Advances in Agronomy, Volume 98

V O LU M E N I N E T Y ADVANCES IN E I G H T AGRONOMY ADVANCES IN AGRONOMY Advisory Board PAUL M. BERTSCH RONA...

Author: Donald L. Sparks

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V O LU M E

N I N E T Y

ADVANCES

IN

E I G H T

AGRONOMY

ADVANCES IN AGRONOMY Advisory Board

PAUL M. BERTSCH

RONALD L. PHILLIPS

University of Kentucky

University of Minnesota

KATE M. SCOW

LARRY P. WILDING

University of California, Davis

Texas A&M University

Emeritus Advisory Board Members

JOHN S. BOYER

KENNETH J. FREY

University of Delaware

Iowa State University

EUGENE J. KAMPRATH

MARTIN ALEXANDER

North Carolina State University

Cornell University

Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D. BALTENSPERGER, CHAIR LISA K. AL-AMOODI

CRAIG A. ROBERTS

KENNETH A. BARBARICK

MARY C. SAVIN

HARI B. KRISHNAN

APRIL L. ULERY

SALLY D. LOGSDON

V O LU M E

N I N E T Y

ADVANCES

E I G H T

IN

AGRONOMY EDITED BY

DONALD L. SPARKS Department of Plant and Soil Sciences University of Delaware Newark, Delaware

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 84 Theobald’s Road, London WC1X 8RR, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2008 Copyright # 2008 Elsevier Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made ISBN: 978-0-12-374355-8 ISSN: 0065-2113 (series) For information on all Academic Press publications visit our website at books.elsevier.com Printed and bound in USA 08 09 10 10 9 8 7 6 5 4 3 2 1

CONTENTS

Contributors Preface

1. Advances in Precision Conservation

ix xiii

1

Jorge A. Delgado and Joseph K. Berry 1. Introduction 2. Geospatial Technologies 3. Identifying Spatial Patterns and Relationships 4. Field Level Flows 5. Connection of Field with Off-Site Transport 6. Watershed Scale Considerations 7. Current Applications and Trends 8. Summary and Conclusions References

2. Reaction and Transport of Arsenic in Soils: Equilibrium and Kinetic Modeling

2 4 9 12 17 22 28 39 39

45

Hua Zhang and H. M. Selim 1. Introduction 2. Environmental Toxicity 3. Arsenic in Soils 4. Biogeochemistry 5. Transport in Soils 6. Modeling 7. Remediation of Contaminated Soils 8. Summary and a Look Ahead References

3. Crop Residue Management for Lowland Rice-Based Cropping Systems in Asia

46 47 48 52 73 81 101 104 105

117

Bijay-Singh, Y. H. Shan, S. E. Johnson-Beebout, Yadvinder-Singh, and R. J. Buresh 1. Introduction 2. Criteria for Evaluating Crop Residue Management Options

118 121 v

vi

Contents

3. 4. 5. 6.

Type and Abundance of Crop Residues Existing and Emerging Residue Management Options Evaluation of Options with Residues Managed During a Rice Crop Evaluation of Options with Residues Managed During a Non-Flooded Crop 7. Crop Residue and Bioenergy Options 8. Summary Acknowledgment References

4. Sampling and Measurement of Ammonia at Animal Facilities

123 125 135 160 181 183 185 186

201

Ji-Qin Ni and Albert J. Heber 1. Introduction 2. A General View of Ammonia Determination 3. Ammonia Sampling 4. Ammonia Concentration Measurement 5. Measurement Methods and Devices 6. Ammonia Concentration Data 7. Summary and Conclusions Acknowledgments References

5. Will Stem Rust Destroy the World’s Wheat Crop?

203 205 206 221 225 243 255 257 257

271

Ravi P. Singh, David P. Hodson, Julio Huerta-Espino, Yue Jin, Peter Njau, Ruth Wanyera, Sybil A. Herrera-Foessel, and Richard W. Ward 1. 2. 3. 4. 5.

Introduction Stem Rust Disease, Pathogen, and Epidemiology Breeding for Resistance Race UG99 and Why it is a Potential Threat to Wheat Production Breeding Strategies to Mitigate the Threat from UG99 and Achieve a Long-Term Control of Stem Rust 6. Conclusion and Future Outlook Acknowledgments References

272 274 277 281 288 305 306 306

6. Genetic Improvement of Forage Species to Reduce the Environmental Impact of Temperate Livestock Grazing Systems

311

M. T. Abberton, A. H. Marshall, M. W. Humphreys, J. H. Macduff, R. P. Collins, and C. L. Marley 1. Introduction 2. Reducing Diffuse Nitrogenous Pollution of Watercourses

312 315

Contents

3. Reducing P Pollution of Watercourses 4. Reducing Emissions to Air 5. Improving Soil Quality and Reducing Flood Damage 6. Enhancing Persistency and Resilience 7. Enhancing C Sequestration in Grasslands 8. Future Prospects Acknowledgments References

7. Mutagenesis and High-Throughput Functional Genomics in Cereal Crops: Current Status

vii

321 325 335 339 341 344 345 345

357

H. S. Balyan, N. Sreenivasulu, O. Riera-Lizarazu, P. Azhaguvel, and S. F. Kianian 1. Introduction 2. Insertional Mutagenesis 3. Non-Transgenic TILLING, DEALING, and DeleteageneTM Approaches 4. Phenomics Platform for Screening Mutagenized Population 5. Outlook Acknowledgments References Index

358 361 380 398 399 401 401 415

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CONTRIBUTORS

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

M. T. Abberton (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom P. Azhaguvel (357) Texas A&M University Agricultural Research and Extension Center, 6500 Amarillo Blvd West, Amarillo, Texas 79106 H. S. Balyan (357) Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut 250 004, India Joseph K. Berry (1) Berry and Associates, Spatial Information Systems, Fort Collins, Colorado 80525 Bijay-Singh (117) Department of Soils, Punjab Agricultural University, Ludhiana 141 004, Punjab, India R. J. Buresh (117) International Rice Research Institute, Los Banõs, Philippines R. P. Collins (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom Jorge A. Delgado (1) USDA-ARS, Soil Plant Nutrient Research Unit, Fort Collins, Colorado 80526 Albert J. Heber (201) Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana 47907 Sybil A. Herrera-Foessel (271) International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico, DF, Mexico David P. Hodson (271) International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico, DF, Mexico

ix

x

Contributors

Julio Huerta-Espino (271) INIFAP-CEVAMEX, 56230 Chapingo, Mexico M. W. Humphreys (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom Yue Jin (271) USDA-ARS, Cereal Disease Laboratory, St. Paul, Minnesota 55108 S. E. Johnson-Beebout (117) International Rice Research Institute, Los Banõs, Philippines S. F. Kianian (357) Department of Plant Sciences, North Dakota State University, Fargo, North Dakota 58105 J. H. Macduff (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom C. L. Marley (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom A. H. Marshall (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom Ji-Qin Ni (201) Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana 47907 Peter Njau (271) Kenya Agricultural Research Institute, Njoro Plant Breeding Research Center (KARI-NPBRC), Njoro, Kenya O. Riera-Lizarazu (357) Department of Crop and Soil Science, Oregon State University, Corvallis, Oregon 97331 H. M. Selim (45) School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, Louisiana 70803 Y. H. Shan (117) College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225009, China

Contributors

xi

Ravi P. Singh (271) International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico, DF, Mexico N. Sreenivasulu (357) Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstrasse-03, Gatersleben 06466, Germany Ruth Wanyera (271) Kenya Agricultural Research Institute, Njoro Plant Breeding Research Center (KARI-NPBRC), Njoro, Kenya Richard W. Ward (271) International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico, DF, Mexico Yadvinder-Singh (117) Department of Soils, Punjab Agricultural University, Ludhiana 141 004, Punjab, India Hua Zhang (45) School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, Louisiana 70803


PREFACE

Volume 98 contains seven comprehensive and timely reviews. Chapter 1 covers advances in precision conservation, including cutting-edge technologies and applications and trends. Chapter 2 deals with equilibrium and kinetic modeling of arsenic reactions and transport in soils and includes background material on the biogeochemistry of arsenic, a toxic element of concern worldwide. Chapter 3 covers crop residue management for lowland rice-based cropping systems in Asia with discussions on existing and emerging residue management options. Chapter 4 is a timely review on sampling and measurement of ammonia at animal facilities, including measurement methods and advances in data collection. Chapter 5 is concerned with stem rust and its effects on wheat production, including breeding efforts and strategies for reducing its impact. Chapter 6 covers genetic improvement of forage species with a goal of reducing the environmental impact of temperate livestock grazing systems. Topics dealing with reducing nitrogen and phosphorus pollution and air emissions are included. Chapter 7 is a comprehensive chapter on the current status of mutagenesis and high-throughput functional genomics in cereal grains. I am grateful for the authors’ outstanding reviews. DONALD L. SPARKS University of Delaware

xiii


C H A P T E R

O N E

Advances in Precision Conservation Jorge A. Delgado* and Joseph K. Berry† Contents 2 4 9 12

1. 2. 3. 4.

Introduction Geospatial Technologies Identifying Spatial Patterns and Relationships Field Level Flows 4.1. Variable erosion and transport (flows of gases, nutrients, and water) 4.2. Precision conservation for management of flows 5. Connection of Field with Off-Site Transport 5.1. Variable flows from field to nonfarm areas 5.2. Precision conservation buffers and riparian zones 6. Watershed Scale Considerations 6.1. Variable hydrology 6.2. Models and tools 6.3. Precision conservation at a watershed scale 7. Current Applications and Trends 8. Summary and Conclusions References

12 16 17 17 20 22 22 22 24 28 39 39

Population growth is expected to increase, and the world population is projected to reach 10 billion by 2050, which decreases the per capita arable land. More intensive agricultural production will have to meet the increasing food demands for this increasing population, especially because of an increasing demand for land area to be used for biofuels. These increases in intensive production agriculture will have to be accomplished amid the expected environmental changes attributed to Global Warming. During the next four decades, soil and water conservation scientists will encounter some of their greatest challenges to maintain sustainability of agricultural systems stressed by increasing food and biofuels demands and Global Warming. We propose that Precision Conservation will be needed to support parallel increases in soil and water conservation

* {

USDA-ARS, Soil Plant Nutrient Research Unit, Fort Collins, Colorado 80526 Berry and Associates, Spatial Information Systems, Fort Collins, Colorado 80525

Advances in Agronomy, Volume 98 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00201-0

#

2008 Elsevier Inc. All rights reserved.

1

2

Jorge A. Delgado and Joseph K. Berry

practices that will contribute to sustainability of these very intensively-managed systems while contributing to a parallel increase in conservation of natural areas. The original definition of Precision Conservation is technologically based, requiring the integration of a set of spatial technologies such as global positioning systems (GPS), remote sensing (RS), and geographic information systems (GIS) and the ability to analyze spatial relationships within and among mapped data according to three broad categories: surface modeling, spatial data mining, and map analysis. In this paper, we are refining the definition as follows: Precision Conservation is technologically based, requiring the integration of one or more spatial technologies such as GPS, RS, and GIS and the ability to analyze spatial relationships within and among mapped data according to three broad categories: surface modeling, spatial data mining, and map analysis. We propose that Precision Conservation will be a key science that will contribute to the sustainability of intensive agricultural systems by helping us to analyze spatial and temporal relationships for a better understanding of agricultural and natural systems. These technologies will help us to connect the flows across the landscape, better enabling us to evaluate how we can implement the best viable management and conservation practices across intensive agricultural systems and natural areas to improve soil and water conservation.

1. Introduction Population growth is expected to increase, and the world population is projected to reach 10 billion by 2050, which will decrease the per capita arable land from 0.23 ha in 1995 to 0.14 ha by 2050 (Lal, 1995). More intensive agricultural production will have to meet the increasing food demands for this increasing population, especially because of an increasing demand for land area to be used for biofuels. These increases in intensive production agriculture will have to be accomplished amid the expected environmental changes attributed to Global Warming. Scientists are projecting future changes of weather patterns that include regions with higher evapotranspiration rates, lower precipitation in some areas, and higher precipitation in other areas, which may contribute to higher erosion rates (Hatfield and Prueger, 2004; Lal, 1995, 2000; Nearing et al., 2004; Pimentel et al., 1995). During the next four decades, soil and water conservation scientists will encounter some of their greatest challenges to maintain sustainability of agricultural systems stressed by increasing food and biofuel demands. Several scientists have reported on the potential impacts of global population increase, increase in greenhouse gases, and potential effects of climate change on soil and water quality and on soil erosion (Hatfield and Prueger, 2004; Lal, 1995, 2000; Nearing et al., 2004; Pimentel et al., 1995). There is a concern that if precipitation patterns continue to change, certain future

Advances in Precision Conservation

3

scenarios may cause conservation practices such as crop residue, no-till, and incorporation of manure to lose effectiveness very rapidly, resulting in dramatic increases in runoff, and higher impacts to soil and water quality (Hatfield and Prueger, 2004). It is also estimated that for every 25.4 mm increase in precipitation rate, erosibility increases by 1.7% (Nearing et al., 2004). Nearing et al. (2004) reported that the relationship between increases in rain, biomass production, and erosion is more complex. Although an increase in rain could increase biomass production, a decrease in biomass may also increase erosion rates. The more difficult area to evaluate was effects of climate change on land use and erosion rates, yet they concluded from their analysis that the average increase in erosibility will be 1.7% per 25.4 mm increase in precipitation. It is important to note that Meisinger and Delgado (2002) reported an average 10–30% of total N inputs in cropping systems are lost due to nitrate leaching. Thus, increases in precipitation and/or more intensive storms could potentially contribute to higher nitrate leaching rates as well. These assessments from Nearing et al. (2004) and Hatfield and Prueger (2004) clearly show the continuing need for soil and water conservation scientists and practitioners to continue looking for alternatives for managing future impacts to soil and water quality. Scientists and conservation practitioners will have to work together with farmers across all types of soils and weather to increase and sustain higher production to meet the demands of the increasing population, while managing for potential changes in weather patterns. This cooperation will also be necessary to develop cropping systems that produce enough to meet the increasing food and biofuel demands while maximizing soil and water conservation. The implementation of soil and water conservation will be necessary for the sustainability of these intensive efforts to maximize agricultural production. New technologies will help us to increase yields per hectare and these technologies will also be applied to understand and manage agricultural systems and to connect the flows from agricultural systems to natural areas in an effort to manage these regions for maximum yield and agroenvironmental sustainability. Precision Conservation was originally defined as a set of spatial technologies and procedures linked to mapped variables, which is used to implement conservation management practices that take into account spatial and temporal variability across natural and agricultural systems (Berry et al., 2003). Contrary to Precision Farming that was oriented to maximize yields in agricultural fields, Precision Conservation connects farm fields, grasslands, and range areas with the natural surrounding areas such as buffers, riparian zones, forest, and water bodies (Fig. 1). The goal of Precision Conservation is to use information about surface and underground flows to analyze the systems in order to make the best viable decisions for application of management practices that contribute to conservation of agricultural, rangeland, and natural areas.

4


Precision conservation Precision Ag Wind erosion

Chemicals

Soil erosion Runoff Leaching

Leaching

Terrain

Leaching

Soils Yield Potassium

3-dimensional Flows Cycles

Coincidence

CIR image

2-dimensional Interconnected perspective

Isolated perspective

Figure 1 The site-specific approach can be expanded to a three-dimensional scale approach that assesses inflows and outflows from fields to watershed and region scales. (From Berry et al., 2003.)

Berry et al. (2003) acknowledged that there could be different degrees of Precision Conservation such as the use of nondigital, non-GIS maps and the use of survey methods that can help in the application of spatial conservation practices. However, the original definition of Precision Conservation is technologically based, requiring the integration of spatial technologies such as global positioning systems (GPS), remote sensing (RS), and geographic information systems (GIS) and the ability to analyze spatial relationships within and among mapped data according to three broad map analysis categories: spatial analysis, surface modeling, and spatial data mining (Fig. 2). Since Berry et al. (2003), several other papers related to the topic of Precision Conservation have been published describing how these new technologies can be applied for maximizing Precision Conservation.

2. Geospatial Technologies New GIS, GPS, RS, modeling, and computer program technologies are rapidly increasing our capacity to analyze large sets of information in space and time. Traditional statistics used for soil and water conservation studies and assessment of best management practices were initially nonspatial and analyzed a data set by fitting a numerical distribution (e.g., standard

Surface modeling

Point samples are spatially interpolated into a continuous surface

53.2 ppm

4.2 ppm

Field sample locations Phosphorus surface

Discrete data spikes

Min = 4.2 Max = 53.2 Avg = 13.4 SDev = 5.2

Spatial data mining 32c,62r

45c,18r

Map surfaces are clustered to identify data pattern groups

P 53.2

Relatively low responses in P, K, and N Relatively high responses in P, K, and N

11.0

Cluster 2 Cluster 1

N

K 412.0

177.0

27.9

32.9

N K P Geographic space

Data space

Clustered data zones

Figure 2 Surface modeling is used to derive map surfaces that utilize spatial data mining techniques to investigate the numerical relationships in mapped data.(From Berry et al., 2005.)

6


normal curve) to generalize the central tendency of the data. The values used for soil and water conservation have traditionally used mean and standard deviation to describe the responses to a traditional conservation practice, informing its numerical distribution without any reference to the spatial distribution of the data sources. The basic assumption for this method of analysis was that these relationships among the data were randomly (or uniformly) distributed in geographic space. Many of the analysis techniques were considered less valid if the data exhibited spatial autocorrelation. New methods and advances in models use spatial technologies to analyze spatial relationships within and among mapped data for highly detailed insight into the field of Precision Conservation and the potential for sitespecific applications that can contribute to environmental sustainability (Berry, 1999, 2003a,b; Mueller et al., 2005; Qiu et al., 2007; Renschler and Lee, 2005; Schumacher et al., 2005). These new soil and water conservation analysis capabilities enabled by GIS can be grouped into two broad map analysis categories: Spatial Statistics, involving numerical relationships of surface modeling and spatial data mining and Spatial Analysis, involving geographical relationships, such as proximity and terrain configuration (Berry, 1999, 2003a,b). These new spatial techniques will contribute to an integrated evaluation of topography, hydrology, weather, management, and other physical and chemical parameters, providing new insight into sitespecific Precision Conservation for management of flow-interconnected agricultural and natural resources. Figure 3 outlines the fundamental differences between the traditional GIS mapping approach and the map analysis approach used in Precision Conservation. Most desktop mapping applications take a set of spatially collected data (e.g., parts per million, kilogram per hectare, etc.), then reduces the data set to a single value (total, average, median, etc.), and ‘‘paints’’ a fixed set of polygons with colors reflecting the scalar statistic of the field data occurring within each polygon. For example, the left side of Fig. 3 depicts the position and relative values for a set of field collected data; the right side shows the derived spatial distribution of the data for an individual reporting parcel. The average of the mapped data is shown as a superimposed plane ‘‘floating at average height of 22.0’’ and assumed to be the same everywhere within the polygon. But the data values themselves, as well as the derived spatial distribution, suggest that higher values occur in the northeast and lower values in the western portion. The first thing to notice in the figure is that the average exists hardly anywhere, forming just a thin band cutting across the parcel. Most of the mapped data is well above or below the average. That is what the standard deviation attempts to reveal—just how typical the computed typical value really is. If the dispersion statistic is relatively large, then the computed typical is not typical at all. The limitation inherent in previous computer

Map analysis Desktop mapping Field data Standard normal curve fit to the data

Spatially interpolated data

34.1% 34.1%

68.3% +/−1 standard deviation

Average = 22.0 StDev = 18.7

22.0

28.2

Discrete spatial object (generalized)

80 60 40 20 0 −20 −40 −60

High = 50

80 60 40 20 Average = 22.0

0 −20 −40 −60

N

Continuous spatial distribution (detailed)

Figure 3 Desktop mapping uses aggregated, nonspatial statistics to summarize spatial objects (points, lines, and polygons), whereas map analysis uses continuous spatial statistics to characterize gradients in geographic space (surfaces).

8


applications arises from the fact that most desktop mapping applications ignore data dispersion and simply ‘‘paint’’ a color corresponding to the average regardless of numerical or spatial data patterns within a parcel. However, the central tendency assumption can be misleading. Assume the data is characterizing a toxic chemical in the soil that, at high levels, poses a serious health risk. The mean values for both the parcel on the left (22.0) and the right (28.2) are well under the ‘‘critical limit’’ of 50.0. Desktop mapping would paint both parcels a comfortable green tone, as their typical values are well below the level of concern. Even when considering the upper-tails of the standard deviations, the limit is not exceeded (22.0 + 18.7 = 40.7 and 28.2 + 19.8 = 48.0). So from a nonspatial perspective, the aggregated results indicate acceptable levels of the chemical in both parcels. However, the lower right portion of the figure portrays a radically different set of conditions. The left and right parcels are displayed as an increasing gradient from low levels (green) through areas that are above the critical limit (red tones). The high regions, when combined, represent a contiguous subarea of nearly 15% of the combined area that likely extends into adjacent parcels. The aggregated, nonspatial treatment of the spatial data fails to uncover the spatial pattern by assuming the average value is everywhere within the parcels. Similar surface modeling investigations can be used to compile point data into a continuous surface representation of data across the landscape to explain any variance. Point density mapping, spatial interpolation, and map generalization are examples of uses of surface modeling. Point density mapping can be used to evaluate the number of aggregate points within a specified distance (e.g., number of occurrences per hectare). Conservation practitioners and scientists will collect point-sampled data to derive maps of nutrient concentrations such as soil carbon. For example, we could use kriging for spatial interpolation of weight-average measurements within a localized area to assess carbon sequestration potential. An example of map generalization is the use of polynomial surface fitting to the entire data set. There are new techniques for spatial data mining that can be used to try to uncover relationships within and among multiple mapped data layers such as water tables, erosion potential, topography, soil texture, yields, vegetative cover, soil depths, and others (Berry, 1999, 2003a). Berry (2002) reported that these procedures, including coincidence summary, proximal alignment, statistical tests, percent difference, level-slicing, map similarity, and clustering can be used to assess similarities in data patterns. Another type of spatial data mining is the use of predictive models that use crop biomass cover (straw biomass production-dependent variable) and the soil nutrient values [soil texture, soil carbonates, topography, hydrology, water levels, and runoff (independent variables)], then quantify the data pattern. As thousands of map locations are analyzed, a predictable pattern


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between crop biomass and the variables may appear. This crop residue production may be correlated to potential for reduction of erosion, of surface runoff, and other soil and water conservation outcomes. Scientists and practitioners can analyze the numerical relationships of spatial patterns inherent in mapped data using surface modeling and spatial data mining. These approaches can be used to explain variance by mapping and analyzing spatial distributions (Berry, 2002).

3. Identifying Spatial Patterns and Relationships For more than 8000 years, we have been using maps with features that identify special locations in the landscape to help us navigate. Precision Conservation is a new way to use advanced technologies to integrate thousands of data points and multiple layers of information contained in maps for management and conservation of the agricultural and natural areas. Specifically, Precision Conservation allows us to identify those management landscape combinations that produce or receive significant impact. Scientists have been using spatial information for soil and water conservation for decades. However, since the development of new computers and GIS technology in the early 1970s, mapped data have changed to digital representations that are linked to larger databases, thereby increasing the number of possible applications for Precision Conservation. These new developments and the capability to integrate thousands of points and multiple map layers of information to analyze spatial and temporal relationships are providing new answers for applications of Precision Conservation. There is even potential to use these map analyses to contribute to air quality conservation. We could use these new analyses to evaluate how conservation practices could be applied to reduce wind erosion from the most sensitive areas. Spatial emissions of trace gases such as nitrous oxide (N2O) and ammonia (NH3) volatilization could also be managed using Precision Conservation. There is potential to use these layers of information to develop Precision Conservation Management plans (Kitchen et al., 2005; Knight, 2005; Lerch et al., 2005). These advances in evolving technologies will continue to increase during the next four decades, which will facilitate and speed the collection and use of thousands of data points and multiple map layers. An example of these new technologies is the mote, a quarter-sized wireless smart sensor that fits anywhere. These smart sensors, initially developed by researchers at University of California at Berkeley and Intel, could have future applications in soil and water conservation. These sensors, called ‘‘smart dust’’ by their developers, Professors Kristofer Pister and Joseph Kahn of University

10


of California at Berkeley, can be scattered, sending information from remote locations. These and other new developments may contribute to the collection of information that will be used to generate maps for use in analysis in the field of Precision Conservation (Berry et al., 2003). Map analysis procedures can be used to study landscape relationships among map features. These analyses can assess the relative position of features in the landscape and their connectivity to flows in the environment. We can use these map analyses to evaluate effective distances, indexes, optimal path connectivity, flows, biomass cover, soil texture, microterrain analysis, elevation, distances to water bodies, and other landscape characteristics. We could simulate the flows over an elevation map to estimate the erosion potential as described by Berry et al. (2003) by using an analysis that follows the downhill path over a terrain. Berry (2003b) described this type of map analysis as a method to account for all the areas sharing common paths (Fig. 4). The ability to model flows and interconnected cycles will benefit from the current evolutionary phase of GIS involving new geo-referencing approaches. In the 1970s, the research and early applications centered on Computer Mapping (display focus) that yielded to Spatial Data Management (data structure/management focus) in the next decade as we linked digital maps to attribute databases for geo-query (left side of Fig. 5). The 1990s centered on GIS Modeling (analysis focus) that laid the groundwork for whole new ways of assessing spatial patterns and relations, as well as for entirely new applications such as Precision Agriculture. Today, in its fourth decade, GIS is centered on Multimedia Mapping (mapping focus) which brings the technology full circle to its beginnings (Berry, 2007b). While advances in virtual reality and three-dimensional visualization can ‘‘knock your socks off,’’ they represent incremental progress in visualizing maps that exploit dramatic computer hardware/software advances. Radical innovation is being addressed by current geospatial research that is refocusing on data structure and analysis (Berry, 2007a). The bulk of the current state of geospatial analysis relies on ‘‘static coincidence modeling’’ using a stack of geo-registered map layers. However, the frontier of GIS research is shifting focus to ‘‘dynamic flows modeling’’ that tracks movement over space and time in three-dimensional geographic space. But a wholesale revamping of data structure is needed to make this leap. The impact of the next decade’s evolution will be huge and will shake the very core of GIS—the Cartesian coordinate system itself, a spatial referencing concept introduced by mathematician, Rene Descartes over 400 years ago. The current two-dimensional square for geographic referencing is fine for ‘‘static coincidence’’ analysis over relatively small land areas, but is woefully lacking for ‘‘dynamic three-dimensional flows.’’ It is likely that Descartes’ two-dimensional squares will be replaced by hexagons

Inclination of a fitted plane to a location and its eight surrounding elevation values

2418

2404

2393

2409

2395

2341

2383

2373

2354

Slope(47,64) = 33.23%

35% 30% 25% 20% 15% 10% 5% 1% 0%

Steep

Moderate Gentle flat

Slope map draped on elevation Slope map

Elevation surface

Flow(28,46) = 451 paths

537 Paths Heavy 256 Paths 123 Paths 64 Paths 32 Paths 16 Paths Moderate 8 Paths 4 Paths Light 2 Paths 1 Paths minimal

Total number of the steepest downhill paths flowing into each location Flow map draped on elevation Slope map

Figure 4 Maps of surface flow confluence and slope are calculated by considering relative elevation differences throughout a project area. (From Berry et al., 2005.)

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Future directions

Revisit analytics (2020s)

2D planar (X,Y data)

3D solid (X,Y,Z data)

Hexagon (6 sides)

Dodecahedron (12 pentagons)

Square (4 sides)

Hexahedron (6 squares)

4) Multimedia mapping (2000s) Revisit Geo-reference (2010s) Contemporary GIS 2) Spatial dB Mgt (1980s) 3) GIS modeling (1990s)

The early years 1) Computer mapping (1970s) Mapping focus Data/structure focus Analysis focus

Figure 5 Current GIS research focuses on revolutionary changes in geo-referencing, data structures, and analytical operations that will greatly advance dynamic flows and cycles modeling directly applicable to Precision Conservation.

(analogous to the pentagon patches forming a soccer ball) that better represent our curved earth’s surface. Current three-dimensional referencing using cubes will be replaced by nesting polyhedrons for a consistent and seamless representation of three-dimensional geographic space (Peterson, 2007). This change in referencing extends the current six sides of a cube for flow modeling to the 12 sides (facets) of a polyhedron (hexagonal polyhedron)—radically changing flow and cycle algorithms, as well as our historical perspective of mapping.

4. Field Level Flows 4.1. Variable erosion and transport (flows of gases, nutrients, and water) Quine and Zhang (2002) reported that eroded areas of the field with depleted nutrients had lower yields. This spatial relationship between erosion and crop yield is complex since other areas with high soil aggregation were also found to show lower yields (Quine and Zhang, 2002). Evaluation of variable erosion on yield production was more clear when long-term


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simulations of the field were conducted. Quine and Zhang (2002) conducted a long-term evaluation of 40 years that clearly showed the future effects of spatial erosions, identifying that the more eroded areas of the field will also have lower yields. These model simulations clearly showed that there is a need to manage fields differently in order to reduce these higher site-specific erosion rates, which will eventually reduce yields in the most affected areas (Quine and Zhang, 2002). The data from Quine and Zhang (2002) show the underlying theory that informs the concepts of Precision Conservation on a field scale. Different spatial patterns of erosion that will affect yield productivity are clearly apparent. If all fields are managed with similar conservation practices, the higher erosion rates from the most affected areas may still continue to lower the yields as crop intensity increases. Precision Conservation proposes that there is a need in those affected areas for conservation managers to consider variable conservation as a means of increasing the sustainability of these systems (Berry et al., 2003, 2005; Mueller et al., 2005; Quine and Zhang, 2002; Schumacher et al., 2005). Schumacher et al. (2005) used a soil displacement of Cesium-137 and the Water and Tillage Erosion model to assess the erosion losses due to water and tillage across the field. They found that both methods were strongly correlated. The areas showing the higher slope were those areas showing the higher tillage and water erosion rates (Fig. 6). Spatial variability of nitrogen dynamics has previously been reported by several scientists. An example of the spatial variability of residual soil NO3-N was presented by Delgado (2001) and Delgado et al. (2001) in a study of the spatial variability for vegetable and small grain systems grown in similarly managed center pivot irrigated systems. The average residual soil NO3-N in the sandy loam zone of the center pivot system was higher than in the loamy sand. Delgado (2001) reported that for center pivot irrigated barley, canola, and potato grown on the loamy sand zone, the average residual soil NO3-N was 20, 44, and 109 kg N ha 1, respectively. The residual soil NO3-N for barley, canola, and potato grown on the sandy loam zone was 42, 51, and 136 kg N ha 1, respectively. Figure 7 shows similar results for residual soil NO3-N for center pivot irrigated corn grown on a sandy coarse soil of Northeastern Colorado (Delgado and Bausch, 2005). Residual soil NO3-N was negatively correlated with the percent sand content across the field. Spatial variability of NO3-N due to leaching has also been reported (Delgado, 2001; Wylie et al., 1995). Delgado (2001) reported that for center pivot irrigated barley, canola, and potato grown on a loamy sand zone, the average NO3-N leached was 32, 39, and 91 kg N ha 1, respectively. The amounts of NO3-N leached from the loamy sand zone were higher than the amounts leached from the sandy loam zone. The average NO3-N leached from center pivot irrigated barley, canola, and potato grown on the sandy loam zone was 29, 13, and 72 kg N ha 1, respectively. Figure 8 shows similar

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Figure 6 Erosion patterns developed from tillage, water, tillage-water, and total erosion (137Cs) modeling of the research field are displayed. Cesium sampling sites are also displayed on a contour map of slope percentage for the field.(From Schumacher et al., 2005.)

results for NO3-N leaching for center pivot irrigated corn grown on a sandy coarse soil of Northeastern Colorado (Delgado and Bausch, 2005). Higher NO3-N leaching increased as the percentage sand increased across the field (Delgado and Bausch, 2005). Best management practices, modeling, and GIS

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Figure 7 Spatial distribution of sand content in the top 1.5 m of soil across different productivity zones (A). Spatial distribution of observed residual soil NO3-N in the top 1.5 m of soil across the different productivity zones during the 2000 growing season (B). (From Delgado and Bausch, 2005.)

can be used to evaluate the effects of management practices on spatially variable NO3-N transport and dynamics across regions (Hall et al., 2001). Spatial variability in emissions of trace gases such as N2O and in methane (CH4) uptake and sink were reported by Mosier et al. (1996). Mosier et al. (1996) reported that for a clay catena of the short grass steppe, the N2O emissions from the swale catena position were 2.5 mg N m 2 h 1 higher than the mid or top slope position of the catena, which averaged 1.4 and

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Figure 8 Spatial distribution of predicted NO3-N leaching from the root zone of corn (1.5 m depth) across the different productivity zones during the 2000 growing season. (From Delgado and Bausch, 2005.)

1.3 mg N m 2 h 1, respectively. Methane uptake rates were lower in the swale position than the mid and upper catena. Similar spatial observations in trace gas emissions have also been reported for Canada by Goddard (2005) and Pennock (2005).

4.2. Precision conservation for management of flows Schumacher et al. (2005) reported that spatial assessment of field erosion and the development of maps from the resultant data can be useful to identify highly sensitive areas of the fields. They recommended that these maps could then be used to develop site-specific conservation practices, including cover crops, organic matter additions, and no till for the site-specific areas that have higher rates of erosion. Berry et al. (2005) reported that creation of Precision Conservation Management Zones (PCMZ) might be a viable approach to enhance soil and water conservation practices. They reported that a combination of Site-Specific Management Zones (SSMZ) (Fleming et al., 1999; Khosla et al., 2002) and PCMZ could maximize economic returns, resource use efficiency, and soil and water conservation. Several studies have shown that with the implementation of SSMZ, grain yields have remained stable or increased, N use efficiencies have increased, and economic returns have been higher (Fleming et al., 1999; Khosla et al., 2002; Koch et al., 2003). Delgado et al. (2005) reported that SSMZ reduced NO3-N leaching compared to traditional management


17

practices. Remote sensing can also be used as a Precision Conservation technique to synchronize applied N with crop N uptake demands, which can increase N use efficiency by almost 50% while sustaining yields and reducing NO3-N leaching by 47% (Delgado and Bausch, 2005). The Bausch and Delgado (2003) method saved 102 kg N ha 1 year 1 with equivalent savings of about $55.00 ha 1 per season. Delgado and Mosier (1996) reported that controlled-release fertilizer and nitrification inhibitors can reduce N losses to the environment and the emissions of N2O. We suggest that a combination of management practices using nitrification inhibitors, controlled-release fertilizer, improved management of N applications that applied N considering N uptake demands (N budgets), split N applications, remote sensing, and management zones can reduce NO3-N leaching for those most sensitive areas. Cabot et al. (2006) used Precision Conservation technology for manure management to track location, timing, and rate of manure application. They reported that it is possible to apply manure more accurately across the landscape using Precision Conservation technology. Sharpley et al. (2007) indicated that this type of management can contribute to improved manure management and to reduced off-site transport (Sharpley et al., 2007). We know that landscape positions have been correlated with trace gas emissions (Goddard, 2005; Mosier et al., 1996; Pennock, 2005). There is potential to use Precision Conservation practices for these site-specific effects across the landscape to improve N management and reduce the spatial emissions of N2O from areas with higher emission rates (Goddard, 2005; Pennock, 2005). Delgado and Mosier (1996) reported that controlled-release fertilizer and nitrification inhibitors can reduce the rate of N2O emissions. We suggest that a combination of management practices using nitrification inhibitors, controlled-release fertilizer, and improved management of N applications that applied N fertilizer considering N uptake demands (N budgets), split N applications, and other localized practices can be used to reduce N2O emissions from the areas of landscape with higher emission rates.

5. Connection of Field with Off-Site Transport 5.1. Variable flows from field to nonfarm areas The connection between field and off-site transport was assessed by Feng and Sharratt (2007) using the wind erosion prediction system and GIS. They used this approach to scale the flows from field to region. They reported that, across the entire region in Washington State, wind erosion was higher in the areas with summer fallow rotations. These unprotected areas were more susceptible to wind erosion losses. The amount of wind erosion

18


from the region was attributed to management, not to the crop land area. For example, summer fallow area represented only 28% of measured land area for Adams County, yet it contributed considerably (an average of 14,250 kg ha 1 in topsoil losses) to soil erosion within the county. In-depth analysis of soil type also found that Mollisols experienced higher wind erosion losses. Feng and Sharratt (2007) were able to identify the most problematic areas in the region, based on management and soil type. Berry et al. (2003, 2005) presented an example of how to use map analysis to assess the potential variable flows from field to surrounding natural areas. They used the new software to assess variable flow over the landscape and to create a potential erosion map superimposed over a topographic map. The map showed the locations where the flows originate and also showed the areas with greater confluence of water. The assessment identified the locations of the field that may have greater potential for concentrated runoff to natural areas. The Berry et al. (2003, 2005) example is straightforward, identifying the areas with the heaviest contribution to flows to adjacent areas and showing how to identify potential hot spots for surface runoff and sediment and agrochemical transport out of the field. These types of analyses can help producers cover these highly sensitive edge areas with Precision Conservation grasses, create buffers along the edge of the fields, or use other viable practices that may also take into account the potential temporal variability of the flows (Fig. 9). There is potential to use GIS software and models to evaluate nonpoint sources of pollutants in the vadose zone (Corwin et al., 1998; Hall et al., 2001). Hatch et al. (2001) reported that site-specific management must evaluate surface and underground flows since some watersheds may not be affected due to erosion. Additionally, the implementation of conservation practices may reduce erosions, but watersheds may have tile flows and the management practices that reduce erosion may increase infiltration and potential for greater NO3-N leaching. Precision Conservation is a threedimensional management scheme that accounts for both surface and underground flows (Berry et al., 2003, 2005). Variable transport of chemicals in shallow underground tile flows will be affected by the composition of the soil matrix, impermeable layers, slope, and others parameters (Vadas et al., 2007). There is potential to manage the sources and sinks for these variable tile flows (Vadas et al., 2007). Vadas et al. (2007) monitored N transport in drainage ditches by monitoring hydrology and groundwater N and P in 26 shallow 3 m wells for 27 months on a heavily ditched poultry farm in Maryland. They concluded that NO3-N leaching losses due to subsurface groundwater were probably occurring across the region. Vadas et al. (2007) reported that, for a poultry farm in Maryland, the groundwater flow to shallow ditches was only intermittent and often ceased during the drier periods across the region. They reported that the

Erosion potential

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Figure 9 Effective erosion buffers around a stream expand and contract depending on the erosion potential of the intervening terrain.(From Berry et al., 2005.)

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groundwater flow to deeper ditches was continuous through time across this region and a source of continuous NO3-N leaching transport. They concluded that the management of ditches, especially the deeper ditches that are continuously receiving the tile flow, presents a tremendous opportunity to reduce the NO3-N leaching losses. Hey et al. (2005) reported that there is potential to strategically locate nutrient management farms where waters with high NO3-N concentration will flow. Hey et al. (2005) envisioned that these site-specific nutrient harvesting farms will have wetlands and riparian buffers that will be used to clean the water. There is potential to employ these ecological engineering practices using Precision Conservation to reduce the transport of nutrients into the surrounding environment (Berry et al., 2003, 2005; Hey et al., 2005). Shuster et al. (2007) conducted a model simulation to evaluate the prospect of enhanced groundwater recharge via infiltration of urban storm water runoff. They evaluated the spatial distribution of expected recharge depth relative to the distribution of soils. Their results indicated strong possibilities for reducing storm water runoff by redirecting this runoff into enhanced recharge areas. Penn et al. (2007) reported that phosphorus sorbing materials (PSMs) can be used to decrease the potential for off-site transport of phosphorus in runoff water. They reported that structures called PSM traps can be installed using PSMs and that these structures can capture runoff phosphorus from large areas of land. The phosphorus removal structure captured 99% of the dissolved phosphorus that flowed through the structure in a 24-h runoff period. The efficiency of such structures installed in the future could be maximized by consulting temporal and spatial studies using models and GIS information that consider the flows and total amount of potential movement through the traps. There is also potential to use denitrification traps to remove NO3-N from underground flows or water flows (Hey et al., 2005; Hunter, 2001). We suggest that Precision Conservation techniques could be used to analyze map and data information to strategically locate these nutrient traps at positions that can maximize the effectiveness in removing phosphorus and nitrates via denitrification.

5.2. Precision conservation buffers and riparian zones Buffers, grass waterways, wetlands, and riparian areas can be good conservation tools with the potential to filter and improve water quality (Dosskey et al., 2002; Hey et al., 2005; Lowrance et al., 2000). These practices can help reduce the transport of chemicals, sediments, and denitrified NO3-N. Dosskey et al. (2005) reported that, to use riparian buffers effectively for Precision Conservation, we need to consider the site-specific characteristics of the flows.


