand Indonesia Turkey Uzbekistan Spain Iraq Egypt Others World total

Proportion of Irrigated area (Mha) agricultural area (%) 50.1 49.8 21.4 17.2 7.3 6.4 5.4 5.0 4.6 4.2 4.0 3.5 3.5 3.3 70.1 255.8

30 52 12 80 39 25 4 29 27 17 89 23 61 100 —

a

Adapted from Postel, 1999.

to 2500 B.C. refer to fertility of the cropland soils. The practice of manuring vineyards in Greece dates back to from 900 to 700 B.C., and the importance of manuring shallow soils is recorded in Greece by Theophrastus around 372–287 B.C. The use of green manures (by plowing under acinium, lupines, vetch, lentils, chickpeas, clover, alfalfa, and other legumes) for soil fertility enhancement dates back to Cato during 234–149 B.C. (Tisdale and Nelson, 1966) and Virgil during 70–19 B.C. The use of lime and saltpeter (potassium nitrate) and mixing fertile and poor soils have been mentioned in ancient writings going back 2000 years. It was J. R. Glauber (1604–1668), a German chemist, who experimentally demonstrated the impact of saltpeter on plant growth. Developing upon the experiments of English scientists (Jethro Tull, Arthur Young, and Francis Home), Justus von Liebig (1803– 1873), a German chemist, laid the foundation for use of chemical fertilizers as a source of plant nutrients. The agricultural experiment station at Rothamsted, United Kingdom, established by J. B. Lawes and J. H. Gilbert, improved upon the work by Liebig and demonstrated the importance of chemical fertilizers in maintaining soil fertility. Application of synthetic fertilizers was an important tool for enhancing agricultural productivity in the second half of the 20th century. The use of synthetic fertilizers has increased dramatically since World War II, especially in the developed countries. The world fertilizer use increased at the rate


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Table VI Trends in World Fertilizer Usea Year 1961–1962 1970–1971 1980–1981 1990–1991 1997–1998 2020–2021 (projected) a

N

P

K

Total

11.6 31.8 60.8 77.2 81.2 —

10.9 21.1 31.7 36.3 33.5 —

8.7 16.4 24.2 24.5 22.6 —

31.2 73.3 116.7 138.0 137.3 220

Values × 106 Mg; IFDC, 1999.

of 4.2 million Mg/year during the 1960s, 4.3 million Mg/year during the 1970s, 2.1 million Mg/year during the 1980s, and its use stabilized during the 1990s (Table VI). The projected fertilizer demand for the year 2020 is 220 million Mg. The use of fertilizer in cereals has seen a meteoric rise from 1960 to 1990. The global cereal production increased from 400 million Mg in 1961–1963 to 1050 million Mg in 1991–1993. The corresponding use of chemical fertilizers was 5 million Mg in 1961–1963 and 65 million Mg in 1991–1993 (Vlek et al., 1997). In the early 1990s, 7 million Mg of N was used on about 70 Mha of irrigated rice that produced a mean yield of 5 Mg/ha (Cassman and Pingali, 1995). Along with chemical fertilizers, there has also been a dramatic increase in the use of pesticides. The global pesticide use in the 1990s was 2.6 million Mg of active ingredients. As much as 85% of all pesticides are used in agriculture and 75% of the global use is in developed countries. Both fertilizer and pesticide use are likely to increase in the developing countries during the early decades of the 21st century.

F. SOIL QUALITY IMPROVEMENT The term “soil quality,” soil’s productivity and environment moderating capacity, received considerable attention during the 1990s (Doran et al., 1994; Doran and Jones, 1996; Karlen et al., 1997; Gregorich and Carter, 1997). The concept has also been a debatable issue (Sojka and Upchurch, 1999). Despite what some claim, the concept of soil quality in one form or another has been used by agricultural philosophers and writers for some 2500 years. In Greece, Theophrastus (327–287 B.C.) recommended abundant manuring of “thin soils” (Tisdale and Nelson, 1966). Virgil realized the importance of “blackish, loose, and crumbling soil,” and advocated a method to assess the volume–weight (bulk density) relationship of soil. Soil color has been used as a criterion for assessing soil fertility for a long time, dark-colored soil being more fertile than the light-colored ones.

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A soil scientist of Moorish Spain, Ibn-Al-Awam, wrote several volumes during the 12th century on agricultural issues. The book “Kitab al-Felhah” or “Book of Agriculture” was translated into Spanish in 1802. The book was brought to public attention in the “Encyclopedia of Islam” (1760–1777). In this book, the author writes “The first step in the science of agriculture is the recognition of soils and of how to distinguish that which is of good quality and that which is of inferior quality” (Vol. 1, p. 23). The best of all soils, according to the author, are the alluvium of river valleys “because of the mud with which they are mixed, for the running water brings sediments removed from the surface of the soil along with dead leaves and manure.”3 The soil quality concept has been used by farmers and revenue officers in South Asia (India) for millennia. According to this procedure, soil quality is expressed on a relative scale of 1–100 (the relative soil quality, for example, can be $0.10 for soil A vs $0.50 for soil B, compared with $1 for a prime soil). Important challenges of modern times are developing objective methods of quantification of soil quality and identifying those indices which scientists can quantify but farmers and land managers can understand and relate to. These were important developments in advancing science of soil and water management. While increasing agricultural production, however, use of these techniques drastically disturbed the natural ecosystem, in some cases, with severe adverse impacts on the environment.

III. THE GREEN REVOLUTION While doomsayers expressed apprehension and pointed fingers, agricultural scientists ushered in the green revolution and saved millions from starvation. The term “green revolution” refers to “rapid increases in wheat and rice yields in developing countries brought about by improved varieties combined with the expanded use of fertilizers and other chemical inputs” (Hazel and Ramasamy, 1991). In a broad sense, the term green revolution also includes other economic changes as well as social and cultural changes that either contributed to the technological and ecological changes or were derived from them (Leaf, 1984). In fact, the adoption of improved varieties and the six concepts/techniques (A through F) described in Section II brought about a quantum jump in food production in the world from 1960 to 1995. From 1950 to 2000, the number of people fed by a single U.S. farmer increased from 19 to 129 (Bond, 2000). The effect of green revolution technology was especially spectacular in South Asia and in northwest India. The food grain production in India quadrupled over the second half of the 20th century, from 50–200 million Mg. The cereal production in 1970 in India was 85 million Mg compared to 170 million Mg in 1995. The wheat production in 3

J. J. Clément-Mullet, 2 vol., 1864–1867, Paris, France.


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India increased from 6 million Mg in 1947 to 72 million Mg in 1999. The average yield of wheat in India increased from 900 kg/ha in 1964 to 2300 kg/ha in 1999 (Swaminathan, 2000). The share of grain production of Indian States Punjab and Haryana rose from 8% in 1965–1966 to 20.5% in 1985–1986 (ICAR, 1998). There was a linear relationship between energy input (fertilizer, irrigation, tillage) and crop yield (Panesar, 1996). The green revolution brought about impressive gains in world agricultural production. In 1950, the global food-grain production was 692 million Mg for the population of 2.5 billion people. It is estimated that as many as two-thirds of the population then lacked adequate food supply. In 1992, the global food-grain production increased to 1952 million Mg for the population of 5.7 billion people, with a 24% gain in per capita grain supply. The world grain production per person increased from 250 kg/year in 1950 to 350 kg/year in 1985 (but declined to 320 kg/year in 1996) (Brown et al., 1998). The per capita calories have increased from 2287 calories/day in 1963 to 2697 calories/day in 1992, despite an increase in the world population (Avery, 1994; WRI, 1998). The relative increase in per capita calories was more in developing countries from 1940 calories/day in 1963 to 2473 calories/day in 1992, an increase of 26%. There has been at least a 32% increase in per capita food production in the developing countries of the tropics since 1970 (Perkins, 1997; Conway, 2001, 1997). Globally, the yield of wheat increased 2.92 percent/year from 1961 to 1979 and 1.78 percent/year from 1980 to 1997. For maize the rate of increase was 2.88 percent/year and 1.2 percent/year for the same period, while the growth rate of rice for the entire period was 1.95 percent/year. Increase in food production was primarily brought about through increase in input of energy (in the form of fertilizer, irrigation, tillage, etc.). Despite the impressive gains of the green revolution, there are large variations in crop yields, among different regions. For example, the world average yield of cereals for the period 1990–1996 was 2.7 Mg/ha, with the highest yield of 8.8 Mg/ha in The Netherlands and the lowest yield of 0.35 Mg/ha in Botswana. The average global yield of wheat is 2 Mg/ha but the record yield is 14 Mg/ha with a possibility of obtaining 21 Mg/ha. For other crops as well, there is an enormous yield gap not only between developed and developing countries, but also between research plot yield and the farmer average yield within a country or an ecoregion (WRI, 1998). Production gains have not been realized especially in SSA (Paarlberg, 1996). On a global scale, therefore, there exists a vast potential to increase crop yield especially in SSA and South Asia. The gap between attainable and actual yield in several regions shows that application of the recommended agricultural practices (RAPs) can bring about yet further increases in crop yields. The need for increasing yield is urgent in regions with rapidly increasing population. However, further increase in crop yield will have to be achieved with less arable land area, less availability of water, and with shortage of labor during the periods of peak demand for the required farm operations.

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Application of the green revolution technologies has been a debatable issue for both biophysical and social reasons (Shiva, 1991; Perkins, 1997). Contamination of natural waters and air pollution are related to excessive and indiscriminate use of fertilizers and pesticides (Sale, 1993; Lohnert and Geist, 1999). Although not all pesticides are equally dangerous and not all farming communities are at equal risks, the health hazards from indiscriminate use of chemicals cannot be ignored. There is also increasing risk of nitrate and phosphate contamination from fertilizers and manures. The global use of nitrogen fertilizer, and along with it the extent of contamination of surface and groundwaters, grew 5-fold from 1960 to 1990. Further, the fertilizer use in developing countries is likely to double by 2020 (WRI, 1998), and with it will increase the risks of environmental contamination. It is well known that hectare for hectare, irrigated land is far more productive than rainfed land. However, its misuse has created water and salt imbalance in the states of Punjab and Haryana in India and in other regions of the world. Irrigation can also increase risks of infectious diseases, especially the dangers of schistosomiasis in West Africa. In all, more than 30 diseases have been linked to irrigation. The problem is not with the technology. The solution to these adverse effects of agricultural technology lies in determining when to apply fertilizers and pesticides, and precisely how much; how much irrigation water to apply, and by what method; and when and how to plow. It is overfertilization, overuse of pesticides, excessive application of irrigation, and unnecessary plowing that have caused the problems. Two severe problems—soil degradation and the accelerated greenhouse effect— require special mention.

IV. RELATION BETWEEN EXTENSIVE AGRICULTURE, SOIL DEGRADATION, AND THE GREENHOUSE EFFECT Despite impressive gains in crop yield and in per capita food supply, land misuse and soil mismanagement during the twentieth century caused problems of soil and environmental degradation. Soil degradation is caused by drastic perturbation of the delicate soil–environment equilibrium, especially in harsh climates and ecologically sensitive ecoregions. Soil degradation is indicative of the degree of the societal care for the land. As Lowdermilk (1939) wrote, “Individuals, nations, and civilizations write their records on the land—a record that is easy to read by those who understand the simple language of the land.” That record, regrettably, is often in the form of extensive and severe soil degradation. As Adlai E. Stevenson4 wrote, “Nature is neutral. Man has wrested from nature the power to make the world 4

Speech in Hartfield, Connecticut, 18 September 1952.


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a desert or to make the desert bloom.” Both soil degradation and the greenhouse effect are creations of human misadventure with nature. Barry Commoner (1966, 1972) clearly stated four laws of ecology that govern effects of human intervention in nature: (1) everything is connected to everything else, (2) everything must go somewhere, (3) nature knows best (nature shows no mercy for mistakes made by humans or any other species), and (4) there is no such thing as a free lunch (nothing comes from nothing). Human mistakes lead to soil degradation and emission of pollutants into the air and water.

