No title

VOLUME O NE HUN DRED T HIRTEE N

ADVANCES IN

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

WARREN A. DICK

MARY C. SAVIN

HARI B. KRISHNAN

APRIL L. ULERY

SALLY D. LOGSDON

VOLUME O NE HUN DRED T HIRTEE N

ADVANCES IN

AGRONOMY EDITED BY

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

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 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA 32 Jamestown Road, London, NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2011 Copyright r 2011 Elsevier Inc. All rights reserved No part of this publication may be reproduced, stored 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 (144) (0) 1865 843830; fax (144) (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

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10 9 8 7 6 5 4 3 2 1

CONTENTS

Contributors Preface

1.

Advances in Agronomy Quantifying Processes of Pedogenesis

ix xi

1

Uta Stockmann, Budiman Minasny, and Alexander McBratney

2.

1. Introduction 2. Conceptual Models of Soil Formation—Factors, Processes, Pathways, Energy 3. Soil Weathering and Production 4. Soil Mixing—Vertical and Lateral Movements 5. Models of Soil Formation Based on the Concept of Mass Balance 6. Conclusions Appendix References

2 5 12 22 33 39 46 68

Irrigation Waters as a Source of Pathogenic Microorganisms in Produce: A Review

75

Yakov Pachepsky, Daniel R.Shelton, Jean E.T. McLain, Jitendra Patel, and Robert E. Mandrell 1. Introduction 2. Concentrations of Microbial Pathogens and Indicator Organisms in Irrigation Waters 3. Implications of Irrigation Water in Spread of Foodborne Diseases 4. Standards, Guidelines, and Risk Assessment 5. Fate and Transport of Pathogenic and Indicator Microorganisms in Irrigation Systems 6. Management and Control of Produce Contamination with Pathogens from Irrigation Waters 7. Research and Development Needs References

3.

76 78 84 91 101 117 121 123

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry of Humification 143 Morris Schnitzer and Carlos M. Monreal 1. Introduction 2. Criticism on Soil HS Research

145 147 v

vi

Contents

3. 4. 5. 6. 7. 8. 9. 10.

4.

Extraction of SOM Analysis of SOM Analysis by Py-FIMS Chemical Structure Chemical Characteristics of HS Spectrometric and Spectroscopic Characteristics of HS Effect of Time on the SOM Structure New Concepts on the Chemical and Microbial Synthesis of HAs and SOM 11. Microbial Humification of Small Organic Compounds into Soil PKs 12. Thermodynamic, Energy, and Kinetic Considerations 13. PKs and the Central Structure of HS and SOM 14. Future Research References

148 150 152 155 156 160 179 180 181 198 202 205 207

Zeolites and Their Potential Uses in Agriculture

219

Kulasekaran Ramesh and Dendi Damodar Reddy

5.

1. Origin and History of Zeolites 2. Classification of Zeolites 3. Structure and Nomenclature of Zeolites 4. Physical and Chemical Properties of Zeolites 5. Major Natural Zeolites of Agricultural Importance 6. Zeolite Nutrient Interactions 7. Agricultural Applications 8. Researchable Issues 9. Conclusions References

221 222 223 224 227 227 229 235 235 235

Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time

243

R.A. Viscarra Rossel, V.I. Adamchuk, K.A. Sudduth, N.J. McKenzie, and C. Lobsey 1. Introduction 2. Proximal Soil Sensing Techniques 3. Proximal Sensors Used to Measure Soil Properties 4. Summary 5. General Discussion and Future Aspects References

245 251 270 274 274 281

Contents

6.

The Role of Knowledge When Studying Innovation and the Associated Wicked Sustainability Problems in Agriculture

vii

293

J. Bouma, A.C. van Altvorst, R. Eweg, P.J.A.M. Smeets, and H.C. van Latesteijn

7.

1. Introduction 2. Current Problems in Dutch Agriculture 3. The Flow of Knowledge When Studying Sustainable Development 4. Case Studies 5. Discussion and Conclusions References

294 299 300 301 319 321

Crops Yield Increase Under Water-Limited Conditions: Review of Recent Physiological Advances for Soybean Genetic Improvement

325

Walid Sadok and Thomas R. Sinclair 1. Introduction 2. Crop Water Use and Yield: A Framework for Trait Identification 3. Traits Influencing Water Conservation 4. Traits Influencing Water Access 5. Traits for Special Sensitivities: Nitrogen Fixation Tolerance to Drought 6. Concluding Remarks References

Index

326 327 331 339 342 344 345

351

CONTRIBUTORS

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

V.I. Adamchuk (241) Bioresource Engineering Department, McGill University, Ste-Anne-de-Bellevue, QC, Canada J. Bouma (291) Professor of Soil Science, Wageningen University, The Netherlands Dendi Damodar Reddy (217) Central Tobacco Research Institute (ICAR), Rajamundry, Andhra Pradesh, India R. Eweg (291) TransForum Innovation Program, The Netherlands C. Lobsey (241) CSIRO Land and Water, Bruce E. Butler Laboratory, Canberra, ACT, Australia Robert E. Mandrell (73) USDA-ARS, Western Regional Research Center, Produce Safety and Microbiology Research Unit, Albany, CA Alexander McBratney (1) Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW, Australia N.J. McKenzie (241) CSIRO Land and Water, Bruce E. Butler Laboratory, Canberra, ACT, Australia Jean E.T. McLain (73) USDA-ARS Arid-Land Agricultural Research Center, Water Management and Conservation Research Unit, Maricopa, AZ Budiman Minasny (1) Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW, Australia Carlos M. Monreal (141) Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Center, Ottawa, ON, Canada

ix

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Contributors

Yakov Pachepsky (73) USDA-ARS Beltsville Agricultural Research Center, Environmental Microbial and Food Safety Laboratory, Beltsville, MD Jitendra Patel (73) USDA-ARS Beltsville Agricultural Research Center, Environmental Microbial and Food Safety Laboratory, Beltsville, MD Kulasekaran Ramesh (217) Indian Institute of Soil Science (ICAR), Nabibagh, Bhopal, Madhya Pradesh, India Walid Sadok (323) Earth and Life Institute, Universite´ Catholique de Louvain, Louvain-la-Neuve, Belgium Morris Schnitzer (141) Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Center, Ottawa, ON, Canada Daniel R. Shelton (73) USDA-ARS Beltsville Agricultural Research Center, Environmental Microbial and Food Safety Laboratory, Beltsville, MD Thomas R. Sinclair (323) Crop Science Department, North Carolina State University, Raleigh, NC P.J.A.M. Smeets (291) Alterra, Wageningen University and Research Center, The Netherlands Uta Stockmann (1) Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW, Australia K.A. Sudduth (241) USDA Agricultural Research Service, Cropping Systems and Water Quality Research, Columbia, MO A.C. van Altvorst (291) Professor of Soil Science, Wageningen University, The Netherlands H.C. van Latesteijn (291) TransForum Innovation Program, The Netherlands R.A. Viscarra Rossel (241) CSIRO Land and Water, Bruce E. Butler Laboratory, Canberra, ACT, Australia

PREFACE

Volume 113 of Advances in Agronomy continues the long-standing tradition of including an eclectic group of reviews on cutting-edge topics in the plant, soil, and environmental sciences by leading experts. Chapter 1 provides a contemporary overview of pedogenesis models and rates of pedogenesis processes. Chapter 2 is a comprehensive and environmentally timely overview of pathogenic microorganisms that are derived from irrigation waters. The implications in terms of foodborne diseases and fate and transport are discussed. Chapter 3 is a critical review of soil organic matter research, including a summary of past results and the application of molecular-based techniques and computational modeling. The review also stresses the need to better integrate biology with chemistry in enhancing our understanding of organic matter structure and reactivity. Chapter 4 covers aspects of zeolites, including their structure and physicochemical properties, as well application of zeolites in agriculture. Chapter 5 is an interesting review on the use of proximal soil sensing to measure soil properties and soil spatial and temporal variability. Chapter 6 is a thought-provoking chapter dealing with translation of agronomic research to enhance innovation and environmental sustainability. Several case studies are presented. Chapter 7 covers recent advances in soybean genetic improvement and impacts on crop yield when water is limiting. I appreciate the fine contributions of the authors. DONALD L. SPARKS Newark, Delaware, USA

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C H A P T E R O N E

Advances in Agronomy Quantifying Processes of Pedogenesis Uta Stockmann, Budiman Minasny, and Alexander McBratney Contents 1. Introduction 2. Conceptual Models of Soil Formation—Factors, Processes, Pathways, Energy 2.1. Factors 2.2. Processes 2.3. Pathways 2.4. Energy 2.5. Summary 3. Soil Weathering and Production 3.1. Production of soil from parent materials 3.2. Chemical weathering of bedrock to soil 3.3. Summary 4. Soil Mixing—Vertical and Lateral Movements 4.1. Bioturbation 4.2. Soil creep 4.3. Rain splash 4.4. Summary 5. Models of Soil Formation Based on the Concept of Mass Balance 5.1. Landscape evolution models 5.2. Modeling soil formation in the landscape 5.3. Summary 6. Conclusions Acknowledgments Appendix References

2 5 5 8 9 10 12 12 13 18 22 22 24 30 30 32 33 34 35 39 39 45 46 68

Abstract Our knowledge of plant and animal growth and development is far superior to that of the evolution of soil, yet soil plays a fundamental role in natural

Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW, Australia Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00001-4

© 2011 Elsevier Inc. All rights reserved.

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ecosystems. To understand the complexity of soil systems, we need to explore processes that lead to its formation. Research in pedogenesis has been focused on formalizing soil-forming factors and processes to ultimately model soil formation in the landscape. Early models described soil formation qualitatively and were mostly limited to a description of soil evolution in the landscape. They led to the development of qualitative models of pedogenesis based on empirical observations and later to quantitative models of pedogenesis based on empirical equations or detailed differential equations derived from fundamental physics. This review highlights the main models of pedogenesis and focuses on models and rates of pedogenic processes such as the production of soil from weathering of parent materials and vertical and lateral movements in the soil profile. It will become clear that field and laboratory work is needed to improve and validate quantitative models of pedogenesis. In order to estimate and verify model parameters, it is therefore of importance to collect real-world data. Keywords: Pedogenesis; soil-forming factors; soil production model; soil mixing; bioturbation; chemical weathering; mass balance

1. Introduction The following review will present and discuss various models of pedogenesis. Since comprehensive reviews on models of soil formation have been presented by Hoosbeek and Bryant (1992), Amundson (2004), Schaetzel and Anderson (2005), and Minasny et al. (2008), here, however, we only summarize the main models and focus on models of soil formation processes such as the weathering of parent materials and soil mixing. Soil is a very complex system composed of a variety of interconnected physical, biological, and chemical processes. It exists at the interface of the atmosphere, biosphere, hydrosphere, and lithosphere. The interface or zone where soil formation processes take place has become known recently as the critical zone (Brantley et al., 2007), where rocks meet life (Fig. 1 and Box 1). Here, soil weathering, soil mixing, and soil erosion processes occur over several timescales, from the colloid (μm), grain (mm), soil horizon (cm), and soil profile (m) scales to the landscape (km) and global (Mm) scale. The importance of soil is also reflected in the recent US National Research Council publication “Landscapes on the Edge: New Horizons for Research on Earth’s Surface” (NAS, 2010) that addresses the challenges and opportunities in Earth surface processes. The Earth’s surface is defined as a dynamic interface where physical, chemical, biological, and human processes cause and are affected by forcings in the Earth system. The book clearly states the importance of pedogenesis: “Soil formation is not, however, only of academic interest. Our food comes from plants grown in soil. The rapid rate of soil erosion due to land use relative to

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Advances in Agronomy Quantifying Processes of Pedogenesis

La

ter

al

Soil

Water flow paths

Regolith Particle trajectory

Critical zone

il t

ran

sp

Bioturbation Erosion

Bedrock

so

or

Soil creep

Erosion

Soil production

Lowering/breakdown of parent material

Material

Process

Soil (material with horizons)

Physical, chemical, biological, and transport

t

Eluviation illuviation processes

Soil production Regolith (parent material for soil Weathering of underlying production/form of parent material weathered “friable” rock with structural characteristics and Biological fresh primary minerals of parent rock) Chemical

Weathering front advance Bedrock

Physical

Uplift

Figure 1 The critical zone. (Source: Based on Anderson et al., 2007 and Graham et al., 2010.)

the slow rate of transformation of rock into soil endangers soil resources worldwide. The fate of soils, the base of agriculture, is of great concern.” To understand the complexity of the soil system, it is important to investigate soil formation processes quantitatively. Ultimately, quantifying pedogenesis should give us answers to questions such as: 1. How does soil form? 2. At what rate does soil evolve over time? and 3. How fast are rates of soil turnover occurring in the soil profile and what influence do they have on pedogenesis? Over the years, soil scientists have formalized concepts and models of soil formation to improve our knowledge of pedogenesis. Based on the degree of computation, these models describe soil formation qualitatively and/or quantitatively. Furthermore, based on the complexity of the structure used in the models, they describe soil formation empirically (functionally) or mechanistically. Generally, functional models are limited to a description of pedogenic factors, but can also be based on empirical equations, whereas mechanistic models are based on the mechanisms that can be formulated as mathematical equations (Hoosbeek and Bryant, 1992). Early models of soil formation were limited to a description of soil evolution in the landscape or were based on simple empirical equations. However, there has been a shift of interest toward mechanistic modeling. Mechanistic models of soil formation implement soil-forming processes

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Box 1

The critical zone

The term critical zone is used quite extensively in recent literature of Earth Sciences. The critical zone is defined as “the external terrestrial layer extending from the outer limits of vegetation down to and including the zone of groundwater,” which “sustains most terrestrial life on the planet” (Brantley et al., 2006; Figure 1). The critical zone is described as the zone where chemical, biological, physical, and geological processes are combined to control the development of soils and ecosystems. It is known as a complex mixture of air, water, biota, organic matter, and earth materials (Brantley et al., 2007). Within the critical zone, a weathering engine transforms bedrock and biomass into soil, the “living skin” of the Earth (Anderson et al., 2007). The weathering engine is driven by physical and chemical weathering processes that fracture, grind, and dissolve the bedrock; and biological “weathering” and turbation processes (Anderson et al., 2007). Within the critical zone, soil acts as an open system that is subject to elemental gain and losses. Studying this central component of the critical zone is imperative, since knowledge of soils is still limited despite their fundamental importance (Brantley et al., 2007). Rates of soil production and loss, bedrock or outcrop weathering, and erosion have been estimated in the literature, but a comparison is often challenging (Brantley et al., 2007). For instance, for undisturbed forested landscapes, rates of soil production and soil loss are assumed to be balanced within the critical zone, varying between 7 and 80 mm per 100 years (0.07 and 0.8 mm yr21). In contrast, weathering rates estimated from field data for the transformation from bedrock to regolith range between 0.05 and 10 mm per 100 years (0.0005
0.1 mm yr21). At present, interdisciplinary research is focusing on exploring how chemical, physical, and biological processes work together within the weathering engine. In 2006 a working group was formed, the so-called Critical Zone Exploration Network (www.czen.org), to emphasize the demand in integrating new tools to estimate the processes within the critical zone from field data and therefore to answer process-orientated research questions (Brantley et al., 2006). Questions related to the formation of soil as a major component of the critical zone that need to be explored and answered are (cited from Brantley et al., 2006): (1) What controls the thickness of the critical zone? Research is focused on how fast and deep weathering of fresh bedrock occurs and what kind of agents are involved, and therefore (2) What controls the rate of chemical and physical weathering? (3) What controls the vertical structure and heterogeneity of the critical zone? Here, research is focused on the mechanisms that ultimately lead to a certain soil type and produce individual soil horizons. (Continued)

Advances in Agronomy Quantifying Processes of Pedogenesis

Box 1

5

(Continued )

In 2007, volume 3 (Number 5) of the magazine Elements explored and reviewed the interdisciplinary knowledge and future research on The Critical Zone focusing on its physical and chemical controls and biogeochemical agents and expressing the need to study the human imprint on the critical zone over the past 250 years (Amundson et al., 2007; Anderson et al., 2007).

to describe soil formation quantitatively. They require a detailed understanding of pedogenic processes. For instance, Bockheim and Gennadiyev (2000) identified a total of 17 soil-forming processes that lead to the formation of a soil profile. These processes include argilluviation, biological enrichment of base cations, andisolization, paludization, gleization, melanization, ferrallitization, podzolization, base cation leaching, vertization, cryoturbation, salinization, calcification, solonization, solodization, silification, and anthrosolization. While there are models that simulate individual process, no mechanistic model can be found in the literature that is able to simulate the listed processes of soil formation simultaneously, resulting in a soil profile. This review will focus on exploring and quantifying the pedogenic processes of the physical and chemical weathering of bedrock, the formation of soil horizons, and the rate of soil mixing processes, short term and long term.

2. Conceptual Models of Soil Formation—Factors, Processes, Pathways, Energy The extent of soil formation is believed to be dependent on local site characteristics. To model the evolution of soil in the landscape, we need to know which factors and processes are important for describing pedogenesis quantitatively. In the following, conceptual models of soil formation are reviewed briefly; they form the basis of mechanistic soil formation models.

2.1. Factors The origin of the soil-forming factors equations presented in the following is discussed in more detail in Box 2.

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Box 2

Background: The equation of soil-forming factors

Jenny’s state factor model is seen as the most well-known model of pedogenesis based on soil-forming factors. This often referred to and cited model states that soil (S) is created as a function of climate (cl), organisms (o), relief (r), parent material (p), time (t), and additional, unspecified factors (. . .) (refer to equation 3). S ¼ f ðcl; o; r; p; tÞ Jenny published his renowned state factor model in 1941 in his book titled “Factors of soil formation. A system of quantitative pedology.” However, previous to Jenny’s state factor model, the soil scientist Shaw also formulated an equation of soil-forming factors. In 1930 Shaw published a formula of “potent factors in soil formation” in the science journal Ecology, describing soil formation as being influenced by parent materials (M ), climatic factors (C), vegetation (V ), and time (T ), as well as the processes of erosion and deposition (D ) (refer to equation 2). It was brought to our attention that Shaw presented his conceptual model of soil formation at the “Second International Congress of Soil Science” in Leningrad in 1930. Following Shaw’s presentation published in the Proceedings of the congress, there is a lengthy discussion of Shaw’s model equation. For instance, a comment was made by Prof. Romell: He “. . .suggested in order not to hurt the feelings of the mathematicians to simply put S equal to a general function of the other symbols” with: S ¼ f ðM; C; V ; T ; DÞ However, Shaw did not develop his model any further before he died suddenly in 1939. Furthermore, we learned that Jenny was also a presenter at the Second World Congress of Soil Science and that he interacted with Shaw in Berkeley (University of California). Subsequently, we hypothesize that Jenny’s factors of soil formation equation was developed following the discussions of Shaw’s paper in 1930. In the Russian soil science community, however, Dokuchaev has been credited with first formulating factors of soil formation in his publication on “Key points in the history of land evolution in the European Russia” in 1886, published in Russian (Florinsky, 2011). Dokuchaev’s work on soil formation was introduced to the Western world through the English translation of Afanasiev’s paper “The classification problem in Russian soil science” in 1927. Afanasiev cited and discussed Dokuchaev’s work including his hypothesis on soil formation. The English translation of Dokuchaev’s hypothesis states that “Every dry land vegetative soil is in all instances a mere function of the following factors of soil formation”: 1. the nature of the parent rock, 2. the climate of the given locality, (Continued)

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Advances in Agronomy Quantifying Processes of Pedogenesis

Box 2 (Continued )

3. the mass and character of vegetation, 4. the age of the country, and finally 5. the relief of the locality. Here, Dokuchaev’s hypothesis is only published as a sentence. However, in a later publication from 1899 (Dokuchaev, 1899) this hypothesis on soil formation is written as the mathematical formula shown in equation 1 (e.g. as associated with Dokuchaev’s work in Volobuyev, 1974 and Schaetzel and Anderson, 2005 where a precise citation of the equation is not given). The factor of relief or topography does not appear in the mathematical equation although Dokuchaev discussed its importance on soil formation (Florinsky, 2011b). Later on, in 1927, Zakharov published a precursor of Jenny’s equation in one of the first fundamental Russian textbooks on soil science (Florinsky, 2011b) where soil (π) formation is a function of parent rock material (М.Г.Π.), organisms (Р.Ж.Орг.), climate (Кл.), age of the terrain (Возр.стр.) and topography (Р 2 ф): π ¼ f ðM:Γ:Π:; P:Ж:Орг:; Кл:; Возр:стр:; Р 2 фÞ We also know that both Shaw and Jenny were aware of Dokuchaev’s earlier work on soil formation factors. Shaw told Jenny “he (Jenny) was going to be the new Dokuchaev” (see “Encyclopedia of Soils in the Environment: Jenny, Hans” (Amundson, 2005)). Jenny acknowledged that his state factor equation included factors first proposed by Dokuchaev, however, emphasized the conceptual differences behind the two “formulas,” which he expressed in the first sentence of his renowned book in 1941: “As a science grows, its underlying concepts change, although the words remain the same” (Amundson, 2005; Jenny, 1941).

Dokuchaev is known as one of the first soil scientists who formulated an equation of soil-forming factors in 1886 (Volobuyev, 1974). The Russian soil scientist linked the formation of soil to environmental factors using a descriptive equation: P ¼ f ðK; O; GÞB

(1)

where P is the soil, K is the climate, O are the organisms, G is the ground or parent rock, and B is the time. In the “Western world,” Shaw (1930) can be seen as the first soil scientist, who published an equation that described “potent” soil-forming factors. In his equation, he stated that soil (S ) is formed from parent materials (M ) by a combination of climatic factors (C ) and vegetation (V ) as a function of time (T). In addition to these soil-forming factors, he

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also included the processes of erosion and deposition (D) to describe soil formation in the landscape: S ¼ MðC þV ÞT þ D

(2)

Although presented as a mathematical equation, Shaw’s soil formation model is only a factorial model, listing the major soil-forming factors. Shaw (1930) emphasized that the influence of these factors in developing soil is not uniform, but rather changes with local conditions. The most well-known conceptual model of soil formation is Jenny’s (1941) state factor model, which is also called the “clorpt” model. It comprises independent variables or state factors that define the state of a soil system. Hence, these state factors are not considered as formers or creators of the soil (S). S ¼ f ðcl; o; r; p; t; t; . . .Þ;

(3)

where cl is the climate, o are the organisms, r is the topography, p is the parent material, t is time, and ... stands for additional, unspecified factors. The state factors are independent from the soil system and vary in space and time (Amundson and Jenny, 1997). In its original form, the state factor model is unsolvable. To be solved, the indeterminate function f needs to be replaced by certain quantitative relationships. Hence, the “clorpt” equation has been formalized in quantitative ways based on empirical field observations, where a single factor is defined by keeping the other factors constant (Minasny et al., 2008). Empirical models were developed to describe soil formation in the form of quantitative climofunctions, biofunctions, topofunctions, lithofunctions, and chronofunctions, mostly based on numerically intensive statistical methods (McBratney et al., 2003; Yaalon, 1975). Based on the “clorpt” model of soil formation, McBratney et al. (2003) formulated the “scorpan” model, which indeed applies empirical quantitative relationships to predict soil properties from landscape attributes at specific locations in the landscape. The “scorpan” model is written as: Sc =Sa ¼ f ðs; c; o; r; p; a; nÞ

(4)

where Sc are the soil classes and Sa are the soil attributes, s is the soil, c is the climate, o are the organisms, r is the topography, p is the parent material, a is age, and n is space or the spatial position. The model is used quite extensively in the field of digital soil mapping to predict the recent state of the soil (soil properties), but is not intended, and cannot be applied, for long-term soil formation predictions.

2.2. Processes One of the first soil scientists who described soil formation as processes instead of factors was Simonson (1959). He considered two processes

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Advances in Agronomy Quantifying Processes of Pedogenesis

important for the evolution of soil, the accumulation of parent materials and the differentiation of soil horizons in the soil profile. Furthermore, he described the evolution of soil types as a function of additions (i.e., organic matter), removals (i.e., soluble salts), transfers (i.e., humus and sesquioxides), and transformations (i.e., primary minerals into secondary minerals) as demonstrated in Fig. 2: s ¼ f ðaddition; removal; translocation; transformationÞ

(5)

The soil-forming processes approach by Simonson (1959) can be seen as one of the conceptual frameworks for mechanistic models of soil formation implementing physical laws (Minasny et al., 2008). However, the original work is still a qualitative description.

2.3. Pathways Johnson and Watson-Stegner (1987) introduced the concept of pathway models. They viewed soil evolution as a result of genetic pathways. Their model considers soil as a complex open system with changes in soil thickness and increasing genetic complexities with time. They stated that soil (S) forms progressively (P) and regressively (R) along interacting pathways: S ¼ f ðP; RÞ

(6)

where P stands for progressive pedogenic conditions, including processes and factors that promote horizonation, developmental (assimilative) upbuilding, and/or subsurface deepening; and R stands for regressive pedogenic conditions, including processes and factors that promote

Additions

Additions Translocations Transformations Removals

Removals

Figure 2 Soil profile evolution as a function of additions, removals, translocations, and transformations.

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haploidization, retardant (nonassimilative) upbuilding, and/or surface removal. Soil evolves along these progressive and regressive pathways, where some might be dominant over others. This is yet another qualitative description.