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Dosskey et al. (2007) reported that we can use soil survey for the identification of the best placement of buffers. They reported that vegetative buffers may have better performance for filtering runoff in some locations than others because of the soil physical and chemical properties of the locations where the buffers will be located. Dosskey et al. (2007) used RUSLE and the Vegetative Filter Strip Model (VFSMOD) to determine the best locations. They concluded that soil surveys may be used as screening tools to guide planners to locations where the buffers will probably have a greater impact on water. Lowrance et al. (2000) reported that the Riparian Ecosystem Management Model (REMM) can be used to evaluate buffers of different shapes and soil depths. These studies from Lowrance et al. (2000) and Dosskey et al. (2005, 2007) show that, in order to maximize soil and water conservation, we need Precision Conservation techniques in which multiple layers of information are evaluated to identify the best placement and shape to maximize buffer efficiency. Peterson and Vondracek (2006) reported that there are about 8340 sinkholes in the karst terrain of southeast Minnesota. They reported that vegetative buffers around these sinkholes will significantly contribute to improved water quality for the region. They used computer models to evaluate effectiveness of the buffer ranging from 2.5 to 30 m wide and found that 30 m wide buffers reduced pollution by 80%. Smith et al. (2006) reported that the ideal buffer width to maximize water quality benefits while minimizing land utilization is difficult to determine. They reported that increasing the buffer width from 9 to 30 m was effective in reducing shallow groundwater NO3-N along the stream bank to below 1 mg l 1. However, because of the severity of NO3-N problems associated with groundwater in the deeper samples, there were not detectable improvements in NO3-N in deeper samples taken after widening of the buffer. They concluded that standardized buffer width recommendations for a variety of landscapes are difficult to generate, but that wider buffers work well in those areas where the water table remains within 1 m of the surface. The previous discussion indicates that there is potential to use variable information across the watershed to identify the best positions of the buffers. There is also potential to use Precision Conservation techniques to employ buffers of different widths that account for the variability of flows (Dosskey et al., 2005). The width of the buffer and its effectiveness will be correlated with both surface flows and water table flows (Dosskey et al., 2005; Smith et al., 2006). This is another example that shows the need to consider surface and underground flows when using vegetative barriers to serve as filters of sediment and/or chemicals. Precision Conservation techniques and computer models can be used to conduct some of these assessments.

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6. Watershed Scale Considerations 6.1. Variable hydrology Qiu et al. (2007) reported that surface runoff is a major contaminant threat to water quality in the USA and proposed that, by incorporating the variable surface area (VSA) hydrology into watershed management practices, we can concentrate our efforts in key areas of the watershed that are the most sensitive. They reported that Hewlett and Hibbert (1967) are credited with the concept of VSAs. Qiu et al. (2007) suggested the need to more closely assess the key management alternatives that will contribute to managing variable source pollution and concluded that Precision Conservation is a good approach to managing this variability. Qiu et al. (2007) reported that managing variable source pollution emphasizes the interconnection between land and water and the different roles varying landscapes play in water resource protection. There is an opportunity to apply these new technologies to address macro- and microscale issues, such as watershed and regional water quality as well as subfield and subwatershed levels (Berry et al., 2003, 2005; Renschler and Lee, 2005). Qiu et al. (2007) reported that the identification of the hydrologically sensitive areas and critical management areas using variable source hydrology will provide the scientific basis for applying Precision Conservation techniques, when applicable. They also reported that it is critical when managing variable source hydrology to also simultaneously assess the area’s temporal variability to identify the most sensitive areas. It is important to consider both the variable source hydrology and the temporal variability to identify those areas that have higher pollution source potential or erosion potentials (Qiu et al., 2007). The same principle applies when trying to identify the areas that will receive the concentrated flows and the season when those concentrated flows will be occurring. It is important to know the sources, flows, and deposition areas to better manage the watershed. It is important to connect these variable flows, at both surface and underground levels to improve management across the watershed (Berry et al., 2003, 2005).

6.2. Models and tools New advances in computer software are allowing for faster integration of information layers used to assess the spatial and temporal flows across the watershed and to identify the best locations for Precision Conservation management practices (Berry et al., 2003, 2005; Dosskey et al., 2005, 2007; Qiu et al., 2007; Renschler and Lee, 2005; Secchi et al., 2007). The initial efforts to identify these spatial erosion impacts by accounting


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for topography and other parameters were reported by Wheeler (1990), Mitasova et al. (1995), Desmet and Govers (1996), Siegel (1996), Mitas et al. (1997), and Wang et al. (2000). Wischmeier and Smith (1965) took initial steps in this direction by using the Universal Soil Loss Equation (USLE) to calculate average soil losses on slope sections (Wischmeier and Smith, 1965). Several scientists followed this initial effort by using USLE extensively on a watershed scale (Foster and Wischmeier, 1974; Williams and Berndt, 1972; Wilson, 1986). Now we have new models and algorithms that account for spatial erosion variabilities using GIS and Digital Elevation Models (DEMs) (Desmet and Govers, 1996). The use of this new software, integration of layers of information with GIS, remote sensing, and computer modeling was reported by Berry et al. (2003, 2005) as an approach for Precision Conservation, facilitating the identification of variable flows and connecting the flows from field to the watershed. The modeling approach to Precision Conservation was used by Secchi et al. (2007) to assess the effect of management practices across the watershed and how to generate more efficient use of the economical resources to reduce environmental impacts (Secchi et al., 2007). These new models and techniques used by Secchi et al. (2007), Renschler and Lee (2005), Qiu et al. (2007), Dosskey et al. (2005, 2007), and Bonilla et al. (2007) can be used to assess hot spots, identify most susceptible locations, and to implement best management practices for Precision Conservation. Some of the models used to evaluate watersheds are the Agricultural NonPoint Source Pollution (AGNPS) model (Young et al., 1987) and the Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1993). FitzHugh and Mackay (2001) used the SWAT model and reported that data aggregation affected model behavior differently depending on whether the watershed was sediment source limited or transport limited. They concluded that it is important to characterize stream channel processes and to improve the selection of subwatershed size to match SWAT. The AGNPS model (Young et al., 1987) that divides the watershed into small discrete square cells representing variability in agricultural practices was used by Bhuyan et al. (2003) to assess erosion at the watershed scale. They used input parameters such as aspect/flow direction, slope, slope shape, slope length, soil erodibility factor (k-factor), C-factor, conservation practice factor (P-factor), soil texture, fertilizer availability, pesticide indicators, and other parameters. Their approach used sediment yields calculated from a modified USLE (Wischmeier and Smith, 1978) with runoff volume calculated by the SCS-CN method (SCS, 1968). The assessment of chemical movement, runoff, and erosion were calculated using the Agricultural Management Systems (CREAMS, Smith and Williams, 1980). They improved their assessment of topography by using databases that included inputs from the DEM. The inclusion of an RS approach improved the efficiency of the evaluation

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and reduced the time needed to evaluate the watershed. They concluded that this new RS-GIS modeling process (DEMs) was effective for the calculation of pollutant levels and chemical transport for small watersheds. Other scientists have used this approach of using multiple models and GIS to increase their ability to process several layers of information to assess transport and pollution levels. Renschler and Lee (2005) used three models and GIS to evaluate the effects of BMPs on both short (4–8 years) and long scales (100 years). They used the Water Erosion Prediction Project (WEPP) for hill slope and small watersheds. They also used the Geospatial interface for WEPP (GeoWEPP) in conjunction with GIS databases. They linked GeoWEPP to the SWAT model to assess larger watershed scales. Renschler and Lee (2005) concluded that this approach allows scientists to generate soil loss and sediment yield predictions within a watershed that can be used to detect hot spots for implementation of preferred management options such as spatially distributed BMPs. Bonilla et al. (2007) used the Precision Agricultural-Landscape Modeling System (PALMS) to estimate spatial water erosion in topographically complex landscapes. Bonilla et al. (2007) reported that PALMS can evaluate the effects of local soil properties and microtopography on changes in soil detachment and deposition across short distances. Bonilla et al. (2007) reported that PALMS also has the capabilities to quantify spatial and temporal erosion, deposition, sediment yield, evapotranspiration, soil evaporation, photosynthesis, plant and soil respiration, infiltration, drainage (with and without tiles), crop growth, yield, and other parameters. As we continue to develop capabilities to process multiple layers of information and to calibrate and validate new models that account for surface and underground flows from fields to natural areas, we will be able to improve the capabilities of Precision Conservation practices that minimize environmental impacts and maximize sustainability of increasingly intensive production systems.

6.3. Precision conservation at a watershed scale Precision Conservation principles are directed to use GIS, RS, and other models to handle large sets of information that consider spatial and temporal variability and allow the identification of variable and temporal flows in the environment. This identification informs decisions that can lead to the site-specific implementation of conservation practices that maximize conservation efforts. Secchi et al. (2007) addressed Precision Conservation with the use of computer models to assess the cost of clean water by assessing pollution reduction at a watershed scale. Using simulation models, they assessed the effect and cost of implementing and evaluating conservation practices designed to reduce phosphorus and nitrate levels.


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Secchi et al. (2007) conducted a simulation suggesting that the cost to reduce nonpoint source pollution levels in Iowa could be very high. They suggested a method by which conservation practices designed to help reduce nutrient and sediment losses can be implemented using model simulations as guides. They identified conservation practices to adopt for their model evaluation based on the physical characteristics of the agricultural lands, in a way similar to the concept of Precision Conservation as described by Berry et al. (2003, 2005). They evaluated the following conservation practices: return of all cropland within 100 ft of a waterway (set aside), retirement of additional land from cropland based on the NRI index, terracing of all land with a slope greater than 7% in western Iowa and land with a slope greater than 5% in the remainder of Iowa, contouring, installation of grass waterways, conversion of significant areas into no-till, and impalement nutrient management planning enacted by reducing N inputs by 10%. Tomer et al. (2007) reported on the spatial patterns of sediment and phosphorus accumulation and flow in a riparian buffer in western Iowa. They proposed that we can use spatial vegetation that accounts for spatial patterns in flow as a mechanism to increase activities such as water use and uptake of nutrients in accordance with the spatial inputs of flows and temporal variability. Tomer et al. (2007) reported that half of the runoff was delivered from mid-April through mid-June. The soil–water phosphorus concentrations at depths of 1.5 m were higher in the riparian zone (where the switch grass was grown) than below the crop areas. The temporal variability when the nutrient flow was higher was during the time when the switch grass (a warm season grass) was not growing. Planting a grass in the lower areas of the buffer that transpires at a higher rate early in the season when sediment is accumulating the fastest contributes to better buffer performance (Tomer et al., 2007). This variable planting of varieties that account for spatial and temporal flows of sediments, nutrients, and water is a useful Precision Conservation technique within these riparian buffers. Strock et al. (2007) reported that appropriately managed ditches can provide an opportunity to manage N and reduce its losses by removing biologically available forms of N via physical and biogeochemical processes. Proper management of ditches, especially deeper ditches, may provide an opportunity to efficiently manage nutrient transport (Strock et al., 2007; Vadas et al., 2007). There are opportunities to use new models, GIS, RS, and GPS techniques to improve the spatial management of ditches across a region. Lowrance et al. (2007) reported on the effects of land use and management on nutrient transport. They found significant differences in runoff between two watersheds, mainly due to land use practices and the use of sediment ponds. They reported that one of the watersheds with sediment ponds using as little as 6.3% of the basin area had significantly cleaner water.

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The installation of plastic covers on 26% of the area of one of the subwatersheds contributed significantly to higher rates of erosion and off-site transport of sediment and total nitrogen. Lowrance et al. (2007) demonstrated in this study that placement of sediment ponds at strategic places in a watershed could help reduce both the off-site transport of sediment and total nitrogen transport. George et al. (2008) put GPS collars on cows and used supplemental feed to manage and monitor behavior (Fig. 10). George et al. (2008) found that they can use supplemental feed to manage cow behavior in a way that considers forest and grassland areas, temporal variability, and water bodies to enhance soil and water conservation. The results from the George et al. (2008) study show the effectiveness of new technologies to connect animal management with potential animal behavior that reduces environmental impacts. The results from George et al. (2008) are important because they show that management can help reduce the amount of time beef cows spend in riparian areas. These results are in agreement with the results of the studies from Bailey et al. (2001), which report that cattle spend more time and graze more forage within 600 m of supplement sites. Several researchers have shown that there is potential to use supplemental feed to manipulate animal behavior (Bailey, 2003; Bailey and Welling

GPS collar

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Figure 10 Potential to use Precision Conservation for animal management and conservation of soil and water. (Adapted from George et al., 2008.)


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1999, 2002; George et al., 2008; McDougald et al., 1989). George et al. (2008) presented their concept and stated the potential application for Precision Conservation, demonstrating that strategic placement of supplemental feed can be effective for soil and water conservation by reducing grazing in riparian patches and sensitive soil areas. The animal management industry can use precision supplemental feeding practices that take landscape and temporal variability into account through reference to spatial technologies such as GIS to implement conservation practices that result in improved environmental outcomes (George et al., 2008). They proposed that animal managers can contribute to Precision Conservation of soil and water by precisely placing nutrient supplements based on management decisions that consider economic returns, as well as environmental and site-specific factors. They also pointed out that there is even greater potential for continued animal management industry contribution to Precision Conservation because supplemental feeding can be moved as needed to prevent degradation of supplement sites. There are several ecological engineering principles that can be applied for Precision Conservation at a watershed scale. Nutrient farming can be used to reduce N losses to the environment (Hey et al., 2005). Hey et al. (2005) reported that, since about one-third of applied N enters drainage systems, there is potential to improve water quality and reduce losses and impacts of nutrients to rivers using drainage and water management to manage location of wetlands. They reported that we need to develop Nitrogen Trading by which nutrient farmers can use denitrification techniques and trade the reduced N with the surrounding environment. They reported that planners and nutrient managers need to evaluate the field management practices connected with streams, water channels, and nutrient farms (wetlands). For effective nutrient farming, we need to consider that denitrification of N in riparian zones can be an important mechanism for N removal from the system (Hey et al., 2005; Schade et al., 2001; Verchot et al., 1997). We propose that if the concept of Nitrogen Trading develops into viable alternative practices, Precision Conservation practices need to be considered to maximize the managed effectiveness of nutrient harvesting and to minimize transport of nutrients downstream. A spatial/temporal N loss evaluation tool such as NLEAP-GIS can be used to quickly identify the management scenario that shows the greatest potential to maximize the reduction in N losses at the field level and minimize N loss impacts to the environment. This temporal and spatial approach, combined with positive reductions in farm N inputs and other management changes that improve nitrogen use efficiencies, could be used to help identify opportunities to use the Nitrogen Trading Tool (Delgado et al. 2008). We suggest that Precision Conservation can be a key component in identifying Nitrogen Trading opportunities.

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7. Current Applications and Trends The Berry et al. (2003) publication about Precision Conservation generated enough interest that the Soil Science Society of America, Canadian Soil Science Society, Mexican Soil Science Society, and the Division of Soil Water and Management and Conservation celebrated a joint symposium entitled: ‘‘Precision Conservation in North America’’ at the November 1–4, 2004 annual meeting in Seattle, Washington. A special issue of selected papers was published in the Journal of Soil and Water Conservation (2005). Most recently, the Chinese Academy of Sciences conducted the first International Conference in Precision Conservation of Soil and Water October 22–24, 2007, at Shijiazhuang, China. Several speakers from Asian countries presented recent advances in GPS, GIS, RS, and other new equipment along with their potential applications for Precision Conservation of soil and water. Precision Conservation was also listed as one of the themes for the 9th International Conference in Precision Agriculture, which will be held in July of 2008 in Denver, Colorado. There is increased interest from both North American and international science communities in the use of new technologies for Precision Conservation of soil and water. Additionally, the USDA NRCS is committed to continued advancement in Precision Conservation and to the development of new tools to support Precision Conservation applications (Knight, 2005). Precision Conservation benefits producers by helping them to efficiently manage their operations (Knight, 2005). There are also benefits for taxpayers and for environmental conservation because Precision Conservation can be used to identify hot spots on the farm and throughout the watershed for a more efficient use of agricultural resources (Knight, 2005). Precision Conservation techniques can identify connections of flow from farm areas to the watershed to help us identify the best location to implement conservation practices that reduce environmental impacts while maximizing use of economical resources (Knight, 2005; Secchi et al., 2007). We have the potential to integrate multiple layers of information to assess spatial erosion variability at a field scale (Bonilla et al., 2007; Mueller et al., 2005; Schumacher et al., 2005). Spatial erosion variability reduces yields at site-specific locations within the field. If this variability in erosion across the field is not properly managed, the yields will continue to decline significantly after decades of uniform management (Quine and Zhang, 2002). There is potential to use Precision Conservation to integrate spatial and temporal information to better implement conservation management practices that account for this erosion variability (Mueller et al., 2005; Quine and Zhang, 2002; Schumacher et al., 2005).


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Precision Conservation can integrate spatial and temporal information to identify and locate the best locations for riparian buffers, grass waterways, ditches, and wetlands within the watershed (Berry et al., 2003, 2005; Dosskey et al., 2002, 2005, 2007; Qiu et al., 2007; Renschler and Lee, 2005; Tomer et al., 2007). We can use these new technologies to connect multiple layers of information for improved rangeland management that integrates spatial variability of resources, including soil and water resources (George et al., 2008). Precision Conservation can also be used to integrate assessment of multiple layers of information that account for temporal variability of hydrology and flows of pollutants (Berry et al., 2003; Dosskey et al., 2007; George et al., 2008; Hey et al., 2005; Qiu et al., 2007; Renschler and Lee, 2005; Secchi et al., 2007). There is potential to use site-specific information of temporal and spatial flows to develop erosion maps that identify highly sensitive areas of the fields or to develop variable hydrology maps that represent variable movement of soil and chemicals across the watershed (Qiu et al., 2007; Schumacher et al., 2005). Table 1 highlights potential-related Precision Conservation practices. Users can integrate spatial and temporal information using GIS and/or models to analyze databases and to develop recommendations for implementation of these conservation practices. For example, we can use models to identify highly sensitive erosion areas of a field which we may then decide to set aside for hay production to reduce the erosion and movement of soil and chemicals out of the field. Alley cropping, conservation crop rotation, cover crops, field borders, riparian herbaceous cover, riparian forest buffers, filter strips, residue management, supplemental feed, sediment ponds, isolated hay production areas with permanent cover, nutrient traps, and buffers are some of the potential conservation practices that may result from an integration of spatial and temporal information about flows and the use of layers of information to develop more effective practices (Table 1). Other nutrient management practices such as remote sensing, site-specific management zones, and Precision Irrigation that contribute to reduced NO3-N leaching were not listed (Delgado and Bausch, 2005; Delgado et al., 2005; Sadler et al., 2005). The practices listed in Table 1 connect the flows from field to natural areas, and contribute to enhanced soil and water conservation. We propose that the application of these conservation practices could be more effective by using new technologies that integrate multiple layers of information spatially and temporally, thereby identifying hot spots. We also propose that the efficiency of these practices could be increased by using variable designs that integrate variable widths, use variable species and varieties, and apply the practices precisely at the hot spots in the field or watershed.

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Table 1 Potential conservation practices that could be used to manage spatial and temporal variability across the landscape to increase precision conservation of soil and watera

Location

Field

Conservation practice

Alley cropping (CODE 311)

Definition

Precision conservation potential

Trees or shrubs are planted in sets of single or multiple rows with agronomic, horticultural crops or forages produced in the alleys between the sets of woody plants that produce additional products.

There is the potential to use new models, remote sensing, and computer software to integrate spatial and temporal information about flows to develop information that can be used to improve site-specific management decisions for alley cropping locations. Spatial soil properties and underground water tables and flows can also be considered. There is also potential to plant single or multiple drills of trees or shrubs, taking these spatial soil properties into consideration. The varieties of trees or shrubs planted in rows could also be changed based on soil property data that may account for different water tables, salinity levels, and site-specific chemical and physical properties. The objective for this application is to determine the number of rows and appropriate species (considering the species’ water use and nutrient uptake) to match the temporal variability of water flows, water tables,

Field

Conservation crop rotation (CODE 328)

Growing crops in a recurring sequence on the same field.

Field

Cover crop (CODE 340)

Crops including grasses, legumes and forbs for seasonal cover and other conservation purposes.

salinity, and precipitation. There is also potential to use this practice to mine and recover NO3-N leached below the roots of shallower crops in areas where NO3-N leaching presents a problem across the field (Allen et al., 2004; Delgado, 1998, 2001; Rowe et al., 1999; Tomer et al., 2007). There is potential to use different crops to reduce soil erosion considering variation in soil types and variable field erosion. In case where bales of straw are removed from the fields, some areas of the field with lower soil organic matter and/or higher erosion potential could be managed differently with full incorporation of crop residue. In highly saline areas of the fields, a more salt-tolerant conservation crop could be planted to manage saline seeps. There is the potential to use deeply rooted crops in some areas of the field that would filter underground water when planted with shallowly rooted crops (Delgado, 1998, 2001; Schumacher et al., 2005). There are potential to use cover crops in the most sensitive areas of the field and natural areas to reduce soil erosion. Cover crops are highly beneficial in most cases. Cover

31 (continued)

Table 1

(continued)

32 Location

Outside field/ natural area


Field border (CODE 386)

Definition

A strip of permanent vegetation established at the edge or around the perimeter of a field.


crop use can be differentiated according to the soil type and N cycling. For fields with spatial variability of soil type, cover crops such as winter cover rye and winter wheat (both effective cover crop scavengers of leached NO3-N) may be planted in areas that have high leaching potential. In the areas of the fields with the finer soil texture and lower leaching potential, leguminous cover crops can be planted to increase nitrogen input into the system (Delgado, 1998, 2001). There is the potential to use new models, remote sensing, and computer software to integrate spatial and temporal information about flows to develop information that can be used to identify the location of concentrated flows of water, nutrients, and sediment from the field. This will allow the identification of hot spots and temporal and spatial patterns at the field border. This information can be used to decide how wide to make the vegetative field border, what species to plant, how deep the root systems should be, and how


Riparian herbaceous cover (CODE 390)

Grasses, grass-like plants, and forbs that are tolerant of intermittent flooding or saturated soils and that are established or managed in the transitional zone between terrestrial and aquatic habitats.

tall and thick the vegetation should be at the surface. Spatial and temporal analysis of the flows coming out of the field could be used to improve the design of field borders that reduce the off-site transport of soil particles, organic matter, chemicals, and water. These field edge practices can maximize farmers’ soil and water conservation, as well as their use of energy and resources. Precision Conservation can determine the best plant type for field borders, whether grass, legumes, or shrubs, considering the potential of each to reduce off-site transport of soil, soil organic matter, and nutrients due to water and wind erosion. These barriers could be precisely designed to eliminate flows from end rows, headlands, and other areas of concentrated flow (Berry et al., 2003; Dosskey et al., 2005; Tomer et al., 2007). There is potential to develop riparian herbaceous cover that accounts for temporal and seasonal site-specific hydrology. There is potential to use models, remote sensing, and computer software to integrate spatial and temporal

33

(continued)

Table 1

(continued)

34 Location


Definition


information and to develop management decisions about the best location(s) for the riparian herbaceous cover. There is also potential to plant variable species across the riparian and herbaceous cover zones to try to synchronize the vegetation growth and water and nutrient use with periods of maximum water flows across the riparian buffers. These riparian zones can be applied to areas adjacent to perennial and intermittent watercourses or water bodies, accounting for the spatial and temporal hydrology. Site-specific hydrology, including water table data and the potential for concentrated flows in extreme cases, can be factored into these decisions to establish a site-specific riparian herbaceous cover that maximizes water quality, using variable widths and species. There is also potential to use the multiple layers of site-specific information to design the best viable shape of the riparian herbaceous cover to account for variable flows and to identify the best placement to maximize buffer affectivity for soil and


Riparian forest buffer (CODE 391)

An area comprised predominantly of trees and/or shrubs located adjacent to and upgradient from watercourses or water bodies.


Filter strip (CODE 393)

A strip or area of herbaceous vegetation situated between cropland, grazing land, or disturbed land (including forestland) and environmentally sensitive areas.

water conservation (Dosskey et al., 2002, 2005, 2005; Hey et al., 2005; Tomer et al., 2007). There is potential to use spatial and temporal information to develop riparian forest buffers that improve and protect water quality by reducing the amount of sediment, nutrient, and surface flows and shallow groundwater chemical movement. There is potential to use the variable hydrology and flow information to identify both the best viable shape for riparian forest buffers to account for variable flows and the best buffer locations for effective management of surface and underground flows (Hey et al., 2005). There is potential to develop filter strips to improve and protect water quality by reducing the amount of sediment and nutrient runoff and movement in surface runoff and shallow groundwater. Sitespecific spatial and temporal information can be used to determine the best locations for filter strips in areas below cropland, grazing land, or disturbed land (including forest land). Filter strips can also be strategically located in areas where

35 (continued)

Table 1

(continued)

36 Location

Field


Seasonal residue management (CODE 344)

Animal Supplemental systems feed

Definition

Managing the amount, orientation, and distribution of crop and other plant residues on the soil surface during a specified period of the year, while planting annual crops on a clean-tilled seedbed, or while growing biennial or perennial seed crops.

Use of supplemental feed to manage cow behavior in a way that considers forest and grassland areas, temporal variability, and


sediment, particulate matter, and/or dissolved concentrated contaminants may be leaving and entering environmentally sensitive areas. These filter strip areas need to be comprised of permanent vegetation, and fully established prior to the first irrigation. This site-specific information can also be used to design the best viable shape for the filter strips (Tomer et al., 2007). There is potential to spatially manage residue to reduce erosion from the most sensitive areas of the field. There is potential to concentrate residue in those areas that are more susceptible to erosion. In case where bales of straw are removed from the fields, some areas of the field with lower soil organic matter and/or higher erosion potential could be managed differently with full incorporation of crop residue (Schumacher et al., 2005). There is potential to use practice to maximize carbon sequestration. There is potential to use Precision Conservation techniques to strategically place supplemental feed to manipulate

water bodies to enhance soil and water conservation.


Sediment ponds

Use of sediment ponds to reduce the movement of soil and chemicals.

Field

Set aside hay areas with permanent cover

Use of set aside hay areas with permanent cover.

animal behavior. Strategic placement of supplemental feed can be effective for soil and water conservation by reducing grazing in riparian patches and sensitive soil areas. Animal managers can take landscape and temporal variability into account through reference to spatial technologies such as geographic information systems (GIS) to implement supplemental feeding-based conservation practices that result in improved environmental outcomes. Supplemental feeding sites can be moved as needed to prevent site degradation (George et al., 2008). There is potential to use sediment ponds to reduce the movement of soil and nutrients from fields and from subwatersheds. There is potential to strategically place these ponds taking variable hydrology and flows into account (Lowrance et al., 2007). Spatial assessment of field erosion and development of maps can be used to identify highly sensitive areas of fields. There is potential to manage the most erosion-sensitive areas by setting aside areas for hay production to reduce the erosion and movement of soil and

37

(continued)

Table 1

(continued)

Location

Field/ natural area

a


Nutrient traps

Definition

Installation of nutrient traps or denitrification traps to remove nutrients from field outflows.


chemicals out of the field (Schumacher et al., 2005). Spatial assessment of field erosion and variable hydrology can be used for development of maps to identify areas with higher flows of phosphorus and nitrates in fields and/or field borders and natural areas. There is the potential to use phosphorus sorbing materials (PSMs) to decrease the potential for off-site transport of phosphorus in runoff water. There is also potential to use denitrification traps to reduce NO3-N concentrations in runoff water or underground water flows (Hunter, 2001; Penn et al., 2007).

Some of these are practices recommended by USDA-NRCS. We suggest that there is potential to apply the concepts of Precision Conservation to these USDANRCS-recommended practices and to other practices included in this report. We suggest that these practices can be implemented site specifically in fields and natural areas by using layers of information that identify hot spots across the landscape. We also suggest that there is potential to use models, map and data analysis software, and Precision Conservation techniques to modify these practices, taking the site-specific spatial and temporal information about flows into consideration. There is also potential to implement these practices using different device shapes and/or species to better account for variable spatial and temporal hydrology and flows. We suggest that there is significant potential to develop new practices for Precision Conservation of soil and water, such as the integration of animal behavior management with soil and water conservation.


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8. Summary and Conclusions There are multiple examples of advances in Precision Conservation published during the last 4 years showing how new spatial technologies and the integration of GPS, GIS, RS, and models can be applied to improve management decisions that contribute to Precision Conservation of soil and water. Precision Conservation can more precisely identify where to locate riparian buffers, sediment ponds, nutrient management farms, and other ecological engineering practices to most effectively reduce environmental impacts from hot spots across the watershed. These technologies can be used to simultaneously consider variable hydrology and temporal flows to identify the best locations for the implementation of conservation practices at the watershed and subwatershed levels. These technologies can also be used to design better buffers to manage flows at field borders, to identify the best locations for phosphorus recovery devices, and to locate potential denitrification trap sites. These new approaches can contribute to better management of variable surface and underground flows across grass waterways, buffers, riparian buffers, ditches, wetlands, and watersheds. New advances even show that there is potential to integrate management of rangeland animal behavior with management practices that account for spatial and temporal variability to enhance Precision Conservation of soil and water resources. With continued increases in population growth and increased demands of land resources for food and biofuel production, maximizing agricultural production is increasingly necessary. Precision Conservation can be used to synchronize best management practices that maximize yields while reducing unnecessary inputs and losses of sediment and other chemicals to the environment. We propose that, as new technological advances continue to emerge, adaptations of Precision Conservation by land owners, managers, farmers, and extension personnel will be widely implemented. These new technologies can contribute to higher efficiency of resource management, economical returns, and environmental sustainability (Berry et al., 2003; Knight, 2005; Secchi et al., 2007). As new advances in computer models, remote RS, GIS, and GPS continue, these technologies will become increasingly accessible for the conservation of agricultural and natural resources. Precision Conservation will play a significant role in maximizing and sustaining agricultural yields while contributing to global sustainability in the 21st century.

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Siegel, S. B. (1996). ‘‘Evaluation of Land Value Study.’’ Stud. Rep. CAA-SR-96–5. U.S. Army Concepts Analysis Agency, U.S. Gov. Print. Office, Washington, DC. Smith, R. E., and Williams, J. R. (1980). Simulation of the surface hydrology. In ‘‘CREAMS: A Field Scale Model for Chemical Runoff, and Erosion from Agricultural Management Systems’’ (W. Knisel, Ed.). Conserv. Res. Rep. 26, USDA-ARS, Washington, DC. Smith, T. A., Osmond, D. L., and Gilliam, J. W. (2006). Riparian buffer and nitrate removal in a lagoon-effluent irrigated agricultural area. J. Soil Water Conserv. 61, 273–281. Soil Conservation Service (SCS). (1968). ‘‘Hydrology.’’ Supplement A, Sec. 4. Eng. Handb. USDA-SCS, Washington, DC. Strock, J. S., Dell, C. J., and Schmidt, J. P. (2007). Managing natural processes in drainage ditches for nonpoint source nitrogen control. J. Soil Water Conserv. 62, 188–196. Tomer, M. D., Moorman, T. B., Kovar, J. L., James, D. E., and Burkart, M. R. (2007). Spatial patterns of sediment and phosphorous in a riparian buffer in western Iowa. J. Soil Water Conserv. 62, 329–338. Vadas, P. A., Srinivasan, M. S., Kleinman, P. J. A., Schmidth, J. P., and Allen, A. L. (2007). Hydrology and groundwater nutrient concentrations in a ditch-drained agroecosystem. J. Soil Water Conserv. 62, 178–188. Verchot, L. V., Franklin, E. C., and Gilliam, J. W. (1997). Nitrogen cycling in Piedmont vegetated filter zones: I. Surface soil processes. J. Environ. Qual. 26, 327–336. Wang, G., Gertner, G., Parysow, P., and Anderson, A. B. (2000). Spatial prediction and uncertainty analysis of topographic factors for the revised universal soil loss equation (RUSLE). J. Soil Water Conserv. 55, 374–384. Wheeler, P. H. (1990). An innovative county soil erosion control ordinance. J. Soil Water Conserv. 45, 374–378. Williams, J. R., and Berndt, H. D. (1972). Sediment yield computed with universal equation: Proc. of the ASCE. J. Hydraul. Div. 98, 2087–2098. Wilson, J. P. (1986). Estimating the topographic factor in the universal soil loss equation for watersheds. J. Soil Water Conserv. 41, 179–184. Wischmeier, W. H., and Smith, D. D. (1965). ‘‘Predicting Rainfall Erosion Losses from Cropland East of the Rocky Mountains.’’ USDA-ARS. Agric. Handb. 282. U.S. Gov. Print. Office, Washington, DC. Wischmeier, W. H., and Smith, D. D. (1978). ‘‘Predicting Rainfall Erosion Losses.’’ Agric. Handb. 537. USDA-ARS, U.S. Gov. Print. Office, Washington, DC. Wylie, B. K., Shaffer, M. J., Brodahl, M. K., Dubois, D., and Wagner, D. G. (1995). Predicting spatial distributions of nitrate leaching in northeastern Colorado. J. Soil Water Conserv. 49, 288–293. Young, R. A., Onstad, C. A., Bosch, D. D., and Anderson, W. P. (1987). ‘‘AGNPS: Agricultural Nonpoint Source Pollution Model: A Large Watershed Analysis Tool.’’ Conserv. Res. Rep. 35. USDA-ARS, U.S. Gov. Print. Office, Washington, DC.

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Reaction and Transport of Arsenic in Soils: Equilibrium and Kinetic Modeling Hua Zhang and H. M. Selim Contents 46 47 48 48 49 51 52 52 56 57 60 62 64 66 69 73 73 78 81 81 84 86 90 97 99 101 104 105

1. Introduction 2. Environmental Toxicity 3. Arsenic in Soils 3.1. Background concentrations 3.2. Anthropogenic sources 3.3. Speciation 4. Biogeochemistry 4.1. Retention mechanisms 4.2. pH dependency 4.3. Effect of solution composition 4.4. Sorption kinetics 4.5. Desorption 4.6. Reaction with sulfides 4.7. Heterogeneous oxidation 4.8. Microbial-mediated reduction and oxidation 5. Transport in Soils 5.1. Transport mechanisms 5.2. Mobility under field conditions 6. Modeling 6.1. Equilibrium thermodynamic models 6.2. Empirical equilibrium models 6.3. Surface complexation models 6.4. Kinetic models 6.5. Transport models 6.6. Field application 7. Remediation of Contaminated Soils 8. Summary and a Look Ahead References School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, Louisiana 70803 Advances in Agronomy, Volume 98 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00202-2

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Hua Zhang and H. M. Selim

Arsenic contamination of the soil and groundwater poses great risk to human and animal health. There is a growing public interest in developing risk assessment framework, environment regulations, and remedial strategies for protecting ecosystems and human from arsenic poisoning. Although extensive research efforts have been made over the past four decades, the prediction of the fate and transport of arsenic in soils are often inaccurate due to the complex biogeochemical reactions of various arsenic species in soil and water environments. In-depth knowledge of factors that influence the behavior of arsenic in aqueous and solid phases are critical in making accurate determinations of the mobility, bioavailability, and toxicity of arsenic in the soil root zone. In this contribution, we present a review of the state of knowledge on reactions and transport of arsenic in soils with emphasis on modeling of the physical, chemical, and biological interactions of arsenic in soil environment. Specifically, we present an overview of (i) biogeochemical mechanisms of arsenic adsorption–desorption, oxidation– reduction, and precipitation–dissolution; (ii) reactive transport mechanisms of arsenic in the natural environment as affected by factors including arsenic species, redox potential, solution chemistry, flow regime, and colloid-facilitated transport; and (iii) equilibrium and kinetic modeling approaches to simulating the geochemical reactions and transport mechanisms of arsenic in porous media. A range of remedial technologies have been reviewed and their effectiveness and feasibility in the removal or in situ stabilization of arsenic in contaminated soils are discussed. Future research needs are also outlined.

1. Introduction Arsenic (As) is a highly toxic element widely present in soils, plants, and water at trace levels. Increasing amounts of arsenic are being introduced into soil and water environments as a result of natural and anthropogenic processes. The U.S. Environmental Protection Agency (USEPA) classified arsenic as a human carcinogen contaminant and lowered the maximum contaminant level (MCL) in drinking water from 50 ppb to 10 ppb (USEPA, 2001). Arsenic concentrations in groundwater above the environmental standard have been observed in many countries including Bangladesh, India, Vietnam, China, and United States, and were attributed to geologic or anthropogenic sources (Nordstrom, 2002; Smedley and Kinniburgh, 2002). In addition, increased use of arsenic-containing compounds, such as pesticides, herbicides, wood preservatives, and livestock feed additives, have introduced large amounts of arsenic into the soil system (Smith et al., 1998). Contamination of ground and surface water by arsenic from soils and aquifers pose significant threat to human health (WHO, 2004). The occurrence of arsenic in groundwater of Bangladesh, resulting from the dissolution of arsenic containing aquifer material, caused massive poisoning of the people living in that area (BGS, 2001). In United States,

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arsenic is a contaminant of concern (COC) at 568 Superfund sites, making it the second most common inorganic contaminant on the National Priority List (USEPA, 2002). The thorough understanding of the fate of arsenic in soil environment is urgently required for environment risk assessment and remediation plan. Recently, extensive research efforts were devoted to unraveling the complex geochemical reactions of arsenic in natural environment. This is reflected by the large volume of literatures published in this area over the past three decades. Numerous laboratory and field studies demonstrated that complex chemical (e.g., adsorption–desorption, oxidation–reduction, precipitation–dissolution), physical (e.g., advection and dispersion), and biological (e.g., biotransformation) processes are involved in regulating the behavior of arsenic in soil, sediment, aquifer, and the surface water environment (Smedley and Kinniburgh, 2002). While our understanding of arsenic cycle was greatly improved in the last several decades, the influence of various environmental factors and their combinations on the retention and transport of arsenic have not been fully explored. Prediction of the mobility of arsenic at contaminated sites was impeded by the lack of knowledge in the hydrogeochemical processes governing arsenic speciation, retention, release, and transport in subsurface. The heterogeneous natural of the geological materials multiplied the complexity of predicting the fate of arsenic existed or released in natural environment. This literature review highlights recent scientific advances toward the understanding of the fate and behavior of arsenic in the soil environment. We present overview of laboratory and field observations and discuss equilibrium and kinetic approaches for describing arsenic retention and transport soils. In addition, we briefly discussed various techniques employed in the remediation of arsenic contaminated sites and identified future research needs.

2. Environmental Toxicity The toxicity of arsenic depends on its chemical form. Organic arsenic compounds are much less toxic than inorganic arsenic. Among the inorganic arsenic, arsine gas (AsH3) is the most toxic form. However, arsine gas rarely exists in the natural environment. Two dominant arsenic forms in the environment, arsenate and arsenite, are highly toxic. As a molecular analog of phosphate, arsenate blocks oxidative phosphorylation, short-circuiting life’s main energy generation system. Arsenite is even more toxic by binding to sulfhydryl groups, impairing the function of many proteins (Oremland and Stolz, 2003). The targets of arsenic toxicity are the respiratory system, the circulatory system, the skin, the nervous system, and the reproductive system, among

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others. Acute arsenic poisoning affects the central nervous system, blood vessels, kidney, and can cause death in 1–3 days (Reigart and Roberts, 1999). Drinking water rich in arsenic over a long period leads to arsenic poisoning or arsenicosis. The health effects of arsenicosis include skin problems (such as color changes on the skin, and hard patches on the palms and soles of the feet), skin cancer, cancers of the bladder, kidney and lung, diseases of the blood vessels of the legs and feet, and possibly also diabetes, high blood pressure and reproductive disorders (WHO, 2004). The USEPA classified arsenic as Group A (human carcinogen) contaminant. Several incidence of arsenic poisoning have been reported in Bangladesh (Nickson et al., 1998), India (Acharyya et al., 1999), Vietnam (Berg et al., 2001), China (Smedley and Kinniburgh, 2002), Taiwan (Chen et al., 1994), and United States (USGS, 2004). The culprit of arsenic poisoning in those range from arsenic in drinking water from geological sources, arsenic released from industrial sources and mining activities, or arsenic in contaminated food (Mandal and Suzuki, 2002). Arsenic enters human body via respiration of arsenic in dust and fumes and ingestion of arsenic in water, soil, and food (Mandal and Suzuki, 2002). The air exposure of arsenic is generally low. However, combustion of arsenic-containing coal may result in locally high arsenic levels in some areas. In general, drinking arsenic contaminated water is the major route of arsenic poisoning around the world. Millions of people suffer from the toxic effects due to drinking of arsenic-rich groundwater (Smedley and Kinniburgh, 2002). Soil ingestion is another important pathway of arsenic poisoning, especially for children (Rodriguez et al., 1999). Arsenic may accumulate in crops, vegetables, and fruits grown on contaminated soil (Meharg and Hartley-Whitaker, 2002). Consumption of arsenic polluted food is thus another serious threat to human health.