A. SOIL DEGRADATION With agricultural expansion came soil degradation; with increase in use of agricultural chemicals came environmental pollution; and with increase in irrigation came salinization. Soil degradation implies decline in soil quality because of its misuse by humans (Lal, 1997d). Severe soil degradation is often a symptom of land misuse, soil mismanagement, and indiscriminate use or abuse of input. Principal processes of soil degradation are physical (crusting, compaction, erosion), chemical (nutrient depletion, leaching, acidification, salinization), and biological (depletion of soil organic matter and reduction in soil biodiversity). 1. Soil Erosion and Desertification Introduction of plowing and turning under of crop residue and weed biomass accentuated risks of accelerated erosion by water on sloping lands and wind on flat terrain. Whereas the natural soil erosion created the most fertile soil in river valleys that were the cradle of modern civilization, onset of the accelerated erosion by plowing toppled many of the same civilizations by washing/blowing away the mere foundation on which they developed. Accelerated erosion is analogous to “cancer” of the land that caused some of the thriving civilizations to vanish (Olson, 1981). Soil erosion, a quiet crisis, has been a challenge to farmers since the time they began to use the land for settled and intensive agriculture. Accelerated soil erosion in the Mediterranean Basin, caused by deforestation of the cedar forest, destroyed granaries of the Roman Empire and toppled the Phoenicians (Eckholm, 1976). Soil erosion in Mesopotamia, the present-day Iraq, converted agricultural land to shifting sand dunes, silted up its ancient irrigation system, and ruined thriving agriculture established since 10,000 B.C. (Lowdermilk, 1939). The ancient kingdoms of Lydia and Sardis, in the present-day Turkey, now lie in ruins among barren lands because of erosion (Beasley, 1972; Lowdermilk, 1939). The demise of Harappan–Kalibangan culture, a pre-Aryan civilization in the Indus Valley, is also attributed to erosion and desert encroachment or desertification (Singh, 1982). The Inca culture in Central America vanished because of the loss of

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shallow topsoil of Mollisols cultivated on steeplands (Olson, 1981). Desertification is a serious problem in arid, semiarid, and subhumid regions (Dregne, 1992), and especially in Central Asia (Babaev, 1999). Soil erosion in these regions has been termed “the quiet crisis” (Brown and Wolf, 1984). 2. Salinization Similar to accelerated erosion, soil salinization is a serious threat to the productivity of irrigated agriculture. The salt problem in irrigated agriculture is not a new issue either. The rise and fall of the Mesopotamian civilization around 5000 to 4000 B.C. has been attributed to the development of irrigated agriculture and to its subsequent failures as a result of rising water tables and soil salinity (Lowdermilk, 1939). The decline of the Native American Indian civilization in the American southwest centuries ago is also attributed to salinization of the soil and water. Total area of all salt-affected soils in the world is estimated at 1.03 billion ha, comprising 412 Mha of saline soils and 618 Mha of sodic soils (Middleton and Van Lynden, 2000). Sustainability of irrigated agriculture is a major issue that needs to be addressed (Pereira et al., 1996). Currently, productivity of 20 Mha of the world’s irrigated land is severely affected by salinity, and that of one-third of the irrigated land is threatened because of contamination with high levels of salts (Frommer et al., 1999). Salinization of irrigated land is a severe problem in the river basins of the Indus, the Tigris, the Euphrates, and the Nile. Soil salinity is also a problem in northeastern Thailand, China, northern Mexico, the western United States, and the central Asian republics. Productivity of irrigated land in Australia is seriously jeopardized by salinity (Anon., 2000). The extent of irrigated land damaged by secondary salinization is estimated at 7 Mha in India, 6.7 Mha in China, 4.8 Mha in Pakistan, 2.4 Mha in Uzbekistan, 1.7 Mha in Iran, 1.6 Mha in Mexico, 1.5 Mha in Thailand, 1.3 Mha in Bangladesh, 1.3 Mha in Afghanistan, and 0.9 Mha in Egypt (Lal, 2000). About 17% of irrigated land in India is already salinized. Middleton and Van Lynden (2000) estimated that 43.8 Mha of land in 11 countries of South and Southeast Asia is affected by secondary salinization. Szabolcs (1985) reported that half of the 250 Mha of the world’s irrigated land is at the risk of salinization, and salinity and waterlogging are affecting 10 Mha of land annually. Abrol et al. (1988) estimated that the area of salt-affected soils in South Asia comprises 82 Mha of saline soils and 2 Mha of sodic soils. Balba (1995) estimated that there are 317 Mha of salt-affected soils in the arid tropics of which 84 Mha are in South and West Asia, 129 Mha in South America, 81 Mha in Africa, 22 Mha in southeast Asia, and 2 Mha in Central America. Excessive use of irrigation water, especially by flood irrigation is a common cause of rise in water table and salinization of the root zone. Flood irrigation is the most primitive form of irrigation, and it has been perpetuated by socioeconomic and political factors, and lack of development of improved and efficient methods.


171

3. Nutrient Depletion Soil degradation by nutrient depletion is a serious issue in SSA and also in South Asia (Van Lynden and Oldeman, 1997; Sayers and Rimmer, 1994). Henao and Baanante (1999) reported that 86% of all countries in Africa show negative balances of nutrients greater than 30 kg of N, P, K/ha/year. The cost of eliminating this vast nutrient balance would amount to $1.5 billion/year for using fertilizer. The nutrient depletion is being caused by subsistence agricultural practices based on low or no off-farm input, residue removal, and use of soil-mining practices for a long period of time. 4. Urbanization Urbanization and conversion of prime land to nonagricultural uses is another cause of decline in arable land area. A considerable amount of prime farmland is being encroached by urban sprawl in developed countries. A large proportion of urban land is being converted to holiday resorts along coastal regions of the Mediterranean in Spain and other countries. Total prime or unique farmland converted to urban land use in the United States during 1982–1992 was nearly 5.7 Mha, which is about 1.5% of all farmland or 3% of total cropland (Hoag, 1999). Area of total farmland being encroached by urban sprawl is about twice this area. Brick making for construction of houses and roads is a major factor affecting the quality of surface soil in many regions with rapidly developing population (e.g., China and India). A large proportion of the cropland is being ruined because the top 1 m of soil is removed for brick manufacture. In Haryana, India, as much as 0.7% of the arable land is affected by brick making annually (Agraval et al., 1995). Soil extraction for brick making is also common in Argentina and elsewhere in South America (Giménez et al., 1992) and the truncated soils are unsuitable for agricultural production (Hurtado et al., 1989). It is difficult to obtain reliable estimates of the global land area affected by soil degradation. The available data are often obtained by subjective methods, and the severity of soil degradation is usually not related to land use, soil management, or agronomic productivity. Oldeman (1994) assessed the global extent of soil degradation at 1966 Mha (Table I) of which 562 Mha is agricultural land, 685 Mha is pastureland, and 719 Mha is forest and woodland. Globally, 15% of the land area is degraded to some extent. The proportion of land degraded in different regions ranges from 5% in North America to 22.9% in Europe. There are several global hot spots of soil degradation which include severe soil erosion by water in foothills of the Himalayas, sloping areas in southern China and southeast Asia, southeastern Nigeria and SSA, Central American hillsides, the Andean Valley, Haiti, and Cerrados of Brazil. In addition, wind erosion is a problem in the West African Sahel and parts of Australia (Scherr and Yadav, 1996).

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R. LAL Table VII Soil-Specific Effects of Different Degradative Processes on Crop Yield

Soil degradative process

Location

I. Compaction A. By land clearing 1. Western Nigeria methods 2. Western Nigeria

Crop

Yield decline (percent/year)

References

Corn Corn

22–36 8–10

Lal (1981a) Hulugalle et al. (1984)

B. By rolling

1. Western Nigeria 2. Western Nigeria 3. Western Nigeria 4. Western Nigeria 5. Pakistan 6. Pakistan

Corn Cowpea Soybeans Cassava Wheat Sorghum

48–75 38–57 25–65 38–47 7–38 0–22

Kayombo et al. (1986) Kayombo et al. (1986) Kayombo et al. (1986) Kayombo and Lal (1986) Ishaq et al. (2001a,b) Ishaq et al. (2001a,b)

C. Simulated harvest traffic

1. NW Ohio 2. NW Ohio 3. NW Ohio

Corn Soybeans Oats

20–25 23–38 19–31

Lal (1997a) Lal (1997a) Lal (1997a)

1. Western Nigeria 2. Western Nigeria

Corn Corn

8–23 8–18

Lal (1997b) Lal (1984)

III. Soil wetness

Central Ohio

Corn

3–18

Fausey and Lal (1989)

IV. Soil erosion

1. Western Nigeria 2. Western Nigeria 3. Tanzania 4. Tanzania 5. Central Ohio 6. Central Ohio 7. Central Ohio 8. North America

Corn Cowpeas Corn Cowpeas Corn Soybeans Corn Corn

II. Decline in soil structure

14–75 16–44 17–48 25–29 2–30 3–27 17–30 2–3

Lal (1981b) Lal (1981b) Kaihura et al. (1996) Kaihura et al. (1996) Fahnestock et al. (1995) Fahnestock et al. (1995) Xu et al. (1997) den Biggelaar et al. (2001)

The magnitude of loss in productivity due to specific degradative processes differs among soils, crops, and ecoregions. The data in Table VII compare effects of physical degradative processes on decline in crop yields in western Nigeria and central northern Ohio. Decline in crop yield because of soil compaction caused by vehicular traffic may range from 8 to 75% for corn, 23–65% for soybeans, 38–57% for cowpeas, and 38–47% for cassava. Methods of deforestation and land development can cause yield reduction due to soil compaction by heavy earthmoving equipment. In central Ohio, soil compaction has only a slight effect of 2–5% in reduction in corn yield but 20–25% reduction may occur in clayey and poorly drained soils of the lakebed region. Similar to corn, compaction-induced reduction in yield on poorly drained soils may be 23–38% for soybeans and 19–31% for oats. The adverse effects of vehicular traffic on crop yield may persist for up to 7 years (Lal, 1997a). In addition to soil compaction, continuous cultivation can also lead to deterioration of soil structure. Yield reduction attributed to decline in soil structure was 8–23% for corn in western Nigeria (Table VII). Accelerated


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soil erosion can lead to reduction in topsoil depth with attendant decline in crop yield especially in soils with a root-restrictive layer at shallow depth. Erosioninduced decline in crop yield in soils of western Nigeria with a root-restrictive gravelly horizon (at 15- to 30-cm depth) ranged from 14 to 75% for corn and 16–44% for cowpeas. In comparison, the maximum yield reduction by severe erosion in soils of central Ohio was 2–30% (Table VII). Effects of soil degradation on yield, however, are masked by use of off-farm input and improved technologies. Severe soil degradation impacts agronomic productivity on-site and environment quality off-site. Estimates of erosion-induced losses in agronomic productivity have been made for Africa and SSA (Lal, 1995a) and the world (Lal, 1998). The regional loss of food production because of erosion is severe in Africa, Asia, and South America. Oldeman (1998) estimated that average loss of productivity since World War II (1945–1990) is 12.7% for cropland and 3.8% for pastureland. He observed that the loss in productivity of cropland is most severe in Central America, Africa, and Asia. The widespread problem of soil degradation is caused by several factors. Soil degradation is a biophysical process fueled by socioeconomic and political factors. In biophysical terms, fragile soils in harsh climates are highly vulnerable to anthropogenic perturbations. In socioeconomic terms, “when people are hungry and poverty stricken, they pass on their suffering to the land.” In some cases, soil degradation is also caused by human greed, short-sightedness, poor planning, and cutting corners for quick economic returns. Aldo Leopold5 wrote, “We abuse land because we regard it as a commodity belonging to us. When we see land as a community to which we belong, we may begin to use it with love and respect.”

B. GREENHOUSE EFFECT The atmosphere is a classical example of a common pool resource, thus it has been overexploited. With industrialization and expansion of agriculture through deforestation and plowing comes emission of gases into the atmosphere. Indeed the atmospheric concentration of three important greenhouse gases (CO2, CH4, and N2O) has been increasing due to anthropogenic perturbations of the global carbon cycle. For example, the preindustrial concentration of CO2 at 280 ppmv increased to 365 ppmv in 1965 and is increasing at the rate of 0.5 percent/year. The concentration of CH4 has increased from 0.8 ppmv to 1.74 ppmv and is increasing at the rate of 0.75 percent/year. The concentration of N2O has increased from 288 ppbv to 311 ppbv and is increasing at the rate of 0.25 percent/year (IPCC, 1996). A considerable part of the increase in atmospheric concentration of CO2 has occurred due to conversion of natural to agricultural ecosystems, soil cultivation, 5

In Stewart Udall’ “The Quiet Crisis,” 1963.