2.4. Energy Models of soil formation based on the concepts of energy describe pedogenic factors and processes implementing the principles of energy or concepts of thermodynamics. The most well-known and cited soil scientist addressing this possibility has been Runge (1973). However, in their review, Minasny et al. (2008) emphasized the work of Volubuyev from Azerbaijan. Volobuyev published various papers on linking pedogenic processes with laws of energy. The most relevant models for estimating soil formation from energy laws are included in the following paragraphs. Runge (1973) presented a different type of factorial model, formulating soil evolution based on energy: S ¼ f ðo; w; tÞ

(7)

where S is the soil, o is the organic matter production (renewing factor), w is the amount of water available for leaching (developing vector), and t is the time. Climate and relief are expressed within the vector w. This energy model relies on gravity as the main source of energy, driving the infiltration of water in the soil, which is responsible for horizonation. The model considers solar energy indirectly in the production process of organic matter. The energy model of Runge (1973) is only useful in a qualitative way because actual quantitative thermodynamical calculations are not implemented in the model (Hoosbeek and Bryant, 1992). In an unpublished thesis, Regan (1977) studied soil formation through energy processes and created an energy model of soil formation. He based his studies on soils derived from limestone and marine washed sands in Florida, USA. Regan (1977) calculated the total amount of energy needed to form soil by implementing the energy from sunlight, the energy flux from carbon dioxide production by organisms, wind, and temperature; the chemical energy of rain; the kinetic energy developed from sloped surfaces; the chemical free energy of phosphorus; and the gravitational energy from uplift processes. Running the energy model, steady-state conditions of soil formation are reached after only 525 years for soils with sandy parent materials, and after 375 years for soils with calcareous parent materials with a rate of B5.65 3 103 kJ m22 yr21. Rates of soil formation with time are obviously underestimated, but nevertheless this model can be seen as a good example for linking the amount of energy needed for soil formation with vegetation and urban growth (Minasny et al., 2008).

Advances in Agronomy Quantifying Processes of Pedogenesis

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The quantification of processes of energy transformation during soil formation was addressed intensively by Volobuyev. For instance, he described the expense of energy in the process of soil formation applicable for all climatic zones as follows (Volobuyev, 1974): Q ¼ Ra ¼ R e 21=mK

(8)

where Q is the expenditure of energy on soil formation, R is the energy of solar radiation, a are the available energy sources, K is the relative wetness, and m is a factor expressing the participation of biota in energy exchange. Following on, Volobuyev and Ponomarev (1977) investigated various thermodynamic aspects of soil-forming processes. They calculated Gibbs free energy (ΔG) and entropy (S) for different soil types from individual Gibbs free energy and entropy values of soil minerals (see Volobuyev and Ponomarev, 1977, Table 1, p. 6), showing that the thermodynamic characteristics of soil minerals vary significantly for the soil types studied. In addition, they identified two soil groups based on their energy expenditure during mineral formation: (1) one that is characterized by a decrease in Gibbs free energy and an increase in entropy and (2) one that is characterized by an increase in Gibbs free energy and a decrease in entropy. Furthermore, Volobuyev et al. (1980) used these Gibbs free energy potentials for soils to predict their infiltration or leaching capacity. They showed that the lower the Gibbs free energy levels of soils, the higher their infiltration capacities. Based on calculations from Volobuyev and Ponomarev (1977) and Volobuyev et al. (1980), Minasny et al. (2008) presented Gibbs free energy and entropy for different soils, rocks, and minerals, as shown in Fig. 3. Soils enriched with SiO2, Al2O3, Fe2O3, CaCO3, and large quantities of residual minerals have low Gibbs free energy (which is “lost” during weathering) and high entropy. In the order of higher energy and lower entropy, this is followed by phyllosilicate minerals, carbonates, and soluble salts. A decrease in Gibbs free energy and an increase in entropy are associated with minerals that have higher intensity of leaching and are more resistant to weathering. Volobuyev (1984) also formulated an energy model to apply Dokuchaev’s equation quantitatively (Eq. (1)):
 Pc wR0:67 Q ¼ Rð6rÞexp 2 (9) mPð6pÞ where Q is the (annual) expense of energy on soil-forming processes, R is the radiant solar energy, P is the relative wetness, m is the biological activity, r is the radiation balance, p is the atmospheric precipitation, w (chemically bound water of mineral soil components) is the rate of mineral transformations in soils, and Pc is water, such as water that is fixed in the mineral,

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1000 900

Gibbsite

800

Kaolinite Oxisols Olivine

Mollisols

600 500

Vertisols

400

S (kJ kg–1)

700

Quartz

Gypsum 300 200 100 0

–16,000 –14,000 –12,000 –10,000 –8000

–6000

–4000

–2000

0

ΔG (kJ kg–1)

Figure 3 Gibbs free energy (ΔG) and entropy (S) for different soils, rocks, and minerals. (Source: Graph is based on Minasny et al., 2008.)

faunal, and floral component of soils. Dokuchaev’s soil-forming factors, as demonstrated in Eq. (1), are represented by R and P (climate, K), by m (organisms, O), by Pc and w (parent rock, G), and (m) by p and r.

2.5. Summary Conceptual models of pedogenesis have been, and are still being, developed for the past 100 years. These include the factorial, processes, pathways, and energy models. However, these models are mostly interpreted qualitatively, although some have been applied in a quantitative way, that is, the factorial model of Jenny that could be solved by applying empirical quantitative relationships to predict soil properties from landscape attributes. These conceptual models can form the basis of mechanistic models.

3. Soil Weathering and Production As described in the previous sections, several processes are responsible for transformations, translocations, additions, and removals in the soil system. Ultimately, these processes and their associated transformations of energy result in the formation of a particular soil profile. Furthermore, the dynamics of the interacting chemical, physical, and biological processes

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are believed to be induced by different parent materials and climates. The horizonation and differentiation of the soil profile is the result of transformation processes like soil weathering and soil mineralization, decomposition and humification, and aggregate formation as well as translocation processes like eluviation and illuviation. A common perception in pedology is that pedogenesis is a product of mostly downward moving processes like leaching that lead to the formation of interrelated layers, the A and B horizons (Huggett, 1998). Pedoturbations, the so-called soil mixing processes, are seen as processes that are working against horizonation rather than promoting it because of possible mixing of surface and subsoil materials induced by mixing agents such as soil biota, soil moisture changes, and periodic freezing and that are resulting in the subsequent homogenization of the layers in the soil profile (Huggett, 1998). The following sections will explore how processes of soil formation are modeled or estimated with field data in the literature.

3.1. Production of soil from parent materials According to NAS (2010): “The breakdown of bedrock—a major factor in Earth surface processes—is among the least understood of the important geological processes.” The evolution of soil has been explained vastly with the help of chronosequences over timescales of up to millions of years. Traditional theories of soil evolution along chronosequences explain soil development progressively under the influence of environmental factors until soil development is in equilibrium (Huggett, 1998). Accordingly, it is believed that the development of a certain soil type is preset in a certain landscape, that is, in the German soil classification scheme; on limestone parent materials, Rendzinas are formed that eventually evolve into brown earths. For instance, chronosequences were created based on conceptual ideas and observations in the field with the help of successional stages of vegetation by placing them in a chronological order and by exploring soil profiles that developed on surfaces of known age (Schaetzel and Anderson, 2005). One discrepancy in formulating chronosequences is the assumption of constant soil-forming factors except time. This is especially unlikely for the soil-forming factors climate and vegetation cover. New views in evolutionary pedogenesis tried to explain the nonlinear behavior of soil development by assuming that soils evolve through continual formation and destruction and, consequently, might progress, regress, or stay constant depending on environmental conditions (Huggett, 1998). Chronosequences can be transformed into chronofunctions by plotting soil and landscape properties against time (or age) using time as the

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independent variable, based on Jenny’s state factor equation (Schaetzel and Anderson, 2005): S ðSoilÞ ¼ ft ðtimeÞcl; o; r; p . . .

(10)

Furthermore, statistical models can be applied to express chronosequence data (soil and landscape properties) mathematically by fitting curves of soil evolution with time. Schaetzel et al. (1994) reviewed types of mathematical functions commonly used in chronofunctions (Fig. 4). As demonstrated in Fig. 4, chronofunctions might be modeled using: 1. 2. 3. 4. 5.

simple linear behavior Y 5a1bt, single logarithmic behavior Y 5a1b(log t), exponential behavior Y 5a exp(bt), power functions Y 5atb, or nonlinear sigmoidal functions Y 5 1/(a1b exp(2t)).

Linear functions suggest that the soil system evolves at a constant rate through time, whereas logarithmic models imply that the soil system is in steady state or will reach a steady state eventually sometime in the future. Nonlinear sigmoidal chronofunctions propose that the soil system evolved along periods of rapid pedogenesis followed by decreasing rates (Schaetzel et al., 1994).

Sigmoid

Soilproperty

Power

Logarithmic

Exponential

Linear

Time

Figure 4 Types of mathematical functions commonly used in chronofunctions. S-shaped or sigmoidal curve, general form of equation: Y 5 1/(a 1 b exp(2 t)); power functions, general form of equation: Y 5atb; logarithmic functions, general form of equation: Y 5a 1 b(log t); exponential functions, general form of equation: Y 5a exp(bt); simple linear functions, general form of equation: Y 5a 1 bt. (Source: Adapted from Schaetzel et al., 1994.)

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Following the concepts of chronofunctions, production rates of soil from bedrock parent materials were assumed to decrease with time and were discussed to follow a linear or nonlinear function of time (Colman, 1981). However, there are two concepts of soil production that have been discussed much more extensively in the geomorphology literature than in pedology (Fig. 5). 3.1.1. The exponential soil production model This model states that the formation of soil declines exponentially with increasing soil thickness. This theory was discussed conceptually by Ahnert (1977). He assumed that the rate of soil production from hillslopes decreases exponentially with increasing thickness of the overlying soil mantle. The exponential decrease of the rate of soil production with increasing soil thickness is believed to be dependent on the soil temperature and water penetration through the soil profile. The exponential decrease of the temperature range with increasing soil depth is seen as a factor for reduced weathering with increasing depth below the soil surface (Minasny and McBratney, 1999). Furthermore, the occurrence of moisture-related processes like freeze-thaw also decreases exponentially under increasing soil depth.

Relative production rate

1

0.8 k2 = 0 0.6 k2/k1 = 3

0.4

0.2 k2/k1 = 1.5 0 0

0.2

0.4

0.6

0.8

1

1.2

Soil thickness (m)

Figure 5 The rate of soil production versus soil thickness. Here, both the exponential and the humped soil production model are presented graphically. Both axes are dimensionless. Soil production is presented graphically depending on different values of the parameter k1 and k2 (see Eqs (12) and (13)). If k2 5 0, the soil production equals a depth-dependent exponentially decreasing soil production function. If k2/k1 $ 0, soil production shows a “humped” function.

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Heimsath et al. (1997, 1999) verified the exponential decline of soil formation with increasing soil depth with field data from the Tennessee Valley in California, USA, and found field evidence for the theory of exponential decline of soil production with increasing soil depth and parameterized the potential weathering rate of bedrock. According to this research, soil production is the greatest when bedrock is just exposed and decreases with increasing soil depth. The derivation of soil production rates (SPRs) from field data became possible with the measurement of in situ terrestrial cosmogenic nuclide (TCN) concentrations from soil parent materials such as bedrock or saprolite, sampled underneath different soil depths. The exponential decline of soil production (SPR) with increasing soil thickness is described as (Dietrich et al., 1995; Heimsath et al., 1997): SPR ¼ P0 expð2bhÞ

(11)

where P0 ([L T21], mm kyr21) is the rate of weathering of bedrock at h equal to 0, h ([L], cm) is the soil thickness, and b ([L21], cm21) is a rate constant, a length scale that characterizes the decline in soil production with increasing soil thickness. 3.1.2. The humped soil production model The second concept explains the conversion of bedrock or saprolite to soil using a humped function (Humphreys and Wilkinson, 2007). This theory was first introduced by Gilbert (1877) and later discussed by Carson and Kirkby (1972). Accordingly, soil production is highest underneath a nonzero soil depth. Hence, the weathering of bedrock is greatest underneath an incipient soil depth and slower underneath exposed bedrock or an already thick soil mantle. The main explanation for the occurrence of a humped model of soil formation is the maximization of chemical and physical weathering processes under an initial soil depth. The presence of water is discussed as a major factor in this phenomenon by Carson and Kirkby (1972). Water is considered as an important agent for chemical weathering of bedrock or saprolite into soil. Carson and Kirkby (1972) give an example for their reasoning: On an exposed site, like bedrock, water tends to run off, which lowers the rate of chemical weathering. This characteristic also applies for very thin soils where water runs off fast because of insufficient pore space in the soil to hold the water. In soils with a thick soil cover, the circulation of water also tends to be slow, which reduces the rate of weathering. Furthermore, because water tends to run off bare rock, processes of freeze-thaw are also limited underneath shallow or deep soil covers. Granger et al. (2001) proposed that the presence of moisture in an

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already thin soil cover seems to be very important for the chemical weathering of granite rock to gruss, which is relatively resistant to weathering. Wilkinson and Humphreys (2005) discussed the role of fauna and flora to explain the occurrence of a humped model of soil production. They argue that animals and plants require a moderate soil mantle to promote soil production processes. Yoo et al. (2005) applied this theory when modeling the population of pocket gophers in relation to soil thicknesses. In addition to mesofauna, plant roots make an important contribution to the weathering of saprolite by disturbing the soil
saprolite interface. Most likely, those disturbances create channels and therefore access for weathering agents (i.e., water) into the soil profile. Biota are also able to penetrate through the soil and saprolite into the zone of unweathered rock. However, Wilkinson and Humphreys (2005) also argue that biota will reach the soil bedrock interface less frequently under a thick soil cover. Minasny and McBratney (2006) presented the humped model as a continuous double exponential function (see Fig. 5):     @e (12) ¼ 2 P0 expð2k1 hÞ 2 expð2k2 hÞ þPa @t where P0 ([L T21], mm kyr21) represents the rate of weathering of bedrock, h ([L], cm) is the soil thickness, k1 represents the rate of mechanical breakdown of the rock materials, and k2 is the rate of chemical weathering, Pa is the weathering rate at steady-state condition ([L T21], mm kyr21) with condition k1 , k2. As k2 equals 0, the humped function is reduced to the depth-dependent exponential soil production function (Eq. (11)). The critical thickness, hc, where weathering is at maximum is given by: hc ¼

lnðk2 =k1 Þ k2 2k1

(13)

An empirical parameterization of the humped model from field data is still to be achieved, although Heimsath et al. (2009) assumed a humped form of soil production for a study site at Arnhem Land in Northern Australia for a landscape dominated by outcrops and soil depths no less than 35 cm. Dietrich et al. (1995) applied the exponential and also the humped model to simulate soil formation in a catchment of the Tennessee Valley in California, USA. With a chosen maximum soil production at 25 cm, the predicted landscape formed is characterized by sharply curved ridges and outcrops. In such an environment with humped soil production, soil depths below the peak of soil production are assumed to be unstable and together with soil erosion this will lead to a stripping of the soil to bedrock. Consequently, no soil depths that are in equilibrium should be

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observed in the field for less than the peak in soil production. However, the modeling results of Dietrich et al. (1995) were more consistent with field observations when using an exponential model. Later work by Heimsath et al. (1997) showed that soil formation indeed followed an exponential function at the study site of 0.077 mm yr21 with no soil mantle. Following Heimsath et al. (1997), a small range of different authors applied the concepts of deriving SPR from TCN data. SPRs derived from TCN range between 0.004 and 0.4 mm yr21 (Heimsath et al., 2000, 2001a, 2001b, 2002, 2005, 2009; Wilkinson et al., 2005). Current research is also focused on finding the conversion rate of bedrock to regolith (Brantley, 2010; Graham et al., 2010). In pedology, bedrock as well as regolith are parent material for soil production. Different from hard fresh rock, regolith is a form of weathered “friable” rock that still has the structural characteristics and fresh primary minerals of the parent rock and is transformed into soil through physical disruptions. Regolith is already seen as a hospitable substrate, in particular for the soil flora. Published rates of regolith formation for hard granitic rocks are relatively low and range between 0.004 and 0.02 m yr21 (Dosseto et al., 2008; Graham et al., 2010).

3.2. Chemical weathering of bedrock to soil The chemical weathering of bedrock to soil is an important process in forming the soil mantle. Rates of chemical weathering have been estimated from both laboratory and field-based studies applying a variety of investigative methods. However, a time-dependent difference between rates in the laboratory and field rates was found (Lasaga et al., 1994). Calculated laboratory rates are much faster when compared to observed field rates by up to five orders of magnitude (Brantley et al., 2007), yet both methods show a decrease of chemical weathering with time. In the laboratory, chemical weathering rates mostly depend on the experimental setup like pH, the percolation rate, and the weathering state of the silicate minerals studied, that is, fresh or partly weathered minerals. For instance, White and Brantley (2003) investigated the long-term dissolution of plagioclase (weathering rate R in mol m22 s21) extracted from fresh and weathered granite. In one of the longest experimental setups, of over 6 years, they determined parabolically decreasing weathering rates of 7.0 3 10214 mol m22 s21 for fresh Panola Granite, and found significantly less chemical weathering rates of 2.1 3 10215 mol m22 s21 for partially weathered Panola Granite, which reached steady state in only 2 months. After extrapolating decreasing weathering rates of fresh plagioclase with time, they concluded it would take several thousand years of reaction to replicate the rate of the naturally weathered plagioclase under identical experimental conditions.

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Chemical fluxes in a watershed can be determined using the solute discharge flux Qi,dis for a chemical species i based on a mass balance approach: V Qi;dis ¼ Ci;dis (14) At where Ci,dis is the chemical concentration of a chemical species i, V is the fluid mass, A is the geographic area of the watershed, and t is the time (White and Blum, 1995). In watershed studies, chemical weathering kinetics of silicate (temperature dependence of dissolution rate) are often modeled empirically with the help of the Arrhenius equation, which describes the dependence of chemical weathering rates on the temperature (WX) in natural systems (White and Blum, 1995): WX ¼ A exp½2ðΔEX =RT Þ

(15)

where A is an empirical constant that incorporates the effects of surface area and surface reactivity, ΔEX is the activation energy for the weathering reaction that releases element X (kJ mol21), R is the universal gas constant (J K21 mol21), and T is the absolute temperature in Kelvin (K). Based on silicon dioxide (SiO2) and sodium (Na) fluxes, activation energies for chemical weathering were calculated to be 59.4 and 62.5 kJ mol21, respectively. A range of chemical weathering rates estimated from field data (usually expressed by M L22 T21) can be found in the literature, a selection is presented in Table A.1, Part 1 and Part 2, found in the appendix. Part 1 summarizes chemical weathering rates based on elemental fluxes (loss and gain) in watersheds and the chemical composition of the parent materials and weathering products studied. For instance, Colman and Dethier (1986) summarized rates of chemical weathering with time based on catchment mass loss from solutes, varying between 0.003 mm yr21 and 0.04 mm yr21 for different climates and parent materials (Dethier, 1986; Paˇces, 1986; Pavich, 1986; Velbel, 1986). In another study, Alexander (1988) calculated rates of chemical weathering based on mass balance equations for 18 watersheds with noncarbonate lithologies. The predicted rates of about 0.0017
0.16 mm yr21 were mostly based on the volume of runoff water and the soil to rock ratio, that is, the mass of soil/mass of bedrock weathered to produce that amount of soil. Summarizing Table A.1, Part 1, we can see that rates of chemical weathering vary between 0.00017 mm yr21 calculated for a tropical environment by Owens and Watson (1979) and 0.473 mm yr21 calculated for a humid tropical environment by Wakatsuki and Rasyidin (1992). However, the majority of compiled chemical weathering rates from these watershed studies ranged between B0.01 and 0.1 mm yr21. A pattern of chemical

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weathering rates being grouped by study areas with similar climate regimes and parent materials was not readily discernible in the data. In the current literature, various studies are now focusing on calculating chemical weathering rates of bedrock in situ to investigate soil formation processes. Calculated rates are presented in Table A.1, Part 2, with Table A.2 listing the main characteristics of the environments studied. The so-called weathering indices are used to estimate the extent of chemical weathering based on mass balance calculations or the ratio between the chemistry of fresh parent rock to that of weathered rock or soil (Taylor and Eggleton, 2001). Generally, the rock to soil ratio of an immobile, slowly weatherable mineral is used to estimate rates of chemical weathering based on its loss and gain. In most of the studies, zircon (Zr) is used. Before discussing the studies that applied this approach, the term “total denudation rate” used in these publications needs to be clarified: The term total denudation rate refers to the combined rate of chemical and physical weathering of rock to soil. In publications of Riebe et al. (2003, 2004a, 2004b), Green et al. (2006), Yoo et al. (2007), Burke et al. (2007), and Burke et al. (2009), total rates of denudation or weathering were substituted with rates of soil production derived with TCN. To estimate the extent of chemical weathering, determined chemical weathering rates (using weathering indices) were subtracted from rates of soil production (using TCN). In the publication of Dixon et al. (2009), however, total denudation rates were calculated by combining determined chemical weathering rates (using weathering indices) and rates of soil production (using TCN). TCN-derived SPRs were therefore substituted with rates of physical weathering. Riebe et al. (2003, 2004a, 2004b) calculated in situ rates of chemical weathering using weathering indices in conjunction with SPRs derived from in situ TCN. In these studies, SPRs are substituted with total denudation rates to estimate the degree of chemical weathering. The conservation of mass equation for the chemical weathering rate as a fraction of the total denudation or weathering rate is written as: W ¼ Dð1
½Zrrock =½Zrsoil Þ

(16)

where W is the chemical weathering flux in [M L22 T21] or [L T21], D is the total denudation rate, and [Zr]rock and [Zr]soil are the concentrations in rock and soil of Zr. Riebe et al. (2003, 2004a, 2004b) estimated chemical weathering to vary between as low as 0.0041 mm yr21 and as high as 0.14 mm yr21. At the study site in Rio Icacos, Puerto Rico (Riebe et al., 2003), results showed that chemical weathering accounted for 58% and 68% of the total loss by chemical and physical weathering. On average 0.021 mm yr21 were

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accounted for by chemical weathering of saprolite and 0.054 mm yr21 were attributed to combined chemical weathering and physical soil weathering. Results from the study site at the Santa Rosa Mountains in Nevada (Riebe et al., 2004b) showed that chemical weathering rates decreased with increasing altitude from 0.02 to 0 mm yr21, suggesting a trend of dominance of physical erosion with increasing altitude. Sparsely vegetated, high-altitude crystalline terrain in particular seemed to be characterized by very slow silicate weathering rates. For 42 different study sites with granitic parent materials located in diverse climate regimes, the data analysis implied that chemical weathering rates increase proportionally with supply rates of fresh material, that is, “supply-limited” weathering (Riebe et al., 2004a). In the majority of the following studies, the conceptual ideas of Riebe et al. (2003, 2004a, 2004b) have been employed. Green et al. (2006) conducted a study in the Bega Valley in south-eastern Australia to quantify chemical weathering along a hillslope, implementing SPRs of the area as analyzed by Heimsath et al. (2000). They calculated that mass loss by chemical weathering accounted for 35
55% of the total mass loss from the hillslope. Results from Burke et al. (2007) indicated a decrease of chemical weathering of saprolite with increasing overlying soil thickness. At the study site Point Reyes in California, USA, chemical weathering accounted for 13
51% of total denudation rates. Assumptions were made that spatial variation in chemical weathering is controlled by the topography of the study sites. Accordingly, weathering rates decrease with slope across the divergent ridge and increase with upslope contributing area in the convergent swale. Results also showed that measurements of saprolite abrasion pH are closely related to weathering indices, assuming that the intensity of chemical weathering decreases linearly with an increase in saprolite pH from 4.7 to 7. Yoo et al. (2007) combined geochemical mass balance with sediment transport to predict rates of soil chemical weathering and transport on hillslopes. This research was conducted at a site in the south-eastern highlands (Frogs Hollow) in south-eastern Australia. Results showed losses of soil chemical weathering rates of 0.029 mm yr21 on the ridge and consequently gains of 0.029 mm yr21 at the lowest slope positions. Additionally, Yoo et al. (2007) investigated soil residence times by linking soil transport and topography. Results indicated residence times for soil of 4 kyr on the ridge to 0.9 kyr at the base of the hillslope. Burke et al. (2009) compared the extent of chemical weathering for field sites in the lowlands and highlands around the area of the Bega Valley in south-eastern Australia and found indications for lower rates of chemical weathering for the highland (47%) compared to the lowland sites (57%). Dixon et al. (2009) applied a similar approach for the Sierra Nevada Mountains in California, USA. Results implied that chemical weathering of saprolite to soil peaks at mid elevations compared to high

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and low elevation sites and that physical erosion rates increase with both saprolite weathering rates and intensity. In contrast to previous publications, total denudation rates were calculated as the sum of total chemical weathering and SPRs.

3.3. Summary Soil-mantled landscapes are believed to be the result of two main concepts of soil formation: (1) the exponential soil production model and (2) the humped soil production model. The exponential decline of soil production with increasing soil thickness was verified with field data in situ. Calculated rates of weathering of parent material are as low as 0.004 mm yr21 and as high as 0.4 mm yr21. The humped model was used to explain soil formation conceptually for some landscapes, but an empirical parameterization is still to be achieved from field data. Laboratory-derived chemical weathering rates of rocks or minerals are generally more rapid than rates estimated from field data. Minimum rates of chemical weathering derived from field data are as low as 0.01 mm yr21 with maximum rates of about 0.5 mm yr21. The majority of chemical weathering rates estimated from catchmentbased mass loss of elements range between 0.01 and 0.1 mm yr21 with mean values of about 0.060 mm yr21. Rates of chemical weathering derived with so-called weathering indices vary from 0 to 0.144 mm yr21 with mean values of about 0.022 mm yr21 and are relatively similar to chemical weathering rates estimated from catchment-based methods (Figs. 6 and 7), although rates derived with weathering indices were generally lower. Rates of total denudation were estimated for all chemical weathering studies based on weathering indices using TCN. Generally, calculated rates of chemical weathering were lower than rates of physical weathering. Both data sets on chemical weathering rates (Table A.1, Part 1 and Part 2) show no distinct pattern of weathering rates being dependent on different climates and parent materials of the sites studied.

4. Soil Mixing—Vertical and Lateral Movements Soil mixing processes ultimately result in the detachment, transport, sorting, and deposition of material within the soil mantle and on its surface (Paton et al., 1995). Consequently, these pedoturbations initialize vertical and lateral movements in the soil mantle.