3. Arsenic in Soils 3.1. Background concentrations The main sources of arsenic in soils come from arsenic containing parent material. The mean value of arsenic abundance in crystal rocks is around 2 mg kg1 with considerable variance between different types of rocks. Arsenic minerals commonly occurred in the environments are arsenopyrite (FeAsS), orpiment, realgar, arsenides, enargite, colbaltite, and proustite. The weathering of those arsenic containing minerals brings dissolved arsenic into soil environment and subsequently adsorbed or precipitated on mineral surfaces. Background concentrations of arsenic in soils vary among soil types, depending on the parent materials from which the soil is derived. According


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to the National Geochemical Survey conducted by The U.S. Geological Survey (USGS), arsenic concentrations of most soils in the United States are well below 10 mg kg1 (USGS, 2004). Researchers reported that background arsenic concentrations of soils in Australian and New Zealand is 0.2–30 mg kg1, and they suggested environmental investigation for the concentrations greater than 20 mg kg1 (Barzi et al., 1996). The study of Bradford et al. (1996) showed that the geometric mean of arsenic concentration in 50 soils from California was 2.8 mg kg1, with a range of 0.6–11.0 mg kg1. Similarly, Chen et al. (2002) reported that the mean arsenic concentration of Florida soils was 0.42 mg kg1, ranging from 0.01 to 50.6 mg kg1, with considerable difference between soil types. The survey of soils in Mississippi found the mean arsenic concentration was 8.25 mg kg1, with a range of 0.26–24.43 mg kg1 (Pettry and Switzer, 2001). It is observed that soil arsenic concentration may correlate with clay content, pH, cation exchange capacity, organic matter content, and most significantly Fe and Al concentration (Bradford et al., 1996; Chen et al., 2002; Ori et al., 1993; Pettry and Switzer, 2001).

3.2. Anthropogenic sources Arsenic is frequently associated with various types of mineral deposits, especially sulfide ore (Foster et al., 1968ab; Paktunc et al., 2003, 2004). The mining process of Pb, Cu, Zn, Co, Ni, and Au often produces tailing of high residual arsenic concentrations due to the presence of arsenic minerals in the ores, such as FeAsS, arsenolite (As2O3), olivenite (Cu2OHAsO4), mimetite (Pb5Cl(AsO4)3, and cobaltite (CoAsS). Soil arsenic concentrations near the mining dump site are reported as high as 30,000 mg kg1, though the levels rapidly decreased with distance away from dump sites (O’Neill, 1995). Large amounts of arsenic containing coal are combusted in power plants worldwide. Combustion of coals adds arsenic containing fly ash into the atmosphere, which eventually accumulate in soils and water (Ishak et al., 2002; Qafoku et al., 1999; Sakulpitakphon et al., 2003). Generally, arsenic may present in coals at concentrations ranging from 2 to 84 mg kg1, depending on the geological background. However, high concentrations of arsenic (1500 mg kg1) within the brown coal from the former Czechoslovakia were reported. Even higher concentration of arsenic (100–9000 mg kg1) within the coal was reported in Guizhou, China as a result of epigenetic mineralization (Liu et al., 2002). Approximately 90% of industrial arsenic in the United States is currently used as a wood preservative. Arsenic-containing compounds, such as chromated copper chromate (CCA), ammoniacal copper arsenate (ACA), and ammoniacal copper zinc arsenate (ACAA), have been extensively used as wood preservatives in order to reduce bacterial, fungal, and insect decay in woods. CCA has been the dominant chemical used to treat wood for decks and other

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outdoor uses, constituting 75% of the pressure treatment wood market by volume. The treated woods commonly contain 1000–5000 mg kg1 of arsenic. Arsenic in those wood preservatives could diffuse into adjacent soil and leach into groundwater. Chirenje et al. (2003) showed that mean soil arsenic concentration as high as 23 mg kg1 close to CCA-treated wood structures compared with less than 3 mg kg1 at distance about 1.5 m away. Similarly, Rahman et al. (2004) concluded that arsenic diffused from CCA-treated wood to adjacent garden soil and found that vegetable crops grown in these gardens can accumulate significant concentrations of arsenic. EPA granted the cancellation and used termination requests affecting virtually all residential uses of CCA-treated wood after Dec. 31, 2003 (USEPA, 2003). Compounds containing arsenic had been extensively used as pesticides, insecticides, herbicides, soil sterilants, silvicides, and desiccants in cotton, orchards, silviculture, and turf since late 1800s. Commonly used arsenical pesticide includes inorganic compounds such as lead arsenate (PbAsO4), calcium arsenate (CaAsO4), magnesium arsenate (MgAsO4), zinc arsenate (ZnAsO4), zinc arsenite [Zn(AsO2)2], and Paris green [Cu(CH3COO)2 3Cu(AsO2)2], as well as organic compounds such as DMAA [dimethylarsonic acid, (CH3)2AsO2H], MSMA [monosodium methane arsenate, CH3AsO3HNa], MAMA [Monoammonium methane arsonate, CH3AsO2NH4OH], and MAA [Methylarsonic acid, CH3AsO2(OH)2] (Reigart and Roberts, 1999). Woolson et al. (1971) reported that surface soil in orchards with history of arsenic pesticide application averaged 165 mg As kg1 versus 13 mg As kg1 in soils without such applications. Before 1968, CaAsO4 was typically used in Louisiana as for cotton defoliation which subsequently resulted in arsenic in soils (Bednar et al., 2002; Ori et al., 1993). As a result of government regulations, several arsenical pesticides have been prohibited in the United States. Nevertheless, considerable amounts of arsenic are retained in by the soil matrix. In Denver, Colorado, some half a century after application of an arsenical herbicide (As2O3 þ PbAsO4) on turf, arsenic concentrations in residential soils at concentrations up to 1440 mg kg1 were measured (Folkes et al., 2001). In the US poultry industry, organic arsenic compounds, such as roxarsone (3-nitro-4-hydroxyphenylarsonic acid), are common feeds for broilers to control coccidial intestinal parasites and improve feed efficiency. Little of these organoarsenicals is retained in the meat and most of the arsenic is rapidly excreted. Thus, poultry litter containing 10–40 mg kg1 of arsenic has been largely recycled as organic amendment to the agriculture fields. It is reported that 20,000–50,000 kg of arsenic is annually introduced into the environment by poultry farmers along the eastern shore of the United States (e.g., Delaware, Maryland, and Virginia) (Arai et al., 2003). Han et al. (2004) found that after 25 years of application of arsenic containing poultry litter, arsenic concentrations were 8.4 and 2.7 mg kg1 in amended and nonamended soils, respectively.


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From 1906 to 1962, dipping solutions containing 1400–2200 mg kg1 of arsenic were widely used among thousands of cattle-dipping vats throughout southern United States in an effort to eradicate ‘‘southern cattle fever,’’ a transmitting disease. These practices have resulted in the contamination of soils and groundwater due to arsenic leaching and disposal from cattle-dipping vats (Thomas and Rhue, 1997). Mclaren et al. (1998) reported that surface soils surrounding cattle-dipping vats in Australia with a history of arsenicals usage were contaminated with arsenic ranged up to 3542 mg kg1.

3.3. Speciation Arsenic can exist in many organic and inorganic forms, depending on the original sources and dominant reactions in soils. Arsenic can form organic compounds by methylation as a biological process, producing both trivalent and pentavalent organoarsenic compounds (O’Neill, 1995; Smith et al., 1998). Environmentally significant arsenic compounds are arsenate and arsenite, because they are soluble in water and toxic. Distribution of arsenate and arsenite in the solution and solid phases is largely determined by adsorption and redox reactions in soils, which will be discussed in the following sections. Arsenic in sediments and soils is bound with solid phases at different strengths. The total arsenic concentration is not necessarily a good indicator of its potential mobility and bioavailability. Several sequential chemical extraction methods have been proposed to apportion soil arsenic into various pools based on the types of extractant used. Sequential extraction methods have been extensively used to characterize the mobility and bioavailability of arsenic in sediments and soils and regarded as operational (Han et al., 2004; Keon et al., 2001; Matera et al., 2003; Mclaren et al., 1998; Rodriguez et al., 2003; Woolson et al., 1973). It should be noted that most extraction procedures were originated from that of Tessier et al. (1979), where the following chemical fractions or heavy metals pools were considered; ion-exchangeable, surficially adsorbed, precipitated, organic chelated, and occluded. Although selective chemical extraction provides empirical determination of the dominant constituents responsible for As retention soils, the procedure does not provide specific information on interfacial reactions between As and various soil constituents. Spectroscopic techniques have been applied to determine the sorption reactions occurring in heterogeneous geological material (Foster, 2003). Spectroscopic analyses, including Fourier transformed infrared (FTIR), Raman spectroscopy, X-ray photoelectron spectroscopy, extended X-ray absorption fine structure (EXAFS), X-ray absorption near-edge spectroscopy (XANES), were able to determine the electronic energy levels of atoms or molecules in the system.

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Therefore, they are useful in determining the structure of a particular solid phase at molecular level. Extensive researches have been conducted in this area and the reader is referred to Foster (2003) among others for a comprehensive review.

4. Biogeochemistry The biogeochemistry of arsenic in heterogeneous soil systems is rather complex, comprising a large array of chemical and microbiological reactions, for example, adsorption–desorption, reduction–oxidation, dissolution– precipitation, acid–base reactions, and biomethylation. Those reactions are affected by a series of environmental conditions such as pH, Eh, soil constituents, electrolytes, microbial activity, temperature, and residence time. A schematic diagram of the biogeochemical reactions of arsenic in soils is presented in Fig. 1. In this section, we briefly discuss biogeochemical reactions in Fig. 1 and focus on the impacts of those reactions on the retention and transport of arsenic in soils. Details of chemical and microbiological reactions of arsenic have been reviewed by others including Smith et al. (1998), Smedley and Kinniburgh (2002), Mahimairaja et al. (2005).

4.1. Retention mechanisms Several processes including ion exchange, surface complexation, precipitation, and surface precipitation contribute to the removal of arsenic from aquatic solution by the soil matrix. Retention of arsenic depends on arsenic

Agricultural input

As (V) Minerals Precipitation Demethylation

Organic As Demethylation

Methylation

Arsine

Agricultural industrial input

Dissolution

Secondary precipitation

Adsorption

Sorbed As (V)

As (V) Methylation Oxidation Demethylation Methylation

As (III)

Reduction Dissolution

Atmosphere

Desorption Reduction Adsorption

Oxidation

Reduction

Sorbed As (III)

Desorption Dissolution Precipitation

As Sulfides

Arsenide Geological industrial input

Figure 1

Schematic diagram of arsenic biogeochemistry in soils.


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concentration in solution, pH, reaction kinetics, arsenic species, competing ligands, as well as soil mineralogical composition. Adsorption processes, especially sorption onto metal oxide surfaces, governs the arsenic distribution in aerobic soils (Livesey and Huang, 1981; Masscheleyn et al., 1991). Sorption of arsenate and arsenite on soils was found to be significantly correlated with extractable Al and Fe contents. For example, Jacobs et al. (1970) found that AsO4 adsorption increased with increasing content of Fe oxide and the removal of Fe and Al oxides eliminated or appreciably reduced AsO4 adsorption in soils. Wauchope (1975) showed that besides Fe and Al content, soil clay content can also affect arsenate and methylarsonate adsorption. Numerous studies have investigated AsO4 and AsO3 adsorption on Fe and Al oxides (e.g., Anderson et al., 1976; Dixit and Hering, 2003; Pierce and Moore, 1980, 1982; Raven et al., 1998). Arsenate and arsenite anions are strongly adsorbed on metal oxides and hydroxides. The dominant process is inner-sphere surface complexation via ligand exchange of As for OH2 and OH in the coordination spheres of surface structural metal atoms (Sun and Doner, 1996; Waychunas et al., 1993). Spectroscopic analysis (Fendorf et al., 1997; Manning et al., 1998) revealed that inner-sphere surface complexes formed between arsenic anions [AsO4 and AsO3] and metal oxides can be mononuclear monodentate, mononuclear bidentate, and binuclear bidentate (Fig. 2). In general, formation of monodentate complex is favored at low As surface coverage while bidentate–binuclear complex occupied highest proportion of mineral surface at highest As coverage (Fendorf et al., 1997). Grossl et al. (1997) measured chemical relaxation via conductivity detection during pressure-jump relaxation experiment and concluded that arsenate adsorption on goethite is a twostep process resulting in the formation of inner-sphere bidentate complex. EXAFS studies of Manning et al. (1998) showed that AsO3 formed bidentate binuclear bridging complexes on goethite surface. Goldberg and Johnston (2001) using FTIR investigated arsenate and arsenite adsorption on amorphous iron and aluminum oxides. Their conclusion is that arsenate form inner-sphere surface complexes on both amorphous Fe and Al oxides, while arsenite form inner-sphere and outer-sphere surface complexes on amorphous Fe oxides and outer-sphere surface complexes on Al oxides. Indirect macroscopic evidences such as shift of point-zero-charge (PZC), ionic strength effect, and OH release stoichiometry have also verified the formation of inner-sphere complex of As on iron oxide surfaces. Shift in PZC with increasing anion concentration can be seen as evidence of strong specific anion adsorption and inner-sphere surface complex formation. Adsorption of anions forming inner-sphere complexes normally shows little or no ionic strength dependence (Hingston et al., 1967). Electrophoretic mobility (EM) measures of Anderson et al. (1976) indicated PZC change to lower pH values as arsenate adsorption increased on amorphous

54


Bidentate comlpexes Mononuclear Monodentate complex

Fe O

O

Fe As

Fe O Fe

O

O As

Fe O

O

O

O

As-Fe 2.85 Å

O Binuclear

Fe

O As-Fe 3.59 Å

Fe

O

O As

Fe

O

O

O As-Fe 3.24 Å

Figure 2 Binding mechanisms of arsenic in soils. (Reproduced with permission from Fendorf et al.,1997.)

Al hydroxide surface. The titration curve produced by Jain et al. (1999) demonstrated that adsorption of arsenate resulted in PZC reduction from 8.5 of pure ferrihydrite to 6.1, adsorption of arsenite resulted in a slightly less reduction of 1.5. Goldberg and Johnston (2001) found PZCs of amorphous iron oxide significantly shifted to increasingly lower pH with increasing AsO4 or AsO3 concentration, but the same results were not observed on amorphous Al oxide. They also showed that ionic strength has little or no effect on AsO4 and AsO3 adsorption on amorphous Al and Fe oxides. Because of their negatively charged surfaces, clay minerals generally have low arsenic adsorption capacity (Frost and Griffin, 1977; Lin and Puls, 2000; Goldberg, 2002; Xu et al., 1988). The proposed adsorption sites of arsenic anions on layer silicates are positively charged AlOH2þ functional groups exposed at crystal edges (Manning and Goldberg, 1996a). Isomorphous substitution of Al by Fe in some clays may contribute to arsenic sorption. Generally, clay minerals with high surface area exhibit strong arsenic sorption.


55

Other soil components such as calcite, sulfide, and organoclay matter may also contribute to As adsorption. Goldberg and Glaubig (1988) quantified arsenate adsorption on calcite and they concluded that at high pH range (>9), soil carbonates may play an important role in arsenate adsorption. Bostick and Fendorf (2003) studied arsenite sorption on iron sulfides and found that at low surface coverage, arsenite retention by sulfides conformed to a Langmuir isotherm. Moreover, the existence of soil organic matter (SOM) can reduce As adsorption due to competition for adsorption sites on mineral surfaces (Grafe et al., 2001) or form aqueous complex with As (Redman et al., 2002). However, Saada et al. (2003) showed evidence that humic acid (HA) nitrogen groups can form organoclay with clay mineral that resulted in enhanced As adsorption capacity. Precipitation is generally considered to contribute a small portion of arsenic retention except in highly contaminated soils (e.g., areas surrounding acid mines). If present at exceedingly high concentrations, direct precipitation, or coprecipitation of arsenic with solid phase Al, Fe, Mn, Mg, and Ca might occur. For example, in As-rich mine tailings piles, precipitates, such as scorodite, parasymplesite [Fe2(AsO4)38H2O], or rauenthalite [Ca3(AsO4)210H2O], may form, often as surface coatings on other mineral grains ( Walker et al., 2006). Alternatively, arsenic anions can substitute anions in secondary minerals including jarosite [KFe3(SO4)2(OH)6], gypsum [CaSO42H2O], calcite [CaCO3], and ettringite [Ca6Al2(SO4)3 (OH)1226H2O] (Myneni et al. 1997). Voigt et al. (1996) observed natural precipitation of mineral hoernesite [Mg3(AsO4)28H2O] in a contaminated soil. Juillot et al. (1999) reported the precipitation of 1:1 Ca arsenate such as weilite [CaHAsO4], haidingerite [CaHAsO4H2O], and pharmacolite [CaHAsO42H2O] and in a minor amounts, Ca–Mg arsenate such as picropharmacolite [(Ca,Mg)3(AsO4)26H2O] in a contaminated industrial site. After equilibrate slurries with varying Ca/As ratios for 4 years, Bothe and Brown (1999) observed the formation of Ca4(OH)2(AsO4)24H2O, Ca5(AsO4)3OH (arsenate apatite), and Ca3(AsO4)232/3H2O using Scanning Electron Microscope (SEM) and X-ray diffraction (XRD). Foster et al. (1998) showed the formation of scorodite [FeAsO42H2O] in mine waste using extended X-ray adsorption fine-structure spectroscopy (EXAFS). On the basis of results from EXAFS spectroscopic analyses, Grafe et al. (2004) reported the formation of adamite like [Zn2(AsO4)OH)] and korinigite precipitation on goethite when AsO4 and Zn were simultaneously introduced in high surface density ratio. Violante et al. (2006) reported the formation of poorly crystalline aluminum arsenate precipitates (a boehmite-like mineral) as influenced by pH, As/Al ratio, and aging. They suggested that arsenate appeared to be occluded within networks of short range ordered materials. Recent studies suggested that surface precipitation, that is, threedimensional growth of a particular surface phase, may occur for arsenate. Surface precipitation assumes anions absorbed on mineral surfaces to attract

56


dissolved Fe or Al. The adsorbed Fe or Al in turn adsorbs more anions, resulting in a multilayer adsorption. Contradictions were reported based on kinetic and spectroscopic studies. Waychunas et al. (1993) ruled out the possibility of arsenate surface precipitation on ferrihydrite with their EXAFS data. However, the possibility of Al–AsO4 surface precipitate formation was not excluded based on spectroscopic studies of arsenate adsorption on Al oxides (Arai and Sparks, 2002). The possibility of surface precipitation of AsO4 on ferrihydrite was suggested by Zhao and Stanforth (2001) through kinetic studies. More recently, Jia et al. (2006) demonstrated the formation of poorly crystalline ferric arsenate surface precipitates (a scorodite-like mineral) in undersaturated condition at low pH. Pedersen et al. (2006) reported that the arsenate was initially associated with the surface of more reactive iron oxides (ferrihydrite and lepidocrocite) but incorporated into the crystalline structure during Fe2þ catalyzed transformation into iron oxides into recrystallization products (goethite and magnetite).

4.2. pH dependency The effect of solution pH on arsenic sorption processes have been investigated on several minerals and soils (Dixit and Hering, 2003; Frost and Griffin, 1977; Goldberg and Glaubig, 1988; Manning and Goldberg, 2002; Manning and Goldberg, 1996; Manning and Goldberg, 1997a; Pierce and Moore, 1982; Smith et al., 1999; Xu et al., 1988). Solution pH controls two fundamental factors; mineral surface potential and arsenic speciation, which in turn impacts arsenic adsorption on mineral surfaces. Metal oxides and other minerals possess pH-dependent variable surface charges. At pH below PZC, adsorption of Hþ is in excess of that of OH, the surface becomes positively charged. The magnitude of negative potential on mineral surfaces increases with increasing pH. On the other side, there exists pH-pKa dependence of both arsenate (pKa1 ¼ 2.3, pKa2 ¼ 6.8, and pKa3 ¼ 11.6) and arsenite (pKa1 ¼ 9.2, and pKa2 ¼ 12.7). At neutral pHs, 2 arsenate exists as negatively charged H2 AsO 4 and HAsO4 , and the negative potential tend to increase as pH increases. In contrast to arsenate, arsenite mostly exists as zero charged H3 AsO03 below pH 9.2. The interaction between PZC of soil minerals and pKa of arsenate or arsenite determine the adsorption envelope, that is, amount of adsorption as a function of pH (Hingston et al., 1967). Arsenate adsorption on oxides and clays appears to be pH dependent with adsorption decreasing with increasing pH. In contrast, arsenite adsorption on soil minerals exhibits parabolic behavior with an adsorption maximum between 8 and 10. Under most conditions, AsO4 adsorbed more strongly than AsO3 on soil components (Manning and Goldberg, 1997a; Smith et al., 1999). However, arsenite is more strongly bound to metal


57

oxides under highly alkaline condition (Goldberg, 2002; Jain and Loeppert, 2000; Manning and Goldberg, 1997a; Raven et al., 1998). These two arsenic oxyanions may compete with each other for adsorption sites. Jain and Loeppert (2000) reported that when both AsO4 and AsO3 concentrations were below 2.08 mmol As kg1Fe, the effect of AsO4 on AsO3 sorption on ferrihydrite was more pronounced than vice versa. At concentrations higher than 3.47 mmol As kg1 Fe, AsO4 did not influence AsO3 adsorption, but AsO3 significantly reduced AsO4 adsorption. Goldberg (2002) concluded that at pH lower than 8, arsenate has higher adsorption capacity on minerals and soils than arsenite and competition between arsenate and arsenite is relatively small. Dixit and Hering (2003) concluded that at lower pH ( 7 (Fig. 3).

4.3. Effect of solution composition Arsenic adsorption in soils can be affected by the presence of ligands that can compete for adsorption sites on mineral surfaces (such as phosphate, silicate, carbonate, and organic acid). Several studies indicate that, because of their similar chemical properties, phosphate [PO4] in soils competes with arsenate [AsO4] for available adsorption sites. Both AsO4 and PO4 are specifically sorbed on mineral surfaces by forming similar types of inner-sphere surface complexes through ligand exchange. Several studies indicated the existence of phosphate substantially suppressed the sorption of arsenate on minerals and soils (Darland and Inskeep, 1997a; Dixit and Hering, 2003; Livesey and Huang, 1981; Melamed et al., 1995; Violante and Pigna, 2002; Roy et al., 1986ab; Smith et al., 2002; Violante and Williams et al., 2003). Competitive sorption between phosphate and arsenate generally depends on the surface properties of the adsorbent, concentrations of AsO4 and PO4, pH, sequence of addition, and residence time ( Violante and Pigna, 2002). Arsenate and phosphate are specifically adsorbed on a similar set of surface sites, although evidence showed some sites are only available for either AsO4 or PO4. On the basis of competitive adsorption of anions on goethite and gibbsite, Hingston et al. (1971) proposed two types of adsorption sites on mineral surface; the first type is available for both anions where competition takes place while the second type of adsorption sites is specifically available for either anions. Violante and Pigna (2002) demonstrated that minerals rich in aluminium (Al) have a greater affinity of phosphate than arsenate, whereas metal oxides and phyllosilicates rich in Fe were more effective in adsorbing AsO4 than PO4. In general, the adsorption of both phosphate and arsenate on Fe/Al oxides decreases with increasing pH (Manning and Goldberg, 1996). Furthermore, Jain and Loeppert (2000) reported that the effect of phosphate on arsenate adsorption on ferrihydrite was greatest at high pH

58


A 100

%As adsorbed

80 60 40 20 0

4

5

6

7 pH

8

9

10

4

5

6

7 pH

8

9

10

B 100

%As adsorbed

80 60 40 20 0

Figure 3 Comparison of AsO4 and AsO3 sorption edges on (A) hydrous ferric oxide (HFO) and (B) goethite in the presence (solid symbols) and absence (open symbols) phosphate: AsO4 (squares) and AsO3 (circles). Total arsenic concentrations are 10 and 25 mM for HFO and goethite, respectively. Total phosphate concentration is 100 mM. Experimental conditions: 0.01 M NaClO4, 0.03 g L1 HFO, or 0.5 g L1 goethite. (Reproduced with permission from Dixit and Hering, 2003.)

than at low pHs. Zhao and Stanforth (2001) confirmed that arsenate and phosphate equally adsorbed on goethite when added simultaneously. When added sequentially, the desorption process was kinetically controlled, with a fraction of both AsO4 and PO4 remained nonexchangeable. Carbonate anions is commonly present at high concentration in soil solution and groundwater. Appelo et al. (2002) using surface complexation modeling calculated carbonate and ferrous ion sorption on ferrihydrite and their displacing effect on sorbed arsenate and arsenite. Their calculation demonstrated that sorption of particularly carbonate at common soil concentrations reduced the sorption capacity of arsenic on ferrihydrite significantly. In contrast, Arai et al. (2004) observed carbonate enhanced AsO4


59

sorption on hematite surface. But when pH was held constant, dissolved carbonate effect was negligible. They suggested that the effect of dissolved carbonate on AsO4 adsorption were influenced by the reaction conditions. In natural systems, the presence of dissolved organic carbon (DOC) may compete with arsenic for adsorption sites on mineral surfaces and inhibit arsenic adsorption. Grafe et al. (2001) showed that the presence of HA and fulvic acid (FA) reduced AsO4 adsorption on goethite surfaces whereas citric acid (CA) indicated no effect. AsO3 adsorption was inhibited by all three organic acids in the order of CA > FA > HA. Redman et al. (2002) observed natural organic matter (NOM) dramatically delayed the sorption kinetic and diminished the sorption maximum of both arsenate and arsenite on hematite. The introduction of NOM displaced sorbed AsO4 and AsO3 from hematite surfaces, on the other side, arsenic similarly displaced NOM from hematite surfaces. They also observed the formation of aqueous complexes between NOM and arsenate or arsenite. Silicic acid is ubiquitous in soil and water environment and strongly adsorb to Fe oxides through ligand exchange. Waltham and Eick (2002) found that the presence of 1.0 mM silicic acid reduced 40% of arsenite adsorption on goethite, whereas it decreased the rate of arsenate adsorption but not the total sorption or capacity. We should mention that several studies have verified that other anions naturally occurring in the soil solution, such as Cl, NO3, and SO42, have little effect on arsenic retention and transport in soils (Livesey and Huang, 1981; Peryea and Kammereck, 1997; Qafoku et al., 1999; Smith et al., 2002). Few studies considered the effect of ionic strength on As behavior in soils. Adsorption of As indifferent to changes in ionic strength was seen as macroscopic evidence for inner-sphere surface complexation. Goldberg and Johnston (2001), Manning and Goldberg (1996a) showed that ionic strength have little effect on arsenite adsorption on Al and Fe oxides at the range of 0.02–0.1M. They observed that ionic strength have greater effect on arsenite than arsenate, indicating arsenite is more weakly bound. However, arsenic adsorption increases with increasing ionic strength was often observed in soils (Manning and Goldberg, 1997; Smith et al., 1999; Williams et al., 2003). Increasing ionic strength lessens electrostatic repulsion between the negatively charged surface and oxyanions and may result in increased adsorption of As onto mineral surface. The type of cations present in the soil system impacts on arsenic adsorption. Some cations may form ion pairs with arsenic oxyanions, resulting in decreasing arsenic activity in the soil solution and decreasing retention on soil surfaces (Myneni et al., 1998). Another effect is that the sorption of some cations on mineral surface may decrease negative charges on mineral surfaces and increase adsorption of arsenic oxyanions. Smith et al. (2002) observed that sorption of both AsO4 and AsO3 was enhanced by the presence of Ca2þ in the solution when compared with Naþ.

60


4.4. Sorption kinetics Traditionally, arsenic sorption studies have been carried out based on batch equilibration experiments conducted within a short period of reaction time. Few studies investigated the effect of long residence time on adsorption of arsenic in soils. However, a slow but significant reaction phase may exist due to diffusion into interparticle and intraparticle spaces, sites of different reactivity, or surface precipitation. The kinetics of arsenic adsorption– desorption must be understood for accurate predictions of the fate of arsenic in the soil environment (Sparks, 1998). Studies demonstrated that adsorption of arsenic on mineral surfaces is perhaps a two-phase reaction with a large amount of arsenic rapidly taken up by the adsorbent initially time and followed by a long plateau phase that can extend to years. Observed retention kinetics of arsenic is likely due to the heterogeneity of the soil surface where multiple chemical and physical processes may take place. Chemical reaction rates of surface complexation between anions and metal oxides are considered rapid. Using a pressure jump relaxation technique, Grossl et al. (1997) calculated a kinetic rate constant of 106.3 s1 for the formation of monodentate inner-sphere surface complex on goethite surfaces. In addition, a forward rate constant of 15 s1 was associated with the succeeding reaction for the formation of bidentate mononuclear surface complex. Because of their rapid reaction rates, surface complexation is not a rate-limiting step of AsO4 adsorption in soils. However, different types of surface complexes (e.g., monodentate, bidentate, mononuclear, binuclear) can be formed on oxide surfaces at high or low surface coverage. This heterogeneity of sorption sites may contribute to observed adsorption kinetics where sorption takes place preferentially on high affinity sites and followed subsequently by slow sorption on sites of low sorption affinity. The development of surface precipitates is a slow process involving multiple reaction steps and may explain in part the slow AsO4 kinetics in soils. Zhao and Stanforth (2001) suggested the slow buildup of surface precipitates as the mechanisms of irreversible AsO4 and PO4 retention on goethite. More recently, the XRD and Raman spectroscopy results of Jia et al. (2006) confirmed the formation of poorly crystalline ferric AsO4 surface precipitates on ferrihydrite under high As/Fe molar ratio, low pH, and extended reaction time. Diffusion of AsO4 to reaction sites within the soil matrix was proposed as an explanation to the time-dependent adsorption by Fuller et al. (1993), Raven et al. (1998), among others. A two-phase process was generally assumed for diffusion-controlled adsorption, with the reaction occurring instantly on liquid–mineral interfaces during first phase whereas slow penetration or intraparticle diffusion is responsible for the second phase. Pore space diffusion model has been employed by Fuller et al. (1993) and Raven

61


As(V) sorbed (mg kg−1)

600 Windsor 450

300

150

0 0

15 30 45 60 As(V) in solution (mg L−1)

75

Figure 4 Time-dependent sorption isotherm of arsenate on Windsor soil. Symbols are for different reaction times of 24, 72, 168, 336, and 504 h (from bottom to top). Solid curves depict results of curve fitting with the Freundlich equation. (Reproduced with permission from Zhang and Selim, 2005.)

et al. (1998) to describe the slow sorption of AsO4 on ferrihydrite. For heterogeneous soil system, the complex network of macro- and micropores may further limit the access of solute to the adsorption sites and cause the time-dependent adsorption. Because of the intrinsic chemical and physical heterogeneity of soils, it is difficult to describe and to accurately predict the kinetics of arsenic adsorption on the soil matrix. Most studies have demonstrated that the residence time significantly affected arsenic retention by soils (Fig. 4). Similar to observations on mineral surfaces, it is widely reported that arsenic sorption is biphasic with an initial rapid reaction followed by a slow (kinetic) phase. For example, Livesey and Huang (1981) concluded that AsO4 adsorption on soils were rapid initially with limited adsorption occurring after 24 h. Elkhatib et al. (1984a) studied kinetic of AsO3 adsorption on surface and subsurface soils and described their results with an Elovich equation and a modified Freundlich equation. They concluded that the initial reaction was rapid with more than 50% of AsO3 sorbed on soils in the first 0.5 h. Their regression results showed that reaction rate can be related to soil clay content or Fe oxide content. Similarly, Carbonell-Barrachina et al. (1996) found that AsO3 sorption on soils was initially rapid and sorption rate decreased with time. Their results showed that sorption processes continued during 50 h of reaction. Corwin et al. (1999) found that Langmuir adsorption coefficient (KL) for arsenate adsorption was 1.70, 2.92, and 3.98 L kg1 after 1, 7, 14 days, respectively. Smith et al. (1999) showed that AsO4 retention by soils was initially rapid, attained apparent equilibrium in less than 1 h, followed by a steady and slow rate for the 72 h investigated. Williams et al. (2003) conducted long-term AsO4 adsorption experiment on an iron oxide containing subsurface soil. They concluded that AsO4

62


sorption exhibit biphasic pattern with a rapid period before 48 h, followed with a slow process for several weeks. The Kd value after 3 weeks was three to four times that after 1 week. Darland and Inskeep (1997b) demonstrated that adsorption of AsO4 on iron oxides continued for at least 96 h. Brouwere et al. (2004) showed that Kd for AsO4 increased on average 1.8 fold between day 2 and 7. Manning and Suarez (2000) showed that the rate of AsO3 adsorption on soils was closely dependent on soil properties including extractable metals, soil texture, specific surface area, and pH.

4.5. Desorption Recent research has shown that desorption of arsenic is highly hysteric and sorbed arsenic is not easily removable from the soil matrix. Observed desorption hysteresis might be due to kinetic retention behavior, such as slow diffusion, and irreversible retention. In contrast to adsorption studies, relatively few work have been done to investigate desorption or release of arsenic from minerals or soils. The effects of residence time on desorption of arsenic from soil minerals or soils are not clear. Lin and Puls (2000) found that desorption of AsO3 and AsO4 from clay minerals was significantly decreased with increasing aging time. They explained this phenomenon by assuming diffusion of arsenic into internal sorption sites, which are not readily accessible by the bulk solution. O’Reilly (2001) showed that a significant amount of AsO4 bound to goethite (>60%) is not readily desorbable by PO43 after 5 months of reaction. However, they found residence time (0.7–4846 h) have little effect on AsO4 desorption from goethite in the presence of 6 mM PO43 at pH 4 and 6. In contrast, Arai and Sparks (2002) found that AsO4 desorption from aluminum oxide surfaces decreased with increasing reaction time (3 days to 1 year). Furthermore, their EXAFS studies provided microscopic evidence of rearrangement of surface complexes and surface precipitation. These different results may due to the different desorption solution used. O’Reilly (2001) used high concentration of phosphate solution while Arai and Sparks (2002) used a complex solution of 0.096M NaCl, 1 mM sodium sulfate, and 2 mM organic buffer. Pigna et al. (2006) studied desorption of AsO4 from Fe and Al oxides by PO4 as a function of residence time and surface coverage. Their data indicate that more AsO4 was desorbed from Al oxides than from Fe oxides. Increased residence time from 24 h to 360 h significantly decreased AsO4 desorption due to rearrangement of AsO4 on mineral surfaces from desorbable into more resistant forms. Desorption or release of arsenic from soil is a complex process since sorbed arsenic species may bound to solid matrices in various energy states. Jacobs et al. (1970) examined the extractability of AsO4 sorbed by soil decreased with increasing equilibration time. The time required to reach equilibrium of arsenic in soils varied from 1 to 6 months depending on soil

63


texture and arsenic level. Woolson et al. (1973) showed that AsO4 solubility in soils decreased with time and reached equilibrium in 4–6 weeks. Elkhatib et al. (1984b) concluded that arsenite desorption soils was hysteretic with only a small fraction of the sorbed AsO3 released after five desorption steps with deionized (DI) water. Carbonell-Barrachina et al. (1996) stated that AsO3 sorption was a reversible process and their data showed about 50% of the sorbed arsenic can be released from soils after five desorption steps in 36 h. Lombi et al. (1999) evaluated the kinetics and reversibility of AsO3 and AsO4 sorption by Fe oxide-coated sand and several soils. They demonstrated that a significant portion of arsenic was converted to less mobile as a subsequent to decreased As in the easily extractable form, and increasing As in the more recalcitrant form. Desorption or release results of Zhang and Selim (2005), which are presented as isotherms in the traditional manner in Fig. 5, demonstrated distinct discrepancies between adsorption and successive desorption isotherms and indicate considerable hysteresis for AsO4 release. This observed hysteresis was attributed to kinetic retention behavior, such as slow diffusion and irreversible retention. Desorption curves also demonstrated that percentage of desorption increased with AsO4 surface coverage, indicating the high sorption affinity at low surface coverage. Under strongly acidic and reducing conditions, iron oxides may be dissolved either biotically or abiotically. It is believed that arsenic associated (adsorbed or precipitated) with oxides will be released into aqueous solution during iron oxide dissolution (Smedley and Kinniburgh, 2002). Pedersen et al. (2006) observed the release of arsenic during the reduction of ferrihydrite, lepidocrocite, and goethite. For ferrihydrite and goethite, they attributed the release of arsenic as a consequence of reduced surface area of iron 600 As(V) sorbed (mg kg−1)

Windsor 450

300 Desorption Adsorption

150

0 0

30 10 20 As(V) in solution (mg L−1)

40

Figure 5 Isotherms of arsenate desorption from different soils based on successive dilution after the last adsorption step for different initial concentrations (Co) of 20, 40, 80, and 100 mg l1. The solid and dashed curves depict results of curve fitting with the Freundlich equation for 504-h adsorption and desorption isotherms, respectively. (Reproduced with permission from Zhang and Selim, 2005.)

64


oxides during dissolution. In contrast, they found arsenic was released from lepidocrocite before the release of Fe. A study by Herbel and Fendorf (2005) investigated the mobilization of arsenic under dynamic flow conditions in ferric hydroxide-coated sands inoculated with arsenate reducing bacteria (Surfurosprillum barnesii strain SES-3). They suggested that the release of arsenic into the aqueous phase is associated with the mineralogical transformation of iron oxides due to microbial reduction.

4.6. Reaction with sulfides Chemical weathering is a major process controlling the geochemical cycling of arsenic in the environment. Arsenic in the parent material is originally present in the form of chemically reduced minerals such as realgar (AsS), orpiment (As2S3), FsAsS, and amorphous As2S3 or AsS (Lengke and Tempel, 2005; Oremland and Stolz, 2003). The weathering process can oxidize arsenic in sulfide minerals to arsenite or arsenate minerals and subsequently release arsenic anions into aqueous phase. The reaction occurring at the interface between sulfide minerals and aqueous solution depends on environmental factors such as pH, redox potential (pe), carbonates, temperature, water flow, and morphology of the minerals. In general, the dissolution arsenic from sulfide minerals is a slow process that can continue for several years. Considerable amount of arsenic will be released into aqueous solution during this process. Lengke and Tempel (2005) studied the oxidative dissolution of amorphous As2S3 and AsS, orpiment, and realgar using mixed flow reactors. The proposed overall reactions can be expressed as:

As2 S3 ðsÞ þ 7O2 ðaqÞ þ 6H2 O ! 2HAsO2 4 ðaqÞ þ þ 3SO2 4 ðaqÞ þ 10H ðaqÞ

AsSðsÞ þ 2:75O2 ðaqÞ þ 2:5H2 O ! HAsO2 4 ðaqÞ þ þ SO2 4 ðaqÞ þ 4H ðaqÞ

ð1Þ ð2Þ

They found that the oxidation rates of arsenic sulfides increase with increasing pH and dissolved oxygen (DO) concentration. Their results demonstrated that at pH value of 7–8, the oxidation rate of arsenic sulfide solids is in the decreasing order of As2S3(am) AsS (am)>orpiment realgar. Arsenite was identified as the dominant senic species released from oxidation process. Calculated values of activation energies suggested that oxidation kinetics of sulfides minerals are controlled by surface reactions, that is, transfer of electron from the sulfide mineral to the oxidant (DO). The oxidative dissolution of FeAsS is commonly observed in many mining sites resulting in the release of high concentrations of arsenic in the


65

effluent. Because As, F, and S can coexist in multiple oxidation states, the dissolution mechanism of FeAsS is rather complex where multiple reactions occur simultaneously or sequentially (Nesbitt et al., 1995). Using X-ray photoelectron spectroscopy (XPS), Nesbitt et al. (1995) reported that As1 was predominant on surfaces of unoxidized FeAsS. Significant oxidation to As5þ and As3þ was observed upon reaction with air saturated water. Using mixed flow reactor, Walker et al. (2006) determined the rate of FeAsS oxidation by DO at pH of 6.3–6.7 and suggested these reaction mechanisms:

4FeAsSðsÞ þ 11O2 ðaqÞ þ 6H2 O ! 4H3 AsO3 ðaqÞ 2þ þ 4SO2 4 ðaqÞ þ 4Fe ðaqÞ

ð3Þ

4Fe2þ ðaqÞ þ O2 ðaqÞ þ 10H2 O ! 4FeðOHÞ3 ðsÞ þ 8Hþ ðaqÞ ð4Þ þ 2H3 AsO3 ðaqÞ þ O2 ðaqÞ ! 2HAsO2 4 ðaqÞ þ 4H ðaqÞ

ð5Þ

Their results indicate that the oxidation rate of FeAsS (10–10.14 mol m2 was independent of DO in the range of 0.3–17 mg l1. However, increasing level of DO resulted in the increasing ratio of AsO4/AsO3 in the effluent. They suggested that the rate limiting reaction step is the slow reduction (electron release) of water at anodic sites on FeAsS surface. Yu et al. (2004) investigated the dissolution of FeAsS in Fe2(SO4)3 solution under acidic condition (pH = 1.8). Their results demonstrated that the dissolution rate of FeAsS increased with increasing concentration of ferric iron [Fe(III)] and temperature. The dissolution rate of FeAsS was higher in ferric chlorite (FeCl3) solution than in ferric sulfate solution. In addition, the dominant species of arsenic releasing was arsenite in Fe2(SO4)3 solution, whereas arsenate was the major form of arsenic in FeCl3 solution. It should be noted that precipitation of iron oxides commonly occurs as a result of FeAsS oxidation. Thus, adsorption of released arsenite and arsenate on iron oxides attenuated arsenic concentration in solution. In anoxic sulfidic environment, reaction of arsenic with sulfide minerals controls the geochemical cycling of arsenic. Using X-ray adsorption nearedge structure (XANES), Reynolds et al. (1999) identified the precipitation of FeAsS in Santa and Palouse soils after 14 days of flooding and those FeAsS precipitation was largely destroyed upon reaeration. Under acidic and reduced conditions, Wilkin and Ford (2002) reported disordered orpiment or alacranite precipitations formed after the reaction between soluble arsenic and H2S. Using As K-edge X-ray absorption spectroscopy (XAS), Farquhar et al. (2002) investigated the mechanisms of the interaction between AsO3 and AsO4 in aqueous solution (pH 5.5–6.5) with the surfaces of crystalline mackinawite (tetragonal FeS) and pyrite (FeS2). They suggested the formation s1)

66


of outer-sphere complexation at low arsenic concentrations and the formation of poorly crystalline arsenic sulfide at high arsenic concentrations. Bostick and Fendorf (2003) studied arsenite sorption on troilite (FeS) and FeS2 and they identified surface precipitation of FeAsS using XAS. They observed that sorption increased with pH. This was explained on the basis of the formation of Fe(OH)3 associated with FeAsS precipitation, because the formation of Fe(OH)3 is favored with increasing pH. Wolthers et al. (2005) reported that the formation of outer-sphere complex between AsO4 and AsO3 and surface of sulfide minerals is a fast process. Thus, sorption isotherms can be described with a Freundlich equation where the distribution coefficient (Kd) for arsenate always reported larger than that for arsenite.