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R. LAL Table VIII Changes in Soil Organic Carbon Content (0–10 cm Depth) with Cultivation of an Alfisol in Western Nigeriaa Treatment

1981

1983

1984

1985

1986

1987

No till + mulch Plow till

10.4 7.5

13.5 13.0

23.1 15.1

15.8 12.9

16.1 11.6

10.0 6.5

a

In g/kg; Lal, 1997c.

biomass burning, draining wetlands and organic soils, and use of nitrogenous fertilizers. Accelerated soil erosion can lead to drastic reduction in soil organic carbon (SOC) content. The loss of SOC pool by conversion of natural to agricultural ecosystems is often more severe in soils of the tropics where temperatures are high and farmers use subsistence agricultural practices based on low external input. Conversion of natural to agricultural ecosystems can have a drastic impact on SOC content. The data in Table VIII from Nigeria show temporal differences in SOC content because of cultivation by two tillage methods. Initial increase in SOC content (between 1981 and 1984) may be because of increase in addition of roots and other biomass to the soil. Subsequent decline (between 1984 and 1987) may be due to degradation of soil quality, decline in biomass returned to the soil, and depletion of SOC pool by mineralization, leaching, and accelerated erosion. The data in Table IX, also from western Nigeria, show that accelerated erosion removed SOC ranging from 53 kg/ha/year on 1% slope to 1297 kg/ha/year on 15% slope in 1972. For 1973, with a higher rainfall than in 1972, the rate of SOC loss was 390 kg/ha/year on 1% slope compared with 3481 kg/ha/year on 15% slope.

Table IX The Loss of Soil Organic Carbon by Accelerated Erosion of an Alfisol in Western Nigeriaa Annual loss of soil organic carbon Slope

1972

1973

1 5 10 15 Rainfall (mm)

53 577 840 1297 824

390 2318 3632 3481 1197

a

In kg/ha/year; Lal, 1976.


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Table X Mean Soil Organic Carbon Content of 0–10 cm Layer for Different Erosion Phases of Alfisols for Three Farms in Central Ohioa Erosion phase

Mean SOC content (g/kg)

Not eroded (wooded) Slightly eroded Moderately eroded Severely eroded Depositional

34.7 11.3 9.8 9.7 14.5

± ± ± ± ±

5.3 1.5 1.3 1.0 2.2

a

Fahnestock et al., 1996.

Consequently, there was a drastic reduction in SOC content of the surface layer prone to erosion. Similar to the results from Nigeria, the data in Table X from on-farm studies in central Ohio also show reduction in SOC content of cultivated soils from 34.7 g/kg to 11.3 g/kg, a reduction of 67%. In general, the reduction in SOC content was more for moderately and severely eroded than slightly eroded and depositional soils. The fate of C transported by wind and water is not properly understood. A considerable proportion of the eroded carbon ends up in different landscape positions, and a considerable amount of this may be mineralized and emitted into the atmosphere. Assuming that 20% of the eroded C is decomposed, it would lead to emission of 1.14 Pg C/year into the atmosphere (Lal, 1995c). The SOC lost from 1094 Mha of global land area affected by water erosion and 549 Mha affected by wind erosion can be large. The data in Table XI show that SOC

Table XI Historic Loss of Carbon because of Land Area Affected by Water and Wind Erosiona SOC loss Severity of erosion

Water erosion

Wind erosion

Total

Light Moderate Strong and extreme Total

1.4–2.1 7.9–13.2 6.7–11.2 16–27

0.5–0.8 2.0–3.0 0.4–0.7 3–5

1.9–2.9 9.9–16.2 7.1–11.9 19–32

a

In Pg C; Lal, 1999b.

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R. LAL Table XII Contribution of C to the Atmosphere from Soil, Biomass, and Fossil Fuel Combustion Source Fossil fuel (1800–1998) Land-use change Soil cultivation Soil erosion Biomass

Emission (Pg C)

References

240–300 81–191 47–58 19–32 19–105

IPCC (2000) IPCC (2000) Lal (1999a) Lal (1999b) (by difference)

loss in the past may be 16–27 Pg C by water erosion and 3–5 Pg C by wind erosion. Similar to the SOC loss by erosion, historic loss of SOC can also be estimated because of land-use change, soil cultivation, and other agricultural activities. The loss of SOC from agricultural soils may be 30–50 Mg C/ha. Estimates of the total cumulative historic loss range from 40 to 537 Pg, with a likely range of 66–90 Pg (Lal, 1999a). Therefore, land-use conversion, agricultural activities, and soil degradation have been important sources of atmospheric enrichment of CO2 and other greenhouse gases. The amount of carbon emitted into the atmosphere is estimated at 25 Pg by erosion and 55 Pg by cultivation, with a total soil contribution of about 80 Pg (Lal, 1999a). The contribution of soil C to the atmospheric enrichment of CO2 needs to be assessed in comparison with those by other anthropogenic activities. From 1800 to 1998, approximately 240–300 Pg C was emitted as CO2 into the atmosphere from fossil-fuel burning and cement production. It is also estimated that 81– 191 Pg C was emitted as a result of land-use change and soil cultivation during the same period (Table XII). In comparison, the contribution of soil is estimated at 66–90 Pg (80 ± 20 Pg) including that by erosion estimated at 19–32 Pg (25 ± 5 Pg) (Lal, 1999a,b). Therefore, conversion of natural to agricultural ecosystems, soil cultivation, and soil degradation has been a major source of atmospheric enrichment of CO2.

V. CHALLENGES OF SOIL AND WATER MANAGEMENT FOR THE 21st CENTURY Achievements in agricultural production through technological developments during the 20th century have been impressive (Bailey, 1999). The relative agricultural production index (1979–1981 = 100) went up from 61.95 in 1961, 81.99 in 1971, and 102.72 in 1981 to an impressive 125.99 in 1991 (WRI, 1994). Similarly, the per capita agricultural production was 89.56 in 1961, 96.60 in


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1971, 100.97 in 1981, and 104.06 in 1991. However, there is no cause for complacency because even greater challenges lie ahead. Achieving global food security is a major issue now more than ever before because there are several regions (e.g., SSA) where food production has severely lagged behind the population growth. With an increase in population at 1.3 percent/year and most of it occurring in developing countries, global demand for cereals will increase by 37% to reach 313 million Mg, and that for roots and tubers will increase by 37% to reach 860 million Mg. Developing countries will account for about 85% of the 690 million Mg increase in global demand for cereals between 1995 and 2020 (Pinstrup-Anderson et al., 1999). Major concerns have been raised about the capacity of populous countries like China to feed themselves (Brown, 1994a,b). The growth rate in grain production in India has declined from 3% in the 1980s to 1.75% in the first six years of the 1990s (Mohanty et al., 1998). The neoMalthusian view of the limits-to-growth hypothesis predicts environmental restrictions on production (Smith, 1995). In addition to calorie intake, malnutrition is also a serious problem in many developing countries (Measham and Chatterjee, 1999). Therefore, the issue is how developing countries (e.g., China, India, and those in SSA) are going to feed themselves without jeopardizing the fragile natural resources (soil and water) which are already under great stress. Many environmentalists have raised serious concerns about the adverse impact of the green revolution technologies (Ehrlich and Ehrlich, 1987, 1991, 1992; Brown, 1998), especially with regard to pollution and eutrophication of natural waters, soil contamination and overall degradation, and the emission of greenhouse gases into the atmosphere and air pollution. In addition to environment, there are also severe socioeconomic and equity issues that have arisen because of the green revolution (Shiva, 1991; Conway, 1999). Access to food and a clean living environment are two of the most basic human rights, which must be respected for all citizens of the planet Earth. The answer to both issues of food security and environment quality lies in adopting strategies for sustainable management of soil resources. Sustainable management of soil resources is important because soil supports life through moderating five basic ecological processes: (1) biomass productivity, (2) restoration and resilience of ecosystems, (3) purification of water, (4) detoxification of pollutants, and (5) cycling of elements (e.g., C, N, P, S, and H2O). Therefore, soil quality must be maintained to sustain these five processes.

VI. TOWARD SUSTAINABLE MANAGEMENT OF SOIL RESOURCES Those holding neo-Malthusian views will be proven wrong also during the 21st century as they were during the 19th and 20th centuries because of the rapid

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R. LAL Table XIII Strategies for Enhancing Food Production and Ameliorating the Environment through Soil Management Strategy

Technological options

Restoration of degraded soils

1. Eroded soil restoration through afforestation and vegetation management 2. Salt-affected soil reclamation 3. Nutrient-depleted soil amelioration 4. Polluted/contaminated soil restoration

Intensification of prime soils

1. Conservation tillage with residue mulch 2. Integrated nutrient management and precision (soil-specific) farming 3. Water conservation including drip irrigation, subirrigation, and water harvesting 4. Improved cropping systems based on frequent use of cover crops

advances being made in soil management and other agricultural technologies capable of enhancing production while ameliorating and restoring the environments. There are two strategies of enhancing food production and improving the environment (Table XIII). The first strategy is to restore degraded soils and ecosystems, and the second is intensification of prime soils. Soil restoration depends on its resilience characteristics and land use and management (Lal, 1997d). Agricultural intensification implies adoption of recommended agricultural practices to enhance productivity and improve environment quality. A holistic approach based on an ecosystem assessment for its potential and restraints is essential in implementing these strategies (Ayensu et al., 1999).

A. SOIL RESTORATION FOR MITIGATING THE GREENHOUSE EFFECT It is our moral duty to restore degraded soils and ecosystems for use by future generations. Theodore Roosevelt6 said, “To waste, to destroy our natural resources, to skin and exhaust the land instead of using it so as to increase its usefulness will result in undermining in the days of our children the very prosperity which we sought to hand down to them amplified and developed.” In addition to losing productivity, degraded soils and ecosystems have also been stripped of a large fraction of their original SOC pool. Albrecht (1938) stated, “Soil organic matter 6

Message to Congress, 1907.


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is one of our most important national resources; its unwise exploitation has been devastating; and it must be given its proper rank in any conservation policy as one of the major factors affecting the level of crop production in the future.” Some argue that organic matter is as vital for soils as blood is for the body (Martius et al., 1999). Therefore, it is important to enhance the SOC pool through restoration and rehabilitation of degraded soils and ecosystems. There are 305 Mha of strongly and extremely degraded soils (Oldeman, 1994). These lands need to be converted to quick-growing perennials whose biomass can also be used as an energy source. Establishing vegetation cover and enhancing soil biodiversity are important to soil restoration. There can be no life without soil, and no soil without life: they have evolved together (Kellogg, 1938). The potential of SOC sequestration through restoration of these severely degraded soils is about 0.1–0.3 Pg C/year. In addition, the biomass produced can be used as biofuel with fossil-fuel offset potential of 0.3– 0.7 Pg C/year. Thus the total potential of restoration of degraded soils is 0.4–1 Pg C/year (Lal, 1999a). Desertification is a serious problem in arid, semiarid, and subhumid regions (Dregne, 1992; Babaev, 1999), and desertification control is a priority issue. There are several promising technological options for desertification control (Singh, 1982; Allen, 1988; Dregne, 1992; Rhoades et al., 1992; Squires et al., 1995; Lal et al., 1999). The potential of restoring degraded soils, in enhancing biomass productivity, and in sequestering C can be realized only through a global, coordinated effort. Most degraded soils and ecosystems exist in developing countries, where institutional support and infrastructure required to achieve the desired goals are in need of major improvement before they can be effective.

B. AGRICULTURAL INTENSIFICATION Agricultural intensification implies “cultivating best soil with best management practices to produce the optimum sustainable yield and save agriculturally marginal lands for nature conservancy.” The optimum sustainable yield is the largest yield that can be obtained without decreasing the ability of agroecosystems as well as the crop itself to indefinitely sustain that yield in the future. The goal is to retire marginal soils and abandon marginal techniques because “marginal soils cultivated with marginal inputs produce marginal yields and support marginal living” (Lal, 2000). The true meaning of agricultural intensification was best stated by Jonathan Swift7 who wrote, “Whoever could make two ears of corn, or two blades of grass, to grow upon a spot of ground where only one grew before, would deserve better of mankind, and do more essential service to his country, than the whole race of 7

“Gulliver’s Travels.”