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Burke et al. (2004b)

Burke et al. (2004a)

Burke et al. (2003)

Burke et al. (2006)

Burke et al. (2009)

Burke et al. (2007) 0 0.05 0.1 Rate of chemical weathering (mm yr–1)

0.15

Figure 6 Box plot distributions of chemical weathering rates derived from weathering indices (Table A.1, Part 2). Graph shows the range in the data sets derived from in situ field data using weathering indices; the line in the middle of the box refers to the median of the distribution.

Weathering indices

Other

0

0.1

0.2

0.3

0.4

0.5

Rate of chemical weathering in (mm yr–1)

Figure 7 Box plot distributions of chemical weathering rates derived from field data (Table A.1, Parts 1 and 2). Graph demonstrates range in weathering indices data sets compared to different methods (generally catchment-based mass balance from solutes); the line in the middle of the boxes refers to the median of the distribution.

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Forms and agents of pedoturbation

Form of pedoturbation

Soil mixing agents

Aeroturbation Anthroturbation Aquaturbation

Gas, air, wind Humans Water (rain splash, eluviation/illuviation processes) Argilliturbation Shrinking and swelling of clays Cryoturbation Freeze-thaw activity, ice crystals Crystalturbation Crystals, such as ice and various salts Faunalturbation (Bioturbation) Animals, including insects Floralturbation (Bioturbation) Plants Graviturbation Mass movements, such as creep Impactturbation Extraterrestrial impacts, such as comets and meteorites, and human-generated impacts, that is, artillery shells and bombs Seismiturbation Earthquakes Source: Based on Schaetzel and Anderson (2005).

Various forms and agents of pedoturbation can be seen in Table 1. Here, soil creep is characterized as mainly induced by gravity-related mass movements (graviturbation). However, creep is also caused by bioturbation via processes like burrowing and the growth and decay of roots, which is followed by the refilling of generated holes from the upslope side. In Table 1, pedoturbation caused by water is restricted to rain splash and movement of water down the profile, excluding overland lateral transport processes and deposition by water, that is, rill or gully soil erosion caused by water. The most relevant pedoturbations for pedogenic studies are soil creep and bioturbation, as well as eluviation and illuviation processes that transport soil particles due to the movement of water down the soil profile.

4.1. Bioturbation The influence of plants and animals on soil formation was not recognized profoundly in early publications of soil science. For instance, Jenny (1941) did not examine the role of animals as part of the o-(organisms)factor “because of lack of sufficient observational data. . .”. Furthermore, Carson and Kirkby (1972) nominated faunal and floral mixing agents as being of secondary or minor importance in causing soil creep. Carson and Kirkby (1972) defined processes of soil creep (only) as diffusion processes or mass movements in the soil profile, mostly caused by reworking

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of the soil surface because of soil moisture and temperature changes such as frost cycling and resulting from the steady application of downhill shear stress. Nevertheless, they acknowledged possible rates of soil creep produced by soil fauna and flora in addition to those resulting from mass movements. On the other hand, Paton et al. (1995) argued that bioturbation processes play a major role in forming a soil profile (Box 3, The Macquarie School of “bioturbation” and Box 4). In the current literature, there is still debate about how much influence bioturbation actually has on soil transport or soil mixing processes

Box 3

The Macquarie School of “bioturbation”

Research from “Macquarie University” in Sydney, Australia, identifies bioturbation processes as one of the key components of pedogenesis. Based predominantly on observations in Australia, Paton et al. (1995) published “A New Global View on Soils,” describing pedogenesis as depending predominantly on bioturbation, slope processes, and the landscape studied, and introducing an alternative view on the genesis of soils in pedology. Geoff Humphreys was credited with first using the term “bioturbation” in pedology by Don Johnson (the term and concept of bioturbation is originally from ichnology and oceanography describing the displacement and mixing of sediment particles and solutes by benthic fauna or flora) (also refer to Box 4). Paton et al. (1995) proposed that the formation of texture contrast soil resulted from interacting slope and pedogenic processes with bioturbation processes being the main drivers in forming the clay-rich subsoil horizon. They argued that the downward movement of clay via lessivage has been overemphasized and in fact plays a minor role in forming duplex soil profiles in landscapes. Therefore, rather than transporting fine materials down the profile via leaching processes, clay and silt-sized materials are moved down the soil profile or downslope by biological agents resulting in an accumulation of coarser materials on the top of the profile. Consequently, the authors put more emphasis on lateral rather than vertical movements in the soil profile. It was noted that especially the deeply weathered so-called duplex soils in tropical Australia would have formed under the proposed circumstances. Furthermore, they used these processes to explain the formation of basal stone layers occurring below the so-called biomantle; the zone that is produced largely by bioturbation in the soil profile ( Johnson, 1990). Stones that are too large for the bioturbating agents to be transported to the soil surface will accumulate below the burrowing depths of the soil fauna. Following on, Wilkinson and Humphreys (2005) concluded that rates of bioturbation are significantly faster than rates of soil production. They (Continued)

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Box 3

(Continued )

hypothesized that soil mixing rates exceed SPRs by up to three orders of magnitude for study sites in south-eastern Australia and based their argument on soil mixing rates by faunal agents of about 0.429
10.6 mm yr21 and SPRs of about 0.013
0.061 mm yr21. In 2009 Wilkinson et al. (2009) reviewed estimates of bioturbation that were extrapolated from short-term observations, mostly recorded by researchers from their school. In this context, the importance of biogenic agents in pedogenic studies was again emphasized. Bioturbation rates for earthworms were reported to be as high as 10
50 t ha21 yr21 (0.833
4.167 mm yr21) with some reaching 100 t ha21 yr21 (8.33 mm yr21). Ants, termites, and vertebrates recorded comparable rates of B1-5 t ha21 yr21 (0.083
0.417 mm yr21); for ants rates of about 5
10 t ha21 yr21 (0.417
0.833 mm yr21) were also noted. Wilkinson et al. (2009) acknowledged the role of Charles Darwin in emphasizing the effect of biota on soil. Darwin can be seen as one of the first scientists who observed and recorded mixing of plant and mineral matter in soil by earthworm activities. He proposed a mixing rate of 0.244 cm2 yr21 for temperate maritime environments. Furthermore, Darwin made an inference that around 26,886 earthworms live in an acre (4046 m2) of pasture soil and that each earthworm would ingest 20 ounces of soil each year (560 g yr21, which translates to a total amount of 15 t of soil annually) (Source: “Pedometron,” The Newsletter of the Pedometrics Commission of the IUSS, No. 20, p. 14). Following research initiated by Humphreys, who died unexpectedly in 2007, Wilkinson is continuing his legacy at “The Macquarie School,” investigating the importance of biological mixing of soils and its implications for pedogenesis.

Box 4 The term “bioturbation” in pedology

At the 19th World Congress of Soil Science in Brisbane, Australia (August 1
6, 2010), we learned that the term “bioturbation” was introduced to pedology by Winfried E. Blum and Robert Ganssen in 1972 in their publication about “Soil-forming processes of the Earth” for the journal Die Erde, at least to the German speaking “soil scientist” (Blum and Ganssen, 1972). Blum and Ganssen (1972) used the term “bioturbation” for explaining “horizon blurring” processes by burrowing animals like earthworms, insects, or small mammals who habituate the soil environment. They noted that bioturbation processes by termites in tropical and subtropical environments resulted in a complete mixing of soil horizons of up to 3 m of depth, destroying pedogenic structures most likely formed by lessivage.

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and therefore pedogenesis, and if bioturbation results in net downslope movement or not (Amundson, 2004; Wilkinson et al., 2009). Biological activity in soils mostly depends on the soil structure including porosity, texture, and stone content; water content; the roots that hold the soil together; and available nutrients. Often the A-horizon or topsoil is the part of the soil profile where most bioturbation processes take place and therefore would be equivalent to the so-called biomantle (Amundson, 2004). It is believed that bioturbation processes are able to either promote the development of horizons (horizonation) in texture contrast soils or impede soil horizonation altogether (haploidization) through the homogenization of the topsoil and the subsoil as well as material transport between the subsoil and the topsoil. Furthermore, it is also believed that bioturbation processes can be responsible for the formation of stone layers, generally occurring at the interface between the A and B horizons (Lobry de Bruyn and Conacher, 1990; Muller-Lemans and van Dorp, 1996; Paton et al., 1995; Wilkinson et al., 2009). Particularly, small mammals like gophers are thought to cause the creation of a stone layer in some areas of the soil profile (Gabet et al., 2003). However, it is also suggested that termites could be partly responsible for their creation because of selective transfer of certain fine soil particle sizes, clay and silt, to the surface (Lobry de Bruyn and Conacher, 1990). 4.1.1. Faunaturbation Agents that predominantly cause faunaturbation are earthworms, ants, termites, gophers, wombats, ground squirrels, and mice (Muller-Lemans and van Dorp, 1996). Hole (1981) classified those agents into two groups, exopedonic (outside the soil) and endopedonic (inside the soil) agents. He also listed 12 soil fauna related activities that take place within a soil body: mounding, mixing, forming voids, back-filling voids, forming and destroying peds; regulating soil erosion, movement of water and air, plant litter, nutrient cycling, biota, and producing special constituents. In the following, rates of soil turnover by faunal agents will be discussed later. Most of the discussed rates are presented in Table A.3. Hole (1981) summarized that Arctic ground squirrels and pocket gophers are able to excavate up to 1.5 mm of soil each year, birds were able to turn over the complete litter layer of a subtropical rainforest floor in Australia in only 3 weeks, and earthworms required only up to 300 years for forming an ochric epipedon by mixing of the topsoil (A2) and the litter layer (O). Samedov and Nadirov (1990) concluded that biota, especially earthworms and woodlice, can increase soil productivity significantly. Earthworms and woodlice promoted the neutralization of acid (pH 3.4
5.5) decay products and their alkalinization (to pH 6.8
8.6) due to digestion of the soil and

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plant residues. They also influenced the total chemical composition of the soil, changing Gibbs free energy of soil (2ΔG) from 2967 to 2992 or to 21063 kJ 100 g21. Furthermore, Samedov and Nadirov (1990) also hypothesized that earthworms and woodlice might participate in the formation of secondary soil minerals with low lattice energies. Different from Hole (1981), Muller-Lemans and van Dorp (1996) reported that it took less than 20 years for earthworms to turn over the topsoil of European grasslands, which resulted in intensive and more or less homogeneous mixing of the topsoil. For earthworm activities, transport rates of around 2 kg m22 yr21 (equivalent to 1.6 mm yr21) of dry matter from deep soil to topsoil are listed. In Amundson (2004), turnover rates of soil by earthworms were cited to be of around 3 years for the upper 10 cm of a Canadian prairie soil and around 700 years for the upper 50 cm of a soil in England, resulting in a homogenization of the upper soil profile. Furthermore, mixing rates of 360 years for the upper 75 cm of soil were reported for ground squirrels in California. Buchan (2010) noted that earthworms are able to digest B90 t of soil per ha each year (equivalent to 7.5 mm yr21). Comparing the listed studies, we can conclude that earthworms seem to play a very active role in intermixing the layers in the soil profile. Gabet et al. (2003) reviewed rates of bioturbation caused by earthworms (between 0.54 and 10 mm yr21), ants (between 0.005 and 1.8 mm yr21), and termites (between 0.013 and 0.41 mm yr21). They summarized that earthworms are able to excavate tunnels up to 5 mm thick in the soil that either collapse or promote the formation of macropores. The latter would increase the soil porosity by 3
10 times. Different from earthworms, they suggested that ants are able to generate relatively deep burrows into the soil. However, ants were classified as selective burrowers who base their burrowing activity on the grain-size of the soil, accordingly being absent in most very fine-grained soils. Gabet et al. (2003) also noted that vertebrates usually dig burrows of up to 25 cm in diameter down to 30 cm below the soil surface with dens reaching depths of up to 1 m. However, the role of vertebrates in reaching and breaking up the parent material and therefore promoting soil formation is assumed to be minor. Yoo et al. (2005, 2007) proposed that an increasing number of fauna participate in soil mixing as soil thickens, especially larger organisms like wombats which were observed to preferentially habituate the base of hillslopes. Naturally, thicker soils are a more preferable habitat for vertebrates because of increased plant productivity, soil moisture, and space for burrowing and nesting. At a study site in the Tennessee Valley in California with known SPRs (Heimsath et al., 1999; McKean et al., 1993), Yoo et al. (2005) investigated and modeled pocket gopher activities

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in relation to sediment transport and soil thickness. Modeling results suggested that pocket gophers spend only about 9 kJ of energy annually on producing downslope transport of soil, which is equivalent to 1% of the total energy expenditure on burrowing. However, it was presumed that most of the energy is used for shearing, mixing, and elevating. Yoo et al. (2005) estimated gopher-induced gross sediment flux to be of 50
140 cm2 yr21, which relates to soil turnover rates of only 40
100 years for the first 50 cm of soil of an area of 1 m2. 4.1.2. Floraturbation Processes that initiate floraturbation are plant growth and decay and also tree throw. Gabet et al. (2003) reviewed the impact of plants on soil mixing rates. They reported that the axial (up to 1.45 MPa) and radial (up to 0.91 MPa) pressures caused by root growth are sufficient enough to break up bedrock and therefore contribute to physical weathering and would also be substantial enough to push up a column of B100 m thick soil. It was also presented that plant roots are able to enter soft rock matrices through small cracks, promoting the breakdown of the rock. After decay, plant roots leave behind macropores that become important preferential pathways for subsurface water flows. Furthermore, they stated that tree throw results in uprooting and consequently in the excavation of soil, leaving a pit behind which is prone to be refilled. Thus, tree throw is another agent that can promote the homogenization of the surface or subsurface soil. Gabet et al. (2003) also discussed a publication of Gill and Jackson (2000) who reported increased turnover rates for roots with increasing temperature and, therefore, implied that sediment flux by root growth and decay could increase with increasing global temperatures. Tonneijck and Jongmans (2008) investigated the vertical distribution of organic matter in volcanic ash soils in Ecuador. They implemented a semiquantitative micromorphological analysis of soil faunal pedofeatures and high-resolution radiocarbon dating. Results implied that bioturbation is highly responsible for the vertical distribution of soil organic matter, with illuviation and roots playing a minor role in transporting soil organic matter in soils. However, the authors assumed that vertical transport of soil organic matter via leaching plays an insignificant role in volcanic ash soils because of large metal to soil organic matter ratios that limited the mobility of soil organic matter. Here, bioturbation did not result in the homogenization of the topsoil and subsoil but in a gradual increase of organic carbon in the soil profile. This was explained by the presence of endogeic species, which tend to move soil horizontally (Anderson, 1988) and transport material over short vertical distances only. Movement over short vertical distances was also explained by the occurrence of upward

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directed shifting of bioturbation in response to soil thickening due to soil organic matter accumulation.

4.2. Soil creep Soil creep defines the slow mass wasting process of soil on a slope, under the influence of gravity (Source: Glossary of Soil Science terms, Soil Science Society of America). Saunders and Young (1983) listed various rates of soil creep in mm yr21, referring to the downslope movement of soil particles near the soil surface. The highest rates were recorded for temperate climates predominantly affecting soil movement in the surface soils down to the first 25 cm with rates of about 0.5
2 mm yr21 for the temperate maritime zone and generally higher rates for the temperate continental zone of about 2
10 mm yr21. For tropical environments, rates of B4
5 mm yr21 were listed. Recorded rates of solifluction, a process often difficult to separate from soil creep, were recorded to be as low as 1 mm yr21 and as high as 300 mm yr21 for polar and montane climate zones with most rates clustered around 10
100 mm yr21. It was also stated that the majority of rates of solifluction affected soil movement down to 50 cm of depth. Heimsath et al. (2002) used optically stimulated luminescence (OSL) to investigate process of soil creep at a study site with known soil production in the Bega Valley in south-eastern Australia. They defined soil creep caused by burrowing agents (e.g., worms, ants, and moles) and tree throw as the main factor for creeping soil. With the help of OSL, they determined when soil grains had last visited the soil surface and measured vertical soil mixing rates of 0.1
0.4 mm yr21. Results indicated that the amount of grains that have visited the surface decreased with increasing soil thickness. Heimsath et al. (2002) used a Monte Carlo simulation to demonstrate particle transport. Results of the simulation assumed that soil creep involved independent movement of mineral grains throughout the soil body and that the grains were reburied or eroded by overland flow upon reaching the soil surface. Kaste et al. (2007) calculated diffusion-like mixing rates of 0.1
0.2 mm yr21 for the Bega Valley in south-eastern Australia using fallout radionuclides (7Be and 210Pb). These mixing rates are comparable to mixing rates of quartz grains of 0.1
0.4 mm yr21 calculated by Heimsath et al. (2002).

4.3. Rain splash Lobry de Bruyn and Conacher (1990) cited annual erosion rates caused by rain splash of 0.25 mm yr21.

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4.3.1. Modeling pedoturbation In their review, Gabet et al. (2003) provided quantitative models of bioturbation and sediment transport. They formulated a general slope-dependent model to determine the horizontal volumetric flux of sediment (qsx) caused by root growth and decay: qsx ¼

ðxrτÞ ðρr Þ

(17)

where x (in m) is the net horizontal displacement of soil, r (in kg m22) is the root mass per unit area, τ (per year) is the root turnover rate, and ρr (in kg m23) is the density of root material. They then calculated values for sediment flux by root growth and decay for three different types of vegetation with 2.1 3 1024 m2 yr21 for temperate grasslands, 6.8 3 1024 m2 yr21 for sclerophyllous shrubs, and 8.8 3 1024 m2 yr21 for temperate forests. Gabet et al. (2003) also quantified the horizontal alteration of soil along a hillslope caused by tree throw applying the following equations: qs ¼

vol distance events events 3 3 3 event event area time 2 xn ¼ xd 2 xu ¼ ðW þ DÞsin θ π

(18) (19)

where qs is the horizontal sediment flux, xn is the long-term net horizontal transport distance, xd is the horizontal distance of displacement of the root plate centroid caused by trees that were falling directly downslope, xu is the horizontal distance of displacement of the root plate centroid caused by trees that were falling directly uphill, W is the width of the root plate, and D is the depth of the excavated pit. Gabet et al. (2003) calculated horizontal sediment flux (qs) of 8 3 1024 m2 yr21 on a 10 slope by substituting Eq. (19) with Eq. (18) for an excavated pit width of 4 m, a pit depth of 0.7 m, a mound volume of 4 m3, and an uprooting rate of 4 trees ha21 yr21 (with qsx equal to 4.8 3 1023 m22 yr21 sin θ). Salvador-Blanes et al. (2007) considered bioturbation processes and the redistribution of soil particles in their model of soil profile evolution. In considering the addition and removal of soil particles to or from the soil profile and also their translocation within the soil profile, the process of horizonation was implemented in the model. Soil mixing was modeled by incorporating parameterized bioturbation processes resulting from earthworm, ant, and termite activity (assumed surface casting of 1
5 kg m22 yr21, cited from Paton et al., 1995). Due to a lack of parameterization, illuviation and eluviation processes were not taken into account. However, running the model resulted in stone layers in the soil profile, which was attributed to high mixing velocities because of bioturbation.

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4.4. Summary Soil mixing rates by faunal agents reported in the literature are relatively fast compared to the earlier discussed rates of chemical and physical weathering (Fig. 8): Lobry de Bruyn and Conacher (1990) published soil mixing rates from ants and termites ranging between B0.002 and 0.950 mm of soil turnover each year (Table A.3). Soil mixing rates from ants and termites taken from the selection of other published data range from 0.002 to 1.8 mm yr21. However, soil turnover rates by ants and termites seem to be of similar orders of magnitude for the compared studies (Fig. 9). Similar to ants and termites, most soil mixing rates caused by earthworms fell into the range of about 0.5
4 mm of soil turnover each year, although much higher rates of 7.5 and 10.6 mm yr21 were also recorded. Higher rates of soil alterations by earthworms may occur because unlike ants and termites earthworms also ingest the soil material, in addition to moving soil particles by mounding and burrowing. Furthermore, turnover rates of the whole topsoil by earthworms were reported to be as fast as 300
700 years and resulted in a homogenization of the soil material. Soil turnover rates recorded for vertebrates range from 0.08 to 1.5 mm yr21 and are very similar to mixing rates recorded for ants, termites, and earthworms.

(2) Soil mixing rates

(1) Chemical weathering rates

0

2

4 6 8 Rates in (mm yr–1)

10

12

Figure 8 A comparison of soil mixing rates and chemical weathering; the line in the middle of the boxes refers to the median of the distribution.

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(7) Soil creep, solifluction (6) Rain splash (5) Vertebrates (4) Termites (3) Earthworms (2) Cicades (1) Ants 0

2

4 6 8 Soil mixing rates (mm yr–1)

10

12

Figure 9 Box plot distributions of soil mixing rates from field data (presented in Table A3). Values of soil mixing rates in mm yr21 are plotted separately for discussed soil mixing agents; the line in the middle of the boxes refers to the median of the distribution.

In situ measured rates of soil creep predominantly caused by burrowing agents and tree throw are much lower than reported assumed turnover rates by ants, termites, and earthworms. These rates, calculated by applying sophisticated laboratory methods to investigate pedogenic processes in situ, range between 0.1 and 0.4 mm of soil turnover each year. However, rates of soil creep determined using other methods are either very similar to mixing rates from ants, termites, and earthworms with 0.5
5 mm yr21 or exceed these rates by far, that is, 10
300 mm yr21 for polar and montane environments. Comparing published rates of soil turnover with chemical weathering rates (Fig. 8) demonstrated that the range in turnover rates of soil by mixing agents exceeds the range of chemical weathering rates significantly.

5. Models of Soil Formation Based on the Concept of Mass Balance Models of soil formation in the landscape couple lateral transport laws from the field of geomorphology with pedogenic processes. For natural environments, downslope movement is generally induced by

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biological and physical processes, aided by gravity (Amundson, 2004). Soil transport is then modeled as random diffusive-like transport or oriented down a slope gradient. In the following, the basis for landscape evolution models is presented, followed by the implementation of soil in landscape evolution models.

5.1. Landscape evolution models Landscape evolution usually results from processes of erosion and deposition. In particular, landscape evolution models replicate long-term hillslope evolution via the principles of the conservation of mass. Additionally, they use transport algorithms to describe the movement of material on slopes (Ahnert, 1977; McKean et al., 1993; Willgoose, 2005). Recent landscape evolution models have successfully coupled hillslope evolution with channel and tectonic processes to model the evolution of natural, undisturbed landscapes spatially as a whole rather than restricted solely to hillslope development (Willgoose, 2005). As described in Minasny et al. (2008), landscape evolution models simulate the downslope transport of sediment with the help of the following simple continuity equation, which describes diffusive-like transport: qs ¼ 2Krz

(20)

where qs is the sediment flux, which is proportional to the slope rz, K is an equivalent to a diffusion coefficient with dimensions (length)2 (time)21, and z is the elevation. The simplest model simulating soil formation in the landscape implements the change in elevation (soil thickness) as a function of material transport (sediment flux): @z (21) ¼ 2rqs @t where z is the elevation (L), t is the time (T), qs is the material flux (L3 T21), and r is a partial derivative vector. Carson and Kirkby (1972) are seen as one of the first researchers to introduce the change of soil thickness into the continuity equation of hillslope evolution models (Eq. (21)). For the transport of soil on a hillslope, the equation has the written form: (soil transport in) 2 (soil transport out over a unit length of slope profile) 2 (increase of soil thickness due to expansion during weathering of bedrock) 5 (decrease of elevation of land surface). The differential equation of the described formula is: @S @y 2ðμ21ÞW ¼ 2 (22) @x @t where S is the mean soil transport, x is the horizontal distance from the divide, μ is the volume of mineral soil produced from the weathering

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unit volume of bedrock, W is the rate of lowering of bedrock surface through weathering, y is the elevation, and t is the time. For the change in soil thickness, the continuity equation has the written form: (Increase of soil depth) 5 (Increase of elevation of land surface) 1 (reduction of elevation of bedrock) or in differential equation form: @z @y @S ¼ þW ¼ μ W 2 (23) @t @t @x where @z/@t is the rate of soil increase with z as the soil thickness. Carson and Kirkby (1972) also presented two limiting processes for the rate of weathering and material transport along a hillslope. The first process is weathering limited; hence the transport process is faster than the weathering rate. The second process is transport limited; hence the rate of weathering is faster than the transport process. If the weathering rate (soil production) and the transport rate (erosion) equal each other, then the soil depth is at a local steady state (Burke et al., 2007). Both equations, described above, can be applied to transport-limited or weathering-limited removal. Dietrich et al. (1995) and Heimsath et al. (1997, 1999) introduced soil into the continuity equation of mass transport along a hillslope (Eq. (21)): @h @e (24) ¼ 2ρr 2 rqs @t @t where h is the soil thickness, ρs and ρr are the bulk densities of soil and rock, e is the elevation of the bedrock
soil interface, t is the time, and qs is the sediment flux in the horizontal direction. Therefore, the change in soil thickness at any point on the hillslope is a function of the divergence of the sediment flux and the rate of conversion from parent material to soil. If the steady-state soil thickness is reached (@h/@t 5 0), then soil production (@e/@t) can be described as: ρs

@e ρ ¼ 2 s Kr2 z ρr @t

(25)

where z is the ground surface elevation. The key variables that drive soil thickness on hillslopes are assumed to be the slope curvature, the transport coefficient, the SPR, and the time (Amundson, 2004).