4.7. Heterogeneous oxidation Oxidation–reduction reactions play an important role in determining arsenic solubility, mobility, bioavailability, and toxicity in soils. Under natural environmental conditions, arsenate [AsO4] and arsenite[AsO3] are the most abundant forms of arsenic (Masscheleyn et al., 1991). In soils and water systems, AsO4 is dominant under aerobic condition and AsO3 under anoxic or anaerobic condition. The distribution and transformation between arsenate [AsO4] and arsenite [AsO3] is largely controlled by the redox condition of the soil environment. Besides soil redox potential (Eh/pe), other factors, such as pH, Fe and Mn oxides, sulfides, organic matter, and microbial activity, also impact reduction and oxidation of arsenic ( Jones et al., 1997). The oxidation–reduction reactions of arsenic can be chemical or biological and dependent on the substances present in the environment. Although AsO3 is not thermodynamically stable under oxidized conditions, high concentrations of AsO3 are frequently observed in water environments in the presence of DO. Nonequilibrium conditions may be due to slow kinetics of arsenite oxidation by oxygen in homogeneous solutions (with a half time of 1 year) (Scott and Morgan, 1995). However, the existence of Mn oxides in soils and water environments can promote heterogeneous oxidation of AsO3 to AsO4 at considerably high reaction rates (Oscarson et al., 1981ab; Scott and Morgan, 1995). There is considerable evidence that heterogeneous oxidation on the surfaces of Mn oxides play an important role in the transformation of AsO4 and AsO3. Oscarson et al. (1981a) showed that Mn oxides can effectively oxidize AsO3 into AsO4, accompanied by the reduction of Mn(IV) to Mn(II). The oxidation rates were unaffected with the insulation from oxygen or the inhibition of biological activity (Oscarson et al., 1981a). The depletion of AsO3 through oxidation on surfaces of birnessite, crytptomelane, and pyrolusite followed first-order kinetics with rate constants dependent on crystallinity and surface area of the minerals (Oscarson et al.,

67


1983a). Through the masking of electron-accepting sites, surface coating of Fe and Al oxides or calcium carbonate decreased the rate constants of AsO3 oxidation by Mn oxides (Oscarson et al., 1983b). Their spectroscopic study showed that birnessite was an active oxidant of AsO3 both in solution and on the goethite surface (Sun and Doner, 1998). Scott and Morgan (1995) demonstrated that AsO3 oxidation by synthetic birnessite is rapid, with a timescale of minutes. They explained this reaction with a four-step multiprocess surface mechanism, (1) adsorption of AsO3; (2) electron transfer from AsO3 to Mn(IV); (3) release of AsO4; (4) release of Mn(II), with the adsorption as the slowest step (Fig. 6). Furthermore, surface spectroscopic studies suggest that the electron transfer process involves two steps with the formation of Mn(III) as an intermediate product (Nesbitt et al., 1998). The reaction stoichiometry can be expressed as:

2MnO2 ðsÞ þ H3 AsO3 ¼ 2MnOOH ðsÞ þ H3 AsO4

ð6Þ

2MnOOH ðsÞ þ H3 AsO3 ¼ 2MnO þ H3 AsO4 þ H2 O

ð7Þ

where MnOOH* is an intermediate reaction product. The oxidation of AsO3 by Mn(III) oxide manganite (g-MnOOH) was shown to be occurring on the timescale of hours (Chiu and Hering, 2000). In addition, chemical and miscroscopy techniques demonstrated the formation of manganese arsenate (Krautite) precipitate at the surface of H-birnessite with high crystallinity (Tournassat et al., 2002). The reaction can be written as follows:

Mn2þ þ H2 AsO4 þ H2 O ¼ MnHAsO4 H2 O þ Hþ

ð8Þ

Although thermodynamically favorable, oxidation rate of AsO3 on surfaces of Fe(III) oxides is rather slow. XPS evidences showed no redox reaction between Fe(III) oxide and AsO3 within 72 h (Oscarson et al., 1981b). EXAFS results of AsO3 sorption showed no evidence of heterogeneous oxidation to AsO4 on goethite surface at pH 6.4 to 8.6 (Manning and Goldberg, 1997b). However, XANES and IR spectral demonstrated that 20% of AsO3 sorbed on goethite surface was oxidized into AsO4 at pH 5.0 after 20 days of incubation, while no AsO4 was detected at pH 8.0 (Sun and Doner, 1998). There is considerable evidence that Fe(II) can catalyze the oxidation of AsO3 by O2 in the presence of iron oxides (Sung and Morgan, 1980, 1981). It was reported that the addition of single oxidants [O2, H2O2, dissolved Fe(III), or iron(III) (hydr)oxides] did not oxidize AsO3. However, partial or complete oxidization of AsO3 was observed in parallel to the oxidation of Fe(II) by O2 or H2O2. A reaction scheme is proposed in which the reaction of Fe(II) with H2O2 forms free radical intermediates, possibly OH radicals at low pH or an Fe(IV) species at higher pH (>5.24), followed by the

68


Mn

O

Mn

Mn

OH

O

O

O

Mn

OH

O

O

OH Mn

O

O Mn

Mn

OH

Mn

O O

Mn

O

O OH

Mn

Mn

O

As OH

O O

Mn

OH

Adsorption of As(III) A B Electron transfer

Equivalent surfaces Mn

O

Mn

C

E

Mn

Mn

Mn

OH

Release of Mn(II)

Mn

OH

OH O

O

OH O

O

O Mn2+

OH

O Mn

OH

OH

O Mn

Mn

Mn

OH O

OH O

Mn

As

O

OH OH

Release of As(V) D

Mn

O

O

O Mn

OH

OH

O Mn

Mn

Mn

OH OH

OH O

Mn

−O

As

O

OH OH

Figure 6 (A) Schematic representation of the cross section of the surface layer of a Mn(IV) oxide and (B) the resulting surface structure following arsenite adsorption, (C) electron transfer, (D) arsenate release, and (E) Mn2þ release. (Reproduced with permission from Scott and Morgan, 1995.)

oxidation of AsO3 to As(IV) by intermediate Fe(IV), then As(IV) is oxidized to AsO4 by molecular oxygen. The oxidation of AsO3 occurs on a timescale of hours and incomplete oxidation was observed even though Fe(II) was in excess (Hug and Leupin, 2003). A reaction transport model was developed to simulate the complex kinetics of Fe(II) catalyzed AsO3 oxidation in sediments (Bisceglia et al., 2005).


69

It is generally accepted that phyllosilicate clay minerals has lower affinity for anions than iron and aluminum oxides and have limited or no capability of oxidizing AsO3 to AsO4. No oxidation of AsO3 in solution was observed after 48 h reaction with suspensions of illite, montmorillonite, kaolinite, vermiculite, ferruginous smectite, microcline, orthoclase, or calcite (Oscarson et al., 1981b). The heterogeneous oxidation of AsO3 to AsO4 in the presence of kaolinite and illite was attributed to MnO2 impurity in clay minerals (Manning and Goldberg, 1997b). Lin and Puls (2000) demonstrated that oxidation of AsO3 to AsO4 occurred at the clay surface, whereas no reduction of AsO4 to AsO3 was observed. The kinetics of AsO3 oxidation in aerated soil and sediment is generally controlled by their reactions on mineral surfaces. Kinetic studies of AsO3 sorption and oxidation by aquifer materials demonstrated that AsO3 removal rate increased with increasing Mn oxides contents (Amirbahman et al., 2006). Production of solid AsO4 species occurred simultaneously with AsO3 adsorption on solid phases (Fig. 7). Natural gradient tracer study with AsO3 demonstrated that arsenic transport in oxic zones was substantially retardation through the oxidation to AsO4 by Mn oxides in the aquifer (Stadler et al., 2001). Using speciation with XANES spectroscopy, Manning (2005) recently reported that, in a batch reaction, AsO3 was either partially or completely oxidized to AsO4 on soil surface. Fitting XANES spectra as linear combinations of several well-characterized AsO3- and AsO4-treated model compounds demonstrated that the reaction products are AsO3 on Fe oxides and AsO4 on Fe and Al oxides as well as clay minerals.

4.8. Microbial-mediated reduction and oxidation Numerous bacteria, fungi, and algae organisms that are capable of reducing or oxidizing arsenic have been identified and isolated. Under anaerobic condition, reduction of AsO4 to AsO3 is generally mediated through two principal biological mechanisms: (1) dissimilatory reduction (respiration) of microbes such as Escherichia coli, Staphylococcus aureus, and Staphylococcus xylsis where AsO4 is utilized as a terminal electron acceptor; (2) detoxification activity controlled by ars genes that encode AsO4 reduction via an AsO4 reductase, followed by AsO3 release from the cell with an efflux pump (Jones et al., 1997; Langner and Inskeep, 2002; Tamaki and Frankenberger, 1992). Bacteria capable of oxidizing arsenite into arsenate was observed many years ago, first in the cattle dips, later in raw sewage and contaminated soils, as well as in mine deposit. The majority of arsenic oxidizing bacteria, such as Alcaligenes, operates through detoxification mechanism utilizing arsenite oxidase. However, bacteria that are capable of chemolithoautotrophic growth using the energy from arsenite oxidation have been documented (Santini et al., 2000).

A

B

30

60

50

Solid As(V)

Solid As(V) 20

40

30 10

20

aq. As(III)

aq. As(III)

Concentration (mm)

10 Solid As(III)

Solid As(III) 0

0 0

50

100

150

C 100

50

0

D

100

150

500 3

80

2

400 aq. as (III)

Aq. As(III) Solid As(V)

aq.As(V)

1

60

300 0 0

40

200

20

100

50 100

150

200

250

Solid As(V)

Solid As(III)

Solid As(III) 0

0 0

50

100

150

0

50

100

150

200

250

Time (h)

Figure 7 Oxidation kinetics of AsO3 by F168–15 material. pH=5.2, and initial [AsO3]=28.3 mM (A), 59.2 mM (B), 88.7 mM (C), and 483.0 mM (D). All lines are model fits to the experimental data. (Reproduced with permission from Amirbahman et al., 2006.)


71

In the soil environment, Macur et al. (2004) showed that bacteria capable of either oxidizing AsO3 or reducing AsO4 coexist and are ubiquitously present. Their unsaturated column study showed that AsO3 was readily oxidized into AsO4, whereas no apparent AsO4 reducing was observed. Langner and Inskeep (2000) investigated microbial reduction of arsenate in the presence of ferrihydrite. They found that an AsO4 reducing, glucosefermenting microorganism was able to rapidly reduce the aqueous AsO4 to AsO3 but not able to reduce AsO4 aorbed on ferrihydrite surface. Although the aqueous AsO4 was highly reduced, the desorption rate of AsO4 from ferrihydrite is too slow to cause increase of arsenic solubility. Moreover, Langner et al. (2001) observed the rapid oxidation (first-order rate constant of 1.2 min1) of AsO3 to AsO4 in stream waters from geothermal springs with the presence of live organisms and high Fe/Al content. Microbial-mediated arsenic reduction through either dissimilatory reduction or detoxification pathways generally demonstrates first-order reaction kinetics with half-life of hours to days, depending on the environmental conditions (Langner et al., 2002). Because of the slow kinetics of biological processes, both AsO4 and AsO3 are often found in the soil environment regardless of the redox conditions. For example, Masscheleyn et al. (1991) documented the persistence of AsO4 under reducing condition and AsO3 under oxidizing condition, which was attributed to the slow reaction kinetics. Onken and Hossner (1996) identified both convergence from arsenite to arsenate and arsenate to arsenite in the soil solution under flooded conditions. Many environmental factors impact the microbial-mediated arsenic reduction and oxidation. McGeehan and Naylor (1994) have shown that the reduction of AsO4 to AsO3 was highly dependent on the sorption process of arsenic. Manning and Suarez (2000) observed that heterogeneous oxidation of AsO3 to AsO4 was controlled by soil properties including pH, content of Al, Fe and Mn oxides. Takahashi et al. (2004) found that arsenic quickly released from flooded paddy soils as a result of reductive dissolution of Fe hydro(oxide) accompanied by reduction from AsO4 to AsO3. Reduction of AsO4 to AsO3 was observed in a natural gradient tracer study in the anoxic zone of a sandy aquifer on Cape Code. Microbial analysis with the sediment material revealed the presence of AsO4 reducing microorganisms (Ho¨hn et al., 2006). Other substances occurring may influence redox chemistry of arsenic in soils. For example, Reynolds et al. (1999) reported that the addition of H2PO4 enhanced arsenic reduction rate in two soils. Senn and Hemond (2002) demonstrated that the existence of nitrate under anaerobic condition can oxidize AsO3 into AsO4. The accumulation of nitrate also produced more As-sorbing Fe(III) oxides. As a result, the presence of nitrate reduced the toxicity of arsenic. Methylation of inorganic arsenic species by aerobic and anaerobic microorganisms produces monomethylarsonic acid (MMA), dimethylarsinic acid

72


(DMA), and trimethylarsine oxide (TMAO). The biological arsenic methylation process in soils can be influenced by abiotic factors, such as pH and temperature. It was demonstrated that several aerobic and anaerobic microorganisms, that is, methanogenic and sulfate-reducing bacteria are accountable for arsenic methylation (Cullen and Reimer, 1989). It is commonly accepted that the biomethylation of arsenic goes through the challenger pathway, where arsenic undergoes an alternating sequence of reduction and oxidative methylation reactions as schematically represented in Fig. 8 (Dombrowski et al., 2005). Once believed to be a detoxification mechanism for arsenic, the methylated species may actually be more toxic and reactive along the methylation pathway. Relatively, few studies have been carried out on the adsorption of methyl-arsenic on minerals and soils. In general, adsorption of arsenic decreases with the methylation. Cox and Ghosh (1994) found that adsorption of MMA(V) and DMA(V) on alumina and hydrous ferric oxide (HFO) was insensitive to changes in ionic strength, indicating that these arsenic species form inner-sphere surface complexes with Fe and Al oxides. In addition, their studies demonstrated that maximum adsorption of MMA(V) and DMA(V) occurs at low pH and the amount of adsorption decreases with increasing pH. Lafferty and Loeppert (2005) observed that adsorption of MMA(III) and DMA(III) on iron oxides was insignificant with pH ranging from 3 to 11.

CH3 + As

−O

OH Arsenite

MMA(III)

OH

HO As

Adding methyl cation

Reduction

n hy lat io et

n tio hy la et M

HO As

CH3

CH3+

CH3

CH3 DMA(III)

••

OH

CH3

CH3

••

As(III) HO As

Reduction

n tio hy la et

CH3

••

−O

OH

CH3

OH

M

Reduction

CH3 + As

+ −O As OH

OH Reduction

TMA(V)

M

OH + −O As OH

DMA(V)

CH3

••

As(V)

MMA(V)

Reduction

Arsenate

As

CH3

CH3 TMA(III)

Figure 8 Schematic of the Challenger pathway for biomethylation, with the alternating sequence of reduction and oxidative methylation of arsenic. (Reproduced with permission from Dombrowski et al., 2005.)


73

5. Transport in Soils 5.1. Transport mechanisms The transport of arsenic in heterogeneous soils is largely controlled by adsorption–desorption processes on solid matrix surfaces. Non-nonlinear or concentration dependent as well as kinetic adsorption–desorption behavior is often regarded as responsible for observed nonequilibrium transport of arsenic in soils. Melamed et al. (1995) observed asymmetrical AsO4 breakthrough curves (BTCs) from columns of an Oxisol and suggested that physical and chemical nonequilibrium as the dominant processes for arsenic movement in soils. Kuhlmeier (1997a) investigated the transport of As in columns of silty and sandy soils sands and quantified time- and concentration-dependent Kd values. In a separate column experiment with a contaminated clayey soil, Kuhlmeier (1997b) suggested that the slow release of arsenic as a result of kinetically controlled or rate limited mass transfer in soils. Darland and Inskeep (1997a,b) found that AsO4 transport exhibited significant retardation, tailing, and poor recovery. Nonequilibrium of AsO4 transport in a subsurface soil with high arsenic sorption capacity was also demonstrated by Williams et al. (2003). Because of the nonequilibrium behavior of arsenic transport in soils, water flow velocity (or residence time) significantly impacts its mobility in soils. Puls and Powell (1992) observed that the distribution factors (Kd) determined from column transport experiments decreased from 3.0 L kg1 to 1.4 L kg1 when the flow rate (q) was doubled. Darland and Inskeep (1997a) demonstrated that increasing pore volume velocity from 0.2 to 90 cm h1 resulted in some tenfolds increase of AsO4 leaching or recovery from saturated sandy columns (Fig. 9). Williams et al. (2003) reported enhanced AsO4 mobility and decreased retardation of BTCs in a subsurface soil by increasing the pore water velocity. Nikolaidis et al. (2004) studied the mobility of arsenic in contaminant lake sediments and found that a significant portion of arsenic was leached due to increased flow velocity. Furthermore, Radu et al. (2005) studied the effect of pore water velocity (0.23 and 2.3 cm/min) on the transport of AsO3 in saturated columns of sand. Their results demonstrated that the increasing pore water velocity increased the mobility of AsO3 in goethite-coated sand columns. A number of column studies have been conducted to evaluate the impact of various conditions on the transport of arsenic in saturated soils. Hiltbold et al. (1974) studied the transport of MSMA in several soils. The BTCs from surface soils exhibited relatively little retardation, whereas extensive retardation was observed in BTCs from the subsoils. The Kd values calculated from batch and column experiments showed distinct

74


Relative concentration (C/C0)

0.6 As recovery in effluent @ PWV 10 PVs 0.2 cm/h 7.24% 1 cm/h 35.6%

0.5 0.4

10 cm/h 90 cm/h

0.3

53.3% 74.3%

0.2 0.1 0.0 0

2

4

6

8

10

Pore volumes

Figure 9 Breakthrough curves of AsO4 at four different pore water velocities (0.2, 1, 10, and 90 cm h1). Column conditions: background 0.01 M KCl; AsO4 pulse 1 pore volume at 133 mM. (Reproduced with permission from Darland and Inskeep, 1997a.)

Relative concentration (C/C0)

1.0

pH 4.5, 0.53 cm min−1 pH 4.5, 0.53 cm min−1, 0.25 mM PO4

0.8

pH 9, 0.53 cm min−1 pH 4.5, 1.6 cm min−1

0.6

0.4

0.2

0.0 0

500

1000

1500

2000

Pore volume (V/V0)

Figure 10 Breakthrough curves of AsO4 under varying pH, phosphate, and pore water velocity. Column conditions: background 0.01 M NaNO3; AsO4 initial concentration at 1 mg l1. (Reproduced with permission from Williams, 2001.)

discrepancies which was attributed to the rather limited residence time of arsenic in the column (high flow rate). Williams et al. (2003) found that increasing ionic strengths from 0.01 M to 0.1 M extended AsO4 adsorption (Fig. 10). They explained that increases in ionic strength lessen the electrostatic repulsion between negatively charged surfaces and oxyanion which results in an increased adsorption of arsenic onto mineral surfaces.


75

Several miscible displacement experiments provided evidence that the presence of PO4 greatly enhanced arsenic mobility in soils. Woolson et al. (1973) found that 77% of arsenic in a sandy soil column was leached due to the application of 0.05 M KH2 PO4 solution. The significant enhanced transport of AsO4 through columns of an aggregated Oxisol by the increasing addition of phosphate was demonstrated by the left shifted BTCs observed by Melamed et al. (1995). Similarly, Peryea and Kammereck (1997) found that the application of phosphate significantly mobilized arsenate and resulted in the leaching of 50% of soil arsenic after 10 pore volumes. Qafoku et al. (1999) studied the effect of competitive anions (phosphate and sulfate) on arsenic transport through a packed Cecil soil column amended with fly ash. They found that with calcium phosphate as the displacing solution, arsenate concentration in the leachate increased an order of magnitude when compared to calcium sulfate as the displacing solution. The displacement of AsO4 by phosphate under dynamic flow conditions were also reported by other researchers including Darland and Inskeep (1997b), Williams et al. (2003), among others. Column experiments were employed to study the effect of carbonate on arsenic transport and found that the presence of 0.1 mM CO3 resulted in a limited increase of AsO4 transport in columns of subsurface soil. Radu et al. (2005) studied the effects of high aqueous carbonate concentrations on the transport of arsenic in a synthetic iron oxide-coated sand columns. They found that increasing carbonate concentrations had relatively little effect on the adsorption and transport of AsO3 and AsO4. A number of experiments demonstrated that AsO4 was generally more mobile under high pH condition which is due to decreased adsorption. For example, Mariner et al. (1996) reported that Kd values measured on excavated core samples of contaminated aquifer material decreased at least tenfold as the pH increases from 8.5 to 11. Darland and Inskeep (1997b) found that increasing the pH from 4.5 to 8.5 caused a retardation or a delay of the AsO4 BTC. A more symmetrical BTC was observed at pH 8.5 due to increased AsO4 mobility. Similar effect of pH was observed by Williams et al. (2003) on a subsurface soil having high iron content. However, Radu et al. (2005) reported early breakthrough for AsO3 at pH 4.5 than at pH 9, as a result of increased adsorption of AsO3 under high pH. The oxidation reduction potential of the geological materials determines chemical speciation of arsenic, hence substantially impacts the extent of arsenic retention and transport. A series of small-scale natural gradient tracer experiment with AsO3 and AsO4 were conducted in oxic, suboxic, and anoxic zones of a shallow sandy aquifer at Cape Cod, Massachusetts by Stadler et al. (2001)and Ho¨hn et al. (2006). Stadler et al. (2001) observed that a significant portion of applied AsO3 was oxidized to AsO4 2.2 m downstream from the injection well in both oxic and suboxic zones. There was relatively little retardation of total arsenic breakthrough relative to

76


conservative Br tracer in suboxic zone. However, substantial retardation (R ¼ 1.3) and irreversible retention (one-third of the applied arsenic) was observed in oxic zone. The transformation from AsO3 to AsO4 in the aquifer was explained by abiotic oxidation on the surface of Mn oxides. Ho¨hn et al. (2006) observed that applied AsO4 was reduced to AsO3 some 1 m downstream of the injection well. This process was attributed to AsO4 reducing microorganisms in the anoxic zone. A spatial snapshots of AsO4 and AsO3 30, 45, 63, and 104 days after injection are illustrated in Fig. 11 using two-dimensional longitudinal transects. Their results demonstrated that the reduction of AsO3 was occurred after nitrate concentration had been attenuated by reaction with Fe(II) and the transport of AsO3 was faster than AsO4 under reducing chemical conditions. Biotransformation between AsO3 and AsO4 as a result of microbial activity might be an important factor contributing to the retention and transport of arsenic in aquifer and vadose zone. Herbel and Fendorf (2006) investigated the mobilization of adsorbed arsenic [AsO4 or AsO3] in saturated columns of ferric hydroxide-coated sands. The sand was inoculated with Surfurosprillum barnesii, an Fe(III) and AsO4 respiring bacterium, or Bacillus benzoevorans, an AsO4 respiring bacterium incapable of Fe(III) reduction. Extensive release of arsenic, predominantly in the form of AsO3, was observed and the desorption was promoted by mildly reducing conditions where limited Fe(II) was present. Another important factor affecting in arsenic mobility in soils is that of colloid facilitated contaminant transport in porous media which was recognized since the early 1990s. Traditionally, contaminants are assumed to be either immobilized due to sorption by the solid phase or mobile in the aqueous phase. However, there is considerable evidence that nonaqueous mobile colloid could transport low solubility contaminants for considerable distances (Kretzschmar et al., 1999). Puls and Powell (1992) conducted column experiments with aquifer material to investigate the possible effect of colloidal iron oxide on facilitating AsO4 transport. They observed substantial mobilization of colloid associated arsenate when flushing the column with DI water. Ishak et al. (2002) investigated arsenic leaching from fly ash through an loamy sand column showed that arsenic levels the leachates correlated with effluent turbidity, which support the supposition that arsenic movement was generally associated with mobilized colloids. Recently, Ghosh et al. (2006) observed that arsenic release from granular ferric hydroxide residuals under mature landfill conditions was associated with suspended particulate matter and mediated by microbial reduction of ferric hydroxides. Zhang and Selim (2007a) evaluated colloid mobilization by changing the background solution from 0.01 M NaCl to DI water and its effect on arsenic(III) transport in soils. Their results revealed that colloid facilitated transport contributed little to arsenic movement under steady flow and constant ionic strength. However, enhanced transport of arsenic

A M2

M3

B M9 M2

M5

M3

M5

M9

2.5 2

5 4

0.02

0.06

2

0.06

0.06

2

30th day

1.5

0.02

0.02

As(V) [mM]

1

As(III) [mM]

2.5 2

2.4

1.2

2 1.5

0.2

0.6

0.4

1.2 0.4

45th day

1 2.5 0.4

0.8

1.6 0.4

1.5

0.

2

0.8 1.2

2

0.2

63th day 1

MSL [m]

2.5 0.4

0.6

2

0.4

1 1.4

0.25

0.25

1

14 1

1

1.4 0.1

1.5 0.1

104th day 1 0

1

2 3 Distance [m]

4

5 0

1

2 3 Distance [m]

4

5

Figure 11 Longitudinal profiles showing concentrations of (A) AsO4 and (B) AsO3 30, 45, 63, and 104 days after starting injection. Mean flow velocity is 0.3–0.4 m d1. (Reproduced with permission from Ho¨hn et al., 2006.)

78


by colloid generation was observed as a result of changing ionic strength and flow interruption (Fig. 12). Preferential flow occurring via distinct flow pathways in clay or sand lenses, cavities, fissures, and other macropores is another factor influencing the movement of arsenic through soils. Few studies have been conducted to investigate arsenic transport through soils when preferential flow conditions are dominant. Simulations conducted by Corwin et al. (1999) demonstrated that, a lysimeter column study, without accounting for preferential flow, 100% of the applied As was isolated in the top 0.75 m over a 2.5-year period. However, when preferential flow was considered and a representative bypass coefficient was used, only 0.59% of applied As moved beyond 1.5 m, which compares favorably with that measured. Hopp et al. (2006) attributed the transport of arsenic in the vadose zone of a former wood preserving site to strong preferential flow patterns which was supported by a dye experiment.

5.2. Mobility under field conditions Elevated concentrations of arsenic in soils and aquifers have caused concern over potential contamination of surface and groundwater from arsenic release due to its leaching. Downward movement of arsenic has been observed in soils contaminated with arsenic pesticides. Isensee et al. (1973) investigated arsenate residual in Metapeake silt loam 14 years after massive application of arsenical herbicides. They found that large amount of arsenic remained in the soil profile and the concentration decreased with depth, indicative of slow As leaching. Hiltbold et al. (1974) studied MSMA transport in surface and subsurface Dothan and Decatur loamy soils and reported that leaching of MSMA was not observed. This is possibly because of the relatively short period after herbicide application (6 years) and high As adsorption of the subsoils. Peryea and Creger (1994) investigated the vertical distribution of arsenic in an orchard soil contaminated by historical application of PbAsO4 insecticides. Their results demonstrated that arsenic was relatively depleted in the surface soil but enriched in subsoil and a significant amounts of As (0.07–0.63 mmol/kg soil) were leached to the depth of 120 cm. Mariner et al. (1996) studied the mobilization of arsenic from buried sodium arsenite waste pits to adjacent waterway through a high-pH groundwater plume. They found that low soil permeability significantly affect transport of As in soils. Corwin et al. (1999) conducted a mesoscale (0.6 m in diameter and 1.83 m in height) weighing lysimeter experiment to assess the mobility of As through the root zone of a Rocky Mountain Arsenal soil. Their results suggested that even though the movement of As is significantly retarded due to adsorptive processes, preferential flow and chemical perturbation might mobilize arsenic above environmental standards. Robinson et al. (2007) investigated the arsenic concentration in soils, stream sediments,

79


8 Olivier DIW leaching Begins

Arsenic concentration (mg L−1)

6

Total Dissolved 4

2

0

10

0

20

30

40

5 Windsor

Arsenic concentration (mg L−1)

4

3 DIW leaching Begins 2

1 Total 0

Dissolved

0

10

20 30 Pore volumes (V/Vo)

40

50

Figure 12 Breakthrough curves of total and dissolved arsenic for the Olivier and Windsor soil columns. Arrows indicate pore volumes when flow interruptions or leaching with deionized (DI) water occurred. Initial AsO3 concentration Co ¼ 10 mg l1. Pore water velocities are 0.83 and 1.03 cm h1 for Olivier and Windsor columns, respectively. (Reprinted with permission from Zhang and Selim, 2007a.)

80


and groundwater near old orchards with historical application of arsenic pesticides in Virginia and West Virginia. Their results indicated that arsenic in pesticide contaminated soils had limited mobility to the groundwater. Soils around historical cattle-dipping vats commonly contain high concentrations of arsenic. There were fears that arsenic in such contaminated soils may be leached to groundwater and cause contamination of the drinking water resources. Mclaren et al. (1998) investigated soils surrounding cattle dips and showed that considerable movement of arsenic down through the soils had occurred. Arsenic in the subsurface soil (20–40 cm) near cattle-dip sites in Australia can be as high as 2282 mg As kg1. However, the migration of arsenic has been found to be slow, controlled by chemical and physical properties of soils. Kimber et al. (2002) analyzed the shallow groundwater around 28 cattle-dipping vats in Australia with piezometers. The highest arsenic concentration (5.69 mg l1) in the groundwater was found adjacent to a contaminated site with soils of sandy texture. The concentration declined to approximately background levels with 20 m distance from the contaminated site. There are many industrial sites contaminated by CCA which may pose a serious threat to the groundwater. Studies demonstrated that the arsenic contained in CCA leached from the wood surface to adjacent soil (Chirenje et al., 2003). Moreover, it is also possible that arsenic may be partially released into aquatic environment causing human health concern. Andersen et al. (1996) sampled soil material and soil solution at a wood impregnation site and provided evidence of both a strong accumulation and a high mobility of As and Cr. Allinson et al. (2000) investigated the release of CCA constituents from undisturbed soil monolith lysimeters of a sandy loam soil. They observed that up to 13% of the applied arsenic was detected in the leachate at 15 cm depth and breakthrough was observed 25 days after CCA application. Hingston et al. (2001) conducted literature review on the leaching studies of CCA-treated wood and they concluded there is insufficient data to quantitatively predict the leaching rate of elements under various environment conditions (pH, salinity, and temperature). More recently, Khan et al. (2006a,b) investigated the leaching of arsenic from CCA-treated wood during service as well as disposal with lysimeter tests. They found cumulatively around 2000 mg arsenic was leached out from landfills in a 1-year period, with inorganic AsO4 and AsO3 as the major species. Hopp et al. (2006) monitored the spatial and temporal variability of As and Cr in the soil pore water at a former wood-preserving site. They found that the high spatial variability (up to three orders of magnitude) of arsenic levels in soil pore water. They suggested that spatial variability is due to heterogeneous and uneven leaching patterns caused by water repellency of the surface soil and preferential flow. In contrast As concentrations in the soil water, only low As concentrations ( 9, while AsO3 forms dominate with pe þ pH < 7 (Sadiq, 1997; Sadiq et al., 1982). However, it is well known that a single measure of redox potential ( pe) does not provide sufficient information for simultaneously determining the ratio of oxidized species to reduced species for all redox couples. Equilibrium arsenic concentration in mixed solid solution system is determined by the solubility of minerals, which is commonly described

83


24 20 1.0

16

H3AsO40 H2AsO4−

pe

8

0.5 HAsO42−

4 H3AsO30

0.0

AsO43−

0

Eh, in volts

12

−4 −8

−0.5

H2AsO3−

−12 −16

0

2

4

6

8

10

12

14

pH

Figure 13 pe-pH diagram for predominant aqueous species of arsenic at equilibrium and 298.15 K and 1 atmosphere pressure. (Reproduced with permission from Nordstrom and Archer, 2003.)

with solubility product constant (Ksp, mol l1). Consider the dissolution of scorodite in neutral solution which can be expressed in the form of:

FeAsO4 2H2 OðsÞ þ Hþ ðaqÞ , H2 AsO 4 ðaqÞ þ FeðOHÞ2þ ðaqÞ þ H2 O

ð16Þ

At equilibrium, this reaction can be described by

2þ Fe ð OH Þ H2 AsO 4 Ksp ¼ ðFeAsO4 2H2 OÞs ðHþ Þ

ð17Þ

To determine if the solution phase is in equilibrium with the scorodite mineral, the ion activity product (IAP) is compared with the solubility product (Ksp) using the saturation index (SI )

SI ¼ log

IAP Ksp

ð18Þ

Solution is at thermodynamic equilibrium with respect to the mineral when SI ¼ 0. When SI > 0, solution is supersaturated and scorodite mineral

84


should precipitate. On the contrary, if SI < 0, solution is undersaturated and scorodite mineral should dissolve. The term of scorodite (FeAsO42H2O) is usually not presented in Eq. (16) because the activity of pure solid phases in standard state is defined as being equal to one. However, most naturally occurring arsenic minerals are deviated from pure solid state due largely to isomorphous ion substitution in the crystalline lattice. This results in the mixture of two or more solid mineral phases, which is called as solid solution. Assuming ideal ion substitution, the free energy of a binary solid solution may be represented by (Davis et al., 1996)

DGi;j ¼ xDGi þ ð1 xÞDGj þ DGmix

ð19Þ

where DGi;j is the standard free energy of the solid solution, x is the mole fraction of mineral component i, and DGi and DGj are the free energies of mineral components i and j. Assuming random mixing, the free energy of mixing (DGmix ) is given by

DGmix ¼ nRT ½x ln x þ ð1 xÞlnð1 xÞ

ð20Þ

Using Eqs. (18) and (19), Davis et al. (1996) calculated the free energy of anhydrous arsenate–phosphate solid solutions of Cu, Fe, Pb, and Zn, and the resulting DGi;j was used to calculate the limiting solubilities. Thermodynamic models have been employed for the simulation of equilibrium distribution of arsenic species regarding arsenic sulfide minerals. For example, Craw et al. (2003) calculated the Eh-pH diagram for As-Fe-S-O system, which showed that FeAsS was relatively stable in the surficial environment. The prediction capacity and quality of thermodynamic models are highly dependent on the data input on stability constants or solubility constants. As pointed out by Nordstrom and Archer (2003), many constants in the published thermodynamic databases are derived from various sources and may suffer from internal inconsistencies.

6.2. Empirical equilibrium models Equilibrium batch experiment, carried out by equilibrating arsenic solution with mineral or soil solid for a certain amount of time (usually 24 h) at constant temperature, is employed to quantify the adsorption capacity and affinity of arsenic in soils. The relationship between the equilibrium concentration in the aquatic solution and the amount adsorbed on the solid surface are commonly described with adsorption isotherms. The L-type and H-type isotherms are usually employed to describe the arsenic adsorption on mineral and soil surfaces. Both types are highly nonlinear (concentration

85


dependent) and indicative of high affinity chemical adsorption, which are commonly described with either an equilibrium model of the Freundlichor Langmuir-type. The Freundlich equation is an empirical adsorption model that can be expressed as

S ¼ KF C N

ð21Þ

where S represents the (total) amount of adsorption (mg kg1), KF is the distribution or partition coefficient (mg kg1 (mg l1)N ), and N is the dimensionless reaction order commonly less than 1. The Langmuir equation has the advantage of providing a sorption maximum Smax (mg kg1) that can be correlated to soil sorption properties. It has the form:

S ¼ Smax

KL C 1 þ KL C

ð22Þ

where KL (L mg1) is a Langmuir coefficient related to the binding strength. Langmuir and Freundlich models can be incorporated into solute transport models to predict the transport of arsenic in soils. The nonlinear adsorption behavior of arsenic was numerically described with Langmuir equation (Darland and Inskeep, 1997a; Livesey and Huang, 1981; Manning and Goldberg, 1997; Pierce and Moore, 1980) and Freundlich equation (Elkhatib et al., 1984a; Puls and Powell, 1992; Smith et al., 1999; Williams et al., 2003). The value of model parameters (KF, N, KL, and Smax) varies considerably among various studies, and depends on several factors such as the soil mineralogy, reaction time, solution pH, initial arsenic concentration, and oxidation state. For arsenate adsorption on a soil from Rocky Mountain area (Corwin et al., 1999), the Langmuir coefficient (KL ) were 1.74, 2.92, and 6.13 L kg1 for temperature of 8, 25, and 40 C, respectively, whereas no significant difference were found between adsorption maxima (66 mg kg1) for three temperatures. The presence of ligands can compete with arsenic for adsorption sites on mineral surfaces. Sheindorf et al. (1981) introduced a modification of Freundlich Eq. (1) in order to account for adsorption when more than one competing ions is present in the solution. Specifically, the SheindorfRebhun-Sheintuch (SRS) model was developed to describe competitive equilibrium sorption for multicomponent systems. Here it was assumed that for a single component, sorption isotherms follow the Freundlich equation. The derivation of SRS equation was also based on the assumption of an exponential distribution of adsorption energies for each component. A general form of the SRS equation may be written as

Si ¼ KFi Ci

l X j¼1

!Ni 1

aij Cj

ð23Þ

86


where l is the total number of components, i and j represent components i and j aij is a dimensionless competition coefficient that describes the inhibition by component j to the adsorption of component i where aij ¼ 1 when i ¼ j. In the absence of competitive sorption, that is, aij ¼ 0 for i 6¼ j, Eq. (22) yields the Freundlich Eq. (20) for a single component. Equation (22) was successfully employed by Roy et al. (1986a,b) to describe the competitive adsorption isotherms of AsO4 and PO4 in several soils. Barrow et al. (2005) have developed a scheme to numerically solve the nonlinear set of equations through iterative improvements and estimate the competitive coefficient with nonlinear optimization. Zhang and Selim (2007b) extended the equation to kinetic form and incorporated it in the solute transport model for the simulation of competitive transport of AsO4 and PO4 in soils.