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politicians put together.” In practical terms, it means careful stewardship of the land to enhance production. Lyndon B. Johnson8 stated it in a matter-of-fact way, “The best fertilizer for a piece of land is the footprints of its owner.” However, the technologies for agricultural intensification may be different in the tropics compared to those for the temperate regions. Many long-term experiments conducted in SSA have given reason to doubt the sustainability of intensified production practices as used in the temperate regions (Lal, 1987; Greenland et al., 1998). The possibility of sustainable growth is also being questioned when high-tech agriculture replaces tropical rainforest ecosystems (Helmuth, 1999). Risks of soil degradation need to be carefully evaluated. 1. Food: The Basic Human Right There is a lively and ongoing debate about the desirability of agricultural intensification, and whether there is a need to enhance productivity to feed the growing human population. Some argue whether human society wants 10–15 billion humans living in poverty and malnourishment or 1–2 billion living with abundant resources and a quality environment. This is a moot point; we are already 6 billion people and the total is increasing. Thus, there is no choice but to produce enough food to meet the needs of the present and future populations. Access to adequate and balanced food is the most basic human right which must not be violated for any of the inhabitants of Earth. Political instability and ethnic conflicts around the world are caused by poverty and hunger. Indeed, “there are not many troubles in the world more alarming than those caused by fire in the pit of an empty stomach.” O. Henry9 appropriately stated, “Love and business and family and religion and art and patriotism are nothing but shadows of words when a man’s starving.” Agriculturists, land managers, and planners must strive hard to eliminate world hunger. 2. Principles Governing Sustainable Management of Soil There is also a strong need to observe scientific principles underlying sustainable management of soil resources. It is a fact that ecosystems utilized by human societies are only sustainable in the long term if the outputs of all components produced balance the inputs into the system. Those who advocate use of low external input ought to realize that it is not possible to take more out of a soil than what is put in it without degrading its quality and jeopardizing the environment. Whether the required amount of plant nutrients to obtain the desired yield is supplied in organic (natural) rather than inorganic (synthetic) form in a matter of availability and logistics. Plants cannot differentiate the nutrients supplied through the organic 8 9

James Reston, Washington Column, New York Times, 8 June 1964. In the “Heart of the West,” 1907.


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Table XIV Recommended Agricultural Practices for Enhancing Productivity and Increasing Density of C in Soil Conventional/traditional practices

Recommended agricultural practice

1. Plow till 2. Residue removal or burning 3. Summer or plowed (bare) fallow 4. Low off-farm inputs

1. Conservation till or no till 2. Residue return as mulch 3. Growing cover crops 4. Judicious use of fertilizers and integrated nutrient management 5. Precision farming or soil/site-specific management 6. Water conservation, water harvesting and recycling, drip/HELPFUL irrigation, subirrigation, and water table management 7. Retiring marginal lands and converting them to nature conservancy 8. Improved cropping/farming systems 9. Integrated watershed management

5. Regular fertilizer use 6. No water control

7. Fence-to-fence cultivation 8. Monoculture 9. Land use along property lines and political boundaries 10. Draining wetland

10. Restoring wetlands

or inorganic sources. The question is of nutrient availability, in sufficient quantity, in appropriate form, and at the time nutrients are needed for the optimum growth and yield. Despite the bulk required, the use of organic amendments (compost, manure, and biosolids) can enhance soil quality and sequester carbon. In view of the present and future population, however, modern agriculture is inextricably dependent on a regular supply of synthetic fertilizers at the required rate. Despite the benefits of using manures on small plots, a massive intervention through fertilizer use has no practical alternatives in a world of growing population. 3. Technologies for Agricultural Intensification There are several options of increasing food supply (Table XIV). Important among these are (1) increasing arable land by additional conversion of natural to agricultural ecosystems and/or restoring degraded soils, (2) increasing yield/ha/crop on existing land, (3) increasing cropping intensity, (4) replacing low-yielding with high-yielding varieties and farming systems, and (5) decreasing postharvest losses (Evans, 1998). The overall goal is to adopt land-saving and recommended agricultural practices (RAPs). The strategy is to deliver nutrients and water directly to the plant roots at the desired rate, in an appropriate form, and at the required time (Fig. 1). Developing appropriate strategies of fertilizer management is crucial to agricultural intensification (FAO, 1999c). Description of RAPs for specific ecoregions are also given by Rhoades et al. (1992), for the use of

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Figure 1 Delivering nutrients and water directly to roots of genetically modified or improved plants through soil-specific management.

saline waters for crop production; by Alberda et al. (1992) and Mortimore (1998), for enhancing food production from drylands; by Uri (1999) and Michalson et al. (1999), for conservation tillage in U.S. agriculture; and by Sayers and Rimmer (1994) and Lal (1995b), for sustainable management of soils in the tropics. Applying farmyard manure and compost to soils leads to enhancing soil quality and sequestering C (Smith and Powlson, 2000). Global adoption of these RAPs can improve productivity and enhance SOC pool in soil. The potential of SOC sequestration in world cropland soils is 0.43–0.57 Pg C/year (Table XV). In addition, C offset through fossil-fuel exchange is 0.30–0.40 Pg C/year. Thus, the total potential of SOC sequestration through agricultural intensification of world cropland is 0.72–0.87 Pg C/year. 4. Agricultural Intensification and Soil Quality Adoption of RAPs can lead to a gradual improvement in soil quality. Accelerated soil erosion and other degradative processes are less prevalent in soils managed with RAPs. Utz et al. (1938) observed that 91 Mha of land in the United States was devastated by severe erosion in the 1930s. The land area affected by severe erosion in the 1990s is only 24 Mha. Most lands eroded during the “dust bowl” era have been restored. Since the introduction of the farm bill, erosion control in the United States is a success story (Trimble, 1999). Similar observations have been


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Table XV Soil Organic Carbon Sequestration Potential of Agricultural Intensification on World Cropland Soilsa Strategy

SOC sequestration potential (Pg C/year)

A. Cropland soils 1. Soil erosion control 2. Soil restoration 3. Conservation tillage and residue management 4. Improved farming/cropping systems

0.08–0.12 0.02–0.03 0.150–0.175 0.18–0.24

Subtotal B. C offset through biofuel production

0.43–0.57 0.30–0.40

Total potential

0.72–0.87

a

Adapted from Lal and Bruce, 1999.

made in Machako, Kenya, by Tiffen et al. (1994) in their book More People, Less Erosion. Adoption of RAPs can restore degraded soils and ecosystems.

VII. RESPECTING “THE DIRT” FOR FEEDING 10 BILLION AND MITIGATING THE GREENHOUSE EFFECT Agricultural planners must develop the capacity to feed 10 billion people by the year 2050. Food must be grown in regions with high population density (e.g, Asia and Africa). Site-specific RAPs have to be developed for these regions to produce the food required to feed the growing population. Soil and water resources and climatic conditions have the capacity to increase productivity to meet the expected demand, provided that known technologies are adopted and new ones developed to address the soil-specific questions. Evans (1998) critically examined several issues including: (1) the amount of arable land required, (2) constraints to agricultural intensification, (3) breaking the yield barriers, (4) introduction of new crops, and (5) adaptation to the potential climate change. There is also a strong need to focus agricultural research on rainfed agriculture that covers more than 80% of the world’s cropland. Enhancing crop yield on rainfed land is a high priority. These are also the lands prone to soil degradation, and adoption of high-yielding varieties and other inputs have lagged behind because of the high risks of crop failure. There is indeed a strong need for “a brown revolution” in these rainfed cropland areas of low productivity (Evans, 1998) and for alleviating soil-related constraints to production in the tropics (Lal, 1987; Greenland et al., 1998).

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While sustainable management of soil and water resources is crucial, use of biotechnology can also play a positive role in combating hunger by developing a new generation of genotypes (Duvick, 1999; Guerinot, 2000; Ye et al., 2000). Transgenic plants can also be used to grow in salt-affected soils (Frommer et al., 1999). The area sown to genetically modified (GM) crops in the world is increasing steadily. The land area planted to GM crops increased in 1999 over 1998 from 14.5 to 21.6 Mha for soybeans, 8.3 to 11.1 Mha for corn, 2.5 to 3.7 Mha for soybeans, and 2.4 to 3.4 Mha for canola. Increase in land area under GM crops increased in 1999 over 1998 from 19.8 to 28.1 Mha for herbicide tolerance, 7.7 to 8.9 Mha for insect tolerance (B); and 0.3 to 2.9 Mha for Bt herbicide tolerance (Ferber, 1999). However, there is a lot of debate and misinformation about the impact of biotechnology on the environment, and the technique has drawn criticism from environmentalists (Ferber, 2000). In the long run, appropriate use of biotechnology can facilitate development of new genotypes with high productive potential under biotic and abiotic stresses and decrease the pesticide use. There is a wide range of agricultural technologies that can be adopted to enhance agricultural productivity. A schematic showing the evolution of these technologies is outlined in Fig. 2. Hand tools were used from 1750 to 1850 when the world population was less than 1 billion. Animal-driven equipment and crop rotations were used between 1850 and 1950 when world population reached 2.5 billion. Innovative technologies used between 1950 and 1975 to feed 3.5 billion people included introduction of mechanized farming, intensive use of fertilizers, and development of input-responsive and high-yielding varieties. Food demand for the population of 6 billion in 2000 is met by reliance on improved cultivars and biotechnological innovations, integrated nutrients and pest management technologies, and new farming systems. The relative agronomic productivity over the 250-year period (from 1750 to 2000) increased 10 times. However, the productivity must be increased by an additional 50% between 2000 and 2050 to meet the demand of the expected population. While improving rainfed agriculture is a high priority, there is also a need to introduce other appropriate agricultural technologies which include (1) use of improved soil, water, and nutrient management to enhance use efficiency of inputs, (2) restoration of soils degraded by erosion, salinization, nutrient depletion, and compaction, (3) use of precision farming to minimize leakage and enhance efficiency of fertilizers, (4) use of conservation tillage and residue mulching, and (5) adaptation of agroforestry measures (Fig. 2). Phosphorus is one of the plant nutrients that is likely to be in short supply, and techniques have to be developed to recycle it and minimize its losses. Soil C sequestration has a potential to decrease emissions and sequester C by 40–70 Pg over the next 50-year period. Soil C sequestration, as a by-product of agricultural intensification, can reduce the rate of increase of atmospheric concentration of CO2. This is indeed a win–win situation. It enhances soil quality and increases agronomic productivity, improves water quality, and helps mitigate the


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Figure 2 Technological innovations for agricultural intensification to meet food demands of the global increase in population.

greenhouse effect. Because of the colossal emissions from fossil-fuel combustion, however, soil C sequestration is a short-term solution to the problem. In the long term, decarbonizing fuel by developing alternate energy sources is the only solution. During the next 50 years, soil C sequestration is the most cost-effective option. It is a bridge to the future because it buys us time during which alternate energy options can take effect.

VIII. CONCLUSIONS It is difficult to assess soil’s contribution to human society in monetary terms. Costanza et al. (1997) estimated the total value of ecosystem services at $33.3 trillion/year. That is nearly twice the value of the U.S. gross national product (GNP) of $18 trillion/year. Out of the total annual value of the ecosystem services,

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soils contribute $17.1 trillon/year to direct services alone. The indirect services of soil contribute an additional $3.0 trillion/year for recreation, $2.3 trillion/year for nutrient cycling, $2.3 trillion/year for water regulation and supply, $1.8 trillion/year for climate regulation, and $0.7 trillion/year for atmospheric gas balance. Human welfare is intimately linked to soil quality. For both issues, food security and environment improvement, the answer lies in soil. In the old Roman Empire, all roads led to Rome. In agriculture, all roads lead back to the soil (Hambidge, 1938), the media that feeds the world. History has taught us that the motto of modern civilization should be “In soil we trust.” Human society must respect the “dirt” (Logan, 1995) that feeds us. The concept of respecting dirt was appropriately worded by Paul Hanley in The London Times, 1997, as follows: “Will everyone please, at some stage during the next few days, take hold of a handful of soil and show it some respect? Grab some, take a long, hard look at it, marvel at its workings, sing in praise of its chemistry, bless its bugs for being, and before placing it back on the ground, say thank you.”