5.2. Modeling soil formation in the landscape In 1975, Yaalon discussed conceptual models of pedogenesis the question, can soil-forming functions like Jenny’s (1941) Yaalon (1975) expressed the need for collecting field data to formation mechanistically. Therefore, he stated that the

and raised be solved? model soil future of

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modeling soil formation relies on replacing statistically based models with physically more significant mathematical models. Yaalon (1975) emphasized the importance of implementing spatial differentiation (catenation), the relief factor (catenary slope), material fluxes on a given pedomorphic surface (vertical and lateral), and different parent materials and time steps in modeling soil formation. Within the proposed model, he considered vegetation as the dependent variable of the ecosystem, varying with soil properties. Huggett (1975) proposed the first mechanistic model of soil formation in the landscape. His publication can be seen as the first application of landscape evolution modeling in soil science. Huggett (1975) introduced the concept of a homomorphic model to simulate the soil system on the catena scale over millennial timescales. A homomorphic modeling approach groups “. . .several components. . .” of the soil system “. . .to form one single element in the model. . .”. More importantly, he suggested simulating soil formation in a threedimensional soil profile. He also proposed to simulate soil in the form of layers more or less organized parallel to the parent material. For instance, within the soil profile the rate of weathering should decrease with depth, whereas the bulk density should increase. Huggett (1975) stated that in terms of landscape evolution contours that concave downslope should lead to convergent flow lines and convex contours should lead to divergent flow lines and that flow should tend to converge in hollows and diverge over spurs. All “thalweg” and valley basins should join in one complex network, based on first- and second-order stream lines. Kirkby (1977, 1985) presented the first comprehensive mechanistic model of soil profile development in interaction with a hillslope model based on mass balance, which can be seen as a valuable basis for quantitative soil profile modeling or even soil catena modeling. To predict the development of soils over time quantitatively, the mass balance model estimates the flow of water through the soil and the uptake of nutrients from the soil. The soil profile development model consists of three parts, the weathering profile, the inorganic profile, and the organic profile. Kirkby’s soil profile model is based on the accumulated amount of “soil deficit,” w, which represents the amount of parent rock converted into a soil mantle: N ð W ¼ ð12PÞdz (26) z¼0

where P is the proportion of bedrock remaining in the soil profile (unweathered rock) and z is the depth below the soil surface. P 5 unweathered parent material, takes values between zero and one.

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The mass balance model for the accumulated soil layer of the soil profile is:
 @w @ ð12PS Þ (27) ¼ J 2S @t @x PS where PS are the properties remaining in the part of the soil profile where mechanical erosion occurs (mostly at the surface), x is the distance from the divide, t is the time, S is the mechanical sediment transport measured in the x direction, and J is the chemical sediment transport. Within the model, inorganic minerals are treated as mixtures of elementary oxides, which dissolve independently to create ions (Gibbs free energy to calculate equilibrium reactions). In the model, the soil organic matter material experiences production, accumulation, vertical mixing, and decomposition. Mixing rates of organic matter act as a diffusion process, transporting organic material from areas of high to those of low concentration. At the surface, accumulated organic matter is transferred downward through vertical mixing in balance with an upward mixing of inorganic material. The most important product of decomposition of organic matter is CO2. Soil CO2 distribution is simulated with gaseous diffusion of the produced CO2. The soil pH is calculated as a result of this CO2 distribution. Kirkby (1985) enhanced his soil profile model by also simulating the processes of percolation, equilibrium solution, leaching of solutes, ionic diffusion, organic mixing, leaf fall, organic decomposition, and mechanical denudation. Each process leads to second-order linear partial differential equations. Minasny and McBratney (1999, 2001) introduced a basic mechanistic quantitative soil formation model based on the conceptual ideas of Huggett (1975) and Heimsath et al. (1997). Their soil formation model considers soil formation spatially at the catena scale and is written for predicting regolith depth and soil formation at the landscape scale over millennial timescales, simulating soil formation over tens of thousands of years. The continuity equation of the model is based on the assumption that the change of soil thickness over time depends on (1) the production of soil from the weathering of parent material and (2) the transport of soil through natural surface erosion. The rate of weathering and the erosive diffusivity are the main parameters used in the soil formation model. The weathering of bedrock is characterized as an exponential decline with increasing soil cover thickness based on Eq. (11), applying concepts from Dietrich et al. (1995) and Heimsath et al. (1997). It is assumed that the weathering rate of bedrock to soil, P0, is mainly controlled by the climate, whereas the empirical constant, b, is believed to depend on the thermal properties of the rock or soil. The transport rate of materials is defined

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similar to Darcy’s law for water transport in soils, whereas the movement of materials within the landscape is expressed in terms of diffusive transport. The erosive diffusivity of the soil material is assumed to be controlled by factors of soil erosion, soil physical properties, and climate. Running the soil formation model over millennial timescales showed that the initial SPR is high and that it slows down exponentially until it reaches steady state at around 40,000 years, applying a value of 0.19 mm yr21 for P0. Based on the soil formation model of Minasny and McBratney (2001), Salvador-Blanes et al. (2007) improved the modeling of in situ long-term soil profile evolution by implementing particle size transformation, horizonation (layers of regolith formed from bedrock with each time step), and bioturbation processes. Horizonation is introduced in the model by taking into account the translocation of particles within the soil profile. However, due to the influence of bioturbation processes, running this model resulted in the formation of stone layers in the soil profile. Running the model also showed that after 10,000 years, a soil profile of about 1.25 m is formed. This profile, however, lacked any horizonation. After a simulation time of 20,000
80,000 years, the thickness of the soil profile increased to a depth of 1.80 m and by that time period a total of three horizons were developed in the soil profile. The soil formation models discussed predict soil profile development in situ or along a hillslope starting from parent materials and ending with the formation of soil being in equilibrium, which was simulated over tens of thousands of years and modeling steps of 100
1000 years. In the following soil formation models, the evolution of soil is not simulated as starting from in situ bedrock. The presence of soil is assumed already and processes that lead to the formation of the soil profiles studied are simulated. Generally, these models predict soil in yearly time steps up to 1000 years. They are not designed to simulate soil profile development from parent rock over millennial timescales, but to model processes in soil. Sommer et al. (2008) presented a soil-landscape genesis model that simulates the evolution of a hummocky agricultural landscape with a time-split modeling approach. Fallout radionuclide data indicated regressive processes of pedogenesis during the past 50 years for the studied landscape. For the progressive period, soil formation was therefore modeled by simulating pedogenic processes, such as decarbonization and carbonization, silicate weathering, and clay translocation, that were quantified by a spatially distributed, coupled water flow and solute transport model. For the modeling of the regressive period, a modified version of the dynamic, physically based EROSION-3D model was used, which allows the spatially distributed modeling of soil erosion and soil deposition. In their modeling approach, Sommer et al. (2008) coupled geological
historical

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information with spatially distributed pedogenic processes that experienced recent changes to better simulate the current development of the landscape studied. Finke and Hutson (2008) introduced a model to simulate formation of calcareous soils formed on loess (15,000 BP to present). Model simulations were based on Jenny’s factors of soil formation (Jenny, 1941) using the LEACHC model. Additional soil-forming processes were implemented to describe the effect of the soil fauna and flora on various soil properties, assuming an annual rate of bioturbation of 30 t ha21 (2.5 mm yr21). Running the model showed that it takes about 1297 years to decarbonize 1 m of loess parent material. Results also showed a clear effect of bioturbation on soil formation. Considering bioturbation processes in the model reduced the decarbonization time of the topsoil and affected clay migration processes.

5.3. Summary The models of soil formation reviewed couple lateral transport laws from modeling hillslope formation in the landscape with pedologic processes. These models simulate soil formation in a quantitative
mechanistic sense based on principles of mass balance. However, some models form soil over millennial timescales starting from parent materials and ending with the formation of soil being in equilibrium, while others simulate soil processes that lead to the formation of a certain soil type over centennial timescales, already assuming the presence of the soil body. An overview of all models presented and discussed in this literature review is given in Table 2. In Table 2, models of pedogenesis are summarized according to the concepts applied in the models listed.

6. Conclusions In pedology, most studies have focused on the prediction of soil properties from landscape attributes at specific sites based on empirical quantitative relationships (McBratney et al., 2003). However, to understand and improve the knowledge of pedogenesis, a mechanistic model that can simulate the development of soil under various conditions or scenarios needs to be formalized. This growing movement in pedology has directed the focus toward mechanistic modeling of soil formation. From this review, we have learned that in order to verify mechanistic models of soil formation, it is essential to quantify pedogenic processes with field data.

Table 2 Summary of models of soil formation presented in this literature review (factors, processes, pathways, energy, and mass balance soil formation models) Model concept

Model description

Model type

Reference

Factorial Soil-forming factors

S 5 f(cl,o,r,p,t, ...)

Qualitative (empirical)

Soil-forming factors

S 5 f(s,c,o,r,p,a,n); S 5 f (time)cl,o,r,p. . .

Quantitative empirical

Dokuchaev (1886), Shaw (1930), Jenny (1941) McBratney et al. (2003); Chronofunctions

Soil-forming processes

S 5 f(addition, removal, translocation, transformation)

Qualitative

Simonson (1959)

Soil evolution along pathways

S 5 f(Progressively, regressively)

Qualitative

Johnson and WatsonStegner (1987)

S 5 f(o,w,t) Soil formation is modeled through quantifying processes of energy, that is, radiation, kinetic energy, and so on Soil formation is modeled via energy expenditure Thermodynamic aspects in soil-forming processes, that is, Gibbs free energy and entropy of soil minerals

Qualitative Mechanistic

Runge (1973) Regan (1977)

Quantitative
empirical
mechanistic Quantitative
empirical
mechanistic

Volobuyev (1974)

Energy Soil energy model Soil energy model

Soil energy model Soil energy model

Volobuyev and Ponomarev (1977)

Mass balance Soil weathering: Soil production function Soil weathering: The “hump” function

SPR 5 P0 exp(2bh) verified with field data

Quantitative
mechanistic

Heimsath et al. (1997)

@e=@t ¼ 2ðP0 ½expð2k1 hÞ 2 expð2k2 hÞ þ Pa Þ

Quantitative
mechanistic

W ¼ Dð1
½Zrrock =½Zrsoil Þ Soil weathering: Coupling chemical
physical weathering

Quantitative
mechanistic

Dietrich et al. (1995), Furbish and Fagherazzi (2001), Minasny and McBratney (2006) Riebe et al. (2003, 2004a, 2004b), Burke et al. (2007, 2009), Green et al. (2006), Yoo et al. (2007)

Pedoturbations: Soil creep

Quantitative
mechanistic

Heimsath et al. (2002)

Quantitative
mechanistic

Huggett (1975)

Quantitative
mechanistic

Minasny and McBratney (1999, 2001), Dietrich et al. (1995), Heimsath et al. (1999)

Monte Carlo simulation of independent movement of soil grains in soil profile

Soil landscape model Catena scale, three dimensional, homomorphic; lateral and vertical fluxes of material Soil landscape model Soil formation over millennial timescales, coupling weathering, diffusive transport and erosive diffusivity, fluxes simulated within the system

(Continued)

Table 2

(Continued )

Model concept

Model description

Model type

Reference

Soil profile model

Soil profile formation in interaction with hillslope processes, fluxes simulated within the soil system Soil profile evolution over millennial timescales, implementing horizonation and bioturbation, fluxes simulated within the system

Quantitative
mechanistic

Kirkby (1977, 1985)

Quantitative
mechanistic

Salvador-Blanes et al. (2007)

Soil landscape evolution via simulating soil-forming processes over centennial timescales, that is, silicate weathering and soil erosion

Quantitative
mechanistic

Sommer et al. (2008), Finke and Hutson (2008)

Soil profile model

Soil process model

Models are grouped based on the concepts used and the type of the model. Source: Based on Minasny et al. (2008).

Advances in Agronomy Quantifying Processes of Pedogenesis

43

By reviewing research related to this topic, we can conclude that processes of pedogenesis have been estimated from field data, particularly rates of soil weathering and rates of soil mixing. However, we also learned that the majority of these estimates were based on watershed studies or observational data and that it is necessary to further investigate soil formation processes in situ to develop models of soil formation further and to be able to model better soil evolution. Colman and Dethier (1986) discussed that the largest constraint in estimating weathering rates (chemical and/or physical) from field studies is the lack of independent age estimates of the weathered materials studied. In recent years, sophisticated laboratory techniques have become available that can be used to quantify such processes of soil formation in situ. In situ terrestrial cosmogenic nuclides (TCNs) and optically stimulated luminescence (OSL) are such tools to investigate pedogenesis quantitatively via numerical dating. Both techniques have been used widely in the fields of geology and geomorphology until they became accessible for pedologyrelated studies. TCN and OSL can now be applied to investigate longterm processes of pedogenesis on millennial timescales; and OSL can also be used to investigate short-term processes of pedogenesis on a timescale of a few hundred years. The concentration of TCN in soil parent materials can be used to investigate processes of soil weathering in situ by deriving rates of soil production. Dating individual sand-sized quartz grains with OSL can be applied to explore processes of soil mixing by estimating rates of potential soil turnover and also to estimate the “age” of soil horizons. In the following paragraphs, we will answer and reflect on our questions about pedogenesis formulated in the introduction to this literature review: 1. How does soil form? We know that soil forms via a combination of interrelated physical, chemical, and biological processes. Traditional concepts in pedology explain soil formation progressively depending on environmental factors and processes along a sequence of preset soil types with soil formation eventually reaching equilibrium. Furthermore, soil profile evolution is described as a product of mostly downward moving processes that lead to the formation of interrelated layers or horizons. Alternately, soil profile evolution is described as depending predominantly on a combination of bioturbation and slope processes. Chronofunctions have been applied to explain soil formation along linear and nonlinear functions, that is, exponential, polynomial, or sigmoidal behavior. However, we learned that the formation of soil from parent materials was predominantly explained by two

44

Uta Stockmann et al.

concepts: (1) the exponential soil production model and (2) the humped soil production model. More recently, sophisticated laboratory methods have made it possible to verify and parameterize the concept of exponential decline of soil formation with increasing soil thickness, using field data over millennial timescales. Soil production following a humped model with maximized soil production at an intermediate soil depth was assumed for some field sites, but a parameterization of this function is still lacking. Evidence for the humped concept comes from observing high rates of chemical and physical weathering under initial soil cover in the field. 2. At what rate does soil evolve? We learned that rates of soil formation are initially fast and decrease with time. We also learned that field data have confirmed the concept of exponential decline of weathering of parent materials to soil with increasing soil thickness in situ (TCN derived). Estimated rates of soil production from field data were as low as 0.004 mm yr21 and as high as 0.4 mm yr21. Modeling soil formation mechanistically over millennial timescales by implementing an exponential soil production model with relatively high rates of weathering of bedrock to soil of 0.19 mm yr21 resulted in steady state after around 40,000 years. Results from studies comparing the rate of chemical and physical weathering in situ showed that chemical weathering rates were generally lower, accounting for less than 50% of the total weathering rate. Mean rates of chemical weathering were recorded to be about 0.022 mm yr21 with a maximum rate of 0.14 mm yr21. 3. How fast are rates of soil turnover occurring in the soil profile, and what influence do they have on pedogenesis? We learned that mixing rates of soil are much faster than rates of soil production from parent materials. Mixing rates as high as 10.6 mm yr21 were recorded, and we assumed that the rates of bioturbation exceed SPRs by up to three orders of magnitude. Particularly, earthworms seem to play a very active role in turbating the soil profile. We can also conclude that in most of the reviewed pedoturbation studies, especially bioturbations impeded the horizonation of a soil profile by homogenizing the soil material. However, bioturbation processes promoting the formation of horizons in the soil profile were also proposed. Determining rates of soil mixing in situ by dating individual soil particles (OSL, fallout radionuclides) resulted in rates of soil turnover of about 0.2 mm yr21. These rates are lower than those estimated from observational field studies, where soil turnover was generally recorded to be higher than 0.5 mm yr21, for a variety of mixing agents.

Advances in Agronomy Quantifying Processes of Pedogenesis

45

Box 5 What defines soil science?

Churchman (2010) from the University of Adelaide identified three aspects that are unique to the discipline of soil science, these aspects distinguish soil science among scientific disciplines generally. These are: 1. the formation and properties of soil horizons, 2. the occurrence and properties of aggregates in soil, and 3. the occurrence and behavior of soil colloids. He added that the possibility that these aspects could be reduced to other sciences is dismissed because they can be explained more usefully at a larger size scale or by a more complex context than those belonging to more basic sciences such as physics or chemistry. The three unique aspects are considered to comprise a research tradition for soil science, which, due to their ultimate irreducibility, is constituted as a special science. These unique aspects cross subdiscipline boundaries so that both soil science and soils should be considered holistically rather than via the separate subdisciplines through which they have often been studied in the past.

Implementing relatively high rates of soil turnover of up to about 4 mm yr21 in mechanistic models of soil formation, however, resulted in the formation of stone layers in the soil profile. Does this result implicate that high rates of bioturbation are questionable? However, within this review, it became apparent that work on quantifying soil-forming processes in situ to verify and improve mechanistic models of soil formation is still minimal and therefore vital (Bockheim and Gennadiyev, 2009; Pelletier and Rasmussen, 2009). Ultimately, a soil model should be able to simulate the formation and generation of the three unique aspects in soil science—soil horizons, soil aggregates, and processes of soil colloids—which is presented further in Box 5.

ACKNOWLEDGMENTS We would like to thank Dr. Tom Vanwalleghem and Dr. Stephen Cattle for their useful comments on this chapter; Dr. David Hammer for suggesting a possible link between Shaw’s work, the 1930 World Conference, and Jenny’s formulation; and Dr. Igor V. Florinsky for his clarification on Dokuchaev’s soil formation formula.

Appendix Table A.1 Part 1: Chemical weathering rates (CWR) derived from elemental fluxes (loss and gain) in watersheds and chemical products of the parent materials and weathering products studied Reference

ID

Alexander (1985) Maryland California California Maryland Wales Wales British Columbia Luxembourg Hong Kong Hong Kong Hong Kong England England Virginia Virginia Java England Idaho

CWR CWR Elevation Climate (mm kyr21) (mm yr21) (m)

5.81 10.79 4.98 19.92 40.67 13.28 54.78 19.92 6.64 5.81 9.96 11.62 14.94 18.26 83 116.2 38.18 64.74

0.00581 0.01079 0.00498 0.01992 0.04067 0.01328 0.05478 0.01992 0.00664 0.00581 0.00996 0.01162 0.01494 0.01826 0.083 0.1162 0.03818 0.06474

Geology

Wyoming Wyoming New Mexico New Mexico New Mexico Hawaii Mexico Mexico Papua Wyoming Colorado Alexander (1988) CA CH, BC, Canada CS, BC, Canada England France Idaho Luxemburg Maryland Minnesota Maryland Maryland N. Hamp. Scotland V1, Victoria V2, Victoria

91.3 166 24.9 49.8 240.7 83 91.3 298.8 282.2 132.8 58.1 11 60 78 55 106 65 46 37 20 5.8 20 29 160 33 57

0.0913 0.166 0.0249 0.0498 0.2407 0.083 0.0913 0.2988 0.2822 0.1328 0.0581 0.011 0.06 0.078 0.055 0.106 0.065 0.046 0.037 0.02 0.0058 0.02 0.029 0.16 0.033 0.057

220 185 945 232 152 478 90 280 98 67 70 300 542 144 203

Adamellite Till/quartz diorite Till/quartz diorite Granite Gneiss Adamellite Metashale Greenstone (metabas.) Till/gabbro Schist Serpentinite Till/gneiss Till/granite Dacite Dacite (Continued)

Table A.1

(Continued )

Reference

ID

Cleaves (1993)

Wash. WF, Wales WP, Wales ZJ, Zimbabwe ZR, Zimbabwe Baltimore, Maryland Pacific Northwest Guayana Shield, South America Rhodesia-Juliasdale

Dethier (1986) Edmond et al. (1995) Owens and Watson (1979) Paˇces (1986)

CWR CWR Elevation Climate (mm kyr21) (mm yr21) (m)

85 26 19 18 1.7 9.1

0.085 0.026 0.019 0.018 0.0017 0.0091

3 10

0.003 0.01

1,150 105 90 160 60 Warm temperate

Geology

Till/quartz diorite Till/wacke Till/wacke Adamellite Adamellite Plagioclase
muscovite
quartz

Cold temperate

Sedimentary and metamorphic Granite

4.4

0.0044

1,900

Tropical

Granite

0.17 8.9

0.00017 0.0089

1,600 724

Tropical Warm temperate

Granite Biotitic gneiss with muscovite, sillimanite, quarzites Biotitic gneiss with muscovite, sillimanite, quarzites Biotitic gneiss with muscovite, sillimanite, quarzites

Rhodesia-Rusape Bohemian Maffif, X-0 X-8

14

0.014

744

Warm temperate

X-7

32

0.032

635

Warm temperate

Pavich (1986) Pavich (1989)

Ruxton (1968) Velbel (1986)

Wakatsuki and Rasyidin (1992)

Virginia Piedmont Appalachian Piedmont Appalachian Piedmont Papua New Guinea Watershed 27, Coweeta Southern Blue Ridge Hubbard Brook

4 20

0.004 0.02

4

0.004

58

0.058

Humid tropical

Volcanic material

37

0.037

Warm temperate

Garnet, plagioclase

37

0.037

Cold temperate

Gneiss

47

0.047

Cold temperate

224

0.224

Iu, basic pyroclastic 473

0.473

Humid subtropical Humid subtropical

Iu, granitic

250

Cool temperate

Occoquan granite Metamorphic rocks Metamorphic rocks

Table A.1 Part 2: Chemical weathering rates (CWR) derived in situ in applying weathering indices and TCN-derived total denudation rates for study sites in and outside Australia (rates of t km22 yr21 were converted to mm yr21 by assuming a bulk density of soil of 1200 kg m23) Reference

ID

Burke et al. (2007)a Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit Pit

6 20 9 7 17 19 5 16 8 15 25 26 14 10 1 24 12 21

Soil thickness (cm)

CWR (published rate)

CWR Total denudation rate (mm yr21) (published rate)

15 15 29 40 40 40 48 55 60 70 70 70 75 80 90 100 120 120

35 m Myr21 44 m Myr21 13 m Myr21 31 m Myr21 6 m Myr21 17 m Myr21 27 m Myr21 5 m Myr21 4 m Myr21 2 m Myr21 4 m Myr21 4 m Myr21 1 m Myr21 7 m Myr21 1 m Myr21 1 m Myr21 1 m Myr21 1 m Myr21

0.035 0.044 0.013 0.031 0.006 0.017 0.027 0.005 0.004 0.002 0.004 0.004 0.001 0.007 0.001 0.001 0.001 0.001

59 m Myr21 59 m Myr21 58 m Myr21 50 m Myr21 50 m Myr21 50 m Myr21 50 m Myr21 36 m Myr21 36 m Myr21 18 m Myr21 18 m Myr21 18 m Myr21 18 m Myr21 28 m Myr21 14 m Myr21 11 m Myr21 11 m Myr21 11 m Myr21

Total denudation rate (mm yr21)

0.059 0.059 0.058 0.050 0.050 0.050 0.050 0.036 0.036 0.018 0.018 0.018 0.018 0.028 0.014 0.011 0.011 0.011

Burke et al. (2009)b FH1 FH2 FH3 FH4 FH5 FH6 FH7 FH8 FH9 FH10 FH-B1 FH-B2 FH-B3 FH-B4 FH-B5 FH-2MP Highland mean (from above) NR0 NR1 NR2 NR3 NR4 NR5 NR6 NR7 NR-A1

52 50 55 65 40 104 58 65 48 55 50 (96) 30 (100) 70 (120) 70 (120) 25 (60) 35 55

3.9 6 1.2 m Myr21 11.7 6 0.6 m Myr21 8.5 6 0.7 m Myr21 7.3 6 0.5 m Myr21 15.2 6 0.7 m Myr21 3.5 6 0.2 m Myr21 8.6 6 0.6 m Myr21 6.9 6 0.6 m Myr21 9.5 6 0.8 m Myr21 0.0 m Myr21 5.0 6 1.1 m Myr21 11.0 6 1.41 m Myr21 5.0 6 0.5 m Myr21 7.0 6 0.4 m Myr21 11.0 6 0.7 m Myr21 7.8 6 1.5 m Myr21 8.1 6 0.8 m Myr21

0.004 0.012 0.009 0.007 0.015 0.004 0.009 0.007 0.010 0.0 0.005 0.011 0.005 0.007 0.011 0.008 0.008

18.7 6 1.0 m Myr21 19.5 6 1.0 m Myr21 17.6 6 1.0 m Myr21 14.4 6 1.0 m Myr21 23.8 6 2.2 m Myr21 6.6 6 1.0 m Myr21 16.6 6 1.0 m Myr21 14.4 6 1.0 m Myr21 20.3 6 1.2 m Myr21 17.6 6 1.0 m Myr21 17.6 6 1.0 m Myr21 27.4 6 4.5 m Myr21 11.4 6 1.0 m Myr21 11.4 6 1.0 m Myr21 30.6 6 4.5 m Myr21 24.5 6 5.0 m Myr21 19.7 6 1.7 m Myr21

0.019 0.020 0.018 0.014 0.024 0.007 0.017 0.014 0.020 0.018 0.018 0.027 0.011 0.011 0.031 0.025 0.020

65 75 72 72 49 30 58 75 60

7.6 6 0.5 m Myr21 6.9 6 0.4 m Myr21 7.2 6 0.2 m Myr21 9.5 6 0.4 m Myr21 12.0 6 0.6 m Myr21 15.6 6 1.0 m Myr21 9.7 6 0.5 m Myr21 6.7 6 0.4 m Myr21 7.2 6 0.6 m Myr21

0.008 0.007 0.007 0.010 0.012 0.016 0.010 0.007 0.007

14.4 6 3.0 m Myr21 11.8 6 2.0 m Myr21 12.6 6 2.0 m Myr21 12.6 6 2.0 m Myr21 19.9 6 4.0 m Myr21 29.1 6 5.0 m Myr21 16.6 6 2.0 m Myr21 11.8 6 1.0 m Myr21 14.2 6 1.0 m Myr21

0.014 0.012 0.013 0.013 0.020 0.029 0.017 0.012 0.014 (Continued)