6.3. Surface complexation models Empirical laws, such as the Langmuir or Freundlich equations, do not provide specific information about the retention mechanisms. As a matter of fact, a number of reactions including ion exchange, outer-sphere surface complexation, inner-sphere surface complexation, and/or surface precipitation can be embraced in simple equilibrium models. Unlike empirical models, surface complexation models (SCMs) are chemical models that give a general molecular description of adsorption phenomena using an equilibrium approach. The models treat arsenic adsorption as inner-sphere surface complexation reactions through ligand exchange mechanism. Because surface sites are explicitly defined as the number of functional groups SOH on mineral surfaces, those model predict a Langmuir-type adsorption isotherm. SCMs are commonly employed to describe the adsorption envelopes, that is, adsorption of arsenic anions as a function of solution pH. Various types of SCM have been proposed based on different assumption of the distribution of surface electrostatic potential. The constant capacitance model (CCM) assumes linear relationship between surface charge (s, mol L1) and surface potential (c, volts), which can be described with (Goldberg, 1992):

s¼

CAs MV c F

ð24Þ

where As is the specific surface area of absorbent (m2 g1), MV is the suspension density of absorbent (g L1), capacitance density C ¼ 1.06 F m2, and Faraday constant F ¼ 9.65 10–4 coulombs mol1. Table 2 gives the reactions used to calculate monodentate and bidentate surface complexation of arsenate on the goethite surface:

Table 2 Surface complexation reactions and constant capacitance model (CCM) intrinsic surface complexation constants for arsenate adsorption on goethite Reaction

Surface hydrolysis reactions ð1Þ XOH þ Hþ ¼ XOHþa 2

ð2Þ XOH ¼ HO þ Hþ

Equilibrium expressions and constants

½XOHþ Fc 2 ¼ 107:31b exp RT ½XOH½Hþ ½XO ½Hþ Fc Ka2 ðintÞ ¼ exp ¼ 108:81b ½XOH RT

Ka1 ðintÞ ¼

Formation of inner-sphere monodentate oxyanion/goethite surface complexes ð3Þ XOH þ H3 AsO4 ¼ XH2 AsO4 þ H2 O þ ð4Þ XOH þ H3 AsO4 ¼ XHAsO 4 þ H2 O þ H þ ð5Þ XOH þ H3 AsO4 ¼ XHAsO2 4 þ H2 O þ 2H

½XH2 AsO4 ¼ 1010 ½XOH½H3 AsO4 þ XHAsO Fc 4 ½H 2 exp KAs ðintÞ ¼ ¼ 105:1 ½XOH½H3 AsO4 RT þ2 2 XAsO ½H Fc 4 3 exp KAs ðintÞ ¼ ¼ 100:55 RT ½XOH½H3 AsO4 1 KAs ðintÞ ¼

Formation of inner-sphere bidentate oxyanion/goethite surface complexes ð8Þ 2XOH þ H3 AsO4 ¼ X2 HAsO4 þ 2H2 O þ ð9Þ 2XOH þ H3 AsO4 ¼ X2 AsO 4 þ 2H2 O þ H

4 KAs ðintÞ ¼

½X2 HAsO4 ¼ 1017 ½XOH2 ½H3 AsO4

5 KAs ðintÞ

þ X2 AsO 4 ½H

Fc exp ¼ 2 RT ½XOH ½H3 AsO4

¼ 1011:4

88


The intrinsic equilibrium reaction constants

½Kþ ðintÞ; K ðintÞ; K1sAsO4 ðintÞ; K2sAsO4 ðintÞ; K3sAsO4 ðintÞ; K1sAsO3 ðintÞ; and K2sAsO3 ðintÞ are commonly obtained through fitting the batch reaction data to the model. The mass balance for the surface functional group is: ½SOHT ¼ ½SOH þ bSOHþ 2 c þ bSO c þ ½SH2 AsO4 2 þ bSHAsO 4 c þ bSAsO4 c þ ½SH2 AsO3 þ bSHAsO3 c

ð25Þ and the charge balance expression is: 2 s ¼ bSOHþ 2 c bSO c bSHAsO4 c 2bSAsO4 c bSHAsO3 c

ð26Þ Constant capacitance model was fitted to the envelope of arsenic adsorption on several pure minerals to obtain the surface complexation constant which can be used to predict the adsorption behavior of arsenic. The model has been successfully employed for the description of adsorption envelope of arsenate adsorption on Al and Fe oxides (Goldberg, 1986), arsenate on calcareous soils (Goldberg and Glaubig, 1988), arsenite on amorphous Al and Fe oxides (Goldberg and Johnston, 2001). Furthermore, the reactions and corresponding reaction constants for competing ligands (e.g., arsenite, phosphate) can be added to the reaction system for the simulation of multicomponent adsorption. The CCM model successfully described the competition of arsenate with phosphate and molybdate on oxides minerals (Manning and Goldberg, 1996a), arsenate with phosphate on clay minerals (Manning and Goldberg, 1996b). In addition, Goldberg (2002) simulated the competition between arsenate and arsenite on Fe and Al oxides as well as clay minerals. However, CCM model predictions can only qualitatively describe the shape of adsorption curves (Goldberg, 2002; Goldberg and Glaubig, 1988; Manning and Goldberg, 1996, 1997; Williams et al., 2003). The diffuse double layer (DDL) model developed by Dzombak and Morel (1990) was frequently used to describe the arsenic sorption on oxides (Dixit and Hering, 2003). Like the CCM model, this model assumes that all surface complexes are inner-sphere surface complexes. However, the DDL model considers a diffuse layer where the relationship between surface

89


charge and surface potential can be described with the Gouy-Chapman equation:

s ¼ 0:1174I 1=2 sinh

zFc 2RT

ð27Þ

where I is ionic strength, z is the valence of the symmetrical electrolyte and commonly takes as unity. Similar to CCM model, a set of equilibrium constants defined for the surface reactions are required as the model parameter. The intrinsic surface complexation constants for HFO, goethite, and magnetite provided by Dixit and Hering (2003) are presented in Table 3. SCMs have been incorporated in geochemical modeling packages such as MINTEQA2 (Allison et al., 1991) and PHREEQC2 (Parkburst and Appelo, 1999). A database compiled by Dzombak and Morel (1990) for the surface reactions of cations and anions on HFO is included in those models and frequently employed to predict the retention and transport of arsenic in soil and water environment. For example, adsorption of arsenic anions during its transport in Benthic sediment has been simulated with the DDL model by Smith and Jaffe (1998) using MINTEQA2. Using DDL model in PHREEQC2, Appelo et al. (2002) simulated the competitive effect of ferrous iron and carbonate on arsenic sorption on ferrihydrite. Table 3 Intrinsic surface complexation constants for hydrous ferric oxide (HFO), goethite, and magnetite (Bixit and Herring, 2003) Intrinsic surface complexation constants

HFO

Goethite

Magnetite

FeOH þ Hþ ¼ FeOHþ 2

7.29

7.47

4.60

8.93

9.51

8.20

29.88

31.00

24.43

26.81

18.10

20.22

¼

þ H2 O Arsenate adsorption constants þ

FeOH þ AsO3 3 þ 3H

38.76

39.93

38.41

¼ FeH2 AsO3 þ H2 O þ

FeOH þ AsO3 3 þ 2H

31.87

32.40

33.02

FeOH ¼ FeO þ Hþ Arsenate adsorption constants þ

FeOH þ AsO3 4 þ 3H ¼ FeH2 AsO4 þ H2 O þ

FeOH þ AsO3 4 þ 2H ¼ FeHAsO 4 þ H2 O þ

FeOH þ AsO3 4 þH FeAsO2 4

¼

FeHAsO 3

þ H2 O

90


6.4. Kinetic models 6.4.1. Kinetic Retention In general, the rate of sorption is a dependent on the following processes: (1) the transport of arsenic anions from bulk solution to the reaction sites on mineral surfaces; and (2) the chemical reaction at the surfaces. Specifically, transport processes include: (i) diffusion in the aqueous solution; (ii) film diffusion at the solid/liquid interface; (iii) intraparticle diffusion in micropores and along pore wall surfaces; and (iv) interparticle diffusion inside solid particles. The chemical processes may include reactions such as ion exchange, formation of inner-sphere surface complexes, precipitation into distinct solid phases, or surface precipitation on minerals (Sparks, 1998). Because of the complexity of the sorption process, it is impractical to derive the mechanism-based reaction rates. Instead, a wide variety of empirical kinetic rate expressions was developed in the last three decades. Those kinetic equations have been widely applied to describe the results from sorption and desorption kinetic experiments. The pseudo first-order equation assumes a fixed amount of adsorption at equilibrium (Seq). It has the form of:

dS ¼ k Seq S dt

ð28Þ

where k is the sorption rate (h1). For the initial condition S ¼ 0 at t ¼ 0, the integrated form of the pseudo first-order equation for adsorption is:

S ¼ 1 ekt Seq

ð29Þ

Similarly, the pseudo second-order equation has the form of:

2 dS ¼ k Seq S dt

ð30Þ

Again for the initial condition S ¼ 0 at t ¼ 0, the integrated form of pseudo second-order equation for adsorption is:

1 1 þ kt ¼ Seq S Seq

ð31Þ

Another kinetic adsorption equation is that of the fraction power equation where,

dS ¼ kt n1 dt

ð32Þ

91


and n is a parameter between 0 and 1. Integration (when S ¼ 0 at t ¼ 0 the familiar adsorption equation,

S ¼ kt n

ð33Þ

which represents nonlinear kinetic adsorption. This equation can be extended to describe desorption as follows. When S ¼ Sc at t ¼ tc, desorption can be expressed as

S ¼ Sc k t n tcn

ð34Þ

Retention of solute by soil is commonly described with the empirical Elovich equation of the form:

dS ¼ aebS dt

ð35Þ

where a is the initial adsorption rate and b is a constant. Assuming abt>>1 and initial condition S ¼ 0 at t ¼ 0, the above rate equations yields this model expressing a linear relationship between S and ln t:

1 1 S ¼ lnðabÞ þ lnðtÞ b b

ð36Þ

This model can also be employed to describe desorption kinetics of solute from the surfaces of adsorbent by applying the condition: S ¼ Sc at t ¼ tc, which yields

1 1

t S ¼ lnðabGÞ þ ln 1 þ b b G

ð37Þ

where G ¼ ebSc =ab tc . Even though adherence to the Elovich model has been proposed as evidence of diffusion-controlled retention mechanism, the use of the model alone to describe kinetic data should not be used for determining retention mechanisms (Fuller et al., 1993). The parabolic diffusion model is based on the assumption of diffusioncontrolled rate-limited process in media with homogeneous particle size. The parabolic diffusion equation was derived from the Fick’s second law of Diffusion in radial coordinate system.

3=2 S 4 Dt 1=2 Dt 1 Dt ¼ 1=2 2 2 1=2 2 Seq p r r r 3p

ð38Þ

For relatively short durations in kinetic studies, the third and subsequent terms may be ignored, thus

1 S 4 D 1=2 1 D ¼ 1=2 2 2 1=2 t Seq p r r t

ð39Þ

92


The above kinetic models have been employed in describing the timedependent sorption of arsenic on minerals and soils. The Elovich and fraction power equations were successfully used by Elkhatib et al. (1984a,b) to describe the sorption and desorption of arsenite by several soils. Prediction results showed that the fraction power equation had higher coefficients of determination (r2 ) than the Elovich equation. Furthermore, the sorption and desorption rate constants k for the fraction power equation were correlated with the Eh and soil content of Fe oxides. Similarly, CarbonellBarrachina et al. (1996) compared Elovich and power equation for arsenite sorption–desorption kinetics and their results showed the Elovich was superior to the power equation. Pigna et al. (2006) performed kinetic studies on the sorption of AsO4 on Fe and Al oxides and desorption by PO4 and were best described by the Elovich kinetic model. Raven et al. (1998) described arsenite and arsenate sorption kinetic results on ferrihydrite using first order, second order, power function, Elovich, and parabolic diffusion equations. The parabolic diffusion equation provided best fit to kinetic data and they suggested that the retention of arsenic on ferrihydrite was diffusion controlled. The time-dependent sorption data demonstrated that a fast reaction is followed by a slow reaction, suggesting that different sorption mechanisms involved in the process. Fuller et al. (1993) developed a pore-space diffusion model with Freundlich equilibrium sorption to describe the timedependent sorption of arsenate on ferrihydrite. Their model has the form of

2 Sa @ C 2@C N 1 @C ¼D e þ KF C þ @t @r 2 r@r N

ð40Þ

where e is the initial porosity, Sa is the reactive surface area, KF and N are Freundlich parameters (Eq. 20). The numerical solution of this model successfully described AsO4 adsorption kinetics on ferrihydrite. The reversible nonequilibrium sorption models have been proposed by many researches to describe the sorption–desorption kinetics involving multiple chemical and physical reaction processes. The reversible first-order kinetic sorption equation has the form of:

@S y ¼ kf C kb S @t r

ð41Þ

where kf (h1) and kb (h1) are the forward and backward reaction rates, respectively. Under equilibrium conditions, that is, @S=@t ¼ 0, Eq. (41) yields linear adsorption equation of S ¼ Kd C. The reversible nth-order (Freundlich-type) kinetic sorption equation is in the form of

@S y ¼ kf C b kb S @t r

ð42Þ


93

where kf and kb are the forward and backward reaction rate coefficients (h1), respectively, b is a nonlinear parameter usually less than 1, t is reaction time (h), r is the soil bulk density (g cm3), and y is the volumetric water content (cm3 cm3). Under equilibrium conditions, that is, @S=@t ¼ 0; Eq. (41) yields Freundlich Eq. (20) assuming KF ¼ kf =kb y=r and N ¼ b. The Langmuir kinetic equation is another reversible type where a concentration maxima (Smax) is assumed,

@S y ¼ kf ðSmax SÞC kb S @t r

ð43Þ

Under equilibrium conditions, @S=@t ¼ 0, Eq. (43) yields the Langmuir equilibrium Eq. (21) where KL ¼ kf =kb y=r. The Freundlich- and Langmuir-type reversible kinetic sorption equations are commonly solved using numerical algorithms such as the fourthorder Runge-Kutta methods. The numerical solutions give us the flexibility of simulating a wide range of initial conditions. The nonequilibrium kinetic equations are incorporated into the dispersion–advection transport model for the simulating dynamics of solute concentration across space and time. For example, the batch kinetic sorption data of Darland and Inskeep (1997a) were described using first-order and nth order reversible adsorption. The first-order forward and backward rate constants (kf and kb) were 2.65 10–1 and 8.75 10–3 h1 for arsenate sorption on acid-washed sand. Because of the complex sorption processes, the simple chemical kinetic models may not be appropriate for describing sorption kinetics in heterogeneous soils where a range of particle sizes and multiple types of reaction sites exists. Recent approaches based on soil heterogeneity and kinetics of adsorption–desorption have been proposed for the purpose of describing the time-dependent sorption of heavy metals in the soil environment. The multireaction model (MRM) kinetic approach presented here considers several interactions of heavy metals with soil matrix surfaces (Amacher et al., 1988; Selim, 1992). Specifically, the model assumes that a fraction of the total sorption sites is kinetic in nature whereas the remaining fractions interact rapidly or instantaneously with solute in the soil solution. The model accounts for reversible as well as irreversible sorption of the concurrent and consecutive type (Fig. 14). The model can be presented in the following formulations:

Se ¼ Ke C n

ð44Þ

@Sk y ¼ k1 C m ðk2 þ k3 ÞSk r @t

ð45Þ

94


Se

Multi-reaction model

Ke k1 C

k2

S1

k3

S2

ks Ss

Figure 14 A schematic diagram of the multireaction model (MRM) with equilibrium, kinetic, and irreversible adsorption sites. Here C is concentration in solution, Se is the amount sorbed on the equilibrium sites, S1 is the amount sorbed on the kinetic sites, S2 is the amount retained on consecutive irreversible sites, and Ss is amount retained on concurrent irreversible sites, where Ke, k1, k2, k3, and ks are the respective rates of reactions. (Reprinted with permission from Zhang and Selim, 2006.)

@Si ¼ k3 Si @t

ð46Þ

@Ss y ¼ ks C r @t

ð47Þ

where Se is the amount retained on equilibrium sites (mgl kg1), Sk is the amount retained on kinetic type sites (mg kg1), Si is the amount retained irreversibly by consecutive reaction (mg kg1), Ss is the amount retained irreversibly by concurrent type of reaction (mg kg1), n and m are dimensionless reaction order commonly less than 1, Ke is a dimensionless equilibrium constant, k1 and k2 (h1) are the forward and backward reaction rates associated with kinetic sites, respectively, k3 (h1) is the irreversible rate coefficient associated with the kinetic sites, and ks (h1) is the irreversible rate coefficient associated with solution. For the case n ¼ m ¼ 1, the reaction equations become linear. In the above equations, we assumed n ¼ m since there is no known method for estimating n and/or m independently. According to model formulation of Fig. 14, the total amount of solute retention (S ) by the soil is:

S ¼ Se þ S k þ S i þ S s

ð48Þ

Moreover, the MRM with nonlinear equilibrium and kinetic sorption successfully described the kinetic data of AsO4 adsorption on Olivier loam and Windsor sand. The model was also capable of predicting AsO4 desorption kinetics for both soils (Fig. 15). However, for Sharkey clay, which exhibited strongest affinity for arsenic, an additional irreversible reaction

95


600 Olivier Adsorption


500

Desorption

400 300 200 100 0 750


Windsor Adsorption

600

Desorption

450

300

150

0 0

200

600 400 Reaction time (h)

800

1000

Figure 15 Arsenate sorbed versus time during adsorption–desorption for Olivier and Windsor soils. Symbols are for different initial concentrations (Co) of 5, 10, 20, 40, 80, and 100 mg l1 (from bottom to top). Solid curves are two-phase multireaction model (MRM) simulations using parameters obtained from nonlinear optimization with adsorption data. (Reprinted with permission from Zhang and Selim, 2005.)

phase was required to predict AsO4 desorption or release with time (Zhang and Selim, 2005). 6.4.2. Kinetic dissolution Oxidative dissolution of arsenic containing sulfide minerals is a multistep process involving diffusive transport of oxidant to mineral surfaces, adsorption of oxidant, interlattice transfer of oxidant, chemical reaction inside crystalline structure, detachment of reaction product from mineral surface, and diffusive transport of reaction product to the bulk solution. The dissolution kinetics

96


of arsenic minerals in the environment is determined by one or more ratelimiting processes. Diffusion-controlled dissolution kinetic can be described with the parabolic diffusion rate law in the form of:

r¼

dC ¼ kp t1=2 dt

ð49Þ

where r is the dissolution rate (mol s1), kp is the reaction rate constant (mol s1/2). A zero-order rate law has been used for the simulation of dissolution kinetics under steady state surface condition, that is, concentration of solutes adjacent to the surface is the same as in the bulk solution:

r¼

dC ¼ ks A dt

ð50Þ

where A is the surface area of the reactive mineral phase. Using mixed flow through experiments and for a range of pH and DO values, Lengke and Tempel (2005) calculated steady state oxidation rate of realgar following first-order reaction: r¼

dC q ¼ C dt A

ð51Þ

where q is the flow rate through the system (L s1), A is the total surface area of solid. The calculated dissolution rate is expressed as a function of DO and pH in the form of:

rAs ¼ 109:63ð0:41Þ ½DO0:51ð0:08Þ ½Hþ rAs ¼ 1011:77ð0:36Þ ½DO0:36ð0:09Þ ½Hþ rAs ¼ 1016:77ð0:68Þ ½DO0:42ð0:07Þ ½Hþ

0:28ð0:05Þ

0:47ð0:05Þ

1:26ð0:09Þ

realgar

ð52Þ

orpiment

ð53Þ

amorphous As2 S3 ð54Þ

In addition, the effect of temperature on the rate constant was described using the Arrhenius equation with activation energy Ea ¼ 64.29.8, 59.1 0.44, and 16.8 5.0 kJ/mol for realgar, orpiment, and amorphous As2S3, respectively. Walker et al. (2006) found that dissolution rate r ¼ 10–10.14(0.03) is essentially independent of DO for FeAsS. 6.4.3. Kinetic reduction–oxidation Heterogeneous oxidation of arsenite on the mineral surface is a slow process involving multiple diffusion and reaction steps. Because of the complexity of the reaction, it is implausible to develop mechanistic rate laws describing

97


element reactions at the molecular level. Instead simple apparent rate laws are commonly employed for the simulation of arsenite depletion in soils. For example, Oscarson et al. (1983a) used a simple first-order rate equation for describing heterogeneous oxidation of arsenite on the surface of manganese oxides, which can be described with dCAsðIII Þ ¼ kox CAsðIII Þ dt

ð55Þ

Integrating this equation gives:

CAsðIII Þ ¼ CAsðIII Þ ekox t

ð56Þ

where kox is the kinetic oxidation rate (h1), CAsðIII Þ is initial AsO3 concentration (mmol L1). Amirbahman et al. (2006) simulated the retention and transformation of arsenite with a set of equilibrium and kinetic reactions. Two types of adsorption sites on the soil surface were characterized. The first represents sites of purely adsorptive type that are not involved in oxidation. The second types of sites are of the oxidative type where AsO3 oxidization takes place. Adsorption on purely adsorptive site was described with fully reversible kinetic equation, whereas AsO3 adsorption on oxidative sites was simulated with equilibrium adsorption followed by first-order irreversible oxidation. Furthermore, concurrent fast and slow reactions were used in combination to simulate reaction kinetics on adsorptive and oxidative sites. The apparent rate constants and density of available oxidative sites were obtained by fitting this model to the experiment data. The transformation between AsO4 and AsO3 as a result of microbial activity is also a kinetic process with great complexity. Similarly, apparent rate laws were adopted by environmental scientists for the simulation of microbial-mediated arsenic transformation ( Jones et al., 2000). Manning and Suarez (2000) treated heterogeneous oxidation and adsorption of arsenite in soils with consecutive kinetic reaction mechanisms, where kox

kad

CAsðIII Þ ! CAsðV Þ ! SAs and give the overall reaction expression CAsðIII Þ SAs ¼ kad 1 ekox t kox 1 ekad t kad kox

ð57Þ

6.5. Transport models The transport of dissolved chemicals through porous media is generally described using the advection–dispersion equation (ADE), sometimes called convection–dispersion equation (CDE). Assuming local equilibrium

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condition (e.g., residence time much larger than time required to complete reaction), the equation can be expressed as:

@C @ @C @C ¼ D v R @t @x @x @x

ð58Þ

where C is solute concentration (M L3), x is distance (L), t is time (T), r is soil bulk density (M L3), y is volumetric water content (L3 L3), D is dispersion coefficient (L2 T1), and v is pore water velocity (L T1) where v ¼ q/y, and q is Darcy’s water velocity (L T1). In addition, the solute retardation factor R is determined by the equilibrium distribution of solute between solid and aqueous phases, where

r R ¼ 1 þ Kd y r R ¼ 1 þ Kf NC N 1 y R ¼1þ

r KL Smax y ð1 þ KL C Þ2

for linear sorption; for Freundlich sorption; and for Langmuir sorption:

The dispersion of solute in soils is a combination of hydrodynamic dispersion and molecular diffusion processes. As a result, the dispersion coefficient D is a function of water content, flow velocity, solute property, and other hydraulic parameters, which can be expressed as,

D ¼ dv þ

D0 t

ð59Þ

where d (L) is the longitudinal dispersivity, D0 is the diffusion coefficient for a particular solute diffusing in bulk water, and t is the tortuosity factor for solute diffusing in pore network inside soil (Brusseau, 1993). A common strategy for estimating the value of dispersion coefficient D is to conduct transport experiment using conservative tracer such as bromide or radioactive tracers such as tritium and chloride-36. Chemical nonequilibrium behavior in soils is likely caused by kinetic reactions occurring at the solid–liquid interfaces, which is often described using one-dimensional steady state transport equation of reactive solute in the following form:

r @S @C @ @C @C þ ¼ D v y @t @t @x @x @x

ð60Þ


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where the total amount of retention (S ) is accounted for using the MRM kinetic approach described in Fig. 14. Zhang and Selim (2006) evaluated eight formulations of MRM model for the simulation of arsenic transport in soils with different properties. As illustrated in Fig. 16, they found the use of independently derived kinetic retention model parameters underestimated the extent of retention and overpredicted arsenic mobility. However, when utilized in an inverse mode, the MRM model provided good predictions of AsO4 BTCs. Nonlinear reversible along with consecutive or concurrent irreversible reactions were the dominant mechanisms in the MRM model.

6.6. Field application Geochemical models (e.g., MINTEQ, PHREEQC) have been adopted to simulate the reactions among multiple chemical species during their transport in soils and aquifers (e.g., Manning and Goldberg, 1996; Smith and Jaffe, 1998). The common strategy is by coupling transport models with equilibrium thermodynamic models with the main aim to demonstrate the effect various properties such as the adsorbents, pH, redox potential, and competing ions on the chemical species in the soil. For example, Smith and Jaffe (1998) formulated a geochemical model which coupled the transport process with various kinetics reactions and simulated the arsenic transport in Benthic sediment. However, because of the uncertainty associated with numerous geochemical parameters, such models can only be viewed as heuristic tools for exploring possible trends in the fate of contaminants as a result of environmental changes. Sracek et al. (2004) summarized several examples of forward or inverse geochemical modeling of the fate of arsenic in the environments. The application of geochemical models requires detailed description of chemical and mineral composition of solution and porous media, soil matrix properties, as well as numerous reaction constants. However, such information are either unavailable or unreliable under most circumstances (Nitzsche et al., 2000). Heterogeneity of the natural porous media also impedes the application of chemical reaction based models. More importantly, sorption processes are often assumed instantaneous (i.e., equilibrium conditions are assumed) in most geochemical models. An effective way of reducing the uncertainty of model simulation is to estimate input parameters from observed data through inverse modeling. Nonlinear least squares regression method is a commonly employed inverse modeling approach. Other optimization algorithms such as simulated annealing and genetic algorithms have been tested, with various degrees of success, to solve inverse modeling problem in groundwater contamination. To overcome the uncertainty in the input parameters to groundwater flow model, Morse et al. (2003) employed Monte Carlo analysis and the generalized likelihood uncertainty estimator (GLUE) methodology for the

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1.0 Olivier column 102 0.8

Measured M1 = k1,k2

C/CO

0.6

M2 = Ke, kirr M3 = Ke,k1,k2

0.4

M4 = k1,k2,k3 M5 = k1,k2,kirr 0.2

0.0 0

20

40 60 Pore volumes (V/VO)

80

100

1.0 Olivier column 102 0.8

C/CO

0.6 Measured M6 = Ke,k1,k2,k3 M7 = Ke,k1,k2,kirr

0.4

M8 = Ke,k1,k2,k3,kirr 0.2

0.0 0

20

40

60

80

100

Pore volumes (V/VO)

Figure 16 Comparison of multireaction model (MRM) model formulations M1–M8 for predicting AsO4 breakthrough curves for Olivier soil column 102. Model parameters were obtained using nonlinear inverse modeling. (Reprinted with permission from Zhang and Selim, 2006.)


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stochastic analysis of capture zone for arsenic contaminated wells in the Zimapan Valley of Mexico. Incorporating extra hydrologic and geologic information reduces the uncertainty in parameter estimation. The heterogeneous nature of the soils and geologic porous media often results in highly nonuniform retention and transport processes in contaminated sites, which restricts the application of deterministic approaches which are often based on the assumption of relatively homogeneous material. To overcome these difficulties, several stochastic advection–dispersion approaches have been developed from theoretical study and field experimentation in the past three decades. Stochastic approaches are theoretically attractive because spatial variability of the soil matrix is explicitly defined using parameters with certain statistical distributions. However, the mathematical complexity hinders the practical application of such approaches in the management of contaminant movement in aquifers and field soils. On the contrary, geostatistical models provide a probabilistic framework for predicting the spatiotemporal distribution of contaminants based on statistical analysis of existing observations without reliance on the underlying mechanisms. Goovaerts et al. (2005) employed multi-Gaussian and indicator Kriging for modeling probabilistically the spatial distribution of arsenic concentrations in groundwater of Southeast Michigan. They found that the use of secondary geological information significantly increased the proportion of variance that can be explained. They attribute the inadequacy of the model for prediction purpose with the quality of the sampling data.

7. Remediation of Contaminated Soils Despite the widespread soil contamination of arsenic, there is no nationwide accepted cleanup standard for arsenic due to soil heterogeneity and policy interpretation (Davis et al., 2001). A survey conducted by the Association for the Environmental Health of Soils (AEHS, 1999) revealed that there were large variations among soil arsenic regulations set by different states across the United States. They reported the notification levels of 2–61 mg kg1, soil screening levels of 0.1–250 mg kg1 for residential area and 2.4–200 mg kg1 for industrial sites, cleanup levels of 0.1–250 mg kg1 for residential area, and 0.85–1000 mg kg1 for industrial sites for the 34 states participated in the survey. In addition, the rationale used for setting the regulation levels was widely diversified, including related regulation limits, background levels, human health risk, and migration to the groundwater. From their survey of Records of Decisions (RODs), Davis et al. (2001) divided the studied sites into four risk categories: industrial, residential, background and ecological risk-based decisions with 84% of the sites were risk driven and 16% were background driven. They reported that a

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wide range of soil arsenic cleanup standard for residential risk goals (2–305 mg kg1) and a narrower background-based clean up goal of 8–21 mg kg1. In addition, there was no apparent temporal trend but considerable geographic differences were observed for arsenic RODs. Engineering approaches used for the remediation of arseniccontaminated sites include isolation, immobilization, toxicity reduction, physical separation, and extraction (Mulligan et al., 2001). The common action for the control heavy metal pollution at heavily contaminated sites is the removal of the surface soil. This method is not always effective, especially at sites with large affected area and long history of pollution. It was found that after 3 years of remediation (cleanup of the tailings and polluted soils, followed by apply sugar-refinery scum and tilling of the soil) at Aznalco´llar pyrite mine spill in Spain, a large portion of the soils in the area remain highly polluted (Aguilar et al., 2004). Isolation or capping of contaminated soils through the construction of physical barriers is a common technique employed at landfills and superfund sites. Such barriers made of steel, cement, bentonite clay, and grout walls reduce the permeability of the waste and limit the movement of groundwater through the contaminated area. Additional layers of sandy soils are employed to prevent upward movement of groundwater by capillary action. The isolation of contaminated sites is generally less expensive than other techniques. However, a common engineering problem associated with landfill capping is that the aging of the clay liner eventually leads to preferential flow in the fractures. Long-term stability of the solidified material requires frequent monitoring at those contaminated sites. Solidification/stabilization (S/S), also known as chemical fixation or encapsulation, is a set of technologies widely applied to treat soils contaminated with cationic heavy metals. The most common form of S/S uses a cement or pozolanic binder to convert the contaminated soil in order to create a monolithic form that limits the contaminant mobility. Miller et al. (2000) evaluated several combinations of cement binders and reagents in their capability to solidify sandy soils contaminated with arsenic. They reported that a mixture of Type I Portland cement and ferrous sulfate was effective in reducing the leaching of arsenic and improved performance was observed when the soil was pretreated with FeSO47H2O followed by Portland cement. This chemical contaminant strategy was successfully implemented in their field study. In situ S/S processes are most suitable for shallow contamination sites using conventional construction equipment. In addition, liming is a common remediation method for immobilizing trace metals released through acid mine drainage. However, as demonstrated by Jones et al. (1997), liming may also result in enhanced As mobilization due to the pH dependence of As sorption reactions. Soil flushing followed by groundwater extraction is a widely used in situ remediation technique for contaminated sites with relatively high


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permeability. Chemical additives are often used to enhance the solubility of the contaminant. Electrokinetic processes, which involve transport of ions and small charged particles through the application of a low intensity electric current between a cathode and an anode imbedded in the soils, are proposed for saturated clay soils with low permeability (Kim et al., 2002). However, several factors limited the suitability of this technique for the treatment of arsenic contaminated soils. First, high sorption capacity and strong binding strength between arsenic and metal oxides substantially retarded the transport of arsenic in soils and limited the extractable amount of arsenic. Second, the rate-limited sorption and transport processes made it practically impossible to achieve the cleanup goal in reasonable time frame with flushing and extraction processes. In addition, arsenic is mixed with other organic or inorganic contaminants in most contaminated sites. But there is no efficient extractant that is capable of simultaneously removing organic and inorganic, anionic, and cationic contaminants. Permeable reactive barrier (PRB) consists of installing a reactive material into the aquifer to induce sequestration and/or transformations of the contaminants and reduce the contaminant concentration in groundwater. Su and Puls (2001, 2003) evaluated several types of zero-valent iron (Fe0) in their capacity to attenuate arsenic concentration using both batch and column experiments. They suggested that Fe0 was a promising material for in situ remediation of contaminated groundwater with relatively low cost. The use of Fe0 in treating arsenic contaminated soils and groundwater have attracted extensive attention over the last 5 years with several new developments appearing in recent literatures. However, to our knowledge, field scale implementations of PRB for arsenic treatment have not been reported. Monitored natural attenuation (MNA) is proposed as an alternative remediation strategy for soils contaminated with inorganic contaminants such as arsenic. In the EPA Directive (USEPA, 1999), MNA is defined as ‘‘physical, chemical, or biological processes that, under favorable conditions, act without human intervention to reduce the mass, toxicity, volume, or concentration of contaminants in soil or groundwater.’’ The establishing of MNA as a potential remediation strategy for a contaminated site requires thorough and adequate site-specific characterization data and analysis. Specifically, there are three tiers of ‘‘lines of evidence’’: (1) historical groundwater and/or soil chemistry data that clearly demonstrate a trend of decreasing contaminant mass and/or concentration; (2) indirect hydrogeologic and geochemical data that demonstrate the type and rate of natural attenuation processes active at the site; and (3) direct field or microbiological data that demonstrate the occurrence of a particular natural attenuation process at the site. Sorption and precipitation are the dominant processes of natural attenuation of arsenic. Biotransformation of arsenic (redox cycling and

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methylation) can influence those natural attenuation processes. The key issue of natural attenuation of arsenic is the reversibility of arsenic sequestration (sorption/precipitation) into the solid phase because the intrinsic toxicity of arsenic was not affected by the immobilization process (Reisinger et al., 2005). Therefore, successful implementation of MNA for arsenic contaminated sites requires detailed site-specific characterization including source identification, plume boundary delineation, time-series monitor of arsenic concentration in soil and groundwater. An extensive list of ancillary data (e.g., pH, redox potential, Fe/Al/Mn contents, soil texture, and organic matter) is required for the evaluation of the feasibility of MNA. Reisinger et al. (2005) provided four examples of arsenic contaminated sites where natural attenuation was deemed acceptable by regulators. Phytoremediation is an emerging technology that uses specially selected and engineered metal-accumulating plants for environmental cleanup. Plants, such as Indian mustard (Pickering et al., 2000) and brake fern (Ma et al., 2001), have the capability to accumulate arsenic and is considered as potential method for treating contaminated soils. Since the discovery of arsenic hyperaccumulation capacity of Pteris vittata (brake fern), extensive researches have been conducted to investigate its physiological mechanisms and its effectiveness for remediation of arsenic contaminated soils. In addition to naturally selected plants, scientists used biotechnology to develop engineered plants that have the capability of accumulating arsenic. Dhankher et al. (2002) have developed a genetics-based phytoremediation strategy for arsenic where arsenic is hyperaccumulated in a plant transformed with the ArsC gene [encoding arsenate reductase (ArsC)]. Even though tremendous research effort was devoted to the identification and cultivation of arsenic accumulating plant, there is limited field evidence demonstrating the effectiveness of the phytoremediation for the treatment of arsenic contaminated soils. As a matter of fact, phytoremediation is limited to shallow soils (root zone) and the reliability of the process is heavily dependent on many environmental factors such as climate, terrain, and contamination level.

8. Summary and a Look Ahead Simulating the fate and transport of arsenic requires an in-depth understanding of the physical, chemical, and biological interactions entangled on the liquid–solid interface in soil environment. Predicting the mobility and bioavailability of arsenic is complicated by the multiple organic or inorganic arsenic species in dissolved, adsorbed, or particulate forms coexisting in the soil system. Environmental factors, for example, pH, redox potential, competing ions, soil mineralogy, flow regime, and microbial activity, should be considered in the models because of their substantial


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impact on arsenic reaction and transport. Thermodynamic models using equilibrium constants derived from the standard Gibbs energy of the chemical compounds are frequently employed for predicting the environmental behavior of arsenic at contaminated sites. But the capabilities of such models are rather limited because chemical equilibrium rarely exists in soils. Recent studies are focused on developing kinetic models that simulate the dynamic distribution of arsenic species and determining the kinetic reaction rates for various chemical and biological reactions. Research is needed for the application of mechanistically based arsenic transport models in making regulatory decision and designing remediation strategy. Areas of research needs included, 1. Kinetics of reactions/release of arsenic on surfaces of soil constituents (organic matter, metal oxides, clay minerals, sulfides, microbes) and quantitative description of mechanistic processes. 2. Speciation of arsenic in solid and aqueous phases as a function of solution composition, soil composition, flow conditions, and microbial activities. 3. Influence of physical and chemical heterogeneity on arsenic retention and transport and stochastic simulation of the spatial distribution of arsenic in geological porous media. 4. Reactive transport models that incorporates complex biogeochemical processes and capable of predicting the fate of arsenic in the environment. 5. Strategies for the remediation of arsenic contaminated soils using combination of physical, chemical, and biological techniques.

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C H A P T E R

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Crop Residue Management for Lowland Rice-Based Cropping Systems in Asia Bijay-Singh,* Y. H. Shan,† S. E. Johnson-Beebout,‡ Yadvinder-Singh,* and R. J. Buresh‡ Contents 1. Introduction 2. Criteria for Evaluating Crop Residue Management Options 2.1. Productivity and profitability 2.2. Environmental impact and sustainability 3. Type and Abundance of Crop Residues 4. Existing and Emerging Residue Management Options 4.1. Rice following rice or a non-flooded crop 4.2. Non-flooded crop following rice 5. Evaluation of Options with Residues Managed During a Rice Crop 5.1. Productivity 5.2. Profitability 5.3. Environmental impact 5.4. Sustainability 6. Evaluation of Options with Residues Managed During a Non-Flooded Crop 6.1. Productivity 6.2. Profitability 6.3. Environmental impact 6.4. Sustainability 7. Crop Residue and Bioenergy Options 8. Summary Acknowledgment References

* { {

118 121 122 122 123 125 129 132 135 135 152 153 158 160 160 174 175 179 181 183 185 186

Department of Soils, Punjab Agricultural University, Ludhiana 141 004, Punjab, India College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225009, China International Rice Research Institute, Los Banõs, Philippines

Advances in Agronomy, Volume 98 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00203-4

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Intensification of rice-based cropping systems in Asia has substantially increased production of food and associated crop residues. The interval between crops in these systems is often brief, making it attractive for farmers to burn residues in the field to hasten and facilitate tillage for the next crop. Open-air burning causes serious air quality problems affecting human health and safety, and it has been banned by many Asian governments. In this chapter, we evaluate for rice-based cropping systems existing and emerging in-field alternatives to burning residues based on criteria of productivity, profitability, environmental impact, and sustainability. In intensive rice monocropping systems, residue is typically managed under conditions of soil flooding and anaerobic decomposition during the rice crop. In systems, where rice is rotated with an upland (non-flooded) crop, there are two major categories: residue of upland crop managed during flooded rice and rice residue managed during the upland crop. One option during the flooded rice crop is incorporation of residues from the previous rice or upland crop into the soil. Many studies have examined incorporation of crop residue during land preparation for flooded rice. In the vast majority of cases there was no significant increase in yield or profit. Residue decomposition in anaerobic flooded soil substantially increases methane (CH4) emission relative to residue removal. Surface retention of residue during rice cropping is challenging to implement because residue must be removed from the field during conventional tillage with soil flooding (i.e., puddling) and then returned. Alternatively, rice must be established without the traditional puddling that has helped sustain its productivity. Mulch is a good option for rice residue management during the upland crop, especially with reduced and no tillage. Mulch can increase yield, water use efficiency, and profitability, while decreasing weed pressure. It can slightly increase nitrous oxide (N2O) emission compared with residue incorporation or removal, but N fertilization and water management are typically more important factors controlling N2O emission than residue management. Long-term studies of residue removal have indicated that removing residue from continuous rice systems with near continuous soil flooding does not adversely affect soil organic matter (SOM). The use of crop residue as a mulch with reduced or no tillage for upland crops should be promoted in rice-based cropping systems. On the contrary, residues from the crop preceding rice on puddled and flooded soil can be considered for removal for off-field uses.