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THE MANAGEMENT OF WHEAT, BARLEY, AND OAT ROOT SYSTEMS S. P. Hoad,1 G. Russell,2 M. E. Lucas,2 and I. J. Bingham3 1

Scottish Agricultural College Crop Science Department Penicuik, Midlothian EH26 0PH, United Kingdom 2 Institute of Ecology and Resource Management University of Edinburgh Edinburgh EH9 3JG, United Kingdom 3 Scottish Agricultural College Agronomy Department Bucksburn, Aberdeen AB21 9YA, United Kingdom

I. Introduction II. Root Characteristics A. Root Systems of the Small-Grained Cereals B. Rooting Depth C. Root Extension Rate D. Root Distribution E. Genetic Variations III. Soil Attributes A. Soil Types B. Effect of Climate C. Soil Attributes Affecting Soil Heterogeneity and Rooting IV. Soil Structure and Root Growth A. Bulk Density B. Compaction C. Affect of Rooting on Soil Structure V. Water and Nutrient Availability A. Water Availability B. Nutrient Availability C. Transport of Water and Solutes through the Soil D. Water Uptake E. Nutrient Uptake VI. Biological Factors A. Soil Microorganisms B. Soilborne Pathogens and Pests C. Rhizodeposition


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S. P. HOAD ET AL. VII. Root : Shoot Allocation A. The Functional Balance B. Affect of Roots on Shoot Growth and Yield C. Affect of Shoot Growth on Rooting VIII. Farming Practices and Rooting A. Crop Rotation B. Tillage and Cultivations C. Fertilization and Crop Nutrition D. Sowing Date and Depth E. Plant Population Density IX. Selection of Appropriate Farming Practices A. Reduction of Lodging Damage B. Reduction of Compaction X. Conclusions A. Circumstances in Which Yield Is Limited by Rooting B. Measures to Improve Yield by Manipulating Roots References

Under many conditions, the root system does not appear to limit crop growth or yield and is more than adequate for maintaining the supply of nutrients and water to the shoot. Reductions in the rate of uptake of soil resources such as water and nitrogen lead to changes in assimilate allocation, which can increase the size of the root system. Limitations to grain yield caused by inadequate rooting mainly occur when soil conditions such as compaction or root damage by soilborne pathogens prevent the plants from accessing the potentially available water and nutrients in the soil. Soil factors which have the largest effect on root growth are penetration resistance, pore distribution, and water and nutrient availability. Root attributes that are repeatedly linked to resource capture are root length, rooting depth, and degree of root–soil contact. There is genetic variation in rooting characteristics and this can be taken advantage of in breeding programs for environments where lodging or drought are problems. Currently we do not know how to manage root systems of wheat, barley, and oats in the way that we can manipulate their leaf canopies. We know that management decisions can affect root systems, but often we do not know the consequences for grain yield and quality. The operations that influence rooting include: rotation, variety choice, cultivations, seed rate, sowing date, nitrogen rate and timing, and plant growth regulator application. Matching the appropriate farming practices to soil, climate, and crop will therefore lessen the probability of root limitations. The unpredictability of climatic events and their interaction with soil type makes it difficult to prescribe the best farming practices for optimizing rooting systems. However an understanding of how root systems behave in changing environments allows the risks of root limitation to be C 2001 Academic Press. minimized.

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I. INTRODUCTION While the influence of canopy size on the grain yield of cereals is now well understood, we know rather less about how yield is affected by the root system. We know that management decisions can affect the rooting pattern of cereals, but it is not clear whether these changes are beneficial. This chapter aims to synthesize the existing knowledge of root systems so that it can be used to identify the situations where rooting might limit cereal yield and to suggest appropriate farming practices that could be used to minimize these constraints. This chapter is focused on temperate climates such as those of northwest Europe although the conclusions should have wider applicability. A conceptual model of the key linkages between agronomic practices and yield, as influenced by changes in the rooting environment or the root system, is shown in Fig. 1. This model provides a framework within which the other results are placed. Effects of farming practices on rooting can be direct or mediated through changes in the soil or on the aboveground parts of the crop. Influences in Fig. 1 are more often bidirectional than one way only. For example, the soil type and chemical environment affect root growth and uptake rates of nutrients and water, but the roots can also affect the structure and chemical environment of the soil, although these effects may not be apparent until the following season.

Figure 1 Linkages between agronomic practice, rooting, and crop yield.

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There is a complex and incompletely understood functional interdependence of the shoot and root. Aboveground conditions can affect the growth of plant roots as a consequence either of the size of the plant or of the relative sink strengths of the above- and belowground parts of the plant. The shoot relies on the root for a supply of nutrients and water while the roots depend on the shoot for a supply of carbohydrates. For example, conditions that are favorable for photosynthesis but not to leaf growth can result in an increase in root growth. Interpretation of field experiments is complicated by the difficulty of making accurate measurements below ground and the uncertainty associated with the contributions of the rhizosphere flora (fungi and bacteria) to the supply and uptake of nutrients and water. Although these contributions are thought to be small in normal arable situations, the close association between plants and their rhizosphere flora blurs the distinction between root and soil. As early as 1926, Weaver (1926) warned of the difficulties in interpretation of root data. Accurate measurement of the root system presents practical difficulties which many root scientists have struggled to overcome (Knof, 1990; Bengough et al., 1992; Vetterlein et al., 1993; Van de Geijn et al., 1994; Kucke et al., 1995; Majdi, 1996; Pages and Bengough, 1997). Although there are now well-established ways of measuring rooting using cores or permanent access tubes, the data collected may not be representative of the crop as a whole (Andrén et al., 1991; Parker et al., 1991; Hansson et al., 1992; Jordan, 1992; de Ruijter et al., 1996).

II. ROOT CHARACTERISTICS A. ROOT SYSTEMS OF THE SMALL-GRAINED CEREALS The root systems of wheat, barley, and oats consist of three to six primary (or seminal) roots growing from the seed and the secondary roots (also called nodal, crown, or adventitious roots) that arise from nodes at the base of the main stem and tillers. Each tiller (shoot) develops its own roots and can thus become independent of other shoots. The primary roots are 0.2–0.4 mm in diameter and in a fully grown crop occupy 5–10% of total root volume. They develop first-, second-, and third-order lateral branches. Secondary roots develop once tillering starts (i.e., 4–12 weeks after germination). They are 0.3–0.7 mm in diameter and form lateral roots (0.1–0.2 mm thick). Laterals may have a horizontal spread of up to 1 m from the main stem. The seed contains relatively large reserves of storage carbohydrate and nutrients ( Marschner, 1998) which allow the initial root system to grow rapidly to considerable depth. Branching often begins before the leaves have unfolded with the result that the plant establishes an early contact with water (Weaver, 1926). It has been suggested that the final root biomass and length do not vary much between


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seasons although the rate of root growth may vary (e.g., Welbank et al., 1974, for barley). However, experiments have not been carried out over a wide enough range of environments to test this hypothesis. In autumn-sown crops, the root mass appears to increase exponentially until the start of rapid aboveground growth and then increases linearly until anthesis (e.g., Gregory et al., 1978, for winter wheat). In many environments translocation of carbon to the roots of cereals effectively stops after anthesis (e.g., Gregory, 1994a, for U.K. conditions). Thus after anthesis the root mass remains constant for some weeks before declining toward harvest due to root death. Many measurements of root biomass and length have now appeared in the literature (Table I). However, compared with aboveground biomass and leaf area index, root measurements are subject to rather larger errors from sampling, cleaning, and measuring even after following the most rigorous experimental procedures. Thus, more confidence can be placed in comparisons in which similar measurement protocols were used than in the absolute values. With that caveat, the biomass of winter wheat roots is typically between 100 and 150 g m−2 at anthesis in the United Kingdom (Table I). Winter barley root biomass is slightly less and is in turn rather greater than for spring crops. It is important to note that these measurements will underestimate total root biomass production whenever root turnover is significant. A survey of total root length in winter wheat gave values from 7.8 to 35.0 km m−2 and root length density in the plow layer (0–200 mm) ranged between 28 to 122 km m−3 (Table I). At depth, root length density declines to less than 1 km m−3 (Barraclough et al., 1989). Total root length appears to increase in conditions of high nitrogen availability (Welbank et al., 1974) and when the soil texture is dominated by sand rather than clay (Andrén et al., 1993, for barley), although it is not clear whether this phenomenon is related to availability of water or soil porosity. The time of sowing can have a large impact on root length. For example, Barraclough (1984) found that wheat sown in September always had more root length than wheat sown in October even though the final root masses were similar. Despite differences in total root mass and total root length, the mean length of individual cereal root members seems to be relatively constant across a wide range of growing conditions (Gregory, 1994a).

B. ROOTING DEPTH Rooting depth, that is, the maximum depth that roots reach, can be difficult to ascertain in the field. In northwest Europe, most soils are derived from glacial drift and are thus inherently variable over short distances. It is therefore not surprising that excavations of soil pits often show that a few roots penetrate much deeper than the rest (i.e., not all plants root to the same depth). Rooting depth should really be estimated as the mean maximum rooting depth of all the plants, which is not feasible in the field. Estimates of rooting depth thus involve an element of

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S. P. HOAD ET AL. Table I Characteristics of Cereal Roots

Crop WW WW

WW WW

WW

WW SW WB SB SB SB SO

Soil texture or type ZCL SL or SCL ZCL ZCL ZCL ZCL SL Degraded chernozem Degraded chernozem SL

ZL ZL ZL L S L SL ZL ZL

Mass (g m−2)

Length (km m−2)

Density (km m−3)

133 126 134a1 125a2 115a3 97a4 105 —

27.6 23.4 35.0a1 28a2 26.0a3 22.0a4 23.5 7.8b1

— — 122a1 73a2 87a3 61a4 60 —

—

14.9b2

—

104c1 110c2 96c3 98 100–150d 90 — 133e 126e 189 80 107

15.0c1 27.0c2 24.0c3 11.8 — 11.3 26.9 24.0e 16.0e — 12.6 11.3

62c1 82c2 28c3 72 — 33 68 — — — 42 34

References Barraclough and Leigh, 1984 Barraclough et al., 1989

Gregory et al., 1978 Habele et al., 1996

McGowan et al., 1984

Welbank et al., 1974 Welbank et al., 1974 Bragg et al., 1984 Andrén et al., 1993 Biscoe et al., 1975 Welbank et al., 1974 Welbank et al., 1974

Note. Measurements of root mass, total root length, and root length density at anthesis in winter wheat (WW), spring wheat (SW), winter barley (WB), spring barley (SB), and spring oats (SO). Root length density refers to the plough layer (0–200 mm). Soil textures: sand (S), loam (L), sandy loam (SL), silt loam (ZL), sandy clay loam (SCL), silty clay loam (ZCL). a Different nitrogen fertilizer and water supplies: 1Nitrogen fertilizer and irrigation; 2 Nitrogen fertilizer and droughted; 3No fertilizer with irrigation; and 4No fertilizer and droughted. b Measured at ear emergence: 1Relatively dry season; and 2Relatively wet and warm season. c Measurements over 3 years: 11975; 21976; and 31977. d Values for several varieties. e Measurements made at harvest.

subjectivity or use a functional criterion such as the deepest depth from which water is being taken up. There seems to be a genetically determined maximum depth of rooting although the actual depth of rooting is largely determined by soil conditions (Gregory, 1994a). The rooting depth increases until anthesis when it usually becomes constant (Gregory, 1994a). A survey of rooting depth (Table II) indicated that individual roots of cereal crops can reach a depth of over 2.0 m


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Table II Maximum Rooting Depth Crop

Soil texture or type

Depth (m)

References

WW

SL SL SCL SCL SL SL L L L L Loess Loess L S and LS ZL L L

1.4 1.0a 1.4b1 1.6b2 2.0 2.0 1.6 1.2 1.7 1.3 0.6 0.3a 0.7 1.4 0.3 1.6 1.8

Barraclough and Weir, 1988

WW WW WW SW WB WB SB SB SB SB SB SO

Barraclough et al., 1989 Gregory et al., 1978 Lupton et al., 1974 Vetter and Scharafat, 1964 Bragg et al., 1984 Vetter and Scharafat, 1964 Kirby and Rackham, 1971 Lipiec et al., 1991 Madsen, 1985 Welbank et al., 1974 Vetter and Scharafat, 1964 Vetter and Scharafat, 1964

Note. Winter wheat (WW), spring wheat (SW), winter barley (WB), spring barley (SB), and spring oats (SO). Soil textures: sand (S), loam (L), loamy sand (LS), sandy loam (SL), silt loam (ZL), sandy clay loam (SCL). a Compacted soil. b Different water supplies: 1Irrigated; and 2Droughted.