Table A.1

(Continued )

Reference

ID

NR2-MP SN-B1 SN-B1B SN-B3 SN-B4 SN-B5 SN-B6 H2B1 H2B2 Lowland mean (from above) Green et al. (2006) Transport distance 0 m Transport distance 3
15 m Transport distance 15
30 m Transport distance 30
45 m

Soil thickness (cm)

CWR (published rate)

CWR Total denudation rate (mm yr21) (published rate)

50 50 65 40 40 55 60 70 60 58

11.2 6 0.5 m Myr21 11.1 6 0.6 m Myr21 6.6 6 0.6 m Myr21 12.2 6 0.9 m Myr21 8.8 6 0.0 m Myr21 8.8 6 0.7 m Myr21 11.8 6 0.3 m Myr21 3.5 6 0.4 m Myr21 6.5 6 0.6 m Myr21 9.1 6 0.5 m Myr21

0.011 0.011 0.007 0.012 0.009 0.009 0.012 0.004 0.007 0.009

NA

20.0 6 1.2 t km22 yr21 0.017

NA

28.6 6 1.0 t km22 yr21 0.024

NA

29.4 6 1.6 t km22 yr21 0.025

NA

23.0 6 1.3 t km22 yr21 0.019

17.6 6 2.0 m Myr21 19.5 6 2.0 m Myr21 14.4 6 1.0 m Myr21 23.8 6 2.0 m Myr21 23.8 6 2.0 m Myr21 17.6 6 1.0 m Myr21 16.0 6 1.0 m Myr21 7.2 6 1.0 m Myr21 16.0 6 1.0 m Myr21 16.6 6 2.0 m Myr21

Total denudation rate (mm yr21)

0.018 0.020 0.014 0.024 0.024 0.018 0.016 0.007 0.016 0.017

Transport distance . 45 m Riebe et al. (2003)c RIS1 RIS2 RIS3 Average soil samples (above) Riebe et al. (2004a) Rio Icacos, Puerto Rico RI-1 RI-4 McNaab Track, New Zealand MT-3 MT-4 MT-5 Chiapas Highlands, Mexico SS Jalisco Highlands, Mexico ST-1 ST-3

NA

19.3 6 2.1 t km22 yr21 0.016

50
150 50
150 50
150

47 6 11 t km22 yr21 59 6 14 t km22 yr21 59 6 13 t km22 yr21 56 6 13 t km22 yr21

0.039 0.049 0.049 0.047

79 6 18 t km22 yr21 101 6 23 t km22 yr21 97 6 2 t km22 yr21 90 6 21 t km22 yr21

0.066 0.084 0.081 0.075

51 6 10 t km22 yr21 59 6 9 t km22 yr21

0.043 0.049

87 6 15 t km22 yr21 97 6 14 t km22 yr21

0.073 0.081

88 6 17 t km22 yr21 115 6 25 t km22 yr21 58 6 10 t km22 yr21

0.073 0.096 0.048

195 6 31 t km22 yr21 235 6 45 t km22 yr21 131 6 20 t km22 yr21

0.163 0.196 0.109

34 6 6 t km22 yr21

0.028

122 6 14 t km22 yr21

0.102

166 6 42 t km22 yr21 84 6 14 t km22 yr21

0.138 0.070

556 6 71 t km22 yr21 212 6 22 t km22 yr21

0.463 0.177 (Continued)

Table A.1

(Continued )

Reference

ID

ST-4 ST-5 Panola Mountain, GA, USA PM Jalisco Lowlands, Mexico RT-1 RT-2 Santa Rosa Mountain, NY, USA SR-1 SR-3 SR-4 SR-6 SR-7 SR-10 Sonora Desert, Mexico CE-3 JC-1

Soil thickness (cm)

CWR (published rate)

CWR Total denudation rate (mm yr21) (published rate)

158 6 69 t km22 yr21 173 6 44 t km22 yr21

0.132 0.144

622 6 72 t km22 yr21 549 6 59 t km22 yr21

0.518 0.458

10 6 2 t km22 yr21

0.008

23 6 3 t km22 yr21

0.019

112 6 25 t km22 yr21 104 6 22 t km22 yr21

0.093 0.087

462 6 50 t km22 yr21 399 6 46 t km22 yr21

0.385 0.333

7 6 3 t km22 yr21 0 6 3 t km22 yr21 15 6 4 t km22 yr21 16 6 4 t km22 yr21 24 6 7 t km22 yr21 22 6 6 t km22 yr21

0.006 0.000 0.013 0.013 0.020 20.002

106 6 11 t km22 yr21 132 6 14 t km22 yr21 144 6 15 t km22 yr21 104 6 11 t km22 yr21 117 6 12 t km22 yr21 117 6 12 t km22 yr21

0.088 0.110 0.120 0.087 0.098 0.098

31 6 11 t km22 yr21 34 6 13 t km22 yr21

0.026 0.028

194 6 32 t km22 yr21 191 6 20 t km22 yr21

0.162 0.159

Total denudation rate (mm yr21)

Fall River, Sierra Nevada, USA FR-2 FR-5 FR-6 FR-8 Antelope Lake, Sierra Nevada, USA AL-4 AL-5 AL-9 AL-10 Adams Peak, Sierra Nevada, USA AP-3 AP-4 AP-5 AP-11 AP-13 Fort Sage, Sierra Nevada, USA A1 A2(s)

99 6 30 t km22 yr21 75 6 16 t km22 yr21 19 6 7 t km22 yr21 7 6 1 t km22 yr21

0.083 0.063 0.016 0.006

485 6 92 t km22 yr21 384 6 54 t km22 yr21 104 6 25 t km22 yr21 40 6 4 t km22 yr21

0.404 0.320 0.087 0.033

15 6 4 t km22 yr21 19 6 20 t km22 yr21 26 6 13 t km22 yr21 12 6 3 t km22 yr21

0.013 0.016 0.022 0.010

72 6 8 t km22 yr21 86 6 11 t km22 yr21 119 6 12 t km22 yr21 91 6 8 t km22 yr21

0.060 0.072 0.099 0.076

24 6 3 t km22 yr21 6 6 4 t km22 yr21 20 6 9 t km22 yr21 14 6 6 t km22 yr21 18 6 6 t km22 yr21

0.020 0.005 0.017 0.012 0.015

140 6 13 t km22 yr21 100 6 9 t km22 yr21 162 6 15 t km22 yr21 93 6 12 t km22 yr21 124 6 12 t km22 yr21

0.117 0.083 0.135 0.078 0.103

5 6 4 t km22 yr21 13 6 4 t km22 yr21

0.004 0.011

83 6 7 t km22 yr21 63 6 17 t km22 yr21

0.069 0.053 (Continued)

Table A.1

(Continued )

Reference

Riebe et al. (2004b)d

ID

A3(s) A4(s) Sunday Peak, Sierra Nevada, USA SP-1 SP-3 SP-8 Nichols Peak, Sierra Nevada, USA NP-1 SR-10 (top of transect) SR-3 SR-1 SR-4 SR-6 SR-7 (base of transect)

Soil thickness (cm)

CWR (published rate)

CWR Total denudation rate (mm yr21) (published rate)

32 6 12 t km22 yr21 111 6 43 t km22 yr21

0.027 0.093

173 6 43 t km22 yr21 0.144 755 6 263 t km22 yr21 0.629

19 6 5 t km22 yr21 3 6 7 t km22 yr21 11 6 5 t km22 yr21

0.016 0.003 0.009

129 6 12 t km22 yr21 93 6 11 t km22 yr21 86 6 12 t km22 yr21

0.108 0.078 0.072 0.000

16 6 7 t km22 yr21 22 6 6 t km22 yr21

0.013 0.0

127 6 12 117 6 12 t km22 yr21

0.106 0.098

0 6 3 t km22 yr21 7 6 3 t km22 yr21 15 6 5 t km22 yr21 16 6 5 t km22 yr21 24 6 7 t km22 yr21

0.0 0.006 0.013 0.013 0.020

132 6 14 t km22 yr21 106 6 11 t km22 yr21 144 6 15 t km22 yr21 104 6 11 t km22 yr21 117 6 12 t km22 yr21

0.110 0.088 0.120 0.087 0.098

Total denudation rate (mm yr21)

Reference

ID

Dixon et al. (2009)e LD-0 LD-1 LD-2 LD-3 LD-4 LD-5 LD-6 Mean WB-0 WB-1 WB-2 WB-3 WB-4 WB-5 WB-6 WB-7 WB-8 Mean BM-2006-1 BM-2006-2 BM-2006-3 BM-2006-4

Soil depth (cm)

6 25 27 40 45 53 75 53 64 70 110 75 75 60 90 80 107 29 102 127

CWRsoil (t km22 yr21)

CWRsaprolite (t km22 yr21)

CWRtotal (t km22 yr21)

Erosion rate (t km22 yr21)

Total denudation rate (t km22 yr21)

0 0 24.4 34.9 38.5 22.7 5.5 18.0 6 6.1 13.0 10.9 0.0 31.3 5.1 21.2 11.4 28.3 5.7 14.1 6 3.6 0 25.2 17.4 31.3

75.4 104.8 34.4 3.8 27.5 19.7 53.4 45.6 6 13.2 39.5 40.7 43.1 0 25.4 15.9 6.4 35.1 18.0 24.9 6 5.3 87.0 66.5 98.1 53.9

75.4 104.8 58.7 38.6 66.1 42.4 58.9 63.6 6 8.4 52.5 51.7 43.1 31.3 30.6 37.1 17.8 63.4 23.7 39.0 6 5.0 87.0 91.7 115.5 85.1

66.2 126.4 80.5 34.3 54.8 37.1 45.7 63.6 6 12.1 57.5 54.2 34.2 34.8 35.1 27.5 23.6 43.2 34.3 38.3 6 3.8 89.5 103.3 131.1 86.2

141.5 231.2 139.3 73.0 120.8 79.5 104.6 127.1 6 20.1 110.0 105.8 77.4 66.1 65.6 64.6 41.4 106.5 57.9 77.3 6 8.2 176.5 195.1 246.6 171.3 (Continued)

Table A.1

(Continued )

Reference

ID

Mean PC-1 PC-2 PC-2006-1 PC-2006-2 PC-2006-3 PC-2006-4 Mean KR-1 KR-2 KR-2006-1 KR-2006-3 Mean

Soil depth (cm)

80 110 107 91 61 71 33 25 64 64

CWRsoil (t km22 yr21)

CWRsaprolite (t km22 yr21)

CWRtotal (t km22 yr21)

Erosion rate (t km22 yr21)

Total denudation rate (t km22 yr21)

24.4 6 7.9 4.2 13.2 0 0 0 21.2 6.4 6 4.0 NA NA 6.5 5.4 6.0 6 0.5

69.0 6 10.6 158.0 67.6 124.1 131.3 172.6 76.1 121.6 6 19.0 4.8 0 17.7 16.7 9.8 6 4.4

93.4 6 5.6 162.2 80.8 124.1 131.3 172.6 97.3 128.1 6 15.9 NA NA 24.3 22.1 23.2 6 1.1

104.4 6 8.1 131.8 59.4 82.0 92.3 122.7 63.2 91.9 6 13.4 NA NA 52.1 41.2 46.7 6 5.4

197.8 6 13.3 294.0 140.2 206.1 223.6 295.4 160.4 220.0 6 29.2 36.0 77.4 76.4 63.3 63.3 6 9.6

Reference

ID

Dixon et al. (2009)e LD-0 LD-1 LD-2 LD-3 LD-4 LD-5 LD-6 WB-0 WB-1 WB-2 WB-3 WB-4 WB-5 WB-6 WB-7 WB-8 BM-2006-1 BM-2006-2 BM-2006-3 BM-2006-4 PC-1 PC-2

Soil depth (cm)

CWRsoil (mm yr21)

CWRsaprolite (mm yr21)

CWRtotal (mm yr21)

Erosion rate Total denudation (mm yr21) rate (mm yr21)

6 25 27 40 45 53 75 53 64 70 110 75 75 60 90 80 107 29 102 127 80 110

0 0 0.020 0.029 0.032 0.019 0.005 0.011 0.009 NA 0.026 0.004 0.018 0.010 0.024 0.005 0 0.021 0.015 0.026 0.004 0.011

0.034 0.048 0.016 0.002 0.013 0.009 0.024 0.018 0.019 0.020 0 0.012 0.007 0.003 0.016 0.008 0.040 0.030 0.045 0.025 0.072 0.031

0.034 0.048 0.036 0.031 0.045 0.028 0.029 0.029 0.028 0.020 0.026 0.016 0.025 0.012 0.040 0.013 0.040 0.051 0.059 0.051 0.075 0.042

0.030 0.057 0.037 0.016 0.025 0.017 0.021 0.026 0.025 0.016 0.016 0.016 0.013 0.011 0.020 0.016 0.041 0.047 0.060 0.039 0.060 0.027

0.064 0.105 0.073 0.046 0.069 0.045 0.050 0.055 0.052 0.035 0.042 0.032 0.037 0.023 0.059 0.029 0.080 0.098 0.119 0.090 0.135 0.069 (Continued)

Table A.1

(Continued )

Reference

a b c d

e

ID

Soil depth (cm)

CWRsoil (mm yr21)

CWRsaprolite (mm yr21)

CWRtotal (mm yr21)

Erosion rate Total denudation (mm yr21) rate (mm yr21)

PC-2006-1 PC-2006-2 PC-2006-3 PC-2006-4 KR-1 KR-2 KR-2006-1 KR-2006-3

107 91 61 71 33 25 64 64

0 0 0 0.018 NA NA 0.005 0.005

0.056 0.060 0.078 0.035 0.002 NA 0.008 0.008

0.056 0.060 0.078 0.052 NA NA 0.013 0.012

0.037 0.042 0.056 0.029 NA NA 0.024 0.019

0.094 0.102 0.134 0.081 NA NA 0.037 0.031

Total denudation rates were taken from Heimsath et al. (2005). Total denudation rates were taken from Heimsath et al. (2006). Riebe et al. (2003) used amalgamated soil samples to determine chemical weathering rates and total denudation rates. Riebe et al. (2004b) used amalgamated soil samples and parent material (49 soil samples, 28 samples of parent material) to determine chemical weathering rates and total denudation rates. Erosion rates equal rates of soil production, here total denudation rates are calculated as the sum of total chemical weathering and erosion rates.

Table A.2

Characteristics of study sites in and outside Australia

Study sites in Australia Reference

Study site

Elevation

Climate

Geology

Green et al. (2006)

Bega Valley, NSW

200 m

Burke et al. (2009)

200 m Snug (lowland), Brown Mountain (escarpment 1,000 m crest), NSW

Warm temperate Rainfall 910 mm yr21 Warm temperate Rainfall 870 mm yr21 Rainfall 690 mm yr21

Granite and granodiorite of the Bega Batholith Ordovician metasediments and Devonian granites

Study sites outside Australia Reference Study site

Elevation

Climate

Geology

Dixon et al. (2009)

Sierra Nevada Mountains, California, USA

1,186
2,991 m

Mixed-conifer to subalpine

Granitic rocks

Point Reyes, California, USA

150 m

Rainfall 330
1,200 mm yr21 Mediterranean

700 m

Rainfall 800 mm yr21 Tropical

Burke et al. (2007)

Riebe et al. (2003)

Rio Icacos, Puerto Rico

Riebe et al. (2004a)

42 different study sites

Riebe et al. (2004b)

Santa Rosa Mountains, Nevada, USA, Transect

2,090
2,750 m

Granitic rocks (quartz diorite, granodiorite) Quartz diorite

Rainfall 4,200 mm yr21 Mean annual temperatures: 2
25 C Granite Mean annual rainfall: 220
4,200 mm yr21 Mean annual temperatures and rainfall Granodiorite from 3.6 C, 650 mm yr21 (base) to 20.4 C, 850 mm yr21 (top)

Table A.3 Soil mixing rates Reference

Location

Soil mixing rates (published rates)

Source: Modified after Lobry de Bruyn and Conacher (1990) Williams (1968) Northern Australia Lee and Wood Northern 470 g m22 yr21 (1971a) Australia Northern Holt et al. (1980) Australia Spain and McIvor Northern (1988) Australia Nye (1955) West Africa 1.25 t ha21 yr21 Maldague (1964) Africa Lepage (1972, Africa 1973) Lepage (1974) Africa 0.67
0.90 t ha21 yr21 Nel and Malan Africa 0.35 t ha21 yr21 (1974) Pomeroy (1976) Africa Wood and Sands Africa 0.3 t ha21 yr21 (1978) 4.0 t ha21 yr21 Aloni et al. (1983) Africa Akamigbo (1984) Africa Bagine (1984) Africa 1.06 t ha21 yr21 Lepage (1984) Africa

Soil mixing rate (mm yr21)

Soil mass moved to surface (mm yr21)

Moving agent

Termites 0.39

5.4

Termites

0.083

Termites

0.917
2.17

Termites

1.47 2

Termites termites Termites

0.104

0.056
0.075 0.029

Termites Termites

0.025

Termites Termites

0.333 0.308 0.088

Termites Termites Termites Termites (Continued)

Table A.3 (Continued ) Moving agent

Location

Soil mixing rates (published rates)

Gupta et al. (1981) Salick et al. (1983) Aloni and Soyer (1987) Nutting et al. (1987) Greenslade (1974) Humphreys (1981) Briese (1982)

India South America Africa

15.9 g m22 day21 4.8 0.02 and 0.78 t ha21 yr21 0.002
0.065 3 t ha21 yr21 0.25

North America

0.07 and 0.56 t ha21 yr21 0.006 and 0.047

Termites

400 cm3 ha21 yr21 8.41 t ha21 yr21 0.35
0.42 t ha21 yr21

0.701 0.029 and 0.035

Ants Ants Ants

,0.05 t ha21 yr21

, 0.004

South Australia Eastern Australia South-eastern Australia Cowan et al. (1985) Eastern Australia Talbot (1953) Michigan America Lyford (1963) America Salem and Hole Wisconsin (1968) America Rogers (1972) America Wiken et al. (1976) America Levan and Stone Wisconsin (1983) America Waloff and Blackith England (1962)

Soil mixing rate (mm yr21)

Soil mass moved to surface (mm yr21)

Reference

0.6 t ha21 yr21 11.36 t ha21 yr21

0.05 0.947

11.36 t ha21 yr21

0.947

0.25

0.07

Ants Ants

20

Ants Ants

0.0002
0.0006 0.133

8.24 t ha21 yr21

0.687

Termites Termites Termites

Ants Ants Ants Ants

Sudd (1969) England Bucher and Argentina Zuccardi (1967) NA Lobry de Bruyn and Conacher (1990) Source: Listed publications Carson and Kirkby NA (1972) Carson and Kirkby NA (1972)

21

11 t ha

21

yr

3 t ha21 yr21

0.917

40
400 mg nest21 day21 Ants 0.007 Ants

0.250

Soil erosion by rain splash

0.003 cm2 yr21 0.10 cm2 yr21

Carson and Kirkby (1972)

NA

0.25 cm2 yr21

Carson and Kirkby (1972)

NA

0.15 cm2 yr21

Muller-Lemans (1996) Gabet et al. (2003) Amundson (2004) (cited from Darwin) Buchan (2010)

Europe

1.7

1.67

Wedging by grass roots Rabbits, burrowing, and refilling Worms, distribution of surface casts Worms, burrowing, and refilling Earthworms

NA

5.4 3 1024 to 0.01 m yr21 0.54
10 0.02 cm22 yr21

Earthworms Earthworms

NA

90 t ha21 yr21

Earthworms (digestion)

7.5

(Continued)

Table A.3 (Continued ) Reference

Location

Soil mixing rates (published rates)

Soil mixing rate (mm yr21)

Wilkinson et al. (2009) Gabet et al. (2003)

Various

10
100 t ha21 yr21

0.833
8.33

Earthworms

NA

0.005
1.8

Ants

Wilkinson et al. (2009) Gabet et al. (2003)

Various

4.5 3 1026 to 1.8 3 1023 m yr21 5
10 t ha21 yr21

0.417
0.833

Ants

0.013
0.41

Termites

Paton et al. (1995)

NA

1.3 3 1025 to 4.1 3 1024 m yr21 1
5 kg m22 yr21

0.833
4.167

Wilkinson and Humphreys (2005) Wilkinson and Humphreys (2005) Wilkinson et al. (2009) Yoo et al. (2005)

South-eastern Australia

10.6

Ants, termites, earthworms Earthworms and ants

South-eastern Australia

0.428
0.714

Ants, termites, and cicades

0.083
0.417

Ants, termites, vertebrates Pocket gopher (simulation model)

NA

Various

1
5 t ha21 yr21

NA

50
140 cm2 yr21

Soil mass moved to surface (mm yr21)

Moving agent

18 t ha21 yr21

Hole (1981)

Arctic climates

Heimsath et al. (2002) Kaste et al. (2007)

South-eastern 10
40 cm kyr21 Australia South-eastern 10
20 cm kyr21 Australia Temperate maritime climates Temperate continental climates Tropical climates

Saunders and Young (1983) Saunders and Young (1983) Saunders and Young (1983) Saunders and Young (1983)

Polar and montane climates

1.5 0.1
0.4 0.1
0.2

Ground squirrel Soil creep

0.5
2

Diffusion-like mixing rates Soil creep

2
10

Soil creep

4
5

Soil creep

1
300 10
100

Solifluction

Note: Soil mixing rates recorded in mass per area per year were converted to mm yr21 by assuming a soil bulk density of 1200 kg m23 in the conversion processes. “Source: modified after Lobry de Bruyn and Conacher, 1990” are taken from the publication by Lobry de Bruyn and Conacher (1990).

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Uta Stockmann et al.

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C H A P T E R T W O

Irrigation Waters as a Source of Pathogenic Microorganisms in Produce: A Review Yakov Pachepsky,
Daniel R. Shelton,
Jean E.T. McLain,† Jitendra Patel,
and Robert E. Mandrell†† Contents 1. Introduction 76 2. Concentrations of Microbial Pathogens and Indicator Organisms in Irrigation Waters 78 2.1. Regional and local differences 80 2.2. Temporal and spatial variabilities 82 3. Implications of Irrigation Water in Spread of Foodborne Diseases 84 3.1. Epidemiological investigations of food poisoning outbreaks implicating irrigated produce 85 3.2. Presence of pathogens in produce irrigated with contaminated water 87 3.3. Increased incidence of disease in areas practicing irrigation with high concentrations of pathogens in water 89 4. Standards, Guidelines, and Risk Assessment 91 4.1. Current standards for microbial quality of irrigation water 91 4.2. Role of microbiological water quality standards 97 4.3. Quantitative microbial risk assessment 98 5. Fate and Transport of Pathogenic and Indicator Microorganisms in Irrigation Systems 101 5.1. Survival of pathogen and indicator organisms in waters suitable for irrigation 103 5.2. Importance of environmental microbial reservoirs for irrigation water quality 107


† ††

USDA-ARS Beltsville Agricultural Research Center, Environmental Microbial and Food Safety Laboratory, Beltsville, MD USDA-ARS Arid-Land Agricultural Research Center, Water Management and Conservation Research Unit, Maricopa, AZ USDA-ARS, Western Regional Research Center, Produce Safety and Microbiology Research Unit, Albany, CA

Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00002-6

© 2011 Elsevier Inc. All rights reserved.