1. Introduction Rice (Oryza sativa L.) is the lifeline of Asia. More than 90% of the world’s total rice crop—or ~570 million tons of the estimated 630 million tons of global rice production in 2006/2007—is produced in Asia (FAO, 2007; USDA, 2007). Modern cultivars of rice with growth duration of

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90–125 days can be cultivated in rotation with one or two additional crops in a year. Intensification and diversification are two main trends of rice-based cropping systems as they have evolved in different agroecological regions in Asia. The most prevalent cropping systems are rice–rice, rice– rice–rice, rice–rice–pulse, rice–wheat (Triticum aestivum L.), rice–oilseed crop, and rice–maize (Zea mays L.). Intensive irrigated rice systems, with two and sometimes three rice crops produced each year in the same field, are a dominant agricultural land use in the lowland tropics and subtropics of Asia (Cassman and Pingali, 1995). Intensive rice-based systems also show great diversity across Asia, where wheat, maize or one of many other secondary crops are grown during the part of the year when rice is not in the field. The rotation of rice and wheat, for example, is a major agricultural production system, which accounts for ~30% of the area of both rice and wheat grown in South Asia (Timsina and Connor, 2001; Ladha et al., 2003). Rice-based cropping systems are the most productive agroecosystems in Asia and produce the most food for the most people. Along with grain yield, these systems generate large amounts of crop residue. Historically, crop residues were often removed from fields for livestock bedding and feed, fuel for cooking, and other off-field purposes. More recently, the off-field uses of crop residues have tended to decrease in parts of Asia even as increasing quantities of crop residues have been produced as crop yields and cropping intensity increase. The intensification of land use results in less time between crops for managing these residues, which can interfere with tillage and seeding operations for the next crop. The lack of alternative uses for crop residues and lack of appropriate mechanization to handle increasing quantities of residue have driven Asian farmers increasingly to burn crop residues as a method of disposal (Flinn and Marciano, 1984; Yadvinder-Singh et al., 2005). Open-field burning of crop residues is recognized as a major contributor to reduced air quality and human respiratory ailments, particularly in China and northwestern India, which represent major irrigated rice ecosystems in Asia. Streets et al. (2003) estimated that 730 Tg of biomass are burned in a typical year from both anthropogenic and natural causes, excluding biofuel. Crop residue burning accounted for 34% of that total. Of the total crop residues burned, China contributed 44%, India 33.6%, Bangladesh 4.4%, Pakistan 4%, Thailand 3.1%, and Philippines 2.8%. The problems of openfield burning straw include atmospheric pollution and nutrient loss. One ton of crop residue on burning releases 1,515 kg CO2, 92 kg CO, 3.83 kg NOx, 0.4 kg SO2, 2.7 kg CH4, and 15.7 kg nonmethane volatile organic compounds (Andreae and Merlet, 2001). These gases and aerosols consisting of carbonaceous matter lead to adverse impacts on human health in addition to contributing to global climate change. Following the IPCC methodology (IPCC, 1996) for estimation of emission from open-field burning of crop residue and assuming 25% of the available residue is burned in the field, the

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estimated emissions in 2000 from open-field burning of rice and wheat straw in India were 110 Gg CH4, 2306 Gg CO, 2.3 Gg N2O, and 84 Gg NOx (Gupta et al., 2004). For every ton of wheat residue burned, an estimated 2.4 kg of N is lost (Kumar et al., 2001), and up to 60% of the S content is lost (Lefroy et al., 1994). Many governments in Asia have made it illegal to burn crop residues, but these laws have been difficult to enforce. There has been increased realization that crop residues are a resource constituting a readily available source of nutrients and organic material for rice farmers. About 40% of the N, 30–35% of the P, 80–85% of the K, and 40–50% of the S absorbed by rice remain in the vegetative parts at maturity (Dobermann and Fairhurst, 2000). Typical amounts of nutrients in rice straw at harvest are 5–8 kg N, 0.7–1.2 kg P, 12–17 kg K, 0.5–1 kg S, 3–4 kg Ca, 1–3 kg Mg, and 40–70 kg Si per ton of straw on a dry weight basis (Dobermann and Witt, 2000). Residue removal can therefore have a significant effect on soil nutrient depletion. Residue management also influences availability of micronutrients such as zinc and iron, and it is an important factor in maintaining the cumulative Si balance in rice (Dobermann and Fairhurst, 2000, 2002). Residues must be carefully managed for obtaining positive effects on soil and crop production and avoiding negative effects such as interference with the planting of crops, N immobilization, and emission of greenhouse gases. The return of crop residues to flooded soils, which are typical in ricebased cropping systems, influences the chemical, physical, and biological soil environment in different ways than the return of residues to aerobic soils prevalent when crops are grown under non-flooded soil conditions. The desired objectives of adopting a particular crop residue management option can be achieved only if the management option is feasible under a given set of soil, climate, and crop management conditions; is compatible with available machinery; and is socially and economically acceptable. This chapter considers existing and emerging in-field management practices. In addition, there are a variety of potentially attractive and competing offfield uses for crop residues such as animal feed, roof thatch, manufacture of paper or cardboard, and biofuel feedstock. Several reviews on crop residue management of rice systems have appeared in recent years. Kumar and Goh (2000) reviewed crop residues in terms of soil quality, soil N dynamics, crop yields, and N recovery. Their chapter primarily deals with the decomposition and turnover rates of residues in relation to nutrient cycling. Yadvinder-Singh et al. (2005) reviewed work on crop residue management in rice-based cropping systems in the tropics, dealing with short- and long-term effects on cycling of C, N, and other nutrients in order to provide necessary understanding for developing suitable new crop residue management options. They also explained the need for evaluating the relative costs of different residue management options for rice-based cropping systems in terms of environmental impact,


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C sequestration, and long-term soil fertility. In this chapter, we describe important in-field residue management options for rice-based cropping systems including China, which did not receive much attention in the earlier reviews, and we evaluate management options based on criteria of productivity, profitability, environmental impact, and sustainability. We include residue removal in our evaluation of in-field options, but we have not detailed off-field options. We exclude high-N residues, such as from green manures, because they are not currently common in rice-based cropping systems in Asia. This chapter focuses on lowland rice-based cropping systems in Asia, in which fields are typically surrounded by earthen levees or bunds to impound water during the period of rice cropping. ‘‘Lowland’’ indicates a cultivation practice of growing rice under either irrigated or rainfed conditions with impoundment of water to flood the soil—typically during land preparation for rice production and during at least part of the rice growing season. The soil is largely anaerobic during the periods of flooding. ‘‘Upland,’’ in contrast, refers to the cropping period or crop in rice-based cropping systems when the soil is not flooded and aerobic. In this chapter, the terms ‘‘upland crop’’ and ‘‘non-flooded crop’’ synonymously refer to the crop (typically not rice) in the cropping system grown without soil flooding. In this chapter, crop residue is defined as the above-ground part of the plant remaining after the grain is harvested. It includes both the stubble left standing during the harvest process and the leaves and stems left over after threshing. Because harvest practices and nomenclature vary across Asia, ‘‘residue removal’’ and ‘‘straw removal’’ can mean different things in different locations and literature. Sometimes it means removal of all biomass from the soil surface upwards; but often it means removal of biomass except the standing stubble, which can represent an important quantity of biomass depending on the height of crop harvest. Roots are also a source of organic material that crops contribute to soil every season, but they are not included in our definition of crop residue. Roots are almost always retained in the soil, and there are few other management options for them in rice-based cropping systems. Composts, animal wastes, and manures produced from residue removed from the field are outside the scope of this chapter.

2. Criteria for Evaluating Crop Residue Management Options In order to make and implement sound decisions about residue management, it is necessary to scientifically understand the short- and long-term effects of different crop residue management practices and to develop residue management technologies that provide agronomic benefit

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in a cost-effective and environmentally acceptable fashion. Crop residue management options are evaluated in this chapter using criteria of productivity, profitability, environmental impact, and sustainability for the cropping system. These criteria coincide with those used in the approach of ecological intensification for intensive crop production systems, which aims to satisfy the increasing demand for food, feed, fiber, and fuel while meeting acceptable standards of environmental quality (Cassman, 1999; Witt, 2003). Success in achieving ecological intensification depends greatly on sustaining yield increases in major irrigated and favorable rainfed cereal systems such as the rice-based cropping systems covered in this chapter. This chapter focuses on evaluating potential large-scale effects of residue management options rather than on the effects of residue management on specific processes, which have already been reviewed for soil fertility (Wilhelm et al., 2004; Yadvinder-Singh et al., 2005), C cycling (Martens, 2000), pesticide interactions with soil microorganisms (Moorman, 1989), soil-borne diseases (Chung et al., 1988), and root health (Allmaras et al., 1988).

2.1. Productivity and profitability Productivity and profitability are criteria directly relevant to farmer’s decision making. The quantifiable indicators of short-term productivity (i.e., 1–3 seasons of a given management option) include grain yield, fertilizer use efficiency (grain yield per unit fertilizer applied), water use efficiency (grain yield per unit water applied), and yield loss due to disease, insect, or weed pressure. Profitability indicators include income from yield less inputs (i.e., labor, fertilizer, seed, machinery, irrigation water, and pesticide). Residue management options can differ in their effects on these indicators of productivity and profitability. We have therefore assessed the residue-associated changes for each indicator for different in-field residue management options as compared to either removing or burning residues.

2.2. Environmental impact and sustainability Environmental impact and sustainability are criteria that are not typically important determinants for farmers in their selection of a particular residue management option, but these criteria can be important for policy making such as with the banning of open-field burning of crop residues. The main short-term (i.e., measurable within 1–3 seasons) environmental impacts associated with residue management include changes in air quality and greenhouse gas emission. Sustainability refers to the medium- and longterm (i.e., 5 years or more) ability of a residue management option to maintain or increase the productivity and profitability of the cropping system. Indicators include trends through time in yield, input use efficiency,


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soil parameters (such as N supply capacity, organic matter, K, S, and bulk density), and profitability.

3. Type and Abundance of Crop Residues The geographical distribution of crop residues in Asia is skewed by the large crop production in India and China. These two countries in 2006/2007 accounted for ~51% of global rice production and ~57% of Asian rice production (FAO, 2007; USDA, 2007). Frolking et al. (2002) by combining remote sensing and ground census data to develop maps for the distribution of rice in China showed 25% of the lowland rice cropland was planted as single rice, 15% was a double-crop rotation with two rice plantings per year (rice–rice), 19% was a double-crop rotation with a single rice planting (rice– other crop), and 41% was a triple-crop rotation with two rice plantings per year (rice–rice–other crop). Rice was rotated on an estimated 4.7 million ha with wheat, 4.5 million ha with rapeseed (Brassica napus L.), and 2.2 million ha with oat (Avena sativa L.). More recently, the area of rice–wheat in China was estimated at 3.4 million ha (Dawe et al., 2004). Yadav and Subba Rao (2001) estimated an area in India of 9.2 million ha for rice–wheat, 2.4 million ha for rice–oilseed, 3.5 million ha for rice–pulse. More recently, the area of rice–wheat was estimated at 10 million ha in India and 13.5 million ha for the Indo-Gangetic Plain, which includes Bangladesh, India, Nepal, and Pakistan (Ladha et al., 2003). The cropping patterns in rice-based cropping systems remain dynamic in response to markets, policies, and labor availability. We estimated total production area, grain yield, and production of residues for rice-based cropping systems in Asia from available 2004 data (Table 1). Rice-based cropping systems are defined as those with at least one rice crop per year grown either as a sole crop or in rotation with rice or a non-flooded crop. In these systems the rice would typically be grown on flooded soil, which markedly influences the management options for residues. Available data for area of a given crop in Asia typically does not specify the rotational system in which the crop is grown. Therefore, the areas for nonrice crops in rice-based cropping systems shown in Table 1 are based on estimations on a country basis. Economic yield data (grain or cane yield) were computed from the online databases (FAO, 2007). The residue-toeconomic yield ratios as listed in Table 1 are based on a range of reported data (Barnard and Kristoferson, 1985; Beri and Sidhu, 1996; Koopmans and Koppejan, 1997; Muehlbauer and Tullu, 1997; Yevich and Logan, 2002). According to these estimations for 2004, rice accounted for ~84% of total residue from rice-based cropping systems while wheat and maize accounted for 9% and all other crops accounted for 7%. The estimations highlight the large quantity of residues produced in rice-based cropping systems in Asia.

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Table 1 Residue production and area for rice and different crops grown in rotation with rice in Asia in 2004

Crop

Rice Wheat Maize Sugarcane Rapeseed Soybean Oats Lentil/Pulses Total

Harvested area (106 ha)

Economic yield (106 t)

Residue-toeconomic yield ratio

134 19 1.4 1.7 7.1 0.3 0.3 2.2

546 57 4.5 107 11 2.7 0.7 1.7

1.4 1.3 2.0 0.17 3.5 2.5 1.5 1.2

Residues (106 t)a

764 74 9 18 38 7 1 2 913

a

Data pertaining to production of residues was computed by multiplying economic yield of different crops with residue-to-economic yield ratios. Source: Data and conversion factors were obtained from FAO (2007), Frolking et al. (2002), Yadav and Subba Rao (2001), Koopmans and Koppejan (1997), Barnard and Kristoferson (1985), Yevich and Logan (2002), Beri and Sidhu (1996), and Muehlbauer and Tullu (1997).

This quantity will increase as yields and intensity of cropping continue to increase. The residues from nonrice crops as a proportion of the total would be expected to increase as rice-based cropping systems diversify. Little data are available on the use of these residues, but there have been several attempts in China and India to estimate the amount returned to soil. Gao et al. (2002) estimated 37% of ~600 million tons of crop residue produced in China are returned to soil, often in the form of rice stubble. In 15 provinces of China with most of the country’s cereal production, crop residues were returned to fields on an estimated 18.2 million ha, comprising of 37% of the total cultivated land in 2000 (Han et al., 2002). In India, an estimated 250 million tons of residues is produced annually in rice–wheat cropping systems in the Indo-Gangetic Plain (Pal et al., 2002). Huge amounts of residues are available either for retaining in fields to enhance productivity and fertility of the soil or for removing from the field for alternative uses, but in many areas of Asia the crop residues produced in rice-based cropping systems have been considered a nuisance by farmers and disposed through burning in fields.


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4. Existing and Emerging Residue Management Options Most lowland rice ecosystems in Asia have a rainy season when climatic conditions favor production of rice rather than other crops. Nonflooded crops are often grown in rotation with rice during the drier season. Soil in irrigated and rainfed lowland ecosystems with sufficient water typically remains flooded for most of the rice-cropping season. The choices for managing crop residue can consequently differ between periods with rice cropping, when anaerobic decomposition of residues predominates, and periods with other crops, when the aerobic decomposition of residues predominates. In intensive nonrice cropping systems with reduced or no tillage such as in Europe, America, and Australia, crop residues are often left in the field after combine harvesting, and seed for the next crop is then sown directly into the residue without plowing. In a rice production system in California, where legislation banned traditional open-field burning of residue, many farmers have adopted a system of retaining residues as a habitat for migratory, foraging waterfowl that hasten the decomposition of the residue during a winter-flood fallow (Bird et al., 2000). Tropical rice production systems in Asia, which are characterized by intensive cropping, do not have such long fallow periods with ample water between crops. Rice production systems in Asia also do not lend themselves to reduced or no tillage options because they are characterized by soil puddling— the plowing and harrowing of soil when flooded. Puddling destroys soil structure, restricting downward movement of water to maintain flooding (Sharma and De Datta, 1986), and soil flooding controls weeds and helps sustain the productivity of rice-based cropping systems. Soil puddling, however, restricts options for surface application of crop residues. Mulching in a puddled field typically necessitates removal of the residue before puddling and then returning it afterwards. Management practices for retention of crop residues are listed for different regions of China in Table 2 and for South Asia in Table 3. The recommended management for crop residues during cropping with flooded rice has typically been incorporation into the soil during land preparation. Yet farmers in Asia have not often in recent years followed this recommendation, electing instead the open-field burning of residues. But with increasingly strict legislation against open-field residue burning, a trend of increasing residue return can be anticipated, either through incorporation or as mulch. The retention of residues on the soil surface as a mulch is often an option during the nonrice crop in rice-based cropping systems, which can be established with

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Table 2 On-field residue management practices in rice cropping systems in different regions of China

Region

Cropping system

Existing residue management practice

Northeast

Rice

Yangtze Valley

Rice–wheat Rice–rapeseed

Southwest

Rice–rice Rice–wheat Rice–rapeseed

South China

Rice–rice Rice–rice– rapeseed

Stubble remaining + shallow plowing Mulching with rice residues in wheat (or rapeseed) season Incorporation in rice season Mulching with rice residues in wheat (or rapeseed) season Incorporation in rice season Mulching with rice residues in rapeseed season Incorporation in rice season

Amount of residue returned per year (t ha–1)

2.3 4.0

4.2

4.9

Sources: Zeng et al. (2001, 2002).

conventional, reduced, or no tillage. A challenge for mulching with reduced and no tillage is to ensure sufficient soil–seed contact after sowing. Methods of harvesting and threshing are critical in determining what happens to crop residue, and are often chosen because of the intended use of the straw. Some of the common harvest methods for rice in Asia include hand-cutting, use of small harvesting machinery, and use of combined harvester-threshers (Gummert and Aldas, 1993; Saunders et al., 1980). When hand-cutting, workers sometimes cut at the soil surface if they want long straw for animal bedding or roof thatch, but they might choose to cut part way up (20–60 cm above soil) to reduce the weight to be carried. The harvested part is carried to a centralized location on- or off-field for threshing to remove the grain from the straw. It can be threshed either manually or mechanically. Manual threshing involves hand flailing, swinging the straw over the head and beating it against a firm object in front that allows easy collection of the grain. If the straw is valuable to the farmer, it can then be stored for future use. With mechanical threshing, the chopped straw accumulates on a pile at the location of threshing. Combined harvesting–threshing machines separate the grain from the straw as they cut the plant and move through the field, retaining the grain and leaving the

Table 3

Existing and emerging in-field residue management practices in rice cropping systems in different regions of South Asia Existing residue management practices and (amount of residue returned per year, t ha–1)

Emerging residue management options and (amount of residue that could be returned per year, t ha–1)

Mulching with rice straw in no-till wheat (5–7) Incorporation of straw and stubble of combine harvested rice in wheat (5–7) Incorporation of combine harvested wheat straw and stubble in rice (1–2) Mulching with rice straw in no-till wheat (~5) Incorporation of manually harvested or combine harvested wheat straw and stubble in rice (~1)

Region

Cropping system

Trans- and Upper IndoGangetic Plain

Rice–wheat

Incorporation of rice and wheat stubble remaining in the field (~1)

Middle- and Lower IndoGangetic Plain

Rice–wheat Rice–oilseed Rice–pulses Rice–jute–rice Rice–vegetable Rice–vegetable– rice Rice–wheat Rice–wheat– pulses

Incorporation of stubble remaining in the field (~1) Rice straw mulch in vegetable production (1–2)

Non-Indo-Gangetic Plain (Terai of Nepal, Bihar, and Uttranchal) South India

Rice–rice Rice–rice– rice Rice–pulses

Source: Based on Gajri et al. (2002) and Pal et al. (2002)

Incorporation of rice and wheat stubble (~1)

Mulching of rice residues in wheat (~4)

Incorporation of rice stubble (~1)

Incorporation of rice straw and stubbles (~4)

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chopped straw spread on the field. It is usually advantageous to leave high stubble and only move the upper portion of the plant through the machine. Combine-harvested fields consequently tend to have relatively tall standing stubble and short pieces of chopped straw laying on the surface. Methods of harvesting and threshing determine the percentage of total biomass left in the field as standing stubble, and the condition and location of the threshed straw. If standing stubble is not burned in the field, it is typically incorporated during land preparation for the subsequent crop. The primary management options, therefore, usually relate to the threshed straw rather than the stubble, although one management decision could be to adjust the harvesting procedure to increase or decrease the proportion of biomass that is threshed. Interventions for management of residues in rice cropping systems can be categorized based on the type of residue and crop in the system as follows: rice residue for a subsequent rice crop, rice residue for a nonflooded crop, and residue of a non-flooded crop for a subsequent flooded rice crop. These three situations can be further simplified into the following two cropping system categories based on the critical distinction of whether soil is flooded or non-flooded during the crop receiving the residues. 1. Rice following rice (common in South China, Southeast Asia, and southern India) and rice following an upland or non-flooded crop (common in Central and North China and parts of South Asia). In both cases, crop residue is managed during a rice crop, typically established on puddled soil. 2. Upland or non-flooded crop following rice (common in the IndoGangetic Plain in South Asia and many parts of China). In this case, rice residue is managed during a crop grown on non-flooded soil. Intensive rice monocropping systems are often the most challenging for managing crop residues because of the short time interval between rice crops. In rotations with rice and a non-flooded crop, the two crops often differ in the magnitude of the challenges for residue management. Management options are affected by the time of year when residue becomes available and the time before the next crop is planted. In the rice–wheat system in the Indo-Gangetic Plain, there is a relatively short time between harvesting rice and sowing wheat, hence the management of rice straw during wheat cropping (i.e., non-flooded crop following rice) is a critical issue. There is a relatively longer fallow after wheat, and wheat straw is valuable for off-field uses, especially as animal feed (Samra et al., 2003). Hence, the management of wheat straw during rice cropping (i.e., rice following a non-flooded crop) is not as critical. In China, however, there is a very short fallow between wheat and rice, often less than 1 week, because of the typically long growth duration of rice, and wheat straw does not have off-field value as in India. Hence, the management of wheat residue during


129

rice (i.e., rice following a non-flooded crop) presents the bigger challenge. In each category of cropping patterns, there are a series of management options differing in relative feasibility and attractiveness.

4.1. Rice following rice or a non-flooded crop Residue management in this category is strongly affected by soil flooding and anaerobic soil conditions during the rice crop. Residues from the previous crop, whether rice or a non-flooded crop, typically must be managed in an environment of historical soil puddling and at least partial anaerobic decomposition. Factors that influence decisions about residue management are harvest method of the previous crop, turnaround time between crops, land preparation practices, availability of implements and labor, and establishment method and water management of the new rice crop. Residue management scenarios vary greatly depending upon whether the previous crop has been harvested manually or by combine harvesters, threshed at locations in field resulting in straw piles, or threshed outside the field. The method of harvesting also determines the extent to which crop residues remain anchored or loose. 4.1.1. Incorporation Residue incorporation into flooded soil has continued to be promoted as an alternative to open-field burning in rice-based cropping systems across Asia. It is a potentially attractive option because rice residues can typically be plowed into the soil as part of the normal tillage operations for preparing the rice field, and residue incorporation therefore does not require an extra step in land preparation. Once incorporated the residue typically decomposes relatively fast thereby potentially providing benefits to the next rice crop. Incorporation also avoids loose residue on the soil surface that could interfere with the preparation of the seedbed or planting of the next crop. Incorporation can reduce the risk of pests and diseases as compared with mulching, and it can potentially increase soil organic C fractions and total organic C. Specific management decisions include tillage method and timing of incorporation relative to rice establishment by transplanting or direct seeding. During land preparation for lowland rice in Asia, the topsoil is typically inverted thereby incorporating crop residues remaining on the soil surface as standing stubble or loose straw. A moldboard plow or disk plow is commonly used for incorporating residues often with a shallow layer of floodwater (Ponnamperuma, 1984). The degree of incorporation varies among tillage systems depending on implement, intensity, and mechanization level (i.e., manual, animal traction, or mechanized) (Sharma and De Datta, 1986). Incorporation of large amounts of fresh residue is labor intensive if suitable machinery is not available (Dobermann and Fairhurst, 2000). When rice is

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harvested using a combine harvester that leaves straw spread in the field, residue can be incorporated into the soil by disking or plowing. If only stubble is retained, the amount of residue incorporated into the soil is determined by the manner and the height of harvesting. Incorporation of residues might not be feasible when long straw clogs field implements. In rice–rice systems in Hunan and Hubei Province of China with only a few days between early and late season rice, a practice for rapid incorporation of residue before immediate establishment of the next crop is cutting the rice residue into ~20–25 cm lengths followed by shallow mechanical incorporation (Zeng et al., 2001). In rice–rice cropping systems with 2–3 months between rice crops, the shallow incorporation of rice residue into aerobic soil soon after harvest rather than delaying the incorporation of residues until preparation of flooded soil immediately before establishment of the next rice crop has been proposed as a practice to accelerate residue decomposition and release of plant-available nutrients (Dobermann and Witt, 2000). Witt et al. (1998) reported rapid decomposition of rice residue following shallow incorporation into aerobic soil, leading to ~50% loss of residue-C within 30–40 days and increased supply of plant-available N. Thuy (2004) in three cropping seasons in the Philippines consistently found comparable or significantly higher KCl-extractable soil ammonium at rice transplanting and uptake of soil N by rice at panicle initiation when residue from the previous rice crop was incorporated into aerobic soil immediately after harvest rather than incorporated later by the traditional practice during puddling ~3 weeks before transplanting. Shallow incorporation of rice residue into aerobic soil after harvest can also help reduce weed growth, save irrigation water by reducing soil cracking, and allow additional time for phenol degradation under aerobic conditions (Dobermann and Witt, 2000). The time interval between incorporation of crop residue and land preparation, flooding, and transplanting the next rice crop is a crucial factor affecting residue management. This time interval influences the extent of residue decomposition before transplanting, depending upon soil and climatic conditions, thereby affecting the beneficial or adverse effects of residue incorporation on young rice seedlings. In intensive irrigated production systems, two or three short-duration crops are typically grown per year. In the Red River Delta of northern Vietnam, for example, rice–rice–maize is a common cropping system. In the Mekong Delta of southern Vietnam where rice is grown continuously, the intensity of rice cropping can reach 6–7 crops in 2 years (Dobermann et al., 2004). In such intensive triple cropping systems, the fallow between two crops can be only a few days. Whereas a relatively long fallow period of 2–3 months provides opportunities to manage residues for hastened decomposition and nutrient release, a fallow of only a few days does not enable appreciable residue decomposition and release of nutrients before establishment of the next rice crop. In such


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cases the incorporation of large quantities of crop residues poses challenges and potential detrimental effects on the subsequent rice crop. 4.1.2. Mulching Adding mulch to flooded rice is usually not feasible because the traditional practice of puddling will incorporate retained crop residue. Mulching typically requires transfer of biomass off of the field before soil puddling and then return of the biomass after land preparation. In rice–rice systems in South China, some farmers do not drain their fields at harvest of the early rice crop and keep the field flooded without tillage during the brief transition period to transplanting of the late rice crop. Straw from the early rice is placed as mulch in rows along the direction of transplanting for late rice, and late rice is transplanted between the rows of straw covered soil (Li, 1991). The continuous flooding helps ensure the soil is sufficiently moist for easy transplanting of late rice, and the mulch helps control weed growth and prevent ratooning of rice. Other rice farmers and researchers in China have been trying reduced and no tillage flooded rice systems that enable surface retention of crop residue. In Anhui, Guangdong, Hunan, Hubei, Jiangsu, and Zhejiang Provinces, farmers have used either the throwing of rice seedlings or the direct sowing of germinated rice seeds as methods for establishing rice in reduced or no tillage fields (Li, 2005). In such systems crop residue is retained on the soil surface. The soil is saturated or flooded during crop establishment, and herbicides are used to control weeds. In Anhui, Jiangsu, and Zhejiang Provinces, some farmers practice relay cropping whereby rice is sown in wheat fields before combine harvesting. The standing stubble of wheat then slowly decomposes during rice cropping. A ground covering rice production system (GCRPS) to save water and increase N efficiency has been developed in South China. It involves growing a lowland rice variety under non-flooded conditions (70–90% of water holding capacity) with the ground covered by rice straw mulch during growth (Fan et al., 2002; Huang et al., 1997; Lin et al., 2002; Luo, 1997; Zhao et al., 1999). Mulching increased soil organic C and total N (Fan et al., 2002; Liu et al., 2003), but grain yield was sometimes lower than when rice was grown under flooded conditions. 4.1.3. Composting Most composting techniques are off-field residue management options in which the produced compost is not returned to the main production field, and as such are not included in this chapter. Some composting can, however, occur in fields (in situ composting), and a small portion of the compost produced off-field can be returned to the main field. One example of in situ composting is where rice straw is piled in the field at threshing sites (Ponnamperuma, 1984). The straw decomposes slowly, largely aerobically,

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and it can then be spread and incorporated into the soil at the beginning of the next season. Constraints of this practice include providing a favorable habitat for rodent pests and promoting excessive immobilization of N under the residue piles. Another type of in situ composting, in which the residue of the previous crop is buried in ditches paralleling rows of transplanted rice, has been examined in China for residues of non-flooded crops, such as wheat and barley, grown immediately before rice (Zhong et al., 2003). Crop residue can be removed from the field, composted alone or with other organic materials originating at the farm such as animal wastes, and then returned to soil as manure for the rice crop. The potential of composting to turn on-farm waste materials into a farm resource makes it an attractive proposition. Traditional methods based on a passive composting approach involve simply stacking crop residues in piles or pits to decompose over a long period with little agitation and management (Misra et al., 2003). The time requirement can be reduced through a few turnings, which slightly enhance passive aeration. Chinese rural composting methods also use a passive aeration approach based on turnings and aeration holes, and they provide output in 2–3 months (Ma, 2004). Low turnover and long time span are major bottlenecks. Traditional passive methods require several months from the time of crop harvest until the compost is ready for use. Composting involves labor input, but it is not capital intensive and does not require sophisticated infrastructure and machinery. Small farmers without manual labor constraints are most likely to benefit from composting technology.

4.2. Non-flooded crop following rice Managing rice residue during a non-flooded crop is somewhat easier than managing it during flooded rice because options for reduced or no tillage and mulching are more feasible. In addition, incorporated residue usually decomposes faster in aerobic than in flooded soil. There are, however, challenges. Major factors affecting residue management decisions include method of rice harvest, the time interval between crops, water management, and method of tillage for the subsequent non-flooded crop. The extent to which rice residue remains anchored after harvest also influences the way it can be managed in the following non-flooded crop. The most common options include incorporation with conventional tillage or surface mulching usually with reduced or no tillage. 4.2.1. Incorporation Incorporation of rice residue into the soil before planting a non-flooded crop has been frequently studied, particularly where rice residue does not have off-field economic use such as for animal fodder, fuel, or industrial purposes. Various options are available for farmers to incorporate crop


133

residues into the soil depending upon availability of machines, financial resources, and amount of straw (Ball and Robertson, 1990). For example, residues can be directly incorporated using a moldboard plow, or they can be chopped using a straw chopper after harvesting with a combine, and then the chopped residue can be easily incorporated into the soil using a disc plow (Sidhu and Beri, 2005). One reason for the popularity of residue incorporation among scientists is that residue can be incorporated during the preparatory tillage for the non-flooded crop, and hence it does not entail extra cost for implementation. Another option is incorporation of residue with a separate field operation several weeks before land preparation for the following crop. This allows more time for the residue to decompose and helps to control weeds. Residue can be incorporated partially or completely into the soil depending on method of cultivation used. The time interval between residue incorporation and planting of the next crop is determined by the cropping calendars and the time needed for residue decomposition. Yadvinder-Singh et al. (2004b), for example, in a rice–wheat cropping system in the northwestern India, observed that rice residue decomposition of ~25% during the pre-wheat fallow period was sufficient to avoid any detrimental effects on wheat yields. Rice and wheat productivity in a 7-year study was not adversely affected when rice residues were incorporated at least 10 days and preferably 20 days before the establishment of the succeeding crop. 4.2.2. Mulching with reduced or no tillage A reduced or no tillage system makes it relatively easy to retain residue on the surface as mulch simply by leaving it on the field during harvest. It is not necessary to remove the residue before tillage and then return it. However, if residue is threshed off-field, it must be transported to and spread on the field, resulting in no saving in labor for handling residues as compared to mulching with conventional tillage. Direct drilling is a method of sowing the crop after rice harvest without cultivation or incorporation of residue. According to Li (1991), no-till sowing of winter crops including wheat, barley (Hordeum murinum L.), and rapeseed is commonly practiced by farmers in rice-based cropping systems in eastern China. Surface seeding of wheat by making small holes in the soil (dibbling) followed by mulching with rice residue (4–6 t ha–1) is practiced on ~60% of the rice–wheat system in the Sichuan Basin (Humphreys et al., 2004). Because the time between rice harvest and wheat sowing is relatively long (60–85 days) in middle and southern China, the spreading of rice residue as mulch immediately after harvest has been examined as a practice for controlling weeds and reducing evaporation during the fallow before the next non-flooded crop (Zeng et al., 2001, 2002). This technique is attractive for farmers growing rapeseed instead of wheat after rice because they can broadcast the seeds, which are

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small enough to fall through the rice mulch to the soil surface. A double zero-tillage system of no-till, direct seeded rice and no-till rapeseed with rice residue as mulch for rapeseed has been developed in Hunan Province (Zou et al., 2004b). Reduced and no tillage for wheat has been increasingly adopted by farmers in the Indo-Gangetic Plain in northwestern India since the late 1990s because it leads to large cost savings through reduced use of fuel and labor (Erenstein et al., 2007). In the eastern part of the Indo-Gangetic Plain, it also facilitates early sowing leading to potential yield benefits, especially after late harvested rice. Rice residue mulching in fields seeded with wheat with reduced and no tillage is also practiced by a small number of farmers in the Terai of Nepal and in eastern Uttar Pradesh and Bihar states of India (Humphreys et al., 2004). The area of reduced and no-till wheat in the Indo-Gangetic Plain has expanded at an exponential rate since the late 1990s, increasing to an estimated 20–30% of the rice–wheat area or 2–3 million ha in 2006 (RWC, 2006). No-till sowing of wheat after combine-harvested rice, however, involves some difficulties including residue accumulation in the furrow openers, traction problems with the drive wheel of the seed drill, difficulty with fertilizer metering systems in the loose straw, and nonuniform sowing depth due to frequent lifting of the drill to clear blockages. A number of approaches are currently being tested for direct drilling into rice residue to solve the problem of machinery clogging and ‘‘hair-pinning’’ when the straw bends but is not cut or buried, resulting in seed remaining on the surface. These include double and triple disc systems (Gupta and Rickman, 2002), the straw thrower (Shukla et al., 2002), and the stubble chopper (Garg, 2002); although none of these approaches has been particularly successful to date. A promising new approach is the ‘‘Happy Seeder,’’ which combines the stubble mulching and seed drilling functions into one machine (Blackwell et al., 2004). The stubble is cut and picked up in front of the sowing tines, which therefore engage bare soil, and deposited behind the seed drill as mulch. The evolution of the technology, leading to a machine called the Comboþ Happy Seeder, is described by Humphreys et al. (2006). Results to date from India suggest that wheat can emerge through 8 t ha–1 of evenly spread rice residue mulch with no detrimental effect (Humphreys et al., 2004), although 4–6 t ha–1 is considered optimum in Sichuan, China. 4.2.3. Mulching with conventional tillage Rice residue can be used as mulch for the following non-flooded crop established after conventional tillage. Not many farmers follow this option because it involves temporarily removing the residue from the field and then returning it after the crop has been planted. This option is more feasible for farmers with small land holdings and sufficient labor (Tang


135

et al., 2004). There are only sporadic reports of rice residue mulching practiced in wheat planted in conventionally tilled fields in the rice–wheat system in South Asia (Zaman and Choudhuri, 1995). 4.2.4. Transfer of biomass as mulch Rice residue can be removed from the field where it is grown and used as mulch for improved production of vegetable crops (Vos et al., 1995), chickpea (Cicer arietinum L.) and mustard (Brassica rapa L.) (Rathore et al., 1999), and crops like bamboo ( Jiang et al., 2002). In China, it is also being used for mulching horticultural crops and tea. The cost of transportation and labor compared with the profits earned by farmers from increased production and saving in inputs like water are important factors in determining the feasibility of this management option. Rice residue can also be removed from the field for a number of useful purposes such as livestock bedding, composting for mushroom cultivation, bedding for vegetables such as cucumber and melon, and conversion to biofuel and biopower.

5. Evaluation of Options with Residues Managed During a Rice Crop We now evaluate residue management options for the category of cropping described in Section 4.1 (Rice following rice or a non-flooded crop) in which residues are managed during a period of rice cropping, usually on puddled and flooded soil. Our evaluation is based on criteria for productivity, profitability, environmental impact, and sustainability as described in Section 2.

5.1. Productivity As reviewed in Section 4.1.1, the incorporation of residue into soil during rice cropping is one of the most studied and promoted alternatives to residue burning across Asia. Much data are therefore available for evaluating its productivity and profitability. When interpreting these data, it is important to remember that large variations among seasons at a given location and among locations can exist in amount of residue incorporated, the time period for residue decomposition before establishment of the rice crop, and the soil aeration status (i.e., primarily aerobic or anaerobic) during residue decomposition. Soil and plant parameters can be affected by whether residues from only one or all crops in a year are incorporated. Mulching of lowland rice with residue of a preceding rice or upland crop is not a very practical option as explained in Section 4.1.2, and consequently it has not been much studied and only limited data are available.

136

Table 4 Effect of rice residue incorporation on grain yield of rice in rice–rice cropping systems in Asia

Experimental details

Kerala, India: Sandy loam soil with pH 5.5, fertilizer N applied at 100 kg ha–1, continuously flooded Same as above but intermittently flooded Indonesia: Acidic soil Acidic soil, fertilizer N at 120 kg ha–1 Acidic soil, no fertilizer N applied Muara, Indonesia: Latosol

Days residue incorporated before transplanting

Amount of rice residue incorporated (t ha–1)

Rice grain yield (t ha–1) Rice residue removed

Rice residue burned

Rice residue incorporated

Season a

4 4 4

3.04 6.15 2.90

– – –

3.18 6.64* 2.10

WS 1972 DSb 1973 WS 1973

28

4 4 4 20

2.75 6.12 2.45 2.78

– – – –

2.92 6.25 2.45 3.06

WS 1972 DS 1973 WS 1973 –

28 14 7 28 14 7 – –

10 10 10 10 10 10 10 20

4.27 4.22 4.00 3.49 3.22 2.84 2.17 2.17

– – – – – –

3.95 3.97 3.81 3.04 3.15 2.95 2.58 2.44

– – – – – – – –

Reference

Vamadevan et al. (1975)

Ismunadji (1978)

137

West Bengal, India: Sandy clay loam soil with pH 7.8, 60 kg N ha–1 in dry season and 40 kg N ha–1 in wet season Los Banõs, Philippines: Maahas clay, averaged for 5 cultivars after 16th crop Tropaqualf, clay soil with pH 6.6, 9-year study Tropaqualf, clay soil with pH 6.6, 9-year study Indonesia: Vertic Tropoquept, pH 6.5, no NPKS applied Same as above with NPKS applied

28 35

10 10

3.88 5.38

– –

4.11 6.04

WS DS

Chatterjee et al. (1979)

–

–

3.2

3.4

4.1*

–

A.B Capati, IRRI, cited by Ponnamperuma (1984)

–

–

8.2c

–

8.7c

WS+DS

–

–

8.3c

8.3c

8.7c

WS+DS

6

2.4

–

2.7

–

6

5.7

–

5.2

–

0

Le Cerff et al. (1985)

(continued)

138

Table 4 (continued)


Uttar Pradesh, India: Soil with pH 8.5 South Korea Shao Shing County, Eastern China: 150 kg N ha–1 Punjab, Pakistan: Soil pH 8.0 Hangzhou, Southeast China: Soil with pH 6.2

Rice grain yield (t ha–1)



Rice residue removed

Rice residue burned


30

–

2.21

–

2.92*

– –

4.3 5.97 5.97 5.38

– – – –

4.8* 6.15 6.17 6.04

–

35

7.5 3 6 10

20

5

5.6

–

5.9

WS

Zia et al. (1992)

Equal to 600 kg organic C ha–1 Equal to 600 kg organic C ha–1

6.20

–

6.19

Early rice

Lu et al. (2000)

6.20

–

6.13

0

153

Season

Reference

Pandey et al. (1985) Han et al. (1991) Li (1991)

DS

Los Banõs, Philippines: Aquandic Epiqualf, silty clay with pH 6.6 Central Java, Indonesia: Acric Tropoqualf, silt loam soil with pH 4.7, rainfed rice Central Thailand: Vertic Tropaquept with pH 5.8, rice residues (C:N¼67:1) contained 21.7 kg N ha1

–

–

5.4 3.0

– –

3.5 3.0

DS WS

Wassmann et al. (2000a)

– –

– –

4.8 4.7

–

5.3 4.6

WS DS

Setyanto et al. (2000)

7

3.75 (þ70 kg N ha1) 3.75 (þ70 kg N ha1) 3.75 (+no N) 3.75 (+no N) 5 (+70 kg N ha–1) 5 (+70 kg N ha–1) 5 (+no N)

4.7

4.2

4.8

DS 1997

Phongpan and Mosier (2003a)

3.8

3.7

3.9

WS 1997

3.7

3.7

4.0

DS 1997

4.0

3.9

4.1

WS 1997

4.4

–

4.1

DS 1998

4.1

–

4.2

WS 1998

3.0

–

3.4

DS 1998

7

7 7

Central Thailand: Ustic Endoaquerts with pH 6.2, rice residues

7 7 7

Phongpan and Mosier (2003b)

139

(continued)

140

Table 4

(continued)


(C:N=67:1) contained 25.4 kg N ha–1 Central Thailand: Ustic Endoaquerts with pH 6.7, rice residues (C:N=67:1) contained 25 kg N ha–1 Andhra Pradesh, India: Sandy clay loam soil with pH 7.5 Anhui, Guangde, China: Loamy clay soil a

Rice grain yield (t ha–1)



Rice residue removed

Rice residue burned


Season

7

5 (+no N)

3.7

–

4.3*

WS 1998

7

5 (+70 kg N ha–1) 5 (+70 kg N ha–1) 5 (+no N) 5 (+no N)

6.0

–

5.6

DS 1999

3.9

–

4.3

WS 2000

4.8 3.9

– –

5.0 4.4

DS 1999 WS 2000

4.4 5.7 4.4 5.7 3

6.2 3.1 6.3 3.2 6.2

6.9 3.4 7.5 3.5 –

7.0 3.5 7.3* 3.7* 6.7

DS 1999 WS 1999 DS 2000 WS 2000

7 7 7

14 3 14 3

Wet season. Dry season. c Yield of dry season+wet season rice crops. * Significantly more than grain yield with residue removed or residue burned treatments at P < 0.05. b

Reference

Phongpan and Mosier (2003c)

Surekha et al. (2003) Li et al. (2003)

Table 5 Effect of incorporation of upland (non-flooded) crop residue on grain yield of rice and residual effects on yield of the following upland crop in rice–upland cropping systems in Asia


Uttar Pradesh, India: Soil with pH 8.5, 2-year study West Bengal, India: Silty clay loam acid laterite soil, 2year study Haryana, India: Clay loam soil, 3-year study Uttar Pradesh, India: Clay loam soil with pH 8.6

Days residue incorporated before transplanting of rice

Kind and amount of upland crop residue incorporated (t ha–1)

30

Grain yield (t ha–1) Upland crop residue removed

Upland crop residue burned

Upland crop residue incorporated

Crop

Reference

Wheat, 0

2.21 4.48

– –

2.82 4.59

Rice Wheat (R)a

Pandey et al. (1985)

10

Wheat, 5

3.74 1.80

– –

4.17* 2.0*

Rice Wheat (R)

Sharma and Mitra (1992)

7–10

Wheat, 5.3

6.97 4.65

7.23 4.84

7.01 4.43

Rice Wheat (R)

30

Wheat, 10

4.10

–

4.45

4.10

–

4.08

2.29

–

2.72

Rice (100% NPK)b Rice (50% NPK)c Wheat (R)

Agrawal et al. (1995) Rajput (1995)

141

(continued)

Table 5

(continued)

142 Experimental details

New Delhi, India: Sandy clay loam with pH 8.1 Guizhou, China: Loamy clay, rice–rapeseed Haryana, India: Sandy loam soil, 25% N applied at the residue incorporation, 3-year study Punjab, India: Typic Ustochrept, sandy loam soil with pH 7.9, 2-year study Punjab, India: Typic Ustochrept loamy sand

Grain yield (t ha–1)

Days residue incorporated before transplanting of rice

Kind and amount of upland crop residue incorporated (t ha–1)

42

Wheat, 5.5

30

Wheat, 6.1

7

Rapeseed, 7.5

60

Wheat, 0

6.83 4.01

51–60

Wheat, 6

5.6 4.8

14

Wheat, 6 (90 kg N+13 kg P+13 kg K ha–1)

6.20c 4.48

Upland crop residue removed

Upland crop residue burned

Upland crop residue incorporated

Crop

Reference

3.3 2.4 3.8 3.6 6.7

3.3 2.3 3.9 3.6

3.6* 2.5 4.0* 3.7 5.9

Rice 1992 Wheat (R) Rice 1993 Wheat (R) Rice

Prasad et al. (1999)

6.85 4.04

Rice Wheat (R)

– –

5.5 4.9

Rice Wheat (R)

Aulakh et al. (2001)

–

5.10 4.33

Rice Wheat (R)

Bhandari et al. (2002)

Zhao and Zhu (2000) Dhiman et al. (2000)

with pH 8.2, 14-year study

Shandong, Lingyi, China: Sandy loam soil Anhui, Guangde, China: Loamy clay soil Shanghai, China: Loamy clay soil Punjab, India: Loamy sand soil with pH 7.6, 12-year study Jiangsu, Wuxi, China: Loamy clay with pH 6.8 a

6.20c 4.48

5.72 3.88

Rice Wheat (R)

7.2

7.8*

Rice

Ma et al. (2003)

Wheat, 1.5

6.2

6.6

Rice

Li et al. (2003)

Wheat, 4.4

8.0

8.8

Rice

52–55

Wheat, 6.4 0.5

5.74 4.41

5.37 4.32

Rice Wheat (R)

10

Wheat, 4.2

7.1

7.3

Rice

Yang et al. (2003) YadvinderSingh et al. (2004a) Zhu et al. (2004)

2

Wheat, 3 (105 kg N +20 kg P +19 kg K ha–1) Wheat, 7.5

– –

– –

(R) denotes that the crop was grown to study the residual effect of crop residues applied to the previous crop listed above. 100% NPK is the blanket recommendation of 120 kg N+13 kg P+25 kg K ha–1 for the region. c 120 kg N+26 kg P+25 kg K ha–1 * Significantly more than grain yield with residue removed or residue burned treatments at P < 0.05. b

143

144

Bijay-Singh et al.