(Kirby and Rackham, 1971; Lupton et al., 1974; Gregory et al., 1978) in favorable conditions. However, the rooting depth is often less than this and winter wheat, for example, may only reach 1.4 m or less where there is a pedological barrier to root growth. Typically, winter wheat roots grow to a depth of 0.75 to 1.0 m by spring and keep growing rapidly downward to reach their maximum by midsummer (Lupton et al., 1974; Gregory et al., 1978). The rooting depth of winter barley can reach 1.7 m (Vetter and Scharafat, 1964) although the difference in the maxima for winter wheat and winter barley may reflect the number and location of measurements rather than any fundamental difference. The rooting depth of spring barley ranges from 0.6 to 1.4 m (Kirby and Rackham, 1971; Bragg et al., 1984; Madsen, 1985; Lipiec et al., 1991). There are rather fewer observations for spring wheat (1.6 m; Vetter and Scharafat, 1964) and oats (1.8 m; Vetter and Scharafat, 1964). The maximum rooting depth achieved is increased by earlier sowing (Barraclough and Leigh, 1984) and by drought (Barraclough et al., 1989). However, these responses are only seen where the roots do not reach a depth at which there is a physical limitation to rooting.

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In fact, mechanical impedance can have a large impact on rooting depth. Martino and Shaykewich (1994) found that the proportion of roots penetrating the soil was inversely related to the soil penetration resistance, and this leads to an associated reduction in the rooting depth on compact soils (Ehlers et al., 1983; Barraclough and Weir, 1988; Lipiec et al., 1991; Unger and Kaspar, 1994). For example, Barraclough and Weir (1988) found that compaction in a sandy loam reduced the rooting depth of winter wheat from 1.4 m at anthesis to 1.0 m, while Lipiec et al. (1991) found a reduction from 0.6 to 0.3 m for spring barley growing in a loess soil in Poland.

C. ROOT EXTENSION RATE Barraclough and Weir (1988) found vertical root extension rates in winter wheat growing in an unimpeded soil of 12, 5, and 18 mm day−1 in autumn, winter, and spring, respectively. Gregory et al. (1978) found the same spring value and an average value of 6 mm day−1 over the whole season (i.e., a depth of 1.20 m would be reached in 200 days). Much of the within-season variation can be explained by temperature and Barraclough (1984) was able to derive a single extension rate of 1.8 mm ◦ C day−1 using a base temperature of 0◦ C. The above-mentioned figures are consistent with this value. However, soil compaction seriously reduces the rates of root extension. For example, Barraclough and Weir (1988) found that the presence of a plow pan reduced the root extension rate of winter wheat to just 1.5 mm day−1 during February, March, and April. During May and June, when the plow pan had been extensively penetrated, the rate of vertical extension increased to 9 mm day−1, which was still only half the rate in unimpeded soils.

D. ROOT DISTRIBUTION Since most of the available nitrogen is usually in the upper part of the soil profile it is not surprising that many authors have reported that cereal roots are distributed unevenly with the bulk of their length in the surface layers (Welbank and Williams, 1968; Welbank et al., 1974; Gregory et al., 1978; Barraclough, 1984; Madsen, 1985; Hansson and Andrén, 1987; Smukalski and Obenauf, 1990). Growth occurs sequentially down the profile and this leads to an exponential decrease in root length density with depth (Barraclough and Leigh, 1984; Barraclough et al., 1989; Haberle et al., 1996). As the root system is both extending downward and proliferating nearer the surface the distribution of roots through the profile changes through the season. For example, Gregory et al. (1978) found 60–70% of the root mass of winter wheat in the 0–0.3 m layer in April and a large amount of growth in deeper layers (0.1–0.5 m) between May and mid-June. McGowan et al. (1984) described the


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general distribution of a mature wheat root system with depth: 60–70% of the root length occurred in the top 0.3 m, another 20–25% occurred within the next 0.3 m, and less than 1–2% occurred below 1.0 m. However, growing conditions can alter the root distribution from the generalized pattern. Qualitative estimates of rooting of cereals have been made routinely, generally after harvest, as part of the process of soil description in England and Wales (e.g., Thomasson, 1971). These profile descriptions show there is large variation in the degree of rooting of cereals below the plow layer as a consequence of natural pedological features such as induration and waterlogging. Researchers have shown how compacted layers of soil impede root growth in deeper layers (Gregory, 1994a). Analysis of the patterns of root distribution in experiments where there was a compacted layer reveal consistent trends. Roots of plants tend to be confined to the surface layers of soil above a plow pan (Barraclough and Weir, 1988; Lipiec et al., 1991; Unger and Kaspar, 1994), at least until the pan weakens and roots manage to break through. Although the rooting depth is reduced, it has been consistently found that there is no effect of soil compaction on the total root length of either wheat or barley (Barraclough, 1984; Barraclough and Weir, 1988; Lipiec et al., 1991; Unger and Kaspar, 1994). Instead, there is a compensatory growth of roots in the surface layers which Lipiec et al. (1991) attributed to a more horizontal mode of root growth. Accumulation of nutrients close to the soil surface is well known to cause a proliferation of roots in the upper layers of soil (Gregory, 1994a). Application of nitrogen (N) fertilizer to barley caused an accumulation (90–97%) of the root mass in the top 0.3 m of the soil (Hansson and Andrén, 1987), while Haberle et al. (1996) found only a few unbranched primary roots below a depth of 0.25 m in fertilized winter wheat. Barraclough (1984) found that nitrogen fertilization increased root growth down to 0.8 m in the presence of adequate supplies of water and to 1.2 m in drought conditions. In northwest Europe, many cereal crops are fertilized at the predicted optimum nitrogen rate. However, the amount of available nitrogen and thus the degree of root proliferation will also depend on the exact timing of fertilizer application, on any losses by leaching or denitrification, and especially on the mineralization of soil organic matter.

E. GENETIC VARIATIONS The root attributes of cereals vary significantly between cultivars (Stoppler et al., 1991; Kujira et al., 1994; Gregory, 1994a; Marschner, 1998), and selecting varieties which are less prone to resource limitations is felt to be important in maximizing crop yield in extreme environments (Gregory, 1994a). Choice of the appropriate variety is influenced by the expected growth conditions and agronomic limitations and is therefore specific to the field and local climate. The

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effects of genotypic variations on root characteristics of crop plants have been reviewed by O’Toole and Bland (1987). Surveys of old and new cereal cultivars show that the modern ones are more responsive to high nutrient availability (Haberle, 1993; Haberle et al., 1995), although they tend to have a lower root fraction (Wahbi and Gregory, 1995). However, Stoppler et al. (1991) found that of four winter wheat varieties growing in a low-input system, the modern cultivars developed a more extensive and deeper root system, which was observed to confer better drought resistance. In contrast Feil and Geisler (1988) showed that the relative yield ranking of new and old cultivars was independent of soil nutrient status. Although some authors have suggested that dwarfing genes are associated with a decrease in the biomass of the root system (Benlarabi et al., 1990), there is evidence that dwarfing genes do not influence the spread of the root system or the total root length (Kujira et al., 1994). Atkinson (1990) found significant variation in the speed of root penetration, specific root length, branching pattern, root density, total root mass, and root hair development in 25 spring barley varieties. The number of root axes and length growth of lateral roots also differs between barley genotypes (Wahbi and Gregory, 1995). Leon and Schwang (1992) used the grid intersection method (Newman, 1966) to evaluate differences in total root length between cultivars of oats and barley and found that yield stability was correlated with root system length. However, the value of such a correlation depends on the range of conditions over which it was evaluated. Adaptability to growing conditions is an important genotypic trait in its own right and can be selected for in plant breeding programs (Zenisceva, 1990). The variations in root system morphology described earlier can lead to differences in structural and mechanical strength of the root system which determine the plant’s susceptibility to lodging. Crops with widely spread, strong secondary roots are more resistant to root lodging, that is, where the whole plant is blown over in strong winds (Ennos, 1991a,b; Crook and Ennos, 1993,1995; Crook et al., 1994). Physiological characteristics also differ between cultivars and can be important in determining the outcome of processes such as nutrient acquisition (Marschner, 1998). The rate of uptake of nutrient per unit root length depends on the nutrient availability but also varies considerably between cultivars (Römer, 1985; Greef and Kullmann, 1992). At the whole plant level, differences in water-use efficiency have been related to the allocation of resources between the root and shoot (Van den Boogaard et al., 1997) and to the rate of photosynthesis per unit of leaf nitrogen (Van den Boogaard et al., 1996b). Gahoonia et al. (1997) estimated that the presence of root hairs increased the total root surface area by 95–341% for winter wheat and 112–245% for barley. The range of values were the result of cultivar differences in the number and length of root hairs. Root hairs of wheat grown in nutrient solution are typically 2.5 to


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3 mm long (Gregory, 1994a) but the length and number of root hairs in the field depends on soil conditions. Root hairs are thought to be especially important in the uptake of less mobile nutrients such as phosphorus (P). The effectiveness of selection for root attributes and other physiological traits on yield of durum wheat was evaluated by Al Hakimi et al. (1998). They found that the use of such criteria led to increased yield in drought-prone environments and to improved yield stability in a wider range of conditions.

III. SOIL ATTRIBUTES A. SOIL TYPES Farmers know that crop yield varies within a field and this has led to developments in precision agriculture. Since weather is essentially constant across a field, this variability must be largely due to soil type and to its effect on root system size and functioning. Thus the soil type is an important factor in decision making. Being able to describe the soil of a field is important in fine-tuning practices and optimizing inputs, as well as for modifying experiences and trial results from other places. However, the categories and scale used in soil mapping are not necessarily appropriate for a farmer, who is able to base decisions at least on observations of surface soil conditions and localized crop growth, even if soil pits have not been dug to expose the subsoil. Likewise, researchers can use soil information to decide how transferable results are from one place to another. The soil type can have a large impact on all of the factors which affect the growth and distribution of roots (Andrén et al., 1993). For example, clay soils and sandy soils differ from each other in their available water capacity (because of the pore size distribution of the soil), nutrient retention and delivery (pore structure, organic matter content, pH, and water availability), penetration resistance, and even temperature. In a survey of 50 spring barley root profiles taken over a range of soil types, plants grown on sandy soils had a mean maximum rooting depth of 0.70 m compared to 1.40 m on loamy soils (Madsen, 1985) and low root length densities could largely be explained by soil texture. Andrén et al. (1993) found that the root number, root length, and mean rooting depth of barley were greater in sand than in clay even though there were no differences in the dry mass of either the root system or the whole plant. They also found that the crop grown in sand contained more N and P than the crop grown in clay. There is evidence that the size of soil aggregates has an influence on the rate of plant growth which is independent of the ability of the root system to take up adequate quantities of water and nutrients (Passioura and Stirzaker, 1993). Crops grown in soil with finer aggregates tend to grow better than those grown on large

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aggregate sizes and this phenomenon may explain the observed lower growth rates and yields of crops grown on excessively loose soils (Hakansson et al., 1998).

B. EFFECT OF CLIMATE Some effects of soil depend on an interaction with climate and weather. For example, the farmer can differentiate fields in terms of earliness, accessibility, and droughtiness, which are virtually permanent features of a soil as there is limited scope for amelioration. Earliness can be used as a relative term within a farm or region referring to the rate at which a soil heats up in spring. It is a common observation in the northern hemisphere that sandy soils on south-facing slopes warm up earlier than clay soils on the level. The reasons are the increased solar radiation load on the slope and the generally lower thermal conductivity and heat capacity of sandy topsoil. At any soil water potential, clay soils contain more water than sandy soils and the bulk density is usually greater so the surface layers warm up more slowly in spring (and cool down more slowly in autumn). Moreover, poor drainage, which is more likely to occur on clay soils, leads to a higher water content and a slower rate of change of temperature. Thus, one of the benefits of drainage is to speed up the rate of warming in spring. Root extension rate depends on temperature, as has already been mentioned, and higher temperatures in the root zone lead to increases in root length density (km m−3) (Sharratt, 1991; Haberle et al., 1996). Differences in temperature between soils are most marked near the surface. Soils containing a large amount of water heat up more slowly in the spring and cool less quickly in the autumn while compaction has been found to reduce daily soil temperature fluctuations (Lipiec et al., 1991). During winter, the air temperature may be too low for significant aboveground growth but the temperature in the soil may be high enough for root growth. Once the soil has dried to field capacity, tractors and farm implements can be driven over most arable soils without damage. The consequences of traversing the land when the soil is wetter, particularly where cultivations are involved, vary from soil to soil. In strongly structured soils with a high organic matter level, the damage may be minimal. In others, a cultivation pan can form in the plow layer restricting root penetration at least temporarily. In others again, a plow pan can form below the plow sole and this again restricts rooting. The following three broad categories of increasing vulnerability to soil damage and restricted rooting have been identified (based on Cannell et al., 1978). 1. Well-drained soils with a stable structure such as those on chalk or limestone, and well-drained loamy soils and coarse sands with a high organic matter content.