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6. Management and Control of Produce Contamination with Pathogens from Irrigation Waters 7. Research and Development Needs References

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Abstract There is increasing evidence that consumption of raw fresh produce is a major factor contributing to human gastrointestinal illness. A wide variety of pathogens contribute to foodborne illnesses, including bacteria (e.g., Salmonella, pathogenic Escherichia coli), protozoa (e.g., Cryptosporidium, Giardia), and viruses (e.g., noroviruses). Large-scale production of produce typically requires some form of irrigation during the growing season. There is a rapidly growing body of research documenting and elucidating the pathways of produce contamination by waterborne pathogens. However, many gaps still exist in our knowledge and understanding. The purpose of this review is to provide a comprehensive approach to the issue, including the most recent research. Topics covered include temporal and spatial variabilities, and regional differences, in pathogen and indicator organism concentrations in water; direct and circumstantial evidence for contaminated water as a source of foodborne pathogens; fate and transport of pathogens and indicator organisms in irrigation systems, and the role of environmental microbial reservoirs; and current standards for irrigation water quality and risk assessment. A concerted effort by researchers and practitioners is needed to maintain food safety of fresh produce in an increasingly intensive food production system and limited and declining irrigation water resources. Keywords: Irrigation water quality; microbial pathogens; produce contamination; water quality standards; microbial risk assessment; environmental microbial reservoirs; control of microbiological irrigation water quality

1. Introduction There is increasing evidence that consumption of raw fresh produce is a major factor contributing to human gastrointestinal illness, due to the potential for contamination with pathogenic microorganisms. Multiple surveys have been performed to determine the local prevalence of pathogenic microorganisms on fruit and vegetables. Several recent books summarized results of these surveys (Fan et al., 2009; Sapers et al., 2009; Warriner et al., 2009). The list of pathogens of interest includes bacteria Campylobacter spp., enterohemorrhagic Escherichia coli (e.g., E. coli O157:H7), enterotoxigenic Staphylococcus aureus, enterotoxigenic Bacillus cereus, Listeria monocytogenes, Salmonella spp., Shigella spp., Yersinia enterocolitica, protozoa Cryptosporidium spp., Cyclospora cayetanensis, Giardia spp., Entamoeba histolytica,

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helminths such as Ascaris spp., and viruses, in particular, adenoviruses, enteroviruses, noroviruses, and rotaviruses. Incidence of foodborne pathogens on fruits and vegetables varies by region and can be extremely high in some developing countries. However, substantial outbreaks continually occur in developed countries. The produce-related illnesses cost United States up to $39 billion annually (Scharff, 2009). Large-scale production of produce typically requires some form of irrigation during the growing season. Consequently, there is a rapidly growing body of research documenting and elucidating the pathways of produce contamination by waterborne pathogens. Excellent reviews by Steele and Odumeru (2004) and Gerba (2009) have recently been published. However, many gaps exist in our knowledge and understanding. Recent Food and Agriculture Organization- and World Health Organization-sponsored workshops have concluded that the role of contaminated water used in the production of vegetable crops as a vector for the transmission of these pathogens to humans is not clear (FAO/WHO, 2008). This review builds upon these previous reviews; however, our intent is to provide a more comprehensive approach to the issue as well as to include the most recent research. No databases on microbial quality of irrigation water have been compiled to date. However, increasing evidence of contamination of produce from irrigation water and increasing scarcity in water resources leave little doubt about the need to pay more attention to the fate and transport of pathogens in irrigation waters. Sources of irrigation water can be generally ranked by the microbial contamination hazard (Leifert et al., 2008): in order of increasing risk, these are potable or rain water, groundwater from deep wells, groundwater from shallow wells, surface water, and finally raw or inadequately treated wastewater. In many countries, surface waters are the predominant source for irrigation. In the United States, for example, the amount of irrigation water increased from 1.076 3 1011 m3 in 2003 to 1.131 3 1011 m3 in 2008. The number of US farms using only groundwater decreased by 9.2%, and the number of farms using only surface water increased by 6.3% (USDA-NASS, 2008). Farms using only surface water were applying 51% more water than farms that used only groundwater for irrigation. The shift to surface water use has coincided with an increase in the popularity of small farms marketing directly to consumers via farmers markets or CSA (Community Supported Agriculture), resulting in a decrease in the average area devoted to vegetable production per farm. A water source that is increasingly used in the United States is treated municipal wastewater. The two states producing the most treated wastewater, Florida and California, report a reuse flow of more than 1.2 billion gallons per day (WaterReuse Foundation, 2006), but use of this water for food crop irrigation occurs only on a very limited scale. At this time, there are no federal regulations governing the use of municipal wastewater to irrigate crops. Nineteen US states regulate the use of wastewater in crop production, but

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these regulations vary widely. Some states require very stringent treatment of effluents to reduce the concentration of pathogens to acceptable levels prior to irrigation, while others utilize site limitations and restriction of crop utilization to allow time for pathogens to decrease to acceptable levels (NRC, 1996). The objective of this chapter is to review the recent research in water quality pertinent to microbiological contamination of produce from irrigation waters and to highlight the relevant information on monitoring, regulation, and control of the microbiological quality of irrigation water. While the types of irrigation systems used in produce farming vary widely (USDA-NASS, 2008), they are usually complex enough to create an ecological environment with multiple potential sources of pathogens for a particular source of water and with potential reservoirs of microorganisms including pathogenic species and strains. Both point and nonpoint sources of microorganisms affect water quality in the sources of irrigation waters. Most sources are affected by weather patterns, presence of animals, water management, and agricultural practices. The site-specific differences in fate and transport of pathogen and indicator organisms in irrigation waters make it imperative to identify the risks of produce contamination for the specific site as related to the specific type of produce and specific irrigation management. This information is the key to establishing realistic and meaningful guidelines on microbial quality of irrigation water.

2. Concentrations of Microbial Pathogens and Indicator Organisms in Irrigation Waters There is a substantial database available on microbial water quality of surface waters throughout the United States and other countries based on indicator organisms. However, this information is of limited value for estimating risk for produce contamination due to deficiencies in location, timing, and/or frequency of sampling. There is very little data on prevalence of specific pathogens. Although reports on the microbial contamination of irrigation water sources are available, these have mostly been conducted “after the fact” subsequent to an outbreak. In addition, surface waters used for irrigation are monitored much less intensively than drinking or recreation water, and not necessarily during periods of peak usage (e.g., during droughts). Note that process water, that is, water used in crop management but not intended for irrigation, such as water used for application of pesticides or cleaning spray equipment, is rarely (if ever) monitored. Even when irrigation water is monitored, indicator organisms rather than actual pathogens are measured in the vast majority of cases. Indicator organisms have been selected mainly to indicate the potentially

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occurring fecal contamination rather than presence or concentration level of any specific pathogen. The major indicator organisms are E. coli, fecal streptococci, and enterococci; other groups of organisms have been recommended (Bacteroides, E. coli specific phages), but none have been widely adopted (Ashbolt et al., 2001). A comprehensive survey of microbial contamination levels in irrigation water has not yet been compiled for the United States (Stoeckel, 2009) or for any other country. We are not aware of any regular reporting on microbial quality of irrigation waters anywhere in the world. This is due, in part, to the cost of extensive sampling. In addition, producers/growers who have begun to collect data on microbial water quality may be reluctant to share these data (Suslow, 2010). Available survey data do, however, show the potential importance of pathogens in irrigation water. ThurstonEnriquez et al. (2002) studied occurrence of human pathogenic parasites in irrigation waters used for food crops in the United States and several Central American countries. They found that 28% of the irrigation water samples tested positive for microsporidia, 60% tested positive for Giardia cysts, and 36% tested positive for Cryptosporidium oocysts. Duffy et al. (2005) observed Salmonella in 9% of irrigation waters analyzed in Texas. A large survey of US groundwater found that 11% of sites were positive for Cryptosporidium, Giardia, or both (Moulton-Hancock et al., 2000). Close et al. (2008) demonstrated that intensive dairying and border-strip irrigation resulted in leaching of E. coli and Campylobacter to shallow groundwater; E. coli and Campylobacter were detected in 75% and 12% of samples, respectively. Chigor et al. (2010) isolated E. coli O157 from 2% of all samples from the river in northern Nigeria used for large-scale irrigation. The prevalence of E. coli O157:H7 and Salmonella in surface waters of Southern Alberta were 1% and 6%, respectively ( Johnson et al., 2003). In the same region of Southern Alberta, E. coli O157:H7 was isolated in 2% of 1608 samples of the surface water supplies over a 2-year period (Gannon et al., 2004). Eight percent of the irrigation water samples collected from six irrigation districts in Alberta, Canada, contained more than 100 fecal coliform (FC) per 100 mL (Cross, 1997). Salmonella were detected in 6% of surface water samples in Greece (Arvanitidou et al., 1997). In a survey of private wells in the Netherlands, Schets et al. (2005) found that 11% of the samples contained fecal indicators, while E. coli O157:H7 was isolated from 3% of the samples. Untreated domestic wastewater contains consistently high concentrations of indicator bacteria as well as pathogens. Kay et al. (2008) reported total coliform counts averaging 7.6 3 1010/100 mL in untreated wastewater, while Cryptosporidium and Salmonella have been reported at average concentrations of 2.63.2 3 102/100 mL and 2.7 3 102/100 mL (Howard et al., 2004; Rose et al., 2001), respectively, in untreated water samples. Modern treatment methods have been shown to be effective in removal of pathogens to below limits of detection in domestic wastewater.

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However, research results indicate that tertiary water treatment, including final disinfection using ultraviolet (UV) light and/or chlorination, is necessary to ensure maximum removal of enteric bacteria, protozoans, and viruses (Al-Sa’ed, 2007; Gerba and Smith, 2005). The databases on surface water and groundwater quality that are available do not necessarily reflect the microbial quality of irrigation water, and may be biased toward contaminated samples because the intensive monitoring is usually conducted at the sites where extensive contamination has been known to occur (Stoeckel, 2009). As previously noted, these databases consist almost exclusively of information on fecal indicator organisms rather than on specific pathogens. Furthermore, though rare reports of correlations between indicator organisms and pathogens exist in the literature (Payment and Locas, 2010; Wilkes et al., 2009), it is widely recognized that indicator organisms are poor predictors of the potential for water to cause gastrointestinal illness (Alonso et al., 2006; Duris et al., 2009; Harwood et al., 2005; Shelton et al., 2011). Consequently, interpretation of data on indicator organisms in terms of concentrations of pathogens remains problematic.

2.1. Regional and local differences Developing countries usually report much higher levels of pathogens in irrigation water than developed countries (Thurston-Enriquez et al., 2002). In developing countries, untreated raw wastewater is often used for produce irrigation. Wastewater irrigation provides a quarter of all vegetables produced in Pakistan. In most parts of Sub-Saharan Africa, irrigated urban and peri-urban farming with highly polluted water sources contributes 60100% of the perishable vegetables sold in most cities (Scott et al., 2004). Fecal indicator concentrations in such waters can reach levels typical for manure and feces. Singh et al. (2010) found concentrations of FC from 105 to 109 MPN/100 mL in waters of Indo-Gangetic riverine system used for irrigation of leafy greens. Irrigation water containing raw sewage or improperly treated effluents from sewage treatment plants may contain hepatitis A, Norwalk viruses, or enteroviruses in addition to bacterial pathogens (Beuchat, 1998). Regional differences in developed countries have also been observed (Kavka et al., 2006). Intraregional differences in microbial quality of surface waters are substantial. For example, in about 3500 surface water samples from Ohio, 35% of the samples contained fewer than 126 colony-forming units (CFU) of E. coli per 100 mL, 13% contained between 126 and 235 CFU/100 mL, 20% contained between 235 and 576 CFU/100 mL, and 32% contained more than 576 CFU/100 mL (Stoeckel, 2009). A study of well water from 268 household and stock wells in an 1100 mi2 area of southeast Nebraska showed that 37% of samples contained FC at levels of up to

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950 FC/100 mL21 of water (Exner and Spalding, 1985). In the absence of established relationships between indicator organisms and specific pathogens, it is impossible to evaluate the microbial contamination potential associated with any of these samples. Manure applications have been often anecdotally implicated in creating differences in microbiological water quality of surface waters. Analysis of point source data in the survey of surface waters in southern Alberta, Canada, showed that predicted manure output from cattle, pig, and poultry feeding operations was directly associated with prevalence of these pathogens ( Johnson et al., 2003). It was concluded that variations in time, amount, and frequency of application of manure to agricultural lands could have influenced levels of surface water contamination. On the other hand, a survey in Iowa under auspices of the USDA Conservation Effects Assessment Project benchmark watersheds (Richardson et al., 2008) found that E. coli populations can be large enough to impair the use of the waterways for contact recreation during much of the summer, but patterns do not always support the assumption that manure is the major source. Microbial quality of well water can be affected by the design of wells, nature of the substrata, depth to groundwater, and rainfall (Gerba, 2009). In the United States, the majority of drinking water disease outbreaks documented are caused by fecal contamination of wells (Reynolds et al., 2008). Close et al. (2008) noted that there is greater filtration of pathogens in finergrained soils compared to coarser-stony soils; however, macropores may enable rapid transport of pathogens through otherwise fine-grained soils (Guber et al., 2005). Deeper soils will generally filter out more pathogens than similarly structured shallow soils, minimizing groundwater contamination. The hydrological regime determines the amount of water available for leaching and transport of the pathogens. It impacts the travel time through the soil and vadose zone to the aquifer. The travel time is also affected by (a) the water content and structure of the soil and vadose zone materials; (b) geochemical properties and organic matter content of the soil and the properties of the microbial cells (affecting the adsorption and desorption of pathogens); and (c) the depth to groundwater table; deeper groundwater tables provide more time for pathogens to die off and/or be filtered before entering the groundwater system. Long-distance transport of pathogens is possible in fractured limestone and clay soils, and gravel sandy soils (Gerba, 2009). A study by Johnson et al. (2010) found high occurrence of viral contamination (averaging B50 MPN 100 L21) in karst aquifers of East Tennessee, and further suggested that co-occurrence rates of viruses and bacterial indicators were higher for karst aquifers than for other aquifer types. Although size of viruses makes them better suited to travel in pore spaces, their interactions with surfaces of the solid matrix can make their transport comparable with the transport of bacteria and parasite oocysts. Unprotected wells routinely have lower microbial water quality than protected wells (Shortt et al., 2003).

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Overall, no explanatory model of interregional differences and intraregional variations in microbial quality of irrigation water has been proposed to date. The minimum set of informative environmental parameters affecting the microbial water quality is lacking. This precludes large-scale estimates of microbial quality of irrigation waters based on environmental correlations.

2.2. Temporal and spatial variabilities Pathogen and indicator organism concentrations in irrigation water sources may exhibit both diurnal and seasonal variabilities as well as be affected by precipitation events. No reports on diurnal variability of pathogen or indicator organism concentrations in irrigation waters have been found in the literature. However, the diurnal variability of E. coli concentrations was documented for surface water sources. The variation coefficient of log10 (concentration) of E. coli measured within a day in three Canadian streams by Meays et al. (2006) was about 0.2, and there was a clear trend to a decrease of concentration from morning to midday. Based on data of Whitman et al. (2004), the AM2PM differences between E. coli concentrations were about 0.5 log on sunny days and 0.2 log on cloudy days in Lake Michigan. On some of the sunny days, the decrease in E. coli concentration from AM to PM was up to 2 logs. Variability in replications for concentrations was substantially larger in the morning than that in the afternoon. The decrease of E. coli concentrations (MPN/100 mL) in Massachusetts streams from 7 AM to 3 PM varied from 0.8 to 0.2 log (Traister and Anisfeld, 2006). The morning-to-afternoon decrease in concentrations of Campylobacter in rivers has also been observed (Eyles et al., 2003; Obiri-Danso and Jones, 1999). Rainfall events inevitably increase concentrations of pathogens and indicator organisms in streams, reservoirs, and ponds due to surface runoff into waterways and release of bacteria from bottom sediments (Pachepsky and Shelton, 2011). Alternatively, dramatic rainfall inputs can dilute surface waters and effectively decrease indicator concentrations. Working in a constructed wetland in Arizona, USA, McLain and Williams (2008) reported increased E. coli concentrations during months of no precipitation (average B400 CFU E. coli/100 mL), compared to samples collected during the summer monsoon (,100 CFU/100 mL) when 70 mm of rainfall was recorded. Seasonal variations in pathogen and indicator organisms were reported for various surface water sources. Patterns of seasonality were quite different in different regions. For example, observers in California usually reported higher concentrations during wetter months (Boehm et al., 2002; Cooley et al., 2007) and after heavy rainfall and related it to increased runoff. Levels of Cryptosporidium and Giardia in the Rio Grande water are much higher during the nonirrigation season (November through April), when the river flow is dominated by wastewater effluent,

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than during the irrigation season when releases from Elephant Butte Reservoir and return flows increase the volume of river water, leading to as much as a 100-fold decrease in pathogen levels (DiGiovanni, 2004). Observers in the United Kingdom, on the other hand, indicated that recurring storm events deplete bacterial reservoirs in the watershed, and therefore the lowest concentrations are found in the end of the sequence of rainfall events (Hunter et al., 1992; Rodgers et al., 2003). On the other hand, Eyles et al. (2003) attributed the highest Campylobacter concentrations in summer to the higher stocking rates and direct access of livestock to surface waters. Gannon et al. (2004) noted that most isolations of E. coli O157:H7 from surface water in southern Alberta occurred in summer. Research in the Eastern United States typically showed highest concentrations in the summer (Cinotto, 2005; Kim et al., 2010; Shelton et al., 2011; Traister and Anisfeld, 2006; Vereen et al., 2007). Seasonality appears to be the consequence of the interplay of land use, water management, weather patterns, and specific organism properties and sources. Patterns other than seasonal or diurnal have also been discovered in stream flow concentrations of microorganisms (Koirala et al., 2008). Few studies exist reporting seasonal variations in microbiological quality of wastewater. Haramoto et al. (2006) examined seasonal quality of tertiarytreated effluent leaving a water treatment plant in Tokyo, Japan, and reported higher concentrations of human norovirus in winter, coinciding with the epidemic season in that country. However, Rock et al. (2009) examined reclaimed water leaving treatment plants in Arizona over a period of 1 year and found no seasonal differences in indicator bacteria (E. coli, enterococci, Salmonella) or viruses. Spatial distributions of pathogen and indicator organisms in surface water sources are usually highly asymmetrical with most sites having relatively low values, with a few sites having high values (Solo-Gabriele et al., 2000; Tate, 2010). Presence of the hot spots in streams sometimes can be associated with point sources of pollution. Hot spots in reservoirs and impoundments could be related to aquatic plant and algal growth (Cinotto, 2005; Dewedar and Bahgat, 1995). Overall, irrigation water from any surface source is likely to contain enteric pathogens at one time or another. Land use and climate affect site-specific concentrations of pathogens that can reach very high levels if raw wastewater is allowed to reach the surface water source. The concentrations and prevalence of pathogens in wastewater and contaminated surface water are much lower than that of indicator organisms. Spatial and temporal distributions of concentrations of pathogens are typically skewed, and datasets contain many relatively low values and few high values. Weather and land use patterns affect pathogen concentrations, but these relationships are difficult to establish due to high variability of pathogen concentrations.

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3. Implications of Irrigation Water in Spread of Foodborne Diseases Direct evidence of irrigation water causing foodborne disease is relatively rare. This is because a “causeeffect” relationship requires that (1) the same pathogenic strain be isolated from the patient, produce, and irrigation sources, and (2) there is a clear sequence of events connecting patient, produce, and irrigation source. In the absence of direct confirmation, the “causeeffect” relationship can only be inferred based on circumstantial or subjective evidence. Figure 1 illustrates the “causeeffect” relationships inferred or hypothesized in research of foodborne diseases caused by consumption of fruits and vegetables. These relationships have been summarized by Steele and Odumeru (2004) as:  isolation of outbreak pathogens from produce and irrigation water (Fig. 1, links B and A), as well as the actual source (links C, B, and A);  epidemiological investigations of food poisoning outbreaks implicating irrigated produce (links B and A), or contamination of produce via other vectors (links D and A);  observations of increased incidence of disease in areas practicing irrigation utilizing highly contaminated wastes (link E). The inability to often identify the locations associated with produce contamination and delays inherent in foodborne outbreak field investigations reflect why the “causeeffect” relationships are often difficult to establish. Nevertheless, contaminated surface water in the vicinity of produce (Mandrell, 2011) and cross-contamination of irrigation water (USFDA and California Food Emergency Response Team, 2008) have been suspected in some large produce outbreaks in the United States.

D

Clinical case(s)

Contaminated produce A

B

Contaminated Irrigation water

C

Environmental sources of contamination

E

Figure 1 Inferences in research of irrigation water as a source of foodborne diseases caused by consumption of fruits and vegetables.

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3.1. Epidemiological investigations of food poisoning outbreaks implicating irrigated produce Despite the general belief that irrigation water poses a potential source of pathogens in foodborne outbreaks, there are relatively few confirmed cases in the United States. Greene et al. (2008) investigated a multistate outbreak of Salmonella Newport infection associated with eating tomatoes in the United States. Contaminated tomatoes were traced back to the eastern shore of Virginia, where the outbreak strain was isolated from pond water used to irrigate tomato fields. Two multistate outbreaks caused by one rare strain, and identification of that strain in irrigation ponds 2 years apart, suggested persistent contamination of tomato fields. So¨derstro¨m et al. (2008) investigated the outbreak of verotoxin-producing E. coli in Sweden caused by the consumption of lettuce that was irrigated by water from a small stream. Identical verotoxin-producing E. coli O157 strains were isolated from the patients and in cattle at a farm upstream from the irrigation point. An E. coli O157:H7 outbreak in the United States associated with shredded lettuce (USFDA and California Food Emergency Response Team, 2008) was traced back to the accidental mixing of well water, intended for irrigation, with water from a dairy manure lagoon. Finally, the same strain of Salmonella was found in irrigation water and in Serrano peppers implicated in an outbreak of Salmonellosis caused by a strain of Salmonella saintpaul (CDC, 2008). Additional studies are suggestive of a link between contaminated irrigation water and contaminated produce, although the evidence is circumstantial. In some instances, pathogens have been subsequently isolated from irrigation sources, although there was not a direct match with the outbreak strains. Duffy et al. (2005) isolated pathogenic bacteria from irrigation water; however, these were determined to be different from those isolated from cantaloupe because DNA fingerprinting was inconclusive. Cooley et al. (2007) reported results of the extensive sampling of a farm associated by traceback with three separate outbreaks in 20022003. The only sample yielding E. coli O157:H7 was a creek sediment sample collected adjacent to Farm A in July 2004. However, the strain was different from clinical strains associated with the three outbreaks. A matching outbreak strain was isolated from the river near where baby spinach was grown associated with a large 2006 outbreak (Cooley et al., 2007; Mandrell, 2011). Although there was no direct evidence that the river water was related to the outbreak, an investigation team hydrologist suspected that the shallow aquifer supplying irrigation well water could have been recharged by this river water (Mandrell, 2011). In other instances, forensic examination of foodborne outbreaks isolated no pathogens from irrigation water. For example, Ackers et al. (1998) reported on a case where irrigation water was implicated in

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outbreaks of E. coli O157:H7 infection from contaminated lettuce. The farm obtained its irrigation water from a nearby pond supplied by several streams that passed through cattle fields. Sampling of water and feces did not yield E. coli O157:H7. However, the environmental sources of potential water contamination were present, including improperly aged compost, feces of possibly infected cattle in the adjacent uphill pasture, cattle access to the streams above the pond used for irrigating the lettuce, and feces of other animal reservoirs of E. coli O157:H7, such as the sheep kept on the farm or deer. Hillborn et al. (1999) implicated irrigation water in an outbreak of E. coli O157:H7 attributed to mesclun lettuce, which was suspected to have been irrigated with water contaminated by dust from cattle grazing land. At the time of the environmental investigation, however, no E. coli O157:H7 was isolated from samples of well water, water from a cattle trough, water sampled from the cattle pasture, and cow or chicken manure. Irrigation water was also implicated in C. cayetanensis infections from raspberries (Herwaldt, 2000). In studies by Wachtel et al. (2002), irrigation water was implicated as a source of E. coli detected on cabbage seedlings irrigated with water inadvertently contaminated by a municipal sewage release; no E. coli were detected on seedlings in an adjacent field irrigated with municipal water. Although the source of the crop contamination could not be demonstrated conclusively because water samples tested negative for E. coli, the authors speculated that the creek water used for irrigation contained pathogenic bacteria associated with human waste or waste from wild animals. Rze˙zutka et al. (2010) noted that Cryptosporidium sp. oocyst-contaminated vegetables originated from Polish districts with the highest numbers of homesteads possessing cattle herds, and no contaminated produce was detected from districts containing lower numbers of cattle-owning homesteads, strengthening the assumption that the origin of the contamination was livestock. In contrast, no studies have established a relationship between irrigation water and disease outbreaks in the United Kingdom (Tyrrel et al., 2006). Consequently, the potential for produce contamination from irrigation water has been established, but it is difficult to quantify the extent of the problem (Groves et al., 2002). Despite public perceptions that irrigation with reclaimed wastewater decreases microbiological food safety, no case of foodborne illness has been attributed to wastewater irrigation in the United States to date, except by unintended cross-contamination (USFDA and California Food Emergency Response Team, 2008). Furthermore, risk assessments so far indicate that human health risks due to tertiary effluent irrigation is much lower than that deemed “acceptable” by public health standards (Zhao et al., 2006).