5.1.1. Grain yield for rice A summary of 51 data sets is reported in Table 4 from rice–rice cropping system experiments designed to assess the effect of incorporating from 3 to 20 t ha–1 rice residue at 0–153 days before establishment of the following rice crop. Only 7 (~14% of the experiments) showed statistically significant increases in grain yield associated with residue incorporation. Table 5 lists studies in which 1.5–10 t ha–1 of residue from an upland (non-flooded) crop was incorporated 2–60 days before establishing the rice crop in India and China. Only in 4 out of 17 comparisons was rice yield significantly increased by incorporation of residue. The effect of residue incorporation on yield of lowland rice can depend on incorporation method, amount of residue, soil characteristics, and timing and amount of fertilizer application (Ponnamperuma, 1984). It was difficult to identify a similarity in terms of region, amount of residue incorporated, or time of incorporation among the data sets showing significant yield increases. In most cases, the effect of residue incorporation was assessed with application of fertilizers, suggesting that benefits in nutrient supply from the residue could have been masked by application of sufficient fertilizer to overcome nutrient limitations to rice. On the contrary, in studies with no application of fertilizer N (Le Cerff et al., 1985; Phongpan and Mosier, 2003a,b,c; Thuy, 2004) there is frequently no significant increase in grain yield associated with residue incorporation. Thuy et al. (2008) in a 3-year study at two locations in China found no significant increase in rice yield following incorporation of wheat or rice residue with and without fertilizer N. Thuy et al. (2008) concluded that incorporated residue had no benefit on N supply during the vegetative growth phase of rice, but N supply at later rice growth stages especially with long-duration rice could be slightly increased. A combined analysis of all data sets for effect of incorporating rice and upland crop residues on yield of the following rice (Tables 4 and 5) revealed no significant trend of increasing yield due to residue incorporation (Fig. 1). The slope and intercept of linear regressions were not different than 1 and 0, respectively, at P < 0.001. Residue of wheat incorporated into rice also did not have a residual effect on the wheat crop that followed rice (Table 5, Fig. 1). A multicountry coordinated research project on management of crop residue for sustainable production concluded that residue incorporation did not lead to higher yields (IAEA, 2003). In some cases the incorporation of crop residues, especially without application of fertilizer N, can reduce rice yield (Thuy, 2004). This is often attributed to short-term immobilization of N following the incorporation of residue with high C:N ratio (Bird et al., 2001; Buresh et al., 2008). The anaerobic decomposition of added residue and associated intensively reduced soil conditions can lead to production and accumulation of aliphatic aromatic acids that can inhibit rice root growth (Chung, 2001; Tanaka et al., 1990) particularly under low temperatures (Cho and


145

A Rice yield (t ha−1) with rice/upland crop residues incorporated

9 With rice residue With upland cropresidue 1:1 line

6

3

0 0

3

6

9

Rice yield (t ha−1) with no residue incorporation

Wheat yield (t ha−1) with wheat residue incorporated in the preceding rice

B 6 Wheat yield 1:1 line 4

2

0 0

2

4

6

Wheat yield (t ha−1) with no residue incorporation in the preceding rice

Figure 1 (A) Relationship between rice yield with and without incorporation of rice or upland crop residue, and (B) wheat yield with and without incorporation of wheat residue into the preceding rice crop. Data are from the literature listed in Tables 4 and 5. The slope and intercept for the linear regressions from A and B were not different than 1 and 0, respectively, at P < 0.001.

Ponnamperuma, 1971), to production of small-molecular-weight organic acids that can have some toxic properties (Rao and Mikkelsen, 1977), and to induced deficiencies of micronutrients especially zinc (Bijay-Singh et al., 1992; Nagarajah et al., 1989). One option for reducing the potential

Table 6 Effect of returning crop residue as mulch to rice fields on grain yield of rice in rice-based cropping systems in Asia

146 Experimental details

Days residue mulched before planting

Grain yield (t ha1) Amount of residue mulched (t ha–1)

Kind of residue

Crop residue mulching in conventionally tilled rice fields 0 Equal to Hangzhou, Rice 600 kg Southeast organic China: Soil C ha1 with pH 6.2, rice straw mulched before transplanting of rice 0 – Rice Guangzhou, China: Grown as upland rice, lowland rice without mulching yielded 6.96 t ha–1 Bhairahawa, – 1.5 Wheat Nepal

Sichuan, China: Fluvaquent grey flood plain soil with pH 7.8

0

–

Wheat

Crop residue removed

Crop residue incorporated

Crop residue mulched

Crop

Reference

6.31

6.44

6.44

Late rice

Lu et al. (2000)

5.98

–

6.59*

Rice

Fan et al. (2002)

3.75

4.2

4.75*

Rice

5.38 (nonflooded rice) 5.74*

Rice

Duxbury and Lauren (2002) Liu et al. (2003)

6.25 (flooded rice) 4.63

Wheat (R)a with 60 kg N ha–1

147

0 Sichuan, China: Loam soil with pH 6.5, nonflooded rice 0 Jiangxi, Yujiang, China: Fluvisol, pH 5.5, organic matter 25.5 g kg–1, rice–rice rotation, nonflooded rice Crop residue mulching in no-till rice Sichuan, Chengdu, China: Sandy loam soil 0 Sichuan, China: Heavy clay soil, permanent bed planting with double zero tillage for rice and wheat Sichuan, Jianyang, China: Loamy clay soil with pH 6.45, no tillage, rice seedling broadcasting

–

Wheat

6.45b 4.62

– –

6.84 5.29*

Rice Wheat (R)

Liu et al. (2005)

5

Rice

4.72c

–

6.75*

Rice

Qin et al. (2006)

7.5

Wheat

6.6

–

6.8

Dryland rice

Ai et al. (2003)

–

Wheat, rice (in ditches)

5.36

–

5.72

Rice on permanent beds

Tang et al. (2004)

5.3

Wheat

8.9

9.3

Rice

Zheng et al. (2005)

(continued)

Table 6 (continued)


Sichuan, Jianyang, China: Loamy clay soil with pH 6.45, rice– rapeseed, no tillage, rice seedling broadcasting a

Days residue mulched before planting

Grain yield (t ha1) Amount of residue mulched (t ha–1)

Kind of residue

Crop residue removed

5.3

Rapeseed

8.8

Crop residue incorporated

(R) denotes that the crop was grown to study the residual effect of crop residues applied to the previous crop listed above. The no mulch flooded treatment yielded 7.2 t ha–1 and was not significantly different than mulched non-flooded yield. –1 The no mulch flooded treatment yielded 6.8 t ha and was not significantly different than mulched non-flooded yield. * Significantly more than grain yield with residue removed treatments at P < 0.05. b c

Crop residue mulched

Crop

Reference

9.4*

Rice

Zheng et al. (2005)


149

detrimental effect of decomposing crop residues on rice seedlings is the type of in situ composting in which the residues are next to but not touching the seedlings in ditches parallel to the rows. This practice, however, did not increase yield of rice in a barley–rice rotation (Zhong et al., 2003). A summary of data from 10 mulching experiments across Asia shows a significant positive effect of rice or upland crop residue applied as mulch on rice grain yield in 4 out of 10 experiments (Table 6 ). In Sichuan Province in China, research has examined the mulching of rice with residue of preceding wheat. The mulch is applied after rice transplanting, and then the mulched rice is not kept flooded to save irrigation water. In two studies (Liu et al., 2003, 2005) the mulch did not increase rice yield, but yield of the wheat crop following rice was significantly increased. The partially decomposed mulch from the rice crop, incorporated during land preparation for the next wheat crop, presumably benefited wheat. The application of mulch after transplanting rice seedlings requires removal of residue from the field and then return as mulch. Following this approach, mulching of rice with wheat residue in Nepal significantly increase rice yield compared to plots without mulch (Duxbury and Lauren, 2002). The application of rice residue before transplanting rice, on the other hand, as examined in Southeast China did not increase rice yield (Lu et al., 2000), probably because the mulch to some extent got incorporated into soil during transplanting of rice. Reduced tillage rather than conventional puddling before rice can result in soil conditions less suitable for transplanting of rice. The throwing of rice seedlings and direct seeding of rice into mulch have consequently been examined as alternatives to transplanting in reduced and no-till rice systems. In a study in Sichuan Province in China, rice yield was significantly increased with establishment by seedling throwing into mulch from residue of a preceding rapeseed crop (Zheng et al., 2005). No benefit of mulching on rice yield was observed when rice and wheat residues were applied as mulch in ditches to rice grown on permanent beds in a double no tillage rice–wheat cropping system (Tang et al., 2004). In a wheat–rice system in Korea, Cho et al. (2001) examined the simultaneous harvesting of wheat and sowing of rice with a sowing device mounted on the combine harvester. Rice seeds broadcast onto the untilled soil surface were covered with wheat straw chopped into mulch by the combine harvester, and comparable rice yields were obtained with no-till, direct sowing of rice and conventional till, transplanting of rice. 5.1.2. Fertilizer use efficiency for rice An important indicator of fertilizer use efficiency is the increase in grain yield per unit of nutrient applied as fertilizer, which is usually referred to as agronomic efficiency. An increase in the agronomic efficiency for a given nutrient occurs when the crop response to the nutrient (i.e., the difference in yield between treatments with and without addition of the nutrient) increases

150

Bijay-Singh et al.

per unit of applied nutrient. As indicated in Section 5.1.1, the incorporation of crop residues seldom significantly increases the yield of rice with and without fertilizer application (Tables 4 and 5). In such cases of no increase in grain yield, the incorporation of crop residues would not increase either the agronomic efficiency of a nutrient or the partial factor productivity of the nutrient (i.e., total grain yield with added nutrient per unit of applied nutrient) unless the incorporation of residue was associated with a reduced rate of applied nutrient. Most studies assessing the effect of residues on rice, however, use similar rates of fertilizer in the treatments with and without the residue. Both increases and decreases in agronomic efficiency of fertilizer N (AEN) have been reported for rice when crop residue is incorporated rather than removed with no change in the rate of fertilizer N. Incorporation of rice residue with 70 kg N ha–1 to dry season rice in Central Thailand reduced AEN by 20–50% (Phongpan and Mosier, 2003a,b,c). This was attributed to a reduced yield response of rice to fertilizer N following incorporation of residue, which slightly increased grain yield without fertilizer N but not with fertilizer N. In a 3-year study at two locations in China, the incorporation of rice or wheat residue typically had no effect on AEN (Thuy et al., 2008). In an experiment in the Philippines, the incorporation of rice residue 20 days before transplanting dry-season rice significantly increased AEN irrespective of whether soil was flooded or aerobic for the 2 months from harvest of the previous rice crop to land preparation for rice (Buresh et al., 2007, unpublished data). This increase in AEN was associated with increased response of rice to fertilizer N with incorporation of residue, which arose because the incorporation of residue significantly reduced yield without fertilizer N. Lower yield without fertilizer N was associated with reduced supply of plant-available N due to immobilization of N following residue decomposition (Thuy, 2004). The higher frequency of yield gains with mulching (Table 6) than incorporation of residue (Tables 4 and 5) suggests mulching might be more likely than incorporation to increase fertilizer use efficiency when fertilizer rates are not adjusted for residue management. An approach for increasing AEN while maintaining or even increasing rice yield is to combine the retention of crop residues with improved management of fertilizer N. Xu et al. (2007) found markedly increased fertilizer N use efficiency for rice when the incorporation of residue from a previous wheat crop was combined with timing and rates of fertilizer N that better matched the needs of the rice crop. This approach of improved matching of fertilizer N to crop needs, referred to as site-specific nutrient management (SSNM) (IRRI, 2007), involves adjusting fertilizer N during early vegetative growth to match crop needs as determined by relatively slow early crop growth and immobilization of N from decomposing residue, and it involves adjusting fertilizer N during tillering and at panicle initiation based on the N status of rice leaves. At locations with excessive use of fertilizer N for rice, such as eastern and southern China, an increase in AEN is largely associated with marked reductions in the use of fertilizer N while


151

maintaining or slightly increasing yield (Peng et al., 2006a). At locations in which existing fertilizer N rates are near or below the optimal, an increase in grain yield, often with the same or more fertilizer N, is required to increase AEN. When fertilizer N is optimally managed for rice, the incorporation of crop residue typically has negligible or only small savings in fertilizer N (Linquist et al., 2006; Thuy et al., 2008). The vegetative portion of mature rice contains ~80–85% of the total plant K, and most of this K is not lost during open-field burning of residue (Dobermann and Fairhurst, 2000). The management of crop residue consequently has a strong effect on the requirements of rice for fertilizer K (Witt et al., 2007). Recommended rates for fertilizer K should consequently be adjusted based on the supply of K from crop residues, even when burnt, in order to ensure high rice yields with high efficiency of fertilizer K use. 5.1.3. Water use efficiency for rice There have been several reports in China of savings in irrigation water associated with mulching rice with crop residue and then growing rice under non-flooded conditions. Yields are often comparable for mulched, non-flooded rice and conventional flooded rice, but water use efficiency (i.e., grain yield per unit of water used) can be markedly higher for nonflooded mulched rice (Fan et al., 2002). Qin et al. (2006) found that total water use in non-flooded rice with and without rice straw mulch was 3.3 and 2.4 times less than for rice grown under flooded conditions. Yields were comparable for mulched, non-flooded rice (6.7 t ha–1) and conventional flooded rice grown without mulch (6.8 t ha–1). But growth of non-flooded rice without mulch significantly reduced yield (4.7 t ha–1). Mulching rice to save water can be particularly attractive in regions with limited rainfall or irrigation water. 5.1.4. Pest and disease pressure for rice Weed pressure is typically minimal in flooded soil, decreasing the importance of mulch as a weed suppressant in lowland rice. However, mulch might help minimize weed competition in production systems without soil flooding when rice seedlings are small. In non-flooded rice mulched with wheat residue in the rice–wheat cropping system in southwestern China, Liu (2005) recorded total weed biomass of 1.3 t ha–1 in mulched plots as compared to 4.4 t ha–1 in plots without mulch. The corresponding uptake of N by weeds was 25 kg N ha–1 in mulched plots and 55 kg N ha–1 in plots without mulch. Residue incorporation in rice monocropping systems has been shown to aggravate fungal diseases including stem rot (Sclerotium oryzae) and sheath spot (Rhizoctonia oryzae-sativae), historically leading to the recommendation of infield residue burning as the best means of disease control (Miller and Webster, 2001; Webster et al., 1981). Residue incorporation at the beginning of the flooded winter fallow was identified as an alternative means of control for stem

152

Bijay-Singh et al.

rot, because the pathogen decomposed faster in the flooded soil ecosystem than on the aerobic surface (Cintas and Webster, 2001). Relatively little is known about the effects of mulch on disease in flooded systems because of the impracticality of mulching after puddling the soil. For sheath rot, the sclerotia floating to the surface of the floodwater during the cropping season were most readily able to inoculate and infect the plant (Miller and Webster, 2001), implying that using infected residue as mulch would be much worse than incorporating it. There is still insufficient knowledge about how residue management of rice–upland crop rotations affects rice diseases. Mosquitoes are another important pest affected by residue management in cropping systems with flooded soil. They require standing water to complete their life cycle, and their larvae grow better in flooded fields in which rice residue has been incorporated (Lawler and Dritz, 2006). Therefore, from the perspective of disease and mosquito control, it would be better to remove residue than incorporate or mulch it. When weeds are a significant problem, which usually occurs only when soil is not flooded during crop establishment and early crop growth, mulch would be a good option although it might incur increased disease pressure.

5.2. Profitability Profitability considers the potential economic gains or losses resulting from observed changes in productivity. In situ incorporation of crop residue during normal tillage before establishment of rice results in no extra cost for managing crop residue provided the normal tillage does not involve more time or energy due to the presence of residue. If cost of land preparation is not altered by the incorporation of residue, then any increase in production can result in net profit for the farmer. Because of potential short-term detrimental effects of anaerobic residue decomposition on the young rice crop, such as immobilization of N and release of organic acids, the preferred practice is typically to incorporate residue several weeks before establishing rice. In such case, a change in the timing of tillage or land preparation practices to accommodate the incorporation of crop residue could result in extra expenditure. The profitability of residue incorporation then depends on the extra costs for field operations rather than only the effect of residue incorporation on grain yield. Even though much information is available on the effect of residue on rice yield (Fig. 1), corresponding information on any additional costs associated with residue incorporation is typically not available. Dawe et al. (2003) examined the profitability of incorporating rice and wheat residue using data from two rice–rice experiments (in China and Malaysia) and 12 rice–wheat experiments (in India) conducted for 10–17 years and representing a wide variety of soil types, climatic conditions, and crop management practices. In the two long-term experiments on rice–rice cropping systems, 5–6 t ha–1 of rice residue were incorporated before


153

each crop. In the other 12 experiments, wheat residue (6–15.8 t ha–1) was incorporated before the rice crop. Recommended levels of fertilizer N, P, and K were applied to all crops. The break-even cost (BEC), defined as the maximum cost that farmers can incur in managing crop residue without losing money, was computed using the average farm-level prices of US$ 0.15 kg–1 for paddy rice and US$ 0.12 kg–1 for wheat and differences in crop yields between NPK and NPK plus crop residue treatments for the annual rotation. Residue incorporation would be profitable when the sum of all additional costs of incorporating residues above the normal operating cost of the farm are less than the BEC. For rice–wheat experiments, the BEC ranged between –23 and 8 US$ ha–1 and averaged –3 US$ ha–1 per crop, suggesting it was usually not profitable to incorporate wheat to rice. In the two rice–rice experiments, the BEC was 45 and 40 US$ ha–1 per crop, indicating the incorporation of rice residues before transplanting of rice was profitable for rice fertilized with NPK provided there were little or no additional costs associated with incorporation of the residue. These positive BEC arose because of slightly higher yields (mean ¼ 0.2–0.6 t ha–1) when residue was incorporated. Reports of increased yield associated with rice residue incorporation are, however, relatively rare (Table 4). Already in the 1970s, Tanaka (1974) observed the economics of residue incorporation did not encourage farmers to regularly adopt the practice even though the incorporation of residue reportedly improved soil conditions for flooded rice. The tendency for more frequent increases in rice yield when residue of the previous crop is mulched (Table 6) rather than incorporated (Tables 4 and 5) suggests mulching could be profitable when the cost of managing residue as mulch is relatively low. The profitably of mulching could also be influenced by other factors such as potential savings in irrigation water. Savings in irrigation cost without loss in rice yield were reported in China when rice was mulched with wheat residue and grown on non-flooded soil (Fan et al., 2005; Liu et al., 2003). Reports of significantly increased yield for wheat following mulched, non-flooded rice (Liu et al., 2005) suggest additional scope for increased profitability of the production system.

5.3. Environmental impact 5.3.1. Air quality Smoke is one of the most serious environmental problems associated with large-scale, open-field burning of crop residues. It pollutes air with a mixture of gases and fine particles, which can lodge deep in our lungs when we breathe. The peak in asthma admissions to hospitals in India coincides with the annual burning of rice residue in surrounding fields (Bijay-Singh and Yadvinder-Singh, 2003). Smoke particles are less than a micron in diameter, which allows them to remain in the atmosphere up to

154

Bijay-Singh et al.

several weeks (Cooke and Wilson, 1996) and therefore spread hundreds to thousands of kilometers before falling back to earth. Large patches of black C aerosol can be detected by satellites over India and China just after the harvest season during large-scale residue burning. As a result of aerosol–radiation–temperature–CO2 interactions, these patches can lead to reduced biomass and grain yield of field crops. Almost any alternative to open-field burning of residue can substantially reduce harmful environmental and health effects of smoke. Streets (2004) predicted that black C emissions in China could be reduced from 75 Gg in 1995 to 56 Gg by 2020 by enforcing laws against residue burning. 5.3.2. Greenhouse gas emissions for rice Another important environmental impact associated with rice residue management is greenhouse gas emissions, including the three main agricultural contributors to climate change: carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Atmospheric concentrations of these gases have been increasing in recent decades due to human activity including agriculture, and they have been shown to contribute to increases in average global temperatures (Houghton et al., 2001). When returned to the field, some of the C in crop residue might be retained in the soil organic matter (SOM) with the rest lost as CO2 or CH4. When residue-C is retained as SOM, there is a net benefit for the greenhouse gas balance because some of the CO2 taken in by the plant is no longer in the atmosphere. When residue-C is lost as CO2, the greenhouse gas balance is neutral because the C came in and out of the plant as CO2. However, when residue-C is lost as CH4, there is a strongly unfavorable consequence for the greenhouse gas balance because each molecule of CH4 has 62 times greater global warming potential than a molecule of CO2 (20-year horizon, Houghton et al., 2001). When soil is anaerobic, the end product of microbial decomposition shifts toward CH4 instead of CO2. Hence, flooded rice systems with retained crop residues are sources of CH4 emission. N2O, which has an even higher global warming potential than CH4 (275 times that of CO2 on a 20-year horizon, Houghton et al., 2001), is formed during aerobic nitrification of ammonium and anaerobic denitrification of nitrate. Under extended periods of soil flooding—as is typical during rice cropping—the predominantly anaerobic soil conditions are not favorable for rapid nitrification– denitrification and emission of N2O (Buresh et al., 2008). The emission of N2O can, however, be important during periods of alternate soil drying and wetting and when soil following a prolonged aerobic period is flooded, such as during land preparation for rice cultivation. The effects of major residue management options on CH4 and N2O emissions from lowland rice are summarized in Table 7. CH4 emission from rice paddies has been measured many times, testing effects of diverse

Table 7 Trends in CH4 and N2O emitted under different residue management options in lowland rice CH4

Management decision

Major options

Best

Crop residue use

Return Removal

Water Continuous management flooding (including preseason fallow or not) Mid-season drainage (one or more drainage periods, intermittent flooding) Mostly aerobic How residue is returned

Incorporation Surface mulch

Timing of residue return

Beginning of preseason fallow Just before rice establishment

N2O

a

Worst

Best

Removal

Return

No clear effect

Aerobic

Continuous flooding

Continuous flooding

No effect Several months before flooding

Close to flooding

Worst

No effectb

Yan et al. (2005) Intermittent flooding

?

References

Bronson et al. (1997b), Yan et al. (2005), Li et al. (2006)

Ishibashi et al. (2005) Yan et al. (2005), Li et al. (2006) (continued)

Table 7

(continued)

Management decision

Type of residue

Overall combination

a

CH4 Major options

Best

High C:N (rice straw) Low C:N (legume residue) Fresh plant material Partially decomposed plant material (compost, manure)

a

N2O Worst

Best

Worst

References

Partially decomposed plant material

Fresh plant material

High C:N

Low C:N

Yan et al. (2005), Zou et al. (2005)

Removal, minimum flooding

Fresh residue applied incorporated just before flooding

Continuous flooding

Mulch with low C:N residue, intermittent flooding

For both gases, ‘‘best’’ means the management practice that results in the lowest gas emission. Lack of timing effect on N2O is based on a model rather than actual data (Li et al., 2006). Note: Methane is in bold because it represents the greater global warming threat in systems that are predominantly flooded b


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variables including residue management, water management, temperature, and soil physical and biochemical properties. In a review of ~1000 seasonal measurements from sites across dominant agroecological zones in Asia, Yan et al. (2005) found the two most important factors controlling CH4 emission were presence or absence of residue followed by water management. Residue return by any method or timing of application or incorporation caused a statistically significant (P < 0.0001) and often very large increase in CH4 emission compared with residue removal. Residue provides a source of easily decomposable organic C, which means (1) anaerobic bacterial populations increase, using up oxygen (O2) followed by other reducible soil components and driving the redox potential down between the transition where CH4 rather than CO2 is end product of decomposition, and (2) methanogenic bacteria have sufficient substrate-C to form CH4 (Yagi and Minami, 1990). The more decomposed the residue before flooding, the less CH4 emitted. The decomposition of residue before soil flooding for rice production can be accomplished by (1) incorporating crop residue soon after harvesting a crop and allowing it to decompose aerobically before soil flooding for the next rice crop (Wassmann et al., 2000a,b,c; Yan et al., 2005), (2) composting the residue off-field (Corton et al., 2000; Yagi and Minami, 1990), or (3) feeding the residue to cattle and returning it as manure (Setyanto et al., 2000; Wang et al., 2000). Although these options were not all directly compared in one study, each of them resulted in lower CH4 emission as compared with the application of fresh straw just before flooding. Because methanogenic bacteria are anaerobes, CH4 formation is minimal under aerobic conditions, meaning that aerobic water management practices mitigate CH4 emission. In many of the experiments cited by Yan et al. (2005), brief mid-season drainage periods as compared to continuous soil flooding significantly reduced total seasonal CH4 emission. The emissions of CH4 from mulched relative to incorporated residue can be strongly influenced by tillage and water management (Hanaki et al., 2002; Harada et al., 2007; Ishibashi et al., 2001, 2005; Xu et al., 2004). When the surface of reduced or no tillage soil was drier than puddled soil due to more rapid percolation, much less CH4 was emitted from the mulched than incorporated residue. But when soil water content was similar between residue management practices, the CH4 emission was also comparable between practices (Ishibashi et al., 2001). We conclude that water management differences have a larger effect than residue placement on CH4 emission, and there is insufficient information to differentiate between incorporated and mulched residue per se (Table 7). In lowland rice cropping systems with continuous soil flooding, N2O emission is not a significant risk regardless of residue management. However, any time the soil is not flooded—during fallow, mid-season drainage, harvest drainage, or water shortage—N2O formation and emission become likely (Table 7). The water management strategies that mitigate CH4 emission simultaneously exacerbate N2O emission. While many have

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reported significant increases in N2O emission following fertilizer N application in partially flooded systems (Xu et al., 2004), there is not a consistent trend in the effect of residue removal or return. Of the available direct comparisons in field or pot experiments, most are not significant and those that are significant are in both directions with returned residue sometimes showing higher and sometimes lower N2O than with residue removed, occasionally even within the same data set (Bronson et al., 1997b; Lou et al., 2007; Ma et al., 2007; Zheng et al., 2000; Zou et al., 2004a, 2005). In the comparison between residue incorporation and surface mulch, N2O emission is often affected more by the soil water regime than the management of residue per se (Harada et al., 2007). One model predicted, without measured data, no effect of the timing of residue return on N2O emission (Li et al., 2006). After water and fertilizer N management, the most important factor determining N2O emission was the type of residue. Incorporation of a high C:N ratio residue like wheat straw decreased N2O emission, presumably through N immobilization, while a low C:N residue like rapeseed cake increased it (Zou et al., 2005). From the perspective of minimizing greenhouse gas emissions from lowland rice, incorporation of fresh residue would be the worst option for CH4 emission, especially where soil is flooded continuously for the month following incorporation (Table 7). However, regardless of residue management, it is important to keep the soil flooded as much as possible to minimize N2O emission. Therefore, a compromise could be to keep the soil flooded most of the time to minimize N2O emission and to remove residue to minimize CH4 emission. Prolonged aerobic decomposition of residue before rice cropping might not be feasible in intensively cropped rice-based cropping systems, and prolonged fallows with aerobic soil can favor formation of nitrate that is subsequently denitrified with formation of N2O upon soil flooding for rice cultivation (Buresh et al., 2008).

5.4. Sustainability 5.4.1. Yield trends with flooded residue management In 14 long-term experiments (2 on rice–rice and 12 on rice–wheat cropping system) explained in the Section 5.2, Dawe et al. (2003) observed that the value of the F-statistic for testing the null hypothesis of identical yield trends in NPK and NPK plus crop residue treatments was never significant at the 5% level, indicating no statistically distinguishable differences in yield trends between the two treatments. The yield trend in the residue treatment was more positive or less negative than the trend in the NPK treatment for 11 of the 16 cases of rice cropping, which included both rice crops in the two rice–rice systems. These differences in yield trends were not statistically significant at the 5% level. Across all sites, there was no consistent yield


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increase from application of straw. For the rice crops in the rice–rice cropping systems, the average rice yield increase during the 11–12-year duration of the experiments due to crop residue application was 0.43 t ha–1 per crop. For the 12 rice crops in the rice–wheat cropping systems, rice yields during the 11–17-year duration of the experiments were reduced in the crop residue treatments by an average 0.40 t ha–1 per crop. 5.4.2. Soil changes with flooded residue management The long-term incorporation of crop residues in flooded rice soil can increase SOM, total soil N, fractions of soil C, and soil biological activity; but it can decrease the availability of zinc (Yadvinder-Singh et al., 2005). Continuous incorporation of crop residues after each crop can eventually increase the N-supplying capacity of rice soils (Eagle et al., 2000; Verma and Bhagat, 1992). Long-term studies indicate the supply of plant-available soil N can increase after 5–10 years of continuous incorporation of crop residues in tropical (Cassman et al., 1996) and temperate area (Bird et al., 2001). The benefits of incorporated residues on SOM and soil N supply, however, seldom translate into increased yield (Section 5.1) or profit (Section 5.2) for flooded rice. The effect of crop residues on properties of flooded soil has already been extensively reviewed by Yadvinder-Singh et al. (2005), and it is consequently not covered in this chapter. A noteworthy feature of flooded rice soils with continuous and intensive rice cropping is the maintenance and even buildup of SOM (Bronson et al., 1997a; Cassman et al., 1995; Witt et al., 2000). Appreciable inputs of N from biological N2 fixation (BNF) in continuous flooded rice production systems contribute to the maintenance of soil N even in the absence of N fertilization (Ladha et al., 2000). Prolonged soil submergence and anaerobic soil conditions can lead to the buildup of phenolic compounds that can immobilize N abiotically, thereby reducing net N mineralization and supply of plant-available soil N (Olk et al., 1996, 2000). However, long-term experiments with continuous cropping of flooded rice in the Philippines reveal no decline in rice yield during the past 10–20 years in zero–N plots receiving ample supplies of other nutrients (Padilla, 2001). Yield of flooded rice without fertilizer N, which presumably reflects the supply of plant-available soil N, was maintained even when all above-ground crop residues were removed for each crop. The results suggest that the inputs of N during continuous soil flooding via BNF and biological N mineralization matched or exceeded any decline in N-supplying capacity arising from abiotic immobilization of N associated with buildup of phenolic compounds. Long-term experiments in the tropics indicate the incorporation of crop residue is not essential for maintenance of SOM and soil N-supplying capacity in continuous rice cultivation on puddled and flooded soil. Pampolino et al. (2008b) examined trends in total soil C and N during 15 years of

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continuous cultivation with two or three flooded rice crops per year in three long-term experiments with incorporation of crop residues and one long-term experiment with removal of all above-ground biomass after each crop. Soil C was maintained during the 15 years in each experiment. In the experiment with removal of all above-ground crop residues and four rates of fertilizer N, soil C increased by 1.5–2.3 g kg–1 and soil N increased by 0.09– 0.15 g kg–1 during the 15 years. Soil N-supplying capacity as determined by anaerobic N mineralization was statistically similar at the start and end of the 15-year period regardless of fertilizer N management. The input of N via BNF as estimated from N balances in a treatment without fertilizer N averaged 81 kg N ha–1 year–1 during the 15 years. Whereas SOM can be maintained in flooded rice–rice systems regardless of residue management, SOM significantly decreased when one flooded rice crop was replaced by conventional till maize with retention of rice and maize residues (Witt et al., 2000; Pampolino et al., 2008a). On the basis of above research findings, we conclude residues from the crop preceding rice on puddled and flooded soil can be considered for removal for off-field uses, without loss in productivity or sustainability of the flooded rice provided fertilizer is appropriate increased to compensate for nutrient removal in the residue. The management of K is particularly important when crop residues are removed because crop residues can markedly increase K availability in soil and decrease the crop response to K application (Chatterjee and Mondal, 1996; Ning and Hu, 1990; Patil et al., 1993; Sarkar et al., 1989).

6. Evaluation of Options with Residues Managed During a Non-Flooded Crop In this section, we evaluate residue management options for the category of cropping system described in Section 4.2(non-flooded crop following rice) in which residues are managed during the period of a crop grown on aerobic soil without flooding. Our evaluation is based on criteria for productivity, profitability, environmental impact, and sustainability as described in Section 2.

6.1. Productivity The two main options available for in-field management of crop residue during the non-flooded (upland) crop in rice-based cropping systems are incorporation into the soil and leaving the residue on the soil surface as mulch. Incorporation typically involves conventional tillage of soil, whereas mulching usually involves reduced or no tillage. While these options are comparable to those available for flooded rice (Section 5), their feasibility and


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implementation markedly differ between a non-flooded crop and flooded rice because of differences in land preparation and tillage options between crops. Mulching of crop residue is much more feasible during a non-flooded crop than flooded rice because of greater opportunities for reduced and no tillage. Most of the information on managing rice residue for non-flooded crops in rice-based cropping systems comes from rice–wheat systems. Although mulching rice residue in conventionally tilled wheat has been attempted by some researchers, mulching in no-till wheat is favored and now facilitated by recent developments of appropriate machinery. 6.1.1. Grain yield for non-flooded crop The incorporation of rice residue before wheat or rapeseed significantly increased yield of the nonrice crop in only 1 of 16 data sets examined from China and India in which 3–7.9 t ha–1 of rice residue was incorporated into soil 10–40 days before sowing of wheat or rapeseed (Table 8). A combined analysis of all data sets revealed no trend of greater yield when rice residue was incorporated rather than removed (Fig. 2). The slope and intercept for the linear regression comparing yield with and without residue incorporation were not significantly different from 1 and 0, respectively, at P < 0.001. Combined data for on-farm experiments with rice–wheat cropping in northwestern India similarly reveal no trend of greater yield of wheat when rice residue was incorporated rather than burnt or removed (Fig. 2). Some of the studies reported for rice–wheat systems in Table 8 investigated the residual effect of rice residue incorporated to wheat on the yield of the next rice crop after wheat, but on all seven cases the incorporated residue had not significant residual effect on yield of the following rice. Table 9 summarizes results for rice–wheat systems in which residue from each crop was incorporated before the following crop, resulting in large amounts of incorporated residue. Wheat yield was significantly increased by incorporated residue in only 1 out of 13 cases. When all data for rice and wheat were combined, no effect of residue on grain yield was detected (Fig. 3). The incorporation of crop residue can have adverse effects on the following crop (Cannell and Lynch, 1984), although in some studies the negative effects of residue incorporation in a rice–wheat cropping system diminished after a few initial years (Dhiman et al., 2000). But in other studies the negative effects were not reversed even after 11 years (Beri et al., 1995). The negative effects on wheat yield can result from immobilization of N by the decomposing residue. This is supported by the observation of Beri et al. (1995) of greater decline in wheat yield at a low rate of N application (0.5 t ha–1 decline at 60 kg N ha–1) than at a high rate of N application (0.08 t ha–1 decline at 180 kg N ha–1). The magnitude of N immobilization depends on the extent of straw decomposition before N fertilization (Bhogal et al., 1997). The immobilization of N is temporary, and it can be followed later in the cropping season by release of N through mineralization. In such case the

162 Table 8 in Asia

Effect of rice residue incorporation on grain yield of upland crop and residual effects on the following rice in rice–upland crop systems

Days residue incorporated before planting


Himachal Pradesh, India: Acidic clay loam soil, chopped rice straw incorporated Himachal Pradesh, India: Silty clay loam soil with pH 5.9, chopped rice straw incorporated up to 20 cm soil depth

28

30

Punjab, Pakistan Haryana, India: Sandy loam soil, 25% N applied at the time of residue incorporation


Grain yield (t ha1) Rice residue removed

Rice residue burned


Crop

Reference

–

2.76 2.37

– –

2.79 2.47

Wheat Rice (R)a

Sharma et al. (1985, 1987)

5

2.6

2.6

2.1

Verma and Bhagat (1992)

5

3.7 2.2

3.6 2.2

3.8 2.2

–

–

3.8 2.91

3.7 –

3.9 3.51*

Wheat (first 3 crops) Rice (R) Wheat (next 2 crops) Rice (R) Wheat

30

–

4.01 6.83

– –

3.72 7.11

Wheat Rice (R)

Salim (1995) Dhiman et al. (2000)

Punjab, India: Typic Ustipsamment, loamy sand with pH 7.3 Shanghai, China: Loamy clay soil Anhui, Guangde: China: Loamy clay soil Punjab, India: Sandy loam soil with pH7.2, 7-year study

Punjab, India: Sandy loam—silt loam, on-farm experiments

20 40

6.4

5.06 5.06

7.5

5.1

5.00 4.89

Wheat

4.7

5.1

Wheat

3

1.7

1.9

Rapeseed

40

7.1–7.9

20

7.1–7.9

10

7.1–7.9

21

5.0–7.0

4.94 6.19 4.94 6.19 4.94 6.19 4.3 4.5 4.5 4.6 4.4

5.17 6.34 5.22 6.29 4.95 6.33 4.5 5.1 5.0 4.3 3.7

Wheat Rice (R) Wheat Rice (R) Wheat Rice (R) Wheat Wheat Wheat Wheat Wheat

– – – – – – – – – – –

a (R) denotes that the crop was grown to study the residual effect of crop residues applied to the previous crop listed above. * Significantly more than grain yield with residue removed or residue burned treatments at P < 0.05.

BijaySingh et al. (2001) Yang et al. (2003) Li et al. (2003) YadvinderSingh et al. (2004b) Sidhu et al. (2007)

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Upland crop yield (t ha−1) with rice residue incorporated

6 Upland crop yield 1:1 line

5

4

3

2

1

0 0

1

2

3

4

5

6

Upland crop yield (t ha−1) with rice residue removed or burned

Figure 2 Relationship between yield of a non-flooded crop (wheat in most cases, and oilseed rape) with and without rice residue incorporation. Data are from the literature listed in Table 8 combined with unpublished data from on-farm experiments with recommended NPK levels in northwestern India (P. R. Gajri, Department of Soils, Punjab Agricultural University). The slope and intercept for the linear regression were not different than 1 and 0, respectively, at P 3.6 million accessions of 30 different crop plants) accessions from 18,000 plant species (SINGER-The System-wide Information Network for Genetic Resources; http://singer.grinfo.net/). These accessions constitute a rich source of diversity, at both the phenotypic and nucleotide sequence levels and could be the main targets for Ecotilling in future. Currently, a number of initiatives on Ecotilling in various cereal species such as rice, maize, and barley have been launched (Table 6).