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2. Chalky clays, and clay soils with a clay or loam topsoil which have been improved by drainage. 3. Sandy soils with a low organic matter content, silty soils, wet alluvial soils, clayey soils that return to field capacity before November, clay soils with a clay or loam topsoil which have not been improved by drainage. Like earliness, droughtiness is partly a meteorological phenomenon and so, other things being equal, fields in low rainfall areas are more droughty than those in wetter parts. In practice, however, differences between soils can be much greater than those between regions. For example, the profile available water in U.K. cereal fields varies from about 60 mm to more than 250 mm, equivalent to 20 to 80 days of potential transpiration in June. In soils at the upper limit, such as deep silty clay loams, drought symptoms will rarely be seen provided there are no structural problems. On the other hand, in a high rainfall area at the margins of arable agriculture, even soils at the lower end of the range may only show an effect of drought in dry years. The actual amount of water available to the crop depends on the texture of the soil (higher in clay and silt soils), the organic matter content, the rooting depth (depth to an impeding layer), and the stone content. In some clay soils, rooting in the subsoil is only possible through structural cracks and wormholes and this means that not all the “available” water can be accessed by crops. On the other hand, crops in shallow soils over chalk can root into the soil parent material itself.

C. SOIL ATTRIBUTES AFFECTING SOIL HETEROGENEITY AND ROOTING Spatial heterogeneity is important because crop roots can respond very differently to different soil conditions. The scale of heterogeneity is crucial because if it can be sensed by a crop root then the plant can respond to it. Indeed, the plant may be even more efficient at using resources than if they are homogeneously distributed (Van Noordwijk and Van de Geijn, 1996). In contrast, scales of heterogeneity which are larger than the individual crop plants cannot be sensed by the root systems and this usually results in a less efficient use of resources. Spatial heterogeneity needs to be considered in three dimensions. Variation with depth can be continuous or can occur in steps such as when there is a cultivation pan. Changes are usually more rapid in the surface soil layers. This is reflected in root growth models which divide the soil into horizontal layers which increase in depth toward the bottom of the soil profile. Yield mapping and soil testing have shown that horizontal soil variation over a distance of a few meters can be as much as that across the whole field (Beckett and Webster, 1971). Farm practices such as tillage, plowing in of crop residues, fertilizer spreading, liming, and farm traffic all contribute to this horizontal heterogeneity.

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Resources which have a heterogeneous distribution in the soil include organic matter (Van Noordwijk et al., 1993; Van Vuuren et al., 1996), mineral nutrients (Robinson et al., 1994; Van Vuuren et al., 1996; Robinson and VanVuuren, 1998), and water (Barraclough, 1989; Tardieu and Katerji, 1991; Gregory, 1994a; Droogers et al., 1997; Brisson et al., 1998). Soil attributes that affect root penetration such as soil structure and bulk density may also vary heterogeneously, for example when there is a layer of soil compaction induced by farm traffic (Davies et al., 1972; Ehlers et al., 1983; Lipiec et al., 1991). Crop root systems demonstrate mechanisms for coping with a heterogeneous distribution of resources. Soil conditions which inhibit root growth in one part of the soil may lead to compensatory growth in other soil zones where conditions are more favorable (Barraclough, 1984; Barraclough and Weir, 1988; Lipiec et al., 1991; Unger and Kaspar, 1994; Gregory, 1994a). Similarly, when the availability of nutrients is low, plants will increase the rate of absorption per unit length of root in zones of relatively high nutrient availability (Robinson et al., 1994). Atkinson (1990) suggested that highly branched root systems are more efficient at exploiting heterogeneous soils, while Robinson and Van Vuuren (1998) concluded that fast growing plants showed a greater plasticity of root growth in response to nutrient patches. In an experiment using localized patches of 15N-labeled organic matter in otherwise N-deficient soil (Van Vuuren et al., 1996), exploitation of the mineralized nitrogen by wheat occurred mainly from an increase in N uptake rate per unit root length (i.e., N inflow). Root proliferation in the patch occurred most vigorously only after most of the mineralized N had been captured and therefore had a limited impact on N capture. Increased N inflow during the first five days after roots entered the patch accounted for 8% of the patch-derived N that the wheat eventually captured. A combination of increased inflow and modest proliferation in the following seven days captured 63% of the total N. The remaining 29% of the N was captured during the period of greatest root proliferation when inflow had decreased to the rate of the controls. Root proliferation response to localized N appears to be correlated strongly with N capture only when plants with different rooting responses compete for N (Robinson et al., 1999). Therefore, attempts to manipulate this response by management or genetics are unlikely to greatly affect N capture in intensively managed cereal monocultures except in the case of competition with weeds.

IV. SOIL STRUCTURE AND ROOT GROWTH A. BULK DENSITY The most important soil conditions affecting root growth appear to be the presence of a system of connected and adequately sized pores which roots can grow


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through combined with a soil structure which allows root penetration and a supply of all the resources required for growth (Gregory, 1994a). The total pore volume can be calculated from the soil bulk density by assuming that the soil particles have a density of 2600 kg m−3 (silica). A soil may have 60% of its volume as pore space, of which 20–30% is occupied by air at field capacity (Davies et al., 1972). However, Stirzaker et al. (1996) pointed out that bulk density alone is insufficient to explain root penetration and the pore size distribution is also important. Even in a soil of high bulk density, there may be pores which make exploration of deeper soils possible. For example, in some clay soils, most of the porosity is in cracks between otherwise impenetrable peds. In a study on the effects of aggregate size on the growth of wheat roots, coarse aggregates and compacted soil structures were found to decrease the root length, root surface area, root fresh weight, shoot weight, and seed yield (Keita and Steffens, 1989). Where there were fine aggregates, roots tended to be thicker and root hairs were longer. Bulk density high enough to restrict rooting can occur naturally toward the bottom of the soil profile or because of cultivation practices.

B. COMPACTION Intensification of U.K. farming systems in the 1960s led to widespread problems of compaction which were reviewed for England and Wales in a report of the Agricultural Research Council (1970). Subsequent changes to machinery and farming practice reduced these problems considerably. Soil compaction occurs when the structure of a soil is changed so that there are fewer large pore spaces. Thus, a heavily compacted soil may have only 30–40% pore space, of which just 5% is filled by air at field capacity, and the bulk density will be higher than in the uncompacted state. Zero tillage tends to result in a higher soil bulk density and an increased penetration resistance in the surface layer (Ehlers et al., 1983). However, the highest bulk densities and penetration resistances occur in soils which have been regularly subjected to passes by heavy farm machinery (Ehlers et al., 1983). These areas of high penetration resistance occur in zones known as “plow pans” which are situated immediately below the tilled layer. For example, Hakansson et al. (1996) found that ordinary farm traffic increased penetration resistance by an average of 40% at a depth of 0.4 m, while Ehlers et al. (1983), working on a loessial soil, found that tillage had induced a plow pan in the 0.25–0.30 m soil layer with a bulk density of 1500 kg m−3 compared to a bulk density of 1280 kg m−3 in the 0.01–0.05 m layer. Lipiec et al. (1991) showed that the use of a five-wheel compaction treatment changed the soil structure by reducing the air-filled porosity and increasing the penetration resistance. Slipping farm vehicle wheels may cause compact layers with a pronounced platy structure (Davies et al., 1972). Because of structural differences between soil types, bulk density is not an appropriate measure of compaction when comparing sites with different soils. For

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this reason, the concept of “degree of compactness” was developed (Hakansson, 1990; Lipiec et al., 1991). The degree of compactness of a soil refers to the ratio of the dry bulk density of the soil to the dry bulk density of the same soil in a compacted reference state. The reference state is determined by loading the soil with a pressure of 200 kPa until drainage ceases (Hakansson, 1990). The usefulness of this measure was confirmed in a series of 100 field experiments on spring barley where it was shown that the maximum crop yield was obtained at the same degree of compactness regardless of soil type. The optimum degree of compactness (87%) was virtually independent of the soil particle size distribution but varied with crop type (Lipiec et al., 1991). Roots grown on soils with a high penetration resistance show characteristic changes in morphology. Cell walls are thickened and there is a decrease in the rate of cell division (Bengough et al., 1997). Mechanically impeded roots are thicker than unimpeded roots because they continue to increase in diameter further behind the growing tip. Their surface is less regular, they tend to be flatter, and their growth tends to follow a more convoluted path through the soil (Lipiec et al., 1991). It has been claimed (Pietola, 1991) that in most fields which are subject to ordinary farm traffic, penetration resistance reaches values which restrict root growth (Hakansson et al., 1996). As Barley et al. (1965) point out, soil strength can be regarded as a property which has a general influence on root elongation rather than as a limiting factor which is only encountered in unusual situations. Root penetration is controlled chiefly by the strength of the soil, and a number of authors have suggested a strong relationship between the rate of vertical root growth and the penetration resistance (Barley et al., 1965; Ehlers et al., 1983; Martino and Shaykewich, 1994). Drying out and the resulting hardening of a clay loam soil may entirely inhibit the penetration of roots into the subsoil (Pietola, 1991). It was recognized relatively early on that the importance of compaction on crop yield depended on the amount and distribution of rainfall during the growing season (Fisher et al., 1975). Tardieu (1994) suggested that under some climatic conditions, the effects of soil compaction on whole plant growth rate and allocation would be pronounced, while under more favorable conditions only small changes would occur. This hypothesis was confirmed by Unger and Kaspar (1994) who noted that even where compaction limited root growth, weather events could enhance or diminish the effect of root limitations on crop growth. There are data (Barraclough, 1984; Barraclough and Weir, 1988) which support the suggestion that the beneficial effects of subsoiling are largest for spring-sown crops in years when a dry summer follows a wet spring. In contrast, a mid-September sowing date, followed by an unusually wet spring resulted in a relatively small effect of compaction on yield (Barraclough and Weir, 1988). With cereals sown late in the autumn or in the spring, penetration of the compact soil layer and access to its store of water is delayed. Barraclough and Weir (1988) concluded that soil compaction is most detrimental during dry years and periods of drought. A further consideration is that when the