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3.2. Presence of pathogens in produce irrigated with contaminated water Despite the limited number of confirmed or circumstantial cases of produce contamination from irrigation water, laboratory studies have elucidated potential mechanisms of produce contamination from waterborne pathogens. Laboratory and field studies show that pathogens and indicator organisms (e.g., generic E. coli and E. coli O157:H7) transmitted from irrigation water to produce can remain viable for variable periods of time depending on environmental conditions (Delaquis et al., 2007). Nonpathogenic E. coli persisted for up to 28 days, whereas E. coli O157: H7 did not survive for more than 14 days in inoculated spinach plants (Patel et al., 2010). Pathogens survive and proliferate in sites where nutrients are available (Delaquis et al., 2007; Kroupitski et al., 2009a) and consequently, the plant rhizosphere has been shown to be a reservoir for opportunistic human pathogenic bacteria (Berg et al., 2005). It is suggested that survival of human pathogens is augmented by inclusion in plant phyllosphere biofilms or internalization within the plant (Heaton and Jones, 2008). Similar to plant-associated bacteria, pathogenic bacteria use cellulose and aggregative fimbriae for their attachment to plant surfaces (Mandrell et al., 2006; Teplitski et al., 2009). Lapidot and Yaron (2009) observed that the transfer of Salmonella to parsley leaves via irrigation water was dependent on curli-forming abilities of the strains. In a recent study, Patel et al. (2011b) reported significantly higher attachment of curli-expressing E. coli O157:H7 on iceberg lettuce and cabbage than the attachment of curli-negative E. coli O157:H7 strains. 3.2.1. Pathogen pathways into plants Pathogens can enter vegetable plants and become internalized, that is, colonize some plant tissues. Early studies suggested that E. coli could be transported into the edible part of lettuce from soil through root system (Solomon et al., 2002b), or that Salmonella Newport could be transported from contaminated roots to the aerial parts of Romaine lettuce seedlings depending on the developmental stage of the plant (Bernstein et al. (2007a, 2007b). However, more recent studies have not confirmed these results. E. coli was found in root tissue but not in shoot tissue of spinach plants grown on inoculated soil (Sharma et al., 2009). Jablasone et al. (2004), Miles et al. (2009), Zhang et al. (2009), and Erickson et al. (2010) found that internalization of E. coli and Salmonella via the root system does not occur or is an extremely rare event. Pathogens may enter aerial portion of plants through stoma, scar tissue, or wounds as a consequence of irrigation water contacting leaf surfaces or from raindrop splashes from the soil surface (Kroupitski et al., 2009b; Materon et al., 2007; Mitra et al., 2009). Guo et al. (2001) observed

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migration of Salmonella from soil directly into the stem scar tissue of green tomatoes. Wound surfaces seemed to be suitable for E. coli to enter iceberg lettuce tissues (Barker-Reid et al., 2009) and promoted its survival (Aruscavage et al., 2008; Brandl et al., 2004). Stomatal cavity was the preferential port of entry of E. coli internalization to the vegetable leaf in experiments of Gomes et al. (2009) with four different varieties of lettuce. A possible pathway from soil to stomata along the wet stem surface has not been explored to date. 3.2.2. Adherence to plants Any pathogen may reach plant surfaces via irrigation water; however, the potential for adherence is both strain and plant specific. For example, strain-specific properties of Salmonella (curli and cellulose) affected its ability to enter parsley plants from contaminated irrigation water in the work of Lapidot and Yaron (2009). For various serotypes of Salmonella, Guo et al. (2002) observed substantial differences in their survival on and in tomato plants. Plants also differ in their propensity to become contaminated with pathogens when irrigated with contaminated water. Quantitative risk assessment models for the use of reclaimed water show that risk varies between crops, with lettuce found to pose a higher risk than cucumber, but comparable to that of broccoli and cabbage (Hamilton et al., 2006). The prevalence of total E. coli was significantly higher in both organic and conventional lettuce than in any other produce varieties in the study of Mukherjee et al. (2004). When E. coli and Clostridium perfringens were added to irrigation water that was supplied in furrows and in drippers, microorganisms were detected on the surfaces of cantaloupe and lettuce, but were never recovered on the bell peppers (Song et al., 2006). Irrigation with contaminated water resulted in concentrations of total coliforms (TC) in amaranthus much higher than in other vegetables in the study of Okafo et al. (2003). Those crops whose edible parts develop on the ground surface, such as lettuce and parsley, were more contaminated with Salmonella than those that grow above the soil surface, like tomatoes and pimento in the work of Melloul et al. (2001). The USDAAMS Microbial Data Program also shows high levels of E. coli on these items (USDA-AMS-MDP, 2009). There are indications that differences between cultivars may influence the extent of contamination from irrigation water to different levels (Barak et al., 2008, for tomatoes, and Mitra et al., 2009, for spinach), although reasons for that are currently unknown. 3.2.3. Effect of the concentration The evidence for whether the initial concentration of pathogens in irrigation water is critical for produce contamination is mixed. For example,

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work of Webb et al. (2008) shows a positive relationship between the E. coli O157:H7 concentrations and incidence on spinach. Whereas no E. coli O157:H7 was detected on spinach plants spray irrigated with water with 102 E. coli O157:H7 CFU mL21 in that study, the pathogen was found at higher concentrations of 104 CFU mL21 and the incidence increased when the concentrations were increased to 106 CFU mL21. Salmonella were undetectable by MPN analysis on spinach plants during a 6-week study with plants irrigated repeatedly with water containing 103 CFU mL21 (Patel and Darlington, 2010). Salmonella persisted at a level of 104 CFU plant21 after 24 h after plants were irrigated with very high levels of the pathogen (106 CFU mL21). These results imply lower concentrations correspond to lower incidence of contamination. On the other hand, Mootian et al. (2009) observed that lettuce irrigated with water containing E. coli O157:H7 in amounts as low as 101 or 102 CFU mL21 may become contaminated. They reported that 30% of the mature plants initially irrigated with contaminated water for 15 days were positive for E. coli O157:H7. The concentration in irrigation water may not necessarily be the dominant factor if the microorganism is able to internalize in produce or colonize it without being outcompeted by the plant internal microbial community of the plant phyllosphere. However, there is no direct evidence that this route of contamination is a significant factor in contamination of any produce.

3.3. Increased incidence of disease in areas practicing irrigation with high concentrations of pathogens in water As long as 60 years ago, Norman and Kabler (1953) observed that poor microbiological quality of irrigation water was associated with the incidence of human pathogens in leafy vegetables. Connections between contaminated irrigation water and clinical studies (Link E in Fig. 1) are typically reported in areas where irrigation water may have unsatisfactory microbial quality, most often having waste origin. Katzenelson et al. (1976) compared the incidence of enteric communicable diseases in 77 kibbutz settlements practicing wastewater spray irrigation with partially treated nondisinfected oxidation pond effluent with disease in 130 kibbutz settlements practicing no form of wastewater irrigation. The incidence of shigellosis, salmonellosis, typhoid fever, and infectious hepatitis was two to four times higher in communities practicing wastewater irrigation during the irrigation season, whereas no differences were found for enteric disease rates during the winter nonirrigation season. A study in Mexico compared incidence of diarrheal disease and microbial quality of the irrigation water in 2320 households irrigating vegetables with either untreated wastewater or

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natural rainfall (Cifuentes, 1998). Rates of diarrhea were significantly higher in households irrigating with untreated wastewater than in households irrigating with rainfall alone. In Morocco, crop irrigation with untreated wastewater caused a significantly higher rate of salmonellosis in children of agricultural workers (39%) than in the children of nonagriculturalists (25%) (Ait Melloul and Hassani, 1999). Populations near microbiologically contaminated surface water sources can be affected via transmission pathways other than irrigation such as aerosols from the surface microlayer as demonstrated in marine environments (Aller et al., 2005), or transfer from domestic animals, insects, etc. Animal and composting facilities are preeminent sources of airborne particulates and dust as well as insects that vector enteric pathogens, however, emission rates, transport, survival, and deposition of particulates and insects carrying E. coli, Salmonella, and other fecal bacteria from these sources currently are not quantified (Duan et al., 2008; Millner, 2009). Insect vectors may harbor and subsequently transmit Enterobacteriaceae and plant pathogens to plants and animals by direct physical contact and their frass (Mitchell and Hanks, 2009). Information about such transport is very limited, but potential vectors for contamination of leafy greens have been identified and studied (Talley et al., 2009). Creation of irrigation water storages affects local ecological systems and can modify pathogen transmission. Ecosystem changes concomitant with irrigation development in Sri Lanka, for example, resulted in long-term changes in the composition of the mosquito fauna, which was characterized by the increasing dominance of species with the potential to transmit human pathogens (Amerasinghe and Indrajith, 1994). Low microbial quality of water can be translated in higher disease incidence not only via agricultural production but also via household uses, including drinking unboiled water (Cifuentes, 1998; Van der Hoek et al., 2001). In summary, transmission of pathogens to produce and their subsequent survival are evident by incidence studies and multiple recent outbreaks described above and in reviews (Mandrell, 2011). However, details of the potential mechanisms of transport have been documented mostly in laboratory studies. More field data are needed to establish reservoirs and patterns of transmission occurring in farm operation environments, and to evaluate the relative importance of various factors such as pathogen concentration, pathogen strain, plant state, irrigation regime, and weather patterns. Results of studies of the incidence of E. coli O157:H7 and Salmonella in watersheds and other environments in a major produce production environment of California emphasize the need for more specific data about these factors (Cooley et al., 2007; Gorski et al., 2011).

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4. Standards, Guidelines, and Risk Assessment With increased recognition of the importance of microbiological quality of irrigation water and its impact on food safety and public health, the need for regulation has become obvious. Guidelines and standards are the two means used to regulate food safety. For the purpose of this review, standards are defined as regulatory documents containing specific numerical limits on concentrations of some microorganisms, whereas guidelines are issuances that do not contain specific concentration limits. Standards may contain elements of guidance. This review is not concerned with the often used distinction that standards are enshrined in law whereas the guidelines are not covered by law (Agriculture and Agri-Food Canada, 2007).

4.1. Current standards for microbial quality of irrigation water Microbiological water quality standards are based on indicator organisms that, albeit not pathogenic, are presumed to correlate with pathogens, thus facilitating estimation of the probability that potential pathogens are present. A large number of microorganisms have been proposed and tested as indicators (Ashbolt et al., 2001), although only a small number of them have been adopted in standards (Table 1). The earliest standards used “TC” as the indicator organism (USEPA, 1973). However, because fecal contamination was considered to be the probable source of pathogens in waters, microorganisms in feces were selected as more appropriate indicators. Subsequently, standards were based on the thermotolerant “FC,” a subset of TC that have the ability to grow and ferment lactose and produce acid and gas at 44.5 C. Most recently, the indicators of choice have become E. coli and, in some cases, fecal streptococci (Table 1). Additional standards have been adopted to include also nematode and/or helminth egg counts (Blumenthal et al., 2000). Microbiological water quality standards for irrigation water should include distinctions between irrigation water sources, method of irrigation, type of crop, and land use (Table 1). For wastewater, an important distinction was introduced between restricted irrigation (i.e., for uses that include crops likely to be eaten uncooked) and unrestricted irrigation for crops that will be cooked (Blumenthal et al., 2000; Marr, 2001). Some states do not allow irrigation of food crops with wastewater effluents of any quality. For example, Florida does not allow spray irrigation with effluent water of edible crops that will not be peeled skinned, cooked, or thermally processed before consumption (USEPA, 2004), though drip and subsurface irrigation are allowed

Table 1

Examples of microbiological water quality standards applied to waters used to irrigate produce Concentration limits

Source

USEPA (1973) Canadian Council ... (1999) Alberta Environment... (1999) Warrington (1988)d Warrington (1988)

FC (fecal coliforms, cell/100 mL)

EC (E. coli, cell/100 mL)

FS (enterococci, NE (nematode cell/100 mL) eggs per liter

Type of crop

TC (total coliforms, cell/100 mL)

NS NS

NS NS

NS 1000a

1000a 100a

NS NS

NS NS

NS NS

NS

NS

NS

100b 200a

NS

NS

NS

NS

NS

NS

Eaten raw

20b

NS

NS

Open to public and grazing NS

Other then eaten raw

200b 200a NS

77b

NS

1000b 1000c 2400a 1000c 2400a 1000c 2400a

385a

100a

NS

NS

NS

200a

200a

NS

NS

NS

Eaten raw

1000a

100a

NS

NS

NS

NS

Eaten raw

NS

1000a

NS

NS

1a

Type of water

Irrigation method

Land use

Surface NS

NS NS

Surface

Williamson NS NS (2002) Anonymous Surface NS (2006) Blumenthal et al. Wastewater NS (2000)

Blumenthal et al. (2000) CSFSGLLGSCf (2009) CSFSGLLGSCf (2009) Vermont Water Agency (2009) Johnson (2009) Johnson (2009) Bahri and Brissaud (2004)

NS

100000a

NS

NS

NS

NS

Eaten processed Eaten raw

NS

NS

NS

NS

NS

Eaten raw

NS

NS

NS

NS

NS

NS

NS

200a

126b 235a 126b 576a 77a

NS

NS

NS NS NS

NS NS Vegetables

NS NS 1000e

200a 576a 1000e

126 NS NS

NS NS NS

NS NS NS

Wastewater NS

NS

NS

Overhead

NS

Drip/ furrow NS

NS

NS Overhead NS Drip Wastewater Overhead, surface

NS—not specified. a Any single measurement. b Moving geometric mean from 5 weekly measurements. c Any from the 5 consecutive weekly measurements. d P. aeruginosa is also limited. e In at least 80% of consecutive measurements. f Commodity Specific Food Safety Guidelines for the Lettuce and Leafy Greens Supply Chain.

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(O’Connor et al., 2008). However, it has been noted that the requirements for treated wastewater are more restrictive, in some cases, than drinking water standards (O’Connor et al., 2008). The National Research Council (NRC, 1996) reported that the quality of treated effluent for most parameters is generally well below the levels measured in the Colorado River and the recommended minimum irrigation water quality criteria. Higher concentrations of indicators are tolerated in surface irrigation when irrigation water does not come in contact with edible parts of plants. Of growing concern in the use of wastewater for irrigation is the potential for these waters to contain organic contaminants (e.g., antibiotics, endocrine disrupting compounds, pesticide residues). Concentrations of these emerging contaminants are as yet unregulated and their effects on public and environmental health are poorly understood (Metcalf and Eddy, 2007). The regional differences between water quality standards can be large. An example can be found in Table 1 where standards are shown for four Canadian provinces: Alberta (Alberta Environment, Environmental Service, 1999), British Columbia (Warrington, 1988), Manitoba (Williamson, 2002), and Saskatchewan (Anonymous, 2006). Another example can be found in Table 2 where the wastewater microbiological quality standards are collected for the US states that have such standards, and allow treated wastewater to be used to irrigate fresh produce. Note that some state standards contain the maximum allowed concentration in any single sample, whereas other states do not include this value. Marked differences in concentrations are approved also for surface versus overhead versus drip irrigation, possibly related to a finding that populations exposed to aerosols from sprinkler irrigation may have increased risks of enteric viral and bacterial infections (Blumenthal and Peasey, 2002). However, it must be noted that increased risk was apparent only from irrigation water containing greater than 105 TC/100 mL orders of magnitude higher than permitted levels (Table 2). It is not clear what is the scientific or epidemiological rationale behind any specific regional standard. Because of the scarcity of information of how microbiological water quality affects pathogen concentrations in produce and therefore consumer health, some regional irrigation water quality standards have been based on microbiological standards for recreational water (So¨derstro¨m et al., 2008, in Sweden, or CSFSGLLGSC, 2009, in California). The use of recreational water standards is considered to be problematic because they were established assuming human health risk posed by full-body contact during swimming, and therefore do not take into account the rapid die-off during postirrigation intervals and

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Table 2 Reclaimed wastewater standards for irrigation of raw eaten crops in US states where such irrigation allowed. Numbers are concentrations of TC or FC, cells/100 mL

State

Irrigation method

Total (TC) Any or fecal (FC) single Sampling coliforms Median sample frequency

Arizona Arizona California Colorado Hawaii Idaho New Jersey New Mexico Oregon Texas Utah Washington

Overhead Surface Overhead Overhead Surface only NS Surface only Surface only Surface only Surface only Overhead Surface only

FC FC TC TC FC TC FC FC TC FC FC TC

0a 200a 2.2a 2.2b 2.2b 2.2b 2.2a 1000 2.2b 20c 0a 2.2b

23 800 23 NS 23 NS 14 NS 23 75 14 23

Daily Daily Daily Daily Daily Daily Daily NS Daily Twice a week Daily Daily

NS—not specified. a Detected in 4 of last 7 daily samples. b Seven-day median. c Median from twice-a-week samples. Source: USEPA (2004).

exposure to environmental stresses associated with crop production (Suslow, 2010). Given that abstention of overhead irrigation is viewed as an important management practice to increase microbial safety of produce (Barker-Reid et al., 2009), the irrigation regime before harvest should be factored in the standards. For example, the California Leafy Greens Marketing Agreement (LGMA) requires a 24 h wait period between irrigation and harvest. Depending on the region, distinctions in standards have been made between irrigation water sources, irrigation method, type of the crop, and land use (Table 1). The permitted concentrations of indicator organisms are usually much higher in case of surface irrigation method where water does not come in contact with edible parts of plants. This assumption requires further scrutiny because it has recently been suggested that (1) pathogens can enter plants via root systems (Bernstein et al., 2007a, 2007b; Solomon et al., 2002a, 2002b), and (2) the in-field splash can transport microorganisms from the soil surface quite far (Boyer, 2008).

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Microbiological standards have been scrutinized and criticized. The two major criticisms are (a) water sampling frequency has never been justified, and (b) indicator organism concentrations do not correlate with concentrations of pathogens. Recommendations on sampling frequency fluctuate widely from annual sampling (Anonymous, 2010b) and sampling not more than 1 month apart (Gombas, 2007; Strang, 2010), five times a month ( Jamieson et al., 2002), to daily (e.g., Table 2). A fairly common requirement is to use the geometric mean from 5 weekly measurements (Table 1). The problem arises from high temporal variabilities in indicator organism concentrations that have been observed in irrigation water sources (see Section 2). The variabilities and ranges of indicator concentrations from the geometric means and maximum of four once-a-week measurements have not been reported, nor their relevance to produce contamination was established. Data on correlations between concentrations of indicator organisms and pathogenic or potentially pathogenic organisms are inconclusive at best. Several studies attempting to find such correlation have found none (Duris et al., 2009; Harwood et al. 2005; Jjemba et al., 2010; Kramer et al. 1996; Shelton et al., 2011). Differences in indicators due to environmental fitness for survival, or even their ability to multiply in the environment, all influence indicator usefulness (Ashbolt et al., 2001). Hence, viral, bacterial, parasitic protozoan, and helminth pathogens are unlikely to all behave in the same way as a single indicator group, and certainly not in all situations. It is worth noting that some drinking water outbreaks occurred from water where coliform standards had been met (Craun et al., 1997; Marshall et al., 1997). Standards for drinking and recreation waters have their origin in epidemiological studies. No such studies have been carried out for irrigation waters. Produce is transported in large lots and dissemination of pathogens from a few contaminated plants to others is possible. This, and the reported independence of pathogen internalization in plants on concentration in irrigation water (see Section 3), challenges the application of concentration-based standards. Concentrations of indicator organisms in water do not reflect the ecology of pathogens and indicators in water sources. Some higher aquatic organisms and soil organisms can harbor both pathogen and indicator microorganisms (Barker et al., 1999; Bichai et al., 2008). Many members of the total coliform group and some so-called FC (e.g. species of Klebsiella and Enterobacter) are not specific to feces, and even E. coli has been shown to grow in natural, aquatic environments (Pachepsky and Shelton, 2011). Hence, whereas indicators representing fecal contamination in temperate waters are E. coli and enterococci, E. coli and enterococci may grow in tropical water and soils (Pachepsky and Shelton, 2011) and alternative indicators should be considered. Use of multiple indicators

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was suggested based on a better understanding of the types, occurrence, and concentration of pathogens (Gerba and Rose, 2003). The difficulty of associating specific indicator concentrations with fresh producerelated health risks does not negate the value of measuring these concentrations. High indicator organism concentrations indicate high levels of fecal contamination and, therefore, an elevated probability that fecal pathogens could be present. However, the use of these measurements is currently a contentious issue aggravated by an extremely low knowledge base regarding health issues related to the quality of irrigation water.

4.2. Role of microbiological water quality standards There are three schools of thinking on the usability of pathogen and indicator concentration-based standards for irrigation water quality: (a) setting indicator-based standards and adhering to them as mandated by regulatory agency or marketing body, (b) using standards as the auxiliary tool in the guidance to control microbial contamination of produce, and (c) not using standards at all. Setting standards may be a convenient regulatory measure. However, it may be difficult to implement. Tyrrel (1999) noted that in the United Kingdom, “one obvious practical problem is that there are many more irrigation abstraction points than potable water abstraction points in the river network which could make compliance with any proposed regulations very difficult to manage.” The irrigation water monitoring infrastructure is absent and no research has been to done to evaluate the feasibility of such infrastructure. Complementary use of standards is advocated in many regional or local guidelines. The Code of Practice for Food Safety in the Fresh Produce Supply Chain in Ireland, for example, states that “testing of agricultural water for microorganisms and chemicals, whilst important, should not be used as the sole method of controlling waterborne hazards. Water testing results can vary considerably and only reflect the water quality at the time of sampling. Growers should focus on the adoption of good agricultural practices to control waterborne hazards and use water testing as a means of validating these practices” (Food Safety Authority of Ireland, 2001). However, which standards to use in such case is an open question. Purely subjective selections have been recommended such as “Water quality standards published by the US Environmental Protection Agency for either swimming, shellfish growing waters, or drinking water may serve as a guideline, depending on how strict public health officials or growers, shippers, and retailers of leafy green vegetables want to be” (Extension Foundation, 2010).

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USFDA (2009) put forward the “Draft Commodity Specific Guidance Documents for Leafy Greens, Melons and Tomatoes” without any reference to microbiological water quality standards. In the absence of scientific data on efficient testing of irrigation water, the regulatory agency provided the list of essential points to look at, questions to ask, and practices to apply to reduce the risk of microbial contamination of leafy greens. A similar situation was acknowledged in using wastewater for irrigation. “The information on biological quality of the water or associated epidemiology is not understood well enough to develop standards. There is no evidence that regulations or standards are needed, but there is sufficient information to indicate that considerable caution and adherence to recommended practices are essential to protect human health” (McFarland et al., 2007). Instead of using “one-size-fits-all” standards, the USFDA guidance emphasizes site-specific analysis for specific crop, pathogen, irrigation system, water sources, and management. One methodology for such analysis—quantitative microbial risk assessment, or QMRA—has been first applied to wastewater irrigation and currently is actively applied to irrigation with water from other sources.

4.3. Quantitative microbial risk assessment Risk assessment, in general, is the characterization and estimation of potential adverse health effects associated with exposure of individuals or populations to hazardous materials or situations. With regard to irrigated produce, adverse health effects may be caused by the ingestion of pathogens with the produce, by inhaling aerosols containing pathogens, by the unintended consumption of contaminated water, etc. The QMRA for irrigation waters, or irrigation QMRA, establishes a relationship between the concentrations of pathogenic microorganisms in irrigation water and the probability of illness. Comprehensive introductions in QMRA in general and in irrigation QMRA have been published (Haas et al., 1999; Petterson and Ashbolt, 2003). QMRA employs two statistical models: the exposure model and the infectivity model. There is no single mathematical formulation for the irrigation QMRA. An outline of one simple implementation of the irrigation QMRA is shown in Fig. 2. The exposure model in Fig. 2 computes the daily dose as the product of concentration of the pathogen in irrigation water, volume of irrigation water interacting with the unit mass of produce, fraction of pathogen in produce that remained infective at harvest time, fraction of pathogen that remained infective between harvest and consumption, and the mass of produce consumed daily. The infectivity model uses the dose and the number of consumption days as inputs and provides the probability of illness as the output. The irrigation QMRA can

CFU mL (cysts, genomes) per g per mL

g

Exposure model

Dose–response relationship

Number of consumption days

II

*

Dose

*

D

Amount of produce consumed daily

*

S2 Fraction of pathogen surviving past harvest till consumption

*

S1

Fraction of pathogen surviving till harvest

*

T Fraction of pathogen transferred to produce

V Amount of irrigation water per gram of produce

Pathogen concentration in irrigation water

C

99

Probability of illness

Irrigation Waters as a Source of Pathogenic Microorganisms in Produce: A Review

CFU (cysts, genomes)

Infectivity model

Figure 2 The outline of the QMRA model to assess the risk of illness due to consumption of produce irrigated with pathogen-contaminated water. (Source: Modified from Stine et al., 2005.)

be developed for specific pathogens, the agricultural management, the water source, the consumer group, and the environmental conditions. The irrigation QMRA is rapidly evolving. It was first developed for the wastewater irrigation and was adopted by the World Health Organization in the development of guidelines for water-related diseases (Fewtrell and Bartram, 2001). Currently, QMRA is applied to other sources of irrigation water as well. Earlier versions of QMRA supported the use of indicator organisms and employed conversion of indicator organism concentrations to concentrations of the pathogen of interest (Blumenthal et al., 2000). Later pathogen-specific irrigation QMRA were developed, notably for viruses on lettuce (Petterson et al., 2001), enteric virus infection on cucumber, broccoli, cabbage, or lettuce (Hamilton et al., 2006), Cryptosporidium and Giardia on irrigated tomatoes, bell peppers, cucumbers, and lettuce (Mota et al., 2009), and norovirus and Ascaris infection (Mara, 2010). Original QMRA formulations for vegetables irrigated with wastewater (Shuval et al., 1997) treated inputs of the exposure model (C, V, T, S1, S2, D in Fig. 2) as constant values. Later it was argued (Tanaka et al., 1998) that these variables are actually uncertain and using multiple results of QMRA, each computed with randomly selected values of the exposure model inputs, would produce a more realistic risk assessment in

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which the uncertainty of the probability of illness could be established. For that purpose, computer programs were developed to perform the Monte-Carlo simulations of QMRA (Mara and Sleigh, 2010). One of the developments in irrigation QMRA applications has been its use to establish irrigation water standards. This application is based on using QMRA in reverse. Inspection of Fig. 2 shows that QMRA with constant C, V, T, S1, S2, and D can be used both as the forward and reverse procedures. The information flow in Fig. 2 is from left to right for the forward procedure and from right to left for the reverse. The reverse procedure uses the given probability of illness as the input value and computes the dose by inverting the doseresponse model. Then, knowing dose and V, T, S1, and S2, one can compute C, that is, find the pathogen concentration in irrigation water that causes the given probability of illness. This method of irrigation water standards development was suggested by Stine et al. (2005) who determined the concentrations of hepatitis A virus (HAV) and Salmonella in irrigation water necessary to achieve a 1:10,000 annual risk of infection. The inputs used in the exposure model were those that provided the worst-case scenario. So far, we are not aware of irrigation water standards that have been developed with reverse QMRA using exposure inputs as random uncertain values. The advantage of QMRA is the potential to tailor it for the case at hand. Irrigation scheduling before harvest, preharvest environmental conditions, and type of crop should be considered in microbial water quality standards for irrigation water (Stine et al., 2005). Where the experimental data were present, irrigation QMRA was developed for specific varieties of vegetables (Hamilton et al., 2006), for specific age groups (Mara and Sleigh, 2009), and for different irrigation methods (Stine et al., 2005), etc. The strength of QMRA is in its provision to assess treatment step performance needs and identify zones for critical control (Petterson and Ashbolt, 2003). The irrigation QMRA obviously has many avenues for improvements. Literature, including some materials of Section 2, shows that the survival of pathogens in produce is a complex process, not necessarily following a simple exponential decay pattern. Long-term persistence of pathogens in produce may undermine the current focus exclusively on the die-off period between the last irrigation and harvest. Another important limitation of existing irrigation QMRA is that currently they are partially or completely relying on laboratory measurements. Field-scale observations and measurement of the exposure model parameters are scarce, and the need for them is acute. Other improvements in QMRA models include modifications in the structure and components of both exposure and infectivity models. This should lead to realization of potential applications of the irrigation QMRA framework in helping to establish guidelines and standards regarding microbial quality of irrigation water.