3.5. DEALING and DeleteageneTM A year before TILLING was reported, Liu et al. (1999) demonstrated the high-throughput PCR detection of deletions by using gene-specific primers to screen DNA pools of deletion lines of C. elegans. In this study, four chemical mutagens including EMS, ENU, DEO and UV-TMP were used to induce deletion mutations. A majority of deletions (average size = 1400 bp) occurred in exons and led to loss of gene function leading to mutant phenotypes. This approach allowed the establishment of gene-function relationship. A similar approach called DeleteageneTM for detection of deletions in fast-neutron induced mutants in Arabidopsis and rice was reported by Li et al. (2001, 2002). For further details with respect to creation of deletion library, pooling strategy, PCR screening, and DeleteageneTM applications refer to the review by Li and Zhang (2002). Large pools of DNA extracted from mutagenized populations generated by fast neutron or chemical deletogens such as DEB, are screened by using primers flanking the targeted genes and by adjusting the PCR conditions to preferentially amplify deletion alleles

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Table 6 A summary of institutions undertaking studies on Eco-TILLING of genes in different cereal species Species

Genes

Institute/University

Barley

Drought tolerant genes (Dhn)

Barley

Barley yellow mosaic and barley mild mosaic virus resistant genes (rym4 and rym5)

Barley

Biotic and abiotic resistance genes Drought related genes (DREB2, TPP, and ERF3) and Protein phosphatase (pp2a4), membrane stability (14–3–3), Class I chitinase (cht) Drought response binding protein 1 (dreb1), Trehalose phosphatase (tps), Viviparous14 (vp14) Alk and waxy gene

Institute for Jordbrugsvidenskab, Denmark. Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany Rothanmsted Research, Harpenden, UK Scottish Crop Research Institute, Invergowrie, UK International Rice Research Institute, Metro Manila, The Philippines Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

Rice

Rice

Sorghum Biotic and abiotic resistance genes Maize

Biotic and abiotic resistance genes

Texas Agricultural Experiment Station, Beaumont, Texas, USA International Rice Research Institute, Metro Manila, The Philippines Fred Hutchinson Cancer Research Center, Seattle, Washington, USA Purdue University, Seattle, USA

represented by amplification products of smaller size than that expected for the wild-type alleles. Deletion mutants were identified for 84% of the targeted loci from an Arabidopsis population of 51,840 individuals (Li et al., 2001; http://www. bio.net/hlo.ypermail/arab-gen/) and the potential application of DeleteageneTM was demonstrated by the analysis of knockout mutations of Arabidopsis transcription factors TGA2, TGA5, and TGA6 suggesting their

Mutagenesis and Functional Genomics in Cereals

397

redundant but essential roles in systemic acquired resistance (Li et al., 2001; Zhang et al., 2003). In the rice functional genomics project at IRRI, Philippines, deletion mutation detection through PCR in defense response genes was optimized in DNA pools of 100 individuals from a population of 8500 DEB and fast neutron induced mutants (Manosalva et al., 2003). Toward identifying rice lines carrying deletion mutation in defense responsive genes, gene-specific (e.g., the untranslated regions of the genes) or gene-family specific (e.g., to conserved regions of gene family members) primers were used in PCR analysis. These primers allowed identification of mutations in two different PAL (phenylalanine ammonia lyase) family members, which were subsequently, confirmed using nested-PCR and sequence analysis (Wu et al., 2005). Studies on functional genomics aimed at exploiting induced deletions by chemical deletogens such as DEB are in progress in diploid wheat (T. monococcum). A method based on the poison– primer approach (Edgley et al., 2002) was applied to detect mutations in complex pools of cereal DNA (Fig. 4). Result (Riera-Lizarazu, unpublished) clearly indicated that insertion/deletion polymorphisms in the waxy gene can be detected up to a ratio of 1:5000 (Fig. 4B). At a 1:5000 ratio, the DNA from the waxy deletion stock represents only 10 pg of the 50 mg of template DNA used in the first round of PCR. Thus, nested PCR with a poison primer allows the detection of a deletion in a complex mixture where the DNA with a deletion is present in about two genome equivalents (DNA content in one disomic cell barley of T. monococcum). Additional protocols based on various simple PCR methods have also been developed that allow detection easily in pools of 1:1000 DNA. The most desirable component of all these approaches is the use of simple PCR and gel-based assays reducing the need for complex machines and enzymes. The genome-wide oligonucleotide (oligo) chips have been suggested as an expedient way to detect mutations (Borevitz et al., 2003). Detection of genetic lesions in rice deletion mutations by using Syngenta GeneChipÒ containing 24-mer oligos representing 24,000 rice genes was recently attempted (Chang et al., 2003). However, it has been shown that the precision of chip-based mutation detection system may be adversely affected by the large deletions and high number of background mutations that may complicate the interpretation of the hybridization signal.

3.6. Allelic series versus knockout mutations Point-mutations-inducing chemical mutagens yield a large proportion of mis-sense mutations, which are discovered through TILLING. While a large majority of mutations caused by insertional (T-DNA and transposons) and deletion (DEALING and Deleteagene) mutagenesis are knockout mutations. Thus these mis-sense and knockout mutation inducing approaches are complimentary. For example, if a knockout mutation leads

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to lethality, it is not possible to characterize the phenotypic changes brought about by the induced mutations in the concerned gene. On the other hand, most of the mis-sense mutations should produce an allelic series representing a range of changes in the phenotype, enabling detailed functional study of the concerned gene. If the mis-sense mutations in the allelic series occur in the conserved residues leading to destabilization of proteins, then conditional mutations are produced. Thus, in the event of the induction of lethal knockout mutations by insertion and deletion mutations, TILLING has the potential to provide an allelic series that can be highly informative for functional genomics. In the case of an allelic series, it is important to estimate the mutation’s ability to damage the protein as this information can determine whether the available mutants are adequate or more screening needs to be done. Toward achieving this goal, software tools have been developed that use protein homology information to predict damaging lesions. These include conserved alignments in blocks database of protein families to predict whether a mis-sense mutation is expected to damage protein function (http://www.proweb.org/coddle) and the sorting intolerant from tolerant (SIFT) program uses PSI-BLAST searching of current databases to assess amino acid conservation (Ng and Henikoff, 2001). SIFT is a general web-based program that is applicable to both natural variation and induced mutations (http://www.blocks.fhcrc.org/~pauline/SIFT. html). SIFT predicts damage with ~70% accuracy with experimental data (Ng and Henikoff, 2001, 2002). Therefore, sequence information can be used as a guide to predict damage to protein caused by random mis-sense mutations, and this approach can be applied generally across organisms.

4. Phenomics Platform for Screening Mutagenized Population Implementing high-throughput phenotypic screening system is a key step for systematic documentation of phenotypic mutants for reverse-genetics analysis. Hence systematic phenotyping of large mutagenized populations based on combination of morphological and biochemical techniques will help to categorize the mutants according to trait of interest. Recently a highthroughput phenomics study was performed in rice and provided detailed phenotypic data for more than 20,000 T1 lines (12 plants per line) and furnished the details of T-DNA integration sites and the consequent phenotypes (Chern et al., 2007). Obviously such trait specific categorization of mutants is valuable in limiting the amount of reverse-genetics screening. The growth stages and data collection methodology platform presented in Arabidopsis demonstrates its significance in gathering phenotypic data over the complete life cycle starting from seed imbibition to vegetative phase


399

by plate-based analysis, and other principal growth stages, flower and seed production by soil-based screening techniques (Boyes et al., 2001). In addition, Kaminuma et al. (2003) demonstrated implementation of computational phenomic technology based on 3D-specific traits at rosette leaves in Arabidopsis. Another example of trait specific screening has been demonstrated by implementing iodine-based staining technique for detection of accumulated starch patterns in leaf between wild-type and AGPase mutant detected using the TILLING approach ( John Lunn, MPI Golm, personal communication). Also iodine staining technique has been implemented to stain barley seeds to estimate amylose content in waxy mutants created using ubiDs barley activation tagging system (Ayliffe et al., 2007). We would also like to highlight the elegant screening technique implemented to screen abiotic stress tolerant mutants. Genetic screening for identifying loci associated with abiotic stress responses signalling has been difficult due to the absence of major visible phenotypes and appropriate screening systems. To tackle these problems Zhu and coworkers evolved an approach first by generating transgenic plants by expressing the luciferase under the control of the stress responsive RD29A promoter and eventually treating the seeds of these plants with EMS to generate large number of mutants (see Ishitani et al., 1997). By looking for the alteration in luciferase expression pattern in mutants under various abiotic stress conditions three major groups of mutants (los-low expression of osmotically responsive genes, cos-constitutive expression of osmotically responsive genes and hos-high expression of osmotically responsive genes) were identified and presented genetic analysis of osmotic and cold stress signal transduction pathways mediated by ABA-dependent and ABAindependent pathways (Ishitani et al., 1997). These representative examples demonstrate the role of phenomics in reporting trait specific mutant phenotypes. Such elegant methods, or modifications thereof, need to be implemented for screening mutants in cereal mutagenized populations aimed at determining gene-function relationship through reverse genetics.

5. Outlook At present, determining gene-function relationships by using reversegenetics methods (T-DNA/transposons) rely heavily on transgenic technology. Due to the recalcitrant nature of cereal crop plants to both the transformation and regeneration, application of insertional-mutagenesisbased reverse genetics has serious limitations. Therefore, alternative and newly emerging non-transgenic, induced mutation-based approaches such as TILLING, DEALING, and DeleteageneTM offer a much needed alternative for the functional analysis of genes in cereal crop plants. However, the success of these approaches depends on generating populations with high

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level of mutational redundancy (>5- to 10-fold mutations of the number of genes in the genome) to ensure detection of mutations in a particular gene with a sufficiently high probability, effective cataloguing, and high-throughput screening of the mutant population (Alonso and Ecker, 2006). Using these approaches, allelic series and knockout mutations can be produced in the target gene conferring subtle to extreme phenotypic alterations and the total loss of function establishing a structure and function relationship. Detection of mutations in induced mutagenesis-based approaches may be hindered by functional redundancy in multigene families, background mutations and the polyploidy in crops such as in wheat and oat. However, encouraging results have been shown by reverse-genetics studies with A. thaliana by knocking out entire gene families uncovering overlapping and specific functions among their members (Okushima et al., 2005; Prigge et al., 2005; To et al., 2004). Second, extensive crossing may help purge background mutations to obtain mutant lines with mutations at only a single locus and also to combine mutant alleles at all the homoeoloci for recovering mutant phenotype in polyploid species. Thirdly, polyploid species such as common wheat also contain major genes that control a particular trait (Friebe et al., 2003). Mutations in such genes may be detected with ease without any complication due to polyploidy (Friebe et al., 2003; Koebner and Hadfield, 2001). Currently, in most of the cereal species, several different cultivars or varieties are being used for reverse-genetics analysis. Although from the stand point of inducing a wide spectrum of mutations, it would perhaps be desirable to use different varieties, but it would be most prudent to use a single variety for which most sequencing information is available. In cereals, the reverse-genetics studies conducted so far have focused on metabolic pathway related genes involved in plant ontogeny. In the future, it would also be interesting to look for mutations in the promoter regions, which may have much larger effect on the phenotypic expression of the trait. Reverse-genetics functional genomics may also help at comparing the functions of closely related genes from related cereal species to address outstanding issues related to evolutionary and developmental biology (Evo-Devo) to understand how the past and present biodiversity arose (Ostergaard and Yanofsky, 2004). Another daunting task that needs to be addressed is the phenotypic characterization of mutagenized population. Thus, the role of phenomics in reporting trait specific mutant phenotypes needs to be considered. Also, systematic forward genetics using reverse-genetics tools that is, simultaneous phenotypic analysis of all the induced mutants needs to be undertaken. This may only be achieved by developing new high-throughput phenomic platforms to examine not only morphological but also biochemical phenotypes. Although induced mutations will continue to be an important component of reverse-genetics analysis, the importance of natural allelic variation


401

to study gene function in plants can not be ignored. Also, future new technologies like sequencing by synthesis and ultra high-throughput sequencing (UHTS) need to be employed for the rapid identification of such alleles (Service, 2006). These and other new emerging sequencing technologies constitute very attractive high-throughput future options for unravelling induced and natural allelic variation for the study of gene-function relationship.

ACKNOWLEDGMENTS The exchange of scientists award by INSA-DFG and the Developing Country Collaboration award by the NSF, USA, to HSB are gratefully acknowledged, during the tenure of which this chapter was conceived and prepared. The DEALING research by ORL and SFK was supported by a grant from the National Science Foundation-Plant Genome Research Program Contract Agreement No. DBI-0321462.

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Index

A Activation tagging, 364–365. See also Cereals and gene trap systems, 375–376 null mutants created by T-DNA, 379 waxy mutants by ubiDs barley, for amylose, 400 Active measurement device, 221–222. See also Ammonia sampling and measurement Aegilops tauschii, 291 Agricultural ammonia emission, 203 Agricultural nonpoint source pollution (AGNPS), 23 Agrobacterium tumefaciens, 362 Agroenvironmental sustainability, 3 Agronomic efficiency of fertilizer N (AEN), 150–151 Agropyron elongatum, 278 Air pollution and flooded/non-flooded systems. See also Emissions ammonia (NH3), 327 greenhouse gas emission control, 334–335 methane emission control, 331–334 nitrous oxide (N2O), 325–326 rumen processes, efficiency of, 328 rumen proteolysis, interspecific variation in, 329–331 WSC and protein content, 328–329 Alcaligenes, 69 Allelic series mutation and knock-out mutation, 399–400 Ammoniacal copper arsenate (ACA), 49 Ammoniacal copper zinc arsenate (ACAA), 49 Ammonia (NH3). See also Tanniferous forage species emission control, in atmosphere, 327 oxidation of, 334 volatilization, 9, 325 Ammonia quick test (AQT), 227 Ammonia sampling and measurement air sampling, 206–207 location and time of, 208–210 methods and devices for, 211–218 sampling method selection, 218–221 volume of, 210 concentration data ammonia measurement standards, 255 data collection, 243–244 error reduction, 245–254 precision and bias, 244–245

concentration measurement features of techniques for, 221–224 techniques selection of, 224–225 determination of, 205–206 emission, pollution and health hazards, 203–204 measurement techniques, 204 ChemcassetteÒ detection system, 239–240 chemiluminescence analyzer, 235–238 electrochemical sensor, 238–239 fourier transform infrared spectroscopy, 230–231 gas detection tubes, 228–230 infrared gas analyzer, 231–234 measuring devices, detection of, 241–243 solid-state/electronic sensor, 240–241 ultraviolet differential optical absorption spectroscopy, 234–235 wet methods, 225–228 Antirhinum majus, 369 Apium graveolens, 392 Arabidopsis spp., 361–365, 375, 382, 385–386, 392, 397, 400 Arabidopsis TILLING Project (ATP), 389–390, 393 ArsC gene, 104 Arsenate (AsO4). See also Arsenic (As) adsorption on amorphous iron and aluminum oxides, FTIR investigation, 53 on oxides and clays, pH dependent, 56 biotransformation, 76 breakthrough curves, 74 diffusion, to reaction sites within soil matrix, 60–61 Freundlich equation and distribution coefficient (Kd) for, 66 and glucose-fermenting microorganism, 71 isotherms of desorption, 63 Langmuir adsorption coefficient (KL) for, 61 occluded within short range ordered materials, 55 and phosphate equally adsorbed on goethite, 58 sorbed vs. time during adsorption–desorption, 95 time-dependent sorption isotherm, 61 Arsenical pesticide, 50

415

416 Arsenic (As) biogeochemistry of, 52 binding mechanisms in soils, 54 desorption, 62–64 heterogeneous oxidation, 66–69 pH dependency, 56–57 precipitation and arsenic retention, 55 reduction and oxidation, microbialmediated, 69–72 solution composition, effect of, 57–59 sorption kinetics, 60–62 sulfides, reaction with, 64–66 contaminated soils, remediation of, 101–104 capping of contaminated soils, 102 phytoremediation, 104 PRB and MNA, 103 solidification/stabilization and soil flushing, 102 sorption and precipitation, 103 empirical equilibrium models, 84–86 equilibrium thermodynamic models, 81–84 geochemical models, application of, 99 GLUE methodology, 99, 101 kinetic models kinetic dissolution, 95–96 kinetic reduction–oxidation, 96–97 kinetic retention, 90–95 in soils, 48–52 compounds containing and in poultry industry, 50 leaching and disposal, 51 surface complexation models, 86–89 toxicity of, 47–48 transport of, 73–78 factor affecting mobility, 75–76 mechanisms, 73–78 mobility and field conditions, 78–80 models, 97–99 Arsenicosis, 48 Arsenite (AsO3). See also Arsenic (As) adsorption capacity on minerals and soils, 57 on metal oxides and hydroxides, 53 biotransformation, 76 heterogeneous oxidation on mineral surface, 96–97 kinetics of oxidation in aerated soil and, 69 NOM and sorption kinetic, 59 oxidation kinetics of, 70 simulation competition on Fe and Al oxides and, 88 in soil solution under flooded conditions, 71 sorption on iron sulfides and, 55 toxicity on binding to sulfhydryl groups, 47 weathering process and oxidation, 64 Arsine gas (AsH3), 47 AsO4 and AsO3 sorption edges, 58 Avena sativa, 123

Index B Bacillus benzoevorans, 76 Barley crop, insertional mutagenesis on, 374, 378 Biofuels, 2 Biological N2 fixation (BNF), 159–160 Biomass production, 3, 8 Biomethylation, 52, 72 Black rust. See Stem rust, of wheat Brassica napus, 123 B. vulgaris, 274 C Caenorhabditis elegans, 385, 390, 397 Carbonate anions in soil, 58 Cauliflower mosaic virus (CaMV), 35S enhancer element, 365, 375 Cereals density of mutations determined in, 389 induce mutations in a variety of plant species, 382 insertion mutagenesis resources in, 376–378 mutagenesis and high-throughput functional genomics in insertional mutagenesis, 362–380 non-transgenic approaches, 381–399 phenomics platform for screening population, 400–401 TILING initiatives in, 393–396 ChemcassetteÒ detection system, 239–240 Chemical deletogens, 385, 397–398 Chemical weathering, 64. See also Arsenic Chemiluminescence (CL) analyzer, 218, 235–238 Chillgard refrigerant leak detection system, 233, 249 Chromated copper chromate (CCA), 49, 80 Citric acid (CA), 59, 228 Closed sampling method, 211–215. See also Ammonia sampling and measurement C:N ratio, 144, 158, 176, 331, 343 Codon Optimizedto Detect Deleterious LEsions (CODDLE) software, 393 Composting, off-field residue management, 131–132 Condensed tannins (CTs), 332 Conservation management, 3, 9, 16, 22, 28 Contaminant of concern (COC), 47 Crop residues, rice-based cropping systems bioenergy production of, 181 flooded soil and, 120 open-field burning of, 119 residue and straw removal of, 121 Crude protein (CP), 328–329 D Dactylis glomerata, 314 Data quality indicators (DQIs), 244

417

Index

DeleteageneTM, 360, 362, 382–383, 386, 390. See also Cereals dealing and, 397–399 Denuder device, 228 Detecting adduct lesions in genomes (DEALING), 360, 362, 381–382, 385, 390, 399 Diepoxybutane (DEB), 384–385, 388, 397–398 Digital elevation models (DEMs), 23–24 Dimethylarsinic acid (DMA), 71–72 Dissolved organic carbon (DOC), 59 Dissolved oxygen (DO), 64–66, 96 DNA pools for Ecotilling, 396 preparation of, 390 to screen deletion lines of C. elegans, 397 Doubled haploid (DH) mutation, 388 Dra¨ger sensor, in ammonia measurement, 238–239 Drosophila melanogaster, 390 Dry methods, 221. See also Ammonia sampling and measurement Dynamic flows modeling, 10. See also Geographic information systems (GIS) E Eco-TILLING, 396–398. See also Cereals Electrochemical (EC) ammonia sensors, 238–239 Electrophoretic mobility (EM), 53 Emissions black C in China, 154 greenhouse gas and enhance C sequestration, 341 for non-flooded crop, 175–176, 179 for rice, 154, 157–158 reducing to air, 325–335 of trace gases, spatial variability in, 15 Environmental protection agency (EPA), 46, 203 Erosion rates, 2 Escherichia coli, 69 Ethyl methane sulfonate (EMS), 382–383, 385, 388–389, 392, 395–397, 401 N-Ethyl-N-nitrosourea (ENU), 384–385 European Triticeae genomics initiative (ETGI), 358 Eutrophication, 316, 321–322, 328 Evapotranspiration, 2, 24 Extended X-ray absorption fine structure (EXAFS), 51, 53, 55–56, 62, 67 F Ferrihydrite, 54, 56–58, 60–61, 63, 71, 89, 92 Fertilizer, non-flooded crop residue management, 167, 172 Festuca arundinacea, 314 Festuca glaucescens, 329

Festuca pratensis, 314 Festulolium loliaceum, 336 Field level flows. See also Precision conservation to reduce N2O emissions, management for, 16–17 variable erosion and transport, 12–16 erosion patterns, 137Cs modeling, 14 predicted NO3-N leaching, spatial distribution of, 16 sand, spatial distribution of, 15 Flood damage control biodiverse mixtures, role of, 338–339 prevention of, 335–337 soil porosity and compaction for, 337–338 Fourier transform infrared spectroscopy (FTIR), 230–231 Full-length cDNA Over-eXpresser (FOX) gene, 377 Fulvic acid (FA), 59 G Gain-of-function mutations, 375–376 Gas detection tubes for ammonia, 228–230 Gasification, 181 Gas manufacturers intermediate standards (GMIS), 250 Gene function determination forward genetics approach, 361 reverse genetics approach, 359–361, 363–364 steps in, 359 Gene trap systems, 365, 376 Geographic information systems (GIS), 10, 12, 14, 20, 23–24, 27, 29 Geospatial technologies. See also Precision conservation desktop mapping, 6–8 surface modeling, 5 gibberellin 2-oxidase gene, 377 Global positioning systems (GPS), 4, 25–26, 28, 39 Global rust initiative, 289. See also Stem rust, of wheat Global warming, 2 Granule bound starch synthase (GBSS) I gene, 395 Grassland enhancing C sequestration in, 341–344 management and food production, 313–314 nutrient budget information for, 316 persistency and resilience genetic variation, implementation of, 340–341 interspecific hybridization, role of, 339–340 role of forage legumes, 334 stabilization of, 338

418

Index

Greenhouse gas emission from fertilizer production, 334–335 and non-flooded crop, 175–179 rice and, 154–158 Ground covering rice production system (GCRPS), 131 H Happy Seeder machine, 175 Humic acid (HA), 55 Hybrid single-particle lagrangian integrated trajectory (HYSPLIT), 284 Hydrous ferric oxide (HFO), 58, 72, 89 I In-field residue management, in rice cropping systems, 127 Infrared gas analyzer, 231–234 Insertion mutagenesis, 362–364. See also Cereals activation tagging, 375 gene trap systems, 376 T-DNA and, 364–369, 378–379 and transposon elements, 369–375, 379–381 Intergovernmental panel on climate change (IPCC), 313 International barley sequencing consortium (IBSC), 358 International rice functional genomics consortium (IRFGC), 376 International rice genome sequencing project, 358 International wheat genome sequencing consortium (IWGSC), 358 Iris fulva, 336 Italian ryegrass (IRG), 314, 329–330 K Knock-out mutation, 364–365. See also Cereals; Mutation T-DNA insertion mutation, 364–365 vs. allelic mutation, 399–400 L Leaf protein, half-life values, 330 Lolium multiflorum, 314, 329–330 Lolium perenne, 314, 330 Long-term experiments, on rice. See also Rice crop with continuous cropping of flooded rice in Philippines, 159 with removal of all above-ground biomass, 160 on rice–rice cropping systems, 152 and soil organic C, 180 Lotus corniculatus, 334 Lotus japonicus, 344

Lowland rice CH4 and N2O emissions from, 154–156 ecosystems in Asia, 124 effect of residue incorporation on yield of, 144 mulch as weed suppressant, importance of, 135, 151 variety under non-flooded conditions, 131 yeild, 144 Lr19 gene, 290 Lr24 gene, 289 Lupinus albus, 323 M Maize crop insertional mutagenesis on, 377–378 mutator transposon insertions in, 380 transposable elements in, 369–370 transposon insertion populations on, 371–372 Maize gene discovery project, 370, 377 Maize genetics cooperative stock center’s Web site, 377 Maize targeted mutagenesis (MTM) project, 377 Maximum contaminant level (MCL), 46 Mean annual nutrient productivity (aNP), 317 Mean residence time (MRT), 317 Medicago sativa, 314 Medicago truncatula, 344 Metal oxides, 53, 56–57, 60, 103, 105 Methane (CH4) contributor to climate change, 154 reduction of emissions, 331–332 tanniferous forage species, role of, 332–334 uptake and, 15–16 Micrometeorological technique for ammonia sampling, 215 Miran 203 infrared analyzer, 207, 234 Molybdopterin synthase gene, 380 Monitored natural attenuation (MNA), 103–104 Monomethylarsonic acid (MMA), 71–72 Mulching, rice-based cropping systems biomass transfer in, 134–135 conventional tillage and, 134 direct drilling and, 133 Happy Seeder approach in, 134 soil puddling and, 130 Multimedia mapping, 10, 12 Multi-point sampling system (MPSS), 215–216 Mutagenesis-based reverse genetics, 360–362 Mutagens for development of mutagenized population, 382–386 point-mutations-inducing chemical, 399 screening mutagenized population, 400–401 (see Cereals) treatment and population size, 386–390 Mutation. See also Mutagens allelic series vs. knockout, 399–400

419

Index

analysis, identification of, 388 deletion mutations in Caenorhabditis elegans, 385 density determined in cereal crop species, 389 detection technique in TILING, 390–393 expression of tagged gene causing knock-out, 365 induced by Mu elements, 370 insertion of nDart1 in OsClpP5 in rice, 379 knockout mutations of Arabidopsis transcription factors, 397 toward more complex virulence, 282 waxy loci with severe, 395 Mu transposon, 369–370, 377 N National air emission monitoring study (NAEMS), 203 National institute of standards and technology (NIST), 249–250 Natural organic matter (NOM), 59 N fertilizer, 17, 150, 172, 316, 320, 334–335 Nitrate leaching, 3, 316, 320, 325 Nitrogen pollution, in watercourses causes of, 315–316 forage legumes, role of, 320 mapping techniques, 318–320 NUE, characteristics of, 316–318 red clover, losses from, 321 Nitrogen trading, 27 3-Nitro-4-hydroxyphenylarsonic acid (Roxarsone), 50 Nitrous oxide (N2O), 9, 154 and CH4 emission, 158, 177–179 emissions control, in atmosphere, 325–326 in oxidation of ammonia, 334 Nondispersive infrared analyzers (NDIR), 231 Non-flooded crop, residue management bioenergy implications for biopower options in, 181 straw characteristics, 182–183 fertilizer efficiency and, 167, 172 grain yield for mulching crop residues effect of, 168–172 N immobilization in, 161 residual effect of rice, 162–163 residue incorporation effects of, 164, 167 rice-wheat systems in, 165–166 Happy Seeder approach in, 175–176 N2O and CH4 emission and, 177–179 P and K management of, 179–180 pest and disease pressure for, 173–174 SOM in, 180–181 water use efficiency for, 173 NO3-N leaching, 14, 20, 29, 31. See also Field level flows; Off-site transport, connection of field with predicted spatial distribution, 16

N use efficiency (NUE), 316–318 Nutrient cycling, crop residue management, 120–121 Nutrient uptake efficiency (NUpE), 317 Nutrient utilization efficiency (NUtE), 317 O Occupational safety and health administration (OSHA), 203 Off-site transport, connection of field with flows from field to nonfarm areas, 17–20 effective erosion buffers, 19 pollutants in vadose zone, GIS software and models for, 18 PSMs, for capturing runoff phosphorus, 20 transport of chemicals in shallow underground tile, 18 ideal buffer width and riparian zones, 21–22 RUSLE and VFSMOD, to determine locations, 21 On-field residue management, rice cropping systems, 126 Open-field burning, rice-based cropping systems, 119 banning of, 122 residue incorporation, 129 Open-path Fourier transform infrared system (OP-FTIR), 218 Open-path sampling, 217–218. See also Ammonia sampling and measurement Opsis AR-500 UV open-path monitor, 235 OryGenesDB database, 376 Oryza sativa, 118 P Passive measurement device, 221–222. See also Ammonia sampling and measurement Path-weighted average (PWA), 217 Permeable reactive barrier (PRB), 103 Phenotype screening system, for mutagen population, 400–401 Phleum pratense, 314 Phosphate (PO4) in soils, 57 Phosphorus pollution control, in watercourses causes of, 321–323 PUE, in rumen, 324–325 P use efficiency, 323–324 Photoacoustic spectrophotometer (PAS), 232–233, 248, 252 Photosynthetic mutant screen (PMS), 377 Phyllosilicates, 57 Pioneer Hi-Bred’s trait utility system of corn, 377 Poa pratensis, 314 Point sampling method, 215–217. See also Ammonia sampling and measurement Point-zero-charge (PZC), 53 Polyphenol oxidase (PPO), 321

420

Index

Population growth, 2 Potential conservation practices, 30–38 Precision agricultural-landscape modeling system (PALMS), 24 Precision conservation buffers and riparian zones, 20–21 different degrees of, 4 environmental impacts and production systems sustainability, 24 on field scale, 13 to generate maps for use in analysis in field of, 10 GIS mapping approach and map analysis, 6–8 hydrologically sensitive area and, 22 to identify hot spots on farm and watershed, 28 to increase for soil and water, conservation practices, 30–38 integration of information and locations for riparian buffers, 29 management and conservation, integration and maps for, 9 for management of flows, 16–17 manure management, technology for, 17 modeling approach to, 23 patterns and relationships, identification of, 9–12 GIS research for, 10, 12 Map analysis procedures, 10–11 multimedia mapping and Cartesian coordinate system, 10 static coincidence analysis vs. dynamic three-dimensional flows, 10, 12 and potential for site-specific applications, 6 to reduce the transport of nutrients, 20 site-specific and three-dimensional scale approach, 4, 13 at watershed scale, 24–27 for animal management and soil and water conservation, 26 Precision conservation management zones (PCMZ), 16 Precision farming, 3 Project aligned related sequences and evaluate SNPs (PARSESNP), 393 Pteris vittata, 104 Puccinia graminis, 273 Puccinia graminis tritici, 272 Pyrolysis, 181 Q Quality assurance and quality control (QAQC), 244, 250–251, 256 R Race Ug99, 273, 278–279. See also Stem rust gene Sr, immunity to, 291 and long-term control, 288–305

markers associated to stem rust resistance genes, 293–294 pandemic, prediction of, 287–288 threat to wheat production, 281–288 Red clover, 313, 315, 321, 333, 338 Remote sensing (RS), 4, 24–25, 28, 39 RescueMu project. See Mu transposon Retrotransposon-tagged mutation, 380 Reverse genetics approach, gene function determination, 359–361 T-DNA insertion line in, 363–364, 378–379 transposon insertion lines in, 379–381 Rice-based cropping systems in Asia, residue management decision tree, 184–185 in-field residue management practices, 127 monocropping systems in, 128 non-flooded crop following rice biomass, transfer of, 134–135 incorporation, 132–133 mulching, 133–134 non-flooded crop, rice following rice composting, 131–132 incorporation, 129–130 mulching, 130–131 nutrient cycling in, 120–121 on-field residue management practices, 126 production area and grain yield in, 123–124 productivity, profitability and environmental impact, 122 residue production and area for, 124 soil puddling in, 125 Rice crop, evaluation for residue management options biological N2 fixation and, 159–160 grain yield effect of incorporation of upland crop residue on, 141–143 effect of rice residue incorporation on, 135–140 incorporation, profitability, 152–153 insertional mutagenesis on, 376–377 monocropping systems, 128, 151–152 residue incorporation effects of, 136–140 T-DNA knockout mutant lines in, 366–368 water use efficiency, 151 yield and residual effect relationship, 145 Rice yield in a barley–rice rotation, 144 with and without fertilizer application, 149 Riparian ecosystem management model (REMM), 21 Riparian zones denitrification of N in, 27 precision conservation and, 3–4 RNAi technique, 360–361. See also Cereals Rosemount gas analyzer, 233–234

421

Index S Seattle TILLING Project (STP). See Arabidopsis TILLING Project (ATP) Secale cereale, 281 Semidwarf wheat varieties, 278–279 Septoria nodorum, 273 Septoria tritici, 273 Silicic acid, 59 Site-specific management zones (SSMZ), 16 Smart dust, 9 Smoke particles, 153–154 Soil aggregation, 12 Soil and water assessment tool (SWAT), 23 Soil erosion, 2, 18, 336, 338 Soil organic matter (SOM), 55, 178, 180–181 Soil puddling, 125 mulching and, 130 Soil quality, improvement of biodiverse mixtures, 338–339 flood tolerance and prevention, 335–337 soil porosity and compaction, 337–338 Solid-state/electronic ammonia sensor, 240–241 Sorghum crop. See Cereals Sorting intolerant from tolerant (SIFT) program, 400 Spatial patterns and relationships. See also Precision conservation GIS research and changes in geo-referencing and, 12 map analysis procedures, 10 maps of surface flow, 11 Sr gene, 278, 280, 290–291. See also Stem rust Staphylococcus aureus, 69 Staphylococcus xylsis, 69 Static coincidence modeling, 10. See also Geographic information systems (GIS) Stem rust, of wheat breeding for resistance, 277–281, 288–289 future perspectives of, 305–306 high-yielding wheat, 296–300 race-specific resistance genes for Ug99, 290–292 race-specific resistance genes in wheat, 292–295 resistant wheat varieties development, 304–305 Sr24 gene breakdown, 289–290 Ug99 resistance of plant, 295–296 world wheat area reduction, 300–304 occurrence of, 273–274 pathogens and epidemiology of, 274–277 race Ug99 of avirulent and virulent genes in, 281–282 epidemic prediction of, 287–288 geographical distribution of, 282–283 migration of, 284–285

wheat germplasm resistance/susceptibility, 285–287 resistance genes, PCR-based markers, 293–294 Surface mulching, 132 CH4 production and, 179 residue incorporation and, 158 and rice residue incorporation in no-till sown wheat, 176 Surfurosprillum barnesii, 76 Syngenta GeneChipÒ , 399 T Tanniferous forage species, 332–334. See also Methane (CH4) Targeting induced local lesions in genomes (TILLING). See also Cereals; Mutagen; Arabidopsis TILLING Project (ATP); Eco-TILLING creation of mutagenized populations, different schemes of, 387 DNA pool preparation, 390 mutagen agents for, 382–386 mutagen treatment, 386–390 mutation detection technique in CELI enzymatic mismatch cleavage of DNA, 392–393 PCR amplification of DNA pools, 390–391 software’s used and emerging techniques, 393–394 T-DNA insertion mutagenesis. See also Cereals; Mutation; Mutagens gene knock-out mutation, 364–365 insertion lines in, 378–379 tagging mutation, 365–369 Thinopyrum elongatum, 289 Thinopyrum ponticum, 278 Tilletia indica, 174 Time weighted average (TWA), 215 Total ammoniacal-N (TAN), 327 Transposon-based gene tagging, 381 Transposon insertion mutagenesis. See also Maize crop Ac/Ds transposons, uses of, 373 barley transposons, 374 insertion lines in, 379–381 maize transposon insertion populations, 371–372t Mu transposons, 370 transposable elements, 369 Trifolium ambiguum, 329 Trifolium nigrescens, 334 Trifolium pratense. See Red clover Trifolium repens L. See White clover Trimethylarsine oxide (TMAO), 72 Triticum aestivum, 119, 272

422

Index

Triticum monococcum, 385, 390, 399 Triticum turgidum, 291 Triticum ventricosum, 278, 281 U Ultraviolet differential optical absorption spectrometer (UV-DOAS), 217–218, 234–235 Universal soil loss equation (USLE), 23 Upland (non-flooded) crop residue incorporation, 141–143 V VECHTA air sampling system, 210 Vegetative filter strip model (VFSMOD), 21 Vesicular arbuscular mycorrhizal fungi (VAM), 323 W Water and tillage erosion model, 13–14 Water erosion prediction project (WEPP), 24 Watershed scale. See also Precision conservation models and tools, 22–24 DEMs, AGNPS and SWAT models, 23–24 WEPP and PALMS, 24 variable hydrology, 22 Water soluble carbohydrate (WSC), 328–329 waxy gene. See Granule bound starch synthase (GBSS) I gene

Wet methods, 221, 225–228. See also Ammonia sampling and measurement Wheat. See also Stem rust, of wheat breeding for rust resistance, 277–281, 288–289 future perspectives of, 305–306 high-yielding wheat for, 296–300 race-specific resistance genes for Ug99, 290–292 resistant wheat varieties development, 304–305 Sr24 gene breakdown, 289–290 Ug99 resistance of plant, 295–296 wheat improvement strategies, race-specific resistance genes in, 292–295 world wheat area reduction, 300–304 crop, insertional mutagenesis on, 378 (see also Cereals) fungal diseases in, 273 production of, 272–273 rust pathogens, dispersal modes of, 275–277 stem rust (see Stem rust, of wheat) White clover, 314, 320, 323, 325, 329, 338 X X-ray absorption near-edge spectroscopy (XANES), 51, 65, 67, 69 Z Zea mays, 119

Precision conservation Precision Ag Wind erosion

Chemicals

Soil erosion Runoff Leaching

Leaching

Terrain

Leaching

Soils Yield Potassium

3-dimensional Flows Cycles

Coincidence

CIR image

2-dimensional Interconnected perspective

Isolated perspective

Jorge A. Delgado and Joseph K. Berry, Figure 1 The site-specific approach can be expanded to a three-dimensional scale approach that assesses inflows and outflows from fields to watershed and region scales. (From Berry et al., 2003.)

Surface modeling

Point samples are spatially interpolated into a continuous surface

53.2 ppm

4.2 ppm

Field sample locations Phosphorus surface

Discrete data spikes

Min = 4.2 Max = 53.2 Avg = 13.4 SDev = 5.2

Spatial data mining 32c,62r

45c,18r

Map surfaces are clustered to identify data pattern groups

P 53.2

Relatively low responses in P, K, and N Relatively high responses in P, K, and N

11.0

Cluster 2 Cluster 1

N

K 412.0

177.0

27.9

32.9

N K P Geographic space

Data space

Clustered data zones

Jorge A. Delgado and Joseph K. Berry, Figure 2 Surface modeling is used to derive map surfaces that utilize spatial data mining techniques to investigate the numerical relationships in mapped data. (From Berry et al., 2005.)

Map analysis Desktop mapping Field data Standard normal curve fit to the data

Spatially interpolated data

34.1% 34.1%

68.3% +/−1 standard deviation

Average = 22.0 StDev = 18.7

22.0

28.2

Discrete spatial object (generalized)

80 60 40 20 0 −20 −40 −60

High = 50

80 60 40 20 Average = 22.0

0 −20 −40 −60

N

Continuous spatial distribution (detailed)

Jorge A. Delgado and Joseph K. Berry, Figure 3 Desktop mapping uses aggregated, nonspatial statistics to summarize spatial objects (points, lines, and polygons), whereas map analysis uses continuous spatial statistics to characterize gradients in geographic space (surfaces).

Inclination of a fitted plane to a location and its eight surrounding elevation values

2418

2404

2393

2409

2395

2341

2383

2373

2354

Slope(47,64) = 33.23%

35% 30% 25% 20% 15% 10% 5% 1% 0%

Steep

Moderate Gentle flat

Slope map draped on elevation Slope map

Elevation surface

Flow(28,46) = 451 paths

537 Paths Heavy 256 Paths 123 Paths 64 Paths 32 Paths 16 Paths Moderate 8 Paths 4 Paths Light 2 Paths 1 Paths minimal

Total number of the steepest downhill paths flowing into each location Flow map draped on elevation Slope map

Jorge A. Delgado and Joseph K. Berry, Figure 4 Maps of surface flow confluence and slope are calculated by considering relative elevation differences throughout a project area. (From Berry et al., 2005.)

Tillage erosion

Water erosion

Tillage–water erosion

Total erosion (cesium-137 measurements)

−1 −1 Mg ha yr

−33

Soil loss net erosion

−22

Accelerated erosion

−11 0

Soil loss T value = −11 Mg ha−1 yr−1

11 22 33 120

Soil gain net deposition

Slope % map and cesium137 sampling sites Slope % 8 7 6 5 4 3 2 1 0 Elevation contour lines are overlaid on all maps elevation labels are shown only on total erosion map

Jorge A. Delgado and Joseph K. Berry, Figure 6 Erosion patterns developed from tillage, water, tillage-water, and total erosion (137Cs) modeling of the research field are displayed. Cesium sampling sites are also displayed on a contour map of slope percentage for the field. (From Schumacher et al., 2005.)

A 100

100

90

80

70

70

60

Sand (%)

90

80

60

50 50

200

200

150

150

100

100

50 0

50


B

0

Jorge A. Delgado and Joseph K. Berry, Figure 7 Spatial distribution of sand content in the top 1.5 m of soil across different productivity zones (A). Spatial distribution of observed residual soil NO3 -N in the top 1.5 m of soil for study one across the different productivity zones during the 2000 growing season (B). (From Delgado and Bausch, 2005.)

250

200

150

150

100 100 50

50

0


250

200

0

Jorge A. Delgado and Joseph K. Berry, Figure 8 Spatial distribution of predicted NO3-N leaching from the root zone of corn (1.5 m depth) in study one across the different productivity zones during the 2000 growing season. (From Delgado and Bausch, 2005.)

Erosion potential

Slopemap

Reclassify

Overlay

Reclassify

3 steep 2 moderate 1 gentle

Flow/slope

Slope_classes Reclassify

33 heavy flow: steep 33 heavy flow: moderate 33 heavy flow: gentle 23 moderate flow: steep 22 moderate flow: moderate 21 moderate flow: gentle 13 light flow: steep 12 light flow: moderate 11 light flow: gentle

Flowmap

Erosion_potential High Moderate Low

Flow_classes 3 heavy 2 moderate 1 light

Effective erosion buffers Effective erosion potential distance

Erosion_potential

Far

Distance

Close

Erosion buffers Streams

Jorge A. Delgado and Joseph K. Berry, Figure 9 Effective erosion buffers around a stream expand and contract depending on the erosion potential of the intervening terrain. (From Berry et al., 2005.)

Advances in Agronomy, Volume 98

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