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zone of compaction is deep in the soil, the amount of water available to the roots is larger, and there is less likelihood of severe water stress (Barraclough, 1984). The responses of crops to subsoil loosening seem to depend on the pattern of rainfall distribution in relation to the timing of root growth and thus the developmental stage of the crop (Bamford et al., 1991). It has been shown that, at least in some circumstances, variations in root growth can be explained by differences in soil water content rather than in bulk density (Ehlers et al., 1983). Other researchers have also found that root elongation is partly a function of the soil water potential and not solely a function of the penetration resistance (Eavis, 1972; Mirreh and Ketcheson, 1973; Baryosef and Lambert, 1981). On the other hand, others have found that soil water content per se had no influence on the rate of root penetration which was solely accounted for by soil strength (Barley et al., 1965; Taylor and Ratliff, 1969; Greacen and Oh, 1972). In a review of the evidence, Ehlers et al. (1980) concluded that for sorghum the extension rates of roots were directly related to the penetration resistance and not to the soil water content per se unless the matrix potential dropped below −0.5 or −1.0 MPa. There is no reason to believe that the roots of wheat, barley, and oats behave any differently. Soil horizons rarely have a uniform bulk density, and roots tend to follow zones of lower penetration resistance (Barraclough and Weir, 1988). In some soils, zero tillage can intensify the soil shrinkage process, leading to heterogeneity of soil structure characterized by vertical planes of weakness or by the presence of channels created by previous plant roots and soil fauna (Ehlers et al., 1983; Stirzaker et al., 1996). Martino and Shaykewich (1994) found that although zero tillage increased the penetration resistance in the top 0.1 m of soil, there was an increase in the proportion of macropores (>100 ␮m) close to the soil surface. These biopores allow crop roots to penetrate compact layers of soil even though they typically occupy less than 1% of the soil volume (Ehlers et al., 1983; Martino and Shaykewich, 1994; Stirzaker et al., 1996). However, growth inside preexisting biopores involves a trade-off between increased access to soil resources and poor root–soil contact that may reduce the absorption of nutrients and water. There may even be a release of an inhibitory signal when more than one root occupies the same pore (Passioura, 1991; Passioura and Stirzaker, 1993; Stirzaker et al., 1996). The walls of biopores also tend to be relatively impenetrable to lateral root growth. Stirzaker et al. (1996) showed that in a growth chamber, barley was found to grow best on soil with a bulk density of 1400 kg m−3. This represents a compromise between soil which was soft enough to allow good root development but was sufficiently compact to give good root–soil contact. On the other hand, Bowen (1981) showed that when soils were at field capacity, the critical maximum bulk densities for root penetration ranged from 1550 kg m−3 on clay loams to 1850 kg m−3 on loamy sands. They quote a critical air-filled porosity of 10% by volume and a penetration resistance of 3 MPa. In a review of the literature Ehlers et al. (1983)

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found that root growth ceased at soil strengths between 2 and 2.5 MPa, although their own data showed that the limiting penetration resistance for root growth in a tilled Ap horizon was 3.6 MPa and in the untilled Ap horizon was 4.6–5.1 MPa. They attribute these higher values of critical penetration resistance to the development of a continuous pore system in the soil, created by worms and the roots from previous crops. Martino and Shaykewich (1994) also suggest a critical soil strength for root penetration of 2 MPa and found that this value was independent of soil type. However, the only measure of soil compaction which seems to be truly independent of soil type is the “degree of compactness” mentioned earlier.

C. AFFECT OF ROOTING ON SOIL STRUCTURE Roots create biopores in the soil which increase the aeration and the infiltration of water to the subsoil and also provide conduits for root growth through soils with high penetration resistance (Ehlers et al., 1983; Passioura, 1991; Passioura and Stirzaker, 1993; Stirzaker et al., 1996). By extracting water from the soil, roots may enhance the drying–wetting cycle which modifies soil structure (Van Noordwijk et al., 1992) particularly in clay-rich soils. Root uptake of water may even dry the soil enough to increase the soil strength so that it cannot itself penetrate it. Root movement through the soil tends to loosen the structure which increases its accessibility to microorganisms and increases the mineralization rate. Although, in the long term, roots increase the aggregate stability through formation of pores and the release of exudates and decaying tissues, in the short term, roots can reduce aggregate stability. In cereal growing systems, however, the main effect of rooting on soil structure probably arises from the roots of break crops and grass leys. For example, when a cereal follows oilseed rape, the old root channels are often colonized by the cereal roots.

V. WATER AND NUTRIENT AVAILABILITY A. WATER AVAILABILITY In dry years, most of the available water in the upper part of the soil profile is taken up by cereals. For example, Fig. 2 compares the driest profile of water content in a field of spring barley in a dry year with the profile at a soil water potential of −1.5 MPa (i.e., the theoretical limit). However, not all the apparently available soil water is necessarily available to plants (Droogers et al., 1997). The bulk density, organic matter content, and porosity of the soil as well as the rooting characteristics of the crop define a hypothetical extraction zone around each root. Subsoiling is


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Figure 2 Soil water profiles for a field of spring barley growing on a sandy loam soil in 1970 near Nottingham, UK. The symbols represent: 䊊, the mineral plus organic components of the soil volume; × , +, and 䊉 are the volumetric water contents corresponding to −1.5 MPa, the driest profile recorded, and field capacity, respectively.

one way of managing the soil to increase water and nutrient availability at depth and cultivar selection may also help. Some authors have found that the actual soil water depletion exceeds the available water once rainfall is taken into account (Tardieu and Katerji, 1991; Brisson et al., 1998). This is because on highly conductive soils, deeper layers which do not contain any roots may still contribute substantially to the water balance, and also the uptake of nutrients, by capillary rise of water. 1. Waterlogging and Aeration Waterlogging occurs when the soil is saturated leading to low oxygen concentrations and the production of toxic compounds such as acetic acid that inhibit root growth (Ellis, 1979). Anaerobic conditions also encourage denitrification and reduce the available soil nitrogen. This is compounded when soluble nutrients such as nitrate are leached to lower levels in the soil profile. Cereal roots can survive short periods of flooding without adverse effects because oxygen dissolves in water in small quantities and because they are capable of anaerobic metabolism when oxygen demand exceeds supply. It has also been reported (Varade et al., 1970) that at least some cultivars of wheat are capable of oxygen transport from the shoot to the roots through aerenchyma tissues. The effect of waterlogging is least at low temperatures when more air can be dissolved in the water and biological activity is depressed leading to lower oxygen requirements by the plant. The importance of aeration of the roots during aggregate penetration has been

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discussed by Van Noordwijk et al. (1992). The oxygen requirements are highest for the growing tip of the root, which during penetration of soil aggregates may be a long way from the nearest oxygen source. Weaver (1926) warned that raising the depth of the water table may cause death of the deeper roots and usually results in a decrease in yield. He also noted that if the subsoil was waterlogged, deeper roots would not develop and this would predispose crops to subsequent drought. In addition to reducing soil nutrient availability, waterlogging disrupts the uptake of nitrogen and this manifests itself as a plant nutrient deficiency (Gales, 1983; Huang et al., 1995). Physiological processes such as photosynthesis, water uptake, and root–shoot hormone relations are affected (Pezeshki, 1994). Comparisons of wheat cultivars show that in waterlogged soils, intolerance is accompanied by high concentrations of iron and manganese in the shoots (Huang et al., 1995). The effects of waterlogging seem to be reduced by increasing nutrient availability through fertilizer application (Gales, 1983; Huang et al., 1995). In a well-drained soil, oxygen uptake by plant roots and microorganisms is balanced by diffusion from the surface through the soil pores. Root growth may be reduced and death rate increased if the amount of oxygen reaching the roots falls below a critical level. Because the rate of oxygen diffusion depends strongly on the size and connectivity of pores it is possible for there to be anaerobic patches in an otherwise well-aerated soil. 2. Drought Drought has several definitions but functionally it occurs when crop growth rate is significantly reduced through lack of water. As the soil dries there are changes in the physical condition of the soil such as increases in strength (Ehlers et al., 1983; Martino and Shaykewich, 1994) and the formation of air gaps between the root and the soil, which reduce the amount of contact and can lead to large increases in the resistance to water and nutrient uptake (Nye, 1992). Drying of the soil surface may also inhibit normal development of the nodal root system (Gregory et al., 1978; Gregory, 1994a). Drought can affect the size of cereal root systems. For example, the root number of five barley cultivars was reduced by drought, and the root volume was reduced by between 51.6% and 56.3% when compared to a well-watered treatment (Khaldoun et al., 1990). Root and shoot growth of winter wheat were also reduced by the imposition of an artificial drought (Barraclough and Weir, 1988), even though water use was not affected until after anthesis. In another experiment, an increase in winter wheat root length at heading from 7.8 km m−2 in one year to 14.9 km m−2 in the following year was attributed to wetter (and warmer) conditions in the second year (Haberle et al., 1996) (Table I). Water and nitrogen availability should really be considered together since nitrogen becomes less available as the soil dries. In one experiment on a water-retentive silty clay loam soil, the combined effect of low water and low nitrogen availability


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reduced the yield of winter wheat by 60% (Barraclough et al., 1989) . Since drought alone reduced the yield by 23% and low nitrogen availability alone by 51%, the effects approach additivity. Drought reduced root growth in the topsoil although there was some compensatory growth in the subsoil as long as there was available nitrogen. Drought increased the depth of rooting from 1.4 to 1.6 m. Although the root length density at depth was less than 1 km m−3, in droughted conditions, and with adequate nitrogen, a rooting density of 1 km m−3 was found sufficient to allow all the available water to be extracted (Barraclough et al., 1989). Winter cereals are probably less susceptible to water shortage than spring cereals because of their earlier and more extensive root development (Gales, 1983). Weaver (1926) suggested that moist surface soil during the early stages of development of a plant would promote a shallow rooting habit which would make a plant vulnerable to drought at later stages of its life cycle.

B. NUTRIENT AVAILABILITY Increasing nitrogen availability was found to increase the nitrogen uptake rate of a wide range of wheat genotypes (Greef and Kullmann, 1992), and this was usually associated with an increase in yield. Application of nitrogen to soils with low available nitrogen can cause an increase in root length throughout the root system (Robinson et al., 1994) although this is not invariably so. There is also an increase in the number of laterals and branching (Weaver, 1926; Feil and Geisler, 1988; Gregory, 1994a), the average length of each lateral root (Welbank et al., 1974; Gregory, 1994a), the depth of rooting (Hansson and Andrén, 1987), and the rooting density (Hansson and Andrén, 1987). In contrast, some authors report a decrease in the length of the main axes and laterals (Weaver, 1926; Greef and Kullmann, 1992). It has long been known that nutrient-rich soil patches lead to localized root proliferation (Gregory, 1994a) and surface applications of nitrogen fertilizer tend to result in high root densities in the surface layers of the soil. In one case, the application of nitrogen fertilizer to barley resulted in no uptake of nitrogen below 0.25 m where only a few unbranched primary roots could be observed (Welbank et al., 1974), although responses as severe as this are probably uncommon. Nitrogen is not the only important nutrient, and phosphate in particular is crucial to proper root development. The importance of P for roots has been known since the 19th century. Weaver (1926) appreciated its importance and recommended that a dressing of phosphate should be applied whenever greater root development was required. However, most of northwest Europe has a long history of P fertilization and it is rare to see symptoms of deficiency provided that enough fertilizer is applied to balance losses in the crop. In soils low in P, phosphates induce the young roots to penetrate rapidly into the deeper layers of soil beneath the surface and this imparts a higher resistance to water shortages later. After 100 days, Weaver (1926) found that

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the depth of rooting of wheat grown with phosphates was twice that of wheat grown on similar soils with a low phosphate status. This is consistent with the observations of Gregory (1994a) who noted that banding of phosphates close to the seed during drilling can stimulate growth and result in fast early development of the crop. In conditions of low pH, rooting is generally reduced. For example, the root length and root fresh weight of wheat plants was found to be correlated with pH over the range 4.0 to 6.0 (Johnson and Wilkinson, 1992).

C. TRANSPORT OF WATER AND SOLUTES THROUGH THE SOIL While leaves hardly deplete CO2 from the air surrounding them due to its rapid rate of diffusion, roots tend to deplete resources in the rhizosphere (Gregory, 1994a). The rate of uptake is therefore also dependent on the rate at which nutrients and water are able to move through the soil medium to the root surface. The mobility of nutrients varies considerably so that while nitrate can move distances of between 10 and 100 mm through the soil to an absorbing root, phosphates can effectively only diffuse from within 1 mm of the root surface. The diffusion of nitrate through the soil medium is also about one order of magnitude faster than the movement of ammonium (Clarke and Barley, 1968). Whereas nutrients move through the soil down concentration gradients, the movement of water is driven by a water potential gradient. In a homogeneous soil, the water content close to the root surface will be lower than in the bulk soil. This difference depends on the rate of transpiration, which drives the uptake of soil water by the roots and by the hydraulic conductivity of the soil. The average rate of water uptake per unit length of root can be estimated by dividing the transpiration rate by the total length of the root system. However, not all parts of the root system are equally active in water uptake. It follows that crops with relatively small root systems will have the largest drawdown in soil water content close to the root (Kage and Ehlers, 1996). Stomata will therefore tend to close at a higher average soil water content in plants which have a lower total root length. Adopting the assumptions of the “single root” model, the most important factors in determining the rate of water transport to the roots are volumetric water content and root length density (Kage and Ehlers, 1996). Root diameter was found to be less important. In practice Kage and Ehlers (1996) found that the rate of water movement through the soil was only limiting in deeper soil layers with very low rooting densities (

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