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5. Fate and Transport of Pathogenic and Indicator Microorganisms in Irrigation Systems Information on the fate and transport of pathogenic and indicator organisms is actively used in water qualityrelated regulatory, engineering and research fields. This information is needed by regulators to inform total maximum daily load estimates in the United States and Programmes of Measures in Europe, both of which are designed to prevent impairment of water quality at locations where compliance is assessed against health-based standards for drinking, bathing, or shellfish harvesting (Kay et al., 2007). Information on fate and transport of pathogenic and indicator organisms is also highly relevant at locations where the microbial quality of irrigation water is of interest. The general layout of fate and transport issues pertinent to irrigation water quality is shown in Fig. 3. The microbial quality of irrigation water is impacted by diverse agricultural, wildlife, and human inputs including runoff from land-applied manure and pasture lands, direct fecal deposition from cattle, overflow from manure lagoons and storage sites, fecal deposition from wildlife, discharge from leaky sewer lines, and subsurface flow from septic drainage fields. In addition, microbial concentrations in the water are dependent on exchange with microbial reservoirs in direct contact with water, including bottom sediment, periphyton, algae, and bank soils. The microbial quality of irrigation water is also impacted by processes that occur during storage and in the distribution system. Changes can occur during transportation from the source to the field. Irrigation Runoff from manured and pasture lands

Directly deposited animal waste

Runoff from waste deposition and storage sites

Runoff Animal waste

Infiltration and subsurface flow

Water source

Bottom sediment and bank soil

Within-field fate and transport processes

Pipe-based irrigation water delivery

Sewage discharge Algae and periphyton

Algae and periphyton

Irrigated crop

Irrigation ditches and canals

Bank soils

Bottom sediment

Biofilms

Figure 3 The layout of the processes affecting the microbial quality of the irrigation waters.

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water transport via irrigation ditches and canals involves interaction with microbial reservoirs of bottom sediment, bank soils, algae, and periphyton, whereas water transport via pipes involves interactions with biofilms in the pipes. Degradation in microbial water quality within transport pipelines has been shown to be especially pronounced in reclaimed water distribution systems. Jjemba et al. (2010) reported regrowth of multiple opportunistic pathogens, including Legionella and Aeromonas, in treated effluent systems leaving four US municipal wastewater plants. Such regrowth has been associated with the presence of high levels of assimilable organic carbon, which serves as an energy source for bacterial growth, in reclaimed water (Ryu et al., 2005; Weinrich et al., 2010). Irrigation water collection, replenishment, and delivery occur in complex ecological systems that affect the microbial quality of water in many ways. Substantial reviews have been published describing both general patterns of microorganism fate and transport in watersheds (Ferguson et al., 2003; Kay et al., 2007), and on specific pathogenic and indicator microorganisms, for example, Salmonella (Gorski et al., 2011; Haley et al., 2009; Walters et al., 2011), E. coli O157:H7 (Cooley et al., 2007), and Cryptosporidium and Giardia (Mohammed and Wade, 2009). One important conclusion is that the current state of knowledge does not allow for accurate predictions of the survival patterns for specific pathogens in specific irrigation water or microbial reservoirs, even though factors of pathogen survival and patterns of pathogen microbial population changes in time are generally known. The heterogeneity of the natural environments is very high and fecal indicator and enteric pathogen contamination are dynamic; it will be complicated to assess the appropriate measurements for natural waters and microorganism characterization (e.g., genotype, fitness) to predict die-off and/or growth of any specific pathogen (van Elsas et al., 2010). The absence of accurate predictions does not preclude using common sense for development of management actions that would aim to keep concentrations of pathogens below epidemiologically acceptable levels. Development of such actions has to rely on the information on fate and transport of microbial pathogens in environmental media present in irrigation systems. Knowing patterns of pathogen survival provides possible ranges of pathogen survival rates and allows quantification of the uncertainty of predictions. Currently there is no single source where existing data on pathogen fate have been collected and can be retrieved. The first database of this type has been developed only recently (Pachepsky et al., 2009). Sources other than water must be considered because of known roles as microbial reservoirs.

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5.1. Survival of pathogen and indicator organisms in waters suitable for irrigation The majority of available data on pathogen and indicator survival have been obtained in water sources potentially suitable for irrigation. Consequently, it must be assumed that the processes occurring in various surface bodies of water are generally applicable to waters intended for irrigation. It must be noted that microbial die-off rates, detailed below, assume no additional inputs of water contamination following the initial measurements of viable populations. However, it has been shown that water quality often degrades quickly in storage ponds, due to inputs from avian or other wildlife (Higgins et al., 2009; McLain and Williams, 2008). Experiments on survival of pathogen and indicator organisms in natural waters usually demonstrate a decrease in microbial concentrations with time. However, the concentration decrease does not necessarily begin with the initial inoculation or input. An initial phase of growth, or lag phase, has been noted in experiments with E. coli and Salmonella in warm estuarine environments (Chandran and Hatha, 2005; Rhodes and Kator, 1988), in experiments with fecal streptococci and E. coli in tropical freshwaters (Wright, 1989), and E. coli in Southern Ontario (Dutka and Kwan, 1980). The duration of the observed growth and lag phase was from 1 to 3 days after which the microbial concentrations decreased. The decrease in concentrations after or in absence of initial delay is typically exponential. At some point in time, the rate of the exponential decay may change, in which case, a biphasic decay is said to occur (Fig. 4). The review by Hellweger et al. (2009) shows that biphasic decay is a common phenomenon for E. coli, as well as for other microorganisms. The rate of decay during the first inactivation phase k1 is defined as the slope of the regression “time versus natural logarithm of concentration” between T1 and T2 in Fig. 4. The reported values of k1 for indicator and pathogen organisms vary by up to two orders of magnitude (Fig. 5). Based on experiments with Salmonella in Poland (Budzin´ ska et al., 2009) and with E. coli in Finland (Korhonen and Martikainen, 1991), inactivation rates k1 in northern surface waters ranged from 0.1 to 0.2 d21; temperature had little effect on the inactivation rates (Fig. 5). In warmer climates, values of k1 are generally higher and the temperature effect is more pronounced (e.g., data of Flint, 1987, for E. coli, Mezrioui et al., 1995, for Salmonella and E. coli, and Wright, 1989, for Salmonella and E. coli, in Fig. 5). The lowest E. coli inactivation rate of 0.03 d21 at the first inactivation phase has been reported for groundwater at temperatures from 4 C to 6 C (Cook and Bolster, 2007).

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0

Log C

I

II

Time

Figure 4 Three characteristic stages of the biphasic microorganism inactivation. Log C 5 Natural log of concentration. 10

log10 k1

1

0.1

0.01 0

5 10 15 20 25 30 35 40 Temperature, (°C)

Figure 5 Examples of microbial inactivation rates at the first phase of inactivation and their dependencies on temperature. —Bogosian et al. (1996), single strain E. coli in nonsterile river water, Missouri, —Chandran and Hatha (2005), E. coli in Cochin estuary water, India, —Wright (1989), E. coli, tropical stream, Sierra Leone, —Hellweger et al. (2009), single strain E. coli, sterile phosphate buffer solution, and —Flint (1987), below and above the sewage outfall, respectively, single strain E. coli, river Sowe, England; —Dutka and Kwan (1980), range of k1 values observed at four stations at the Lake Michigan; —Easton et al. (2005), k1 values for E. coli O157:H7 (top symbol) and generic E. coli —Mezrioui et al. (1995), E. coli, summer 85, autumn 85, winter (bottom symbol); 86, summer 86, E. coli single strain, stabilization pond system, Mediterranean; —Rhodes and Kator (1988), E. coli, Fox Mill Run stream, Chesapeake Bay, —Korhonen and Martikainen (1991), single strain E. coli, a lake in Finland, —Budzi´nska et al. (2009), Salmonella Seftenberg, water from the Borowno Lake, Poland, —Cook and Bolster (2007), E. coli O157:H7, groundwater, south central Kentucky. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this book.

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It has been suggested that the temperature dependence of the k1 rate can be simulated with the Arrhenius equation, transformed to the form k1 ¼ k1;20 θt 220

(1) 

where t is time, k1,20 is the rate of the exponential inactivation at 20 C, and θ is the temperature sensitivity parameter (Mancini, 1978). This equation can be transformed to log k1 ¼ log k1;20 þ ðt 220Þlog θ

(2)

that is, log k1 has to be linearly related to the temperature if model (1) is applicable. Data in Fig. 5 illustrate the applicability of this model. The model (1) works reasonably well where all inactivation experiments are conducted in parallel using the water collected from the same site (e.g., data of Bogosian et al., 1996, or Flint, 1987, in Fig. 5). However, model (1) does not work well when inactivation experiments are conducted with water from the same site but in different seasons (see data of Mezrioui et al., 1995, Rhodes and Kator, 1988, in Fig. 5, both datasets are shown with symbols connected with lines). Values of k1 also varied significantly when measurements were made at the same time but at different locations (see data of Dutka and Kwan, 1980, in Fig. 5). Therefore, model (1) should be corrected for composition of water in different seasons. The proper correction factor is currently unknown. The value of θ in Eqs (1) and (2) reflects the sensitivity of inactivation rates to temperature. It varies between 1.02 and 1.05 for data in Fig. 5. These numbers are substantially smaller than the value of 1.07 obtained by Mancini (1978) and used in watershed microbial modeling, for example, as the default value of the temperature adjustment factor (Parajuli et al., 2009). Nutrients are a substantial factor in determining bacteria survival in water. For example, differences in nutrient contents can explain variation in E. coli inactivation rates in river water above and below sewage outfall (data of Flint, 1987). McFeters and Stuart (1972) observed that E. coli could grow in autoclaved water taken below a sewage outfall but not above the sewage outfall, probably because of the differences in the nutrient content. Hutchison et al. (2005) reported an increase in the survival of E. coli O157:H7 when manure was added to water. In some circumstances, the value of pH can be a substantial factor in E. coli survival. Whereas pH in the range from 5.5 to 8 did not affect E. coli survival, very low and very high pH substantially decreased survival (McFeters and Stuart, 1972). Sjogren and Gibson (1981) demonstrated that a bacterium such as E. coli exhibits an increased potential for survival in aqueous environments by utilizing a proton gradient generated by a lowering of the pH.

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Sunlight is the most important inactivating factor in determining survival of fecal indicator bacteria, E. coli, and pathogens such as Salmonella typhimurium in surface waters. Populations of E. coli and S. typhimurium decreased by 3.5 logs and 4.5 logs, respectively, under direct sunlight in waters of the Cochin estuary (Chandran and Hatha, 2005). The inactivation rate of E. coli under direct sunlight was about an order of magnitude larger than in darkness in an irrigation pond (Maϊga et al., 2009); the value of the k1 rate constant was about 19 d21, which is much higher than anything shown in Fig. 5, which represents only experiments without direct sunlight. Inactivation of E. coli and enterococci declines with depth (Barcina et al., 1990; Maϊga et al., 2009; Murphy et al., 2010). The majority of published data is on the survival of enteric bacteria in saline as opposed to freshwaters. Since the light absorbance of freshwaters is substantially higher than that of seawater, caution should be exercised when using survival data from elevated salinity environments. An understanding of the mechanism(s) responsible for biphasic inactivation is currently lacking. The second phase (phase II in Fig. 4) can exhibit several behaviors relative to the first: faster decrease (Cook and Bolster, 2007), substantially slower decrease (Easton et al., 2005), essentially stationary (Dutka and Kwan, 1980), or even an increase indicative of growth (Hellweger et al., 2009). Some hypotheses have been put forward (Hellweger et al., 2009), but have never been tested. Phenotypic variants could explain biphasic inactivation, with sensitive cells killed and more resistant cells selected and eventually growing slowly. Indigenous biota of the water source can have multiple effects on microorganism survival. Both ameba and algae have been shown to enhance the survival of Campylobacter, a waterborne pathogen that otherwise has a very fast die-off (Axelsson-Olsson et al., 2010). Algae have been shown to harbor substantial populations of Salmonella (Byappanahalli et al., 2009) and Salmonella enterica and E. coli O157 have been reported to survive in protozoa, and be released from protozoal vacuoles as viable cells (Brandl et al., 2005; King et al., 1988). The water source ecosystem controls the level of nutrients and the degree of water transparency, and can prevent sunlight that causes inactivation (Dewedar and Bahgat, 1995). However, little is known about the water ecosystem as the environmental factor of indicator and/or pathogen microorganism survival. Different pathogens and/or indicator organisms can have substantially different inactivation patterns in surface waters. For example, Easton et al. (2005) reported that E. coli O157:H7 was inactivated about two times faster than generic E. coli. E. coli have been reported to survive longer than either Campylobacter jejuni (Cook and Bolster, 2007; Korhonen and Martikainen, 1991) or Streptococcus faecalis (Dutka and Kwan, 1980), while Y. enterocolitica typically survived much longer than E. coli (Boyle et al., 2008; Lund, 1996). In contrast, McFeters and Stuart (1972) reported that

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Salmonella survival was somewhat similar to that of E. coli in many natural settings. Shigella spp. have been found to survive longer than the usual indicators (McFeters et al., 1974). In untreated river water, the die-off of E. coli and enterococci was approximately 10 times faster than die-off of oocysts, but die-off rates of C. perfringens were lower than those of oocysts (Medema et al., 1998). These results are probably just a snapshot of the capabilities that exist among different strains of each organism and dependent upon the source associated with the strains. Responses of different microorganisms to environmental factors may also differ. For example, Salmonella spp. populations exhibited significantly less die-off and stress than did E. coli at water temperatures of ,10 C (Rhodes and Kator, 1988). In brackish water, the survival rate of S. typhimurium has been reported to be both higher (Mezrioui et al., 1995) and lower (Chandran and Hatha, 2005) than that of E. coli. Die-off rates specific for a pathogen in question have to be used for environmental estimates and modeling. Corrections for the stress factors have to be introduced. Overall, temperature, pH, availability of nutrients, and radiation are traditionally viewed as the leading factors of survival for both indicator and pathogenic microorganisms in natural waters and other environments. For a specific site, multiple regression equations can be developed to reflect the effect of these factors on survival (Fallowfield et al., 1999). This indicates an opportunity for the eventual development of a process model of indicator and pathogen survival in irrigation waters, which currently does not exist. However, there are probably additional, but less obvious, factors important in selecting specific pathogen variants (cells) based on fitness in unknown environmental niches.

5.2. Importance of environmental microbial reservoirs for irrigation water quality Irrigation systems are complex, and some of their compartments can serve as reservoirs where indicator and pathogen microorganisms can survive and multiply. The knowledge about these reservoirs is uneven. Whereas bottom sediments and bank soils have received substantial attention (Pachepsky and Shelton, 2011), aquatic biota as a pathogen reservoir in irrigation systems has only recently become a topic of research, and little if anything is known about biofilms in irrigation systems and drainage networks as pathogen reservoirs. Again, these are some additional factors that may select for different fitness characteristics among pathogens and relevant to water-related pathogen contamination of produce. 5.2.1. Bottom sediments It has been known for some time that substantial populations of pathogenic and indicator microorganisms are harbored in freshwater bottom

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sediments. Testing sediments to evaluate bacterial pollution was first proposed more than 100 years ago (Savage, 1905). Geldreich (1970) characterized bottom sediments as a reservoir for fecal pollution “fallout” from overlying water. However, the relative importance of sediments as bacterial habitats and as a source of waterborne FC and E. coli was not recognized until recent publications describing that resuspension of sediments, rather than runoff from surrounding lands, can create elevated E. coli concentrations in water (Pachepsky and Shelton, 2011). Various pathogenic microorganisms have been detected in freshwater sediments, including bacteria such as Mycobacterium avium (Whittington et al., 2005), Salmonella (van Donsel and Geldreich, 1971), Clostridium botulinum type E (Perez-Fuentetaja et al., 2006), Aeromonas hydrophila and Shigella spp. (Obasohan et al., 2010), and Campylobacter (Abulreesh et al., 2006); protozoa such as Cryptosporidium (Searcy et al., 2006a) and Giardia (Crokett, 2004); and various viruses (Miura et al., 2009). Nevertheless, much less is known about pathogens in sediments as compared with indicator organisms in sediments (Cahoon and Song, 2009). Extremely large variations have been recorded in microorganism concentrations in sediments from different sources as well as within a single stream or water body. Literature reports for values of E. coli concentrations in sediments vary from 1 to 500,000 MPN or CFU per g dry weight (Pachepsky and Shelton, 2011). Strongly asymmetric distribution functions have been found where replicate samples have been taken (Berry et al., 2007). It is not uncommon to find two to five orders of magnitude differences between maximum and minimum concentrations observed at the same site or in the same watershed. In addition, bacterial concentrations in sediments may quickly decrease up to half order of magnitude per cm (Garzio-Hadzick et al., 2010), with depth. Comparisons of microorganism contents in sediment and in the water column above it have inevitably led to the conclusion that sediments are the dominant reservoir for waterborne microorganisms. Numerous authors have observed that concentrations of FC in sediment are multiple-fold higher than that in the water column. Davies-Colley et al. (2004) analyzed data from agricultural streams in New Zealand and concluded that most of the time the water in the agricultural streams contained only a tiny fraction (about 1/1000) of the total FC contamination in the stream; the rest resided in the streambed from where it could be released by floods. Brandl (2006) noted that E. coli and S. enterica survive well in water sediments, and seasonal flooding of fields with overflowing stream water has been suspected for E. coli O157:H7 outbreaks associated with leafy greens (Cooley et al., 2007). Sediments and flooding are risk factors of potential crop contamination. Correlations between E. coli and FC concentrations in sediment and in the water column are usually not significant. At any given sampling site, the measured bacterial concentrations are derived from multiple

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locations upstream of the sampling site. An additional factor is stream depth versus sampling depth. Even during periods of sediment resuspension, if sediment-borne organisms are not distributed throughout the entire water column, water samples taken near the surface may not be representative of the total load. Finally, the diurnal oscillations in E. coli concentrations in stream water may be up to one and a half orders of magnitude (see Section 1). This difference may compromise correlations with E. coli concentrations in sediment, although nothing is known about the diurnal oscillations of E. coli concentrations in sediments. High variability of concentrations in time is characteristic for sedimentborne bacteria. In fact, this variability in conjunction with resuspension is a likely explanation for the erratic variations in indicator organism concentrations frequently reported in water quality monitoring (Chawla et al., 2003; Giddings and Oblinger, 2004). Temporal variability is due to the relative rates of bacterial growth/die-off and to episodic resuspension and redistribution of sediments due to rainfall events, while spatial variability is due to streambed heterogeneity. Seasonal dynamics in E. coli concentrations in sediments have been documented by several authors. Goyal et al. (1977) observed that the numbers for canal sediments were higher in winter than in summer and attributed these differences to lower die-off rates in winter months. On the other hand, Crabill et al. (1999) encountered three orders of magnitude of higher sediment FC concentrations in the summer versus the winter. The authors suggested the frequent flushing of sediments during the winter melt as a possible cause of the decrease of the sediment FC population in winter. Based on a large-scale survey of the Rio Grande basin, Hartke et al. (2005) found no E. coli O157 in sediments during the summer; however, its prevalence varied from 0% to 80% in fall, winter, and spring samples. The year-to-year variations in E. coli concentrations in sediment have been attributed to climatic conditions (Cinotto, 2005). The spatial variability of E. coli and FC concentrations has often been attributed to the differences in sediment particle size distributions. Regression analysis has confirmed a significant direct relationship between the percentage of clay and silt particles and FC and E. coli concentrations in estuarine and riverine sediment samples in Northern California (Atwill et al., 2007). Garzio (2009) observed an increase in sediment E. coli concentrations with increasing silt content in the sediment of a Maryland creek. The same trend was observed by Niewolak (1989) across 10 observation sites on a river in Poland. On the other hand, Cinotto (2005) reported the highest median concentration of E. coli in the 0.1250.5 mm size range of natural sediments. The contradictory reports on the effect of sediment texture on the size of E. coli and FC populations are probably related to the multiplicity of ways in which the particle size distribution can affect the persistence of these organisms. Coarse sediments may not

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provide sufficient protection from the environment to allow the persistence of a substantial concentration of bacteria; for example, with too much exposure, bacteria may be subject to the effects of sunlight inactivation or protozoan grazing. On the other hand, availability of nutrients may be better in coarse sediments (Cinotto, 2005). Differences in sediment organic matter contents have been postulated as a possible explanation of the spatial variation in E. coli and FC populations, with mixed results. No correlation between organic matter content and total FC concentration was found in streambed sediments from a tropical rainforest (Buckley et al., 1998). On the contrary, FC concentrations in the sediments were reported to be significantly higher in the presence of organic matter (Ferguson et al., 1996). Bacterial loads associated with human or animal presence and/or activities could, in some cases, be related to the elevated concentrations of E. coli in sediments. Crabill et al. (1999) observed that locations of creek recreational use (activity in water) coincided with increased concentrations of sediment-borne FC. This led the authors to the conclusion that recreational use served as the FC distribution system. Giddings and Oblinger (2004) suggested that the high E. coli densities at one of their sites was due to fecal contamination by domestic animal operations and home sites upstream, and the large area of sediment accumulation from upstream sources. The distance from the source of pollution affects concentrations of FC and E. coli in sediments when the source of fecal pollution is clearly defined. Goyal et al. (1977) observed an inverse relationship between FC in sediment and the distance from the sewage outfalls in the canals of the Texas West Coast. A similar strong dependence was documented by Haller et al. (2009) near the water treatment outlet at the Lake Geneva. Time spent by birds at the observation sites at the Chesapeake Bay strongly correlated (r 5 0.79) with FC concentrations in sediments (Hussong et al., 1979). FC numbers increased 100-fold in the sediments of water bodies following their colonization by water fowl in Poland (Niewolak, 1989). A wide range of E. coli die-off rates in sediments have been reported in the literature. Interpretation of these data, however, is problematic, since, as previously discussed, die-off rates are highly dependent on the ability of introduced strains to adapt to and persist in specific sediment habitats. In an early study by van Donsel and Geldreich (1971), the authors reported a 90% die-off of both FC and Salmonella spp. in 7 days in various sediments. By comparison, in the study of Davies et al. (1995), 85 days were required to reach 90% FC die-off. A common way to generalize data on FC and E. coli survival data in sediments is to use the exponential die-off model equation 1. The inactivation rates found in the literature vary significantly. The inactivation rates of E. coli in inoculated sediment from lakes of the Eastern United States

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were about 0.54 d21. Jamieson et al. (2004) reported a value of k1 5 0.15 d21 in sediments of Southern Ontario creeks. An FC inactivation rate constant of 0.07 d21 was published for sediment from Hillsborough River, FL (Anderson et al., 2005). The biphasic die-off (Fig. 4) of E. coli in sediments was demonstrated by Garzio-Hadzick et al. (2010). Die-off rates strongly depended on temperature, particle size distribution, and organic matter content in sediment in this work. E. coli survival has been observed to be longer in sediments than in the water column (Craig et al., 2004). Inactivation rates in sediment can be an order of magnitude lower than those for the water column ( Jamieson et al., 2004; Anderson et al., 2005). Decrease in temperature, increase in organic matter content, and increase in clay content, especially smectite clays, improve survival of bacteria in sediments. The effect that nutrients and pollutants in solution and sediment have on FC survival is uncertain. Nutrients can both support growth of the bacterium in question ( Jeng et al., 2005) and cause an increase in competition and predation (Banning et al., 2003). The presence of fine solid material devoid of biota may stimulate initial E. coli growth (Desmarais et al., 2002; Laliberte and Grimes, 1982). 5.2.2. Resuspension and settling Resuspension of sediments may account for much of the measured waterborne FC and E. coli during or shortly after rainfall events. The concentrations of E. coli and FC in streams during storm events are usually two to three orders of magnitude higher than in the baseflow conditions (Hunter et al., 1992). At least three resuspension mechanisms can act during the high flow events (Wilkinson et al., 2006). A steepfronted wave, with wave height much greater than the preceding water depth, can effectively suck organisms from the bottom sediment and hold them in the turbulent wave front. A less steep front or falling wave can lift organisms but not draw them in the wave overrun. Finally, the steady-flow stochastic erosion of bed and/or bank sources, resulting from high flow turbulence, can maintain FC concentrations elevated above those encountered at lowser rates of flow. Davies-Colley et al. (2004) described storm chasing studies that showed that FC pollution in streams typically peaks well ahead of stream flow peaks. This timing reflects bacteria being resuspended from sediments by accelerating currents rather than subsequent wash-in. The increase of E. coli concentrations in a water column has been noted due to sediment resuspension, which is unrelated to rainstorms. Phillip et al. (2009) recorded a fourfold increase in mean E. coli densities and a threefold increase in total suspended sediments in the water column due to the recreational wading in a small tropical stream. Recreational boating has been implicated in elevated E. coli concentrations in lake

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water (An et al., 2002). Irrigation water intake can cause substantial resuspension of sediment (Paulos et al., 2006) and become a possible reason for transport of microorganisms to fields. Association of microorganisms with sediment particles in soils is often assumed to be a function of the clay content. One available empirical equation for E. coli is Kd 5 1021.6 6 0.9CLAY1.98 6 0.7, where Kd is the partition coefficient, that is, the ratio of the associated concentration in cell g21 and concentration in water cell mL21, CLAY is the percentage of clay particles , 0.002 mm in soil, 2 , CLAY , 50 (Pachepsky et al., 2006). The schematic subdivision of resuspended bacteria into free-floating versus attached to individual solid particles may be misleading. Bacteria frequently persist in the environment in heterogeneously distributed microcolonies or biofilms. Very little is known about the relative stability of sediment biofilms. Pettibone et al. (1996) observed that a few large flocculated particles accounted for most of the volume of resuspended sediment-borne FC in a study conducted with waters from the Buffalo River before and after ship passage. Resuspension of flocculated, bacteria-laden particles affects bacteria transport and deposition as well as survival. Sediment flocs can be a dominant form of sediment transport in freshwater fluvial systems (Droppo and Ongley, 1994). Since bacteria are small and light, their settling rates are extremely low (1.6 m d21 as estimated by Cizek et al. (2008)). However, settling rates of sediment-associated bacteria are significantly higher due to the density of sediment particles (Gannon et al., 1983). The straightforward representation of settling in streams based on Stokes law, that is, as the pure effect of the effects of viscosity and gravity, has been recently questioned. Cooley et al. (2007) indicated that the hyporrheic exchange, that is, exchange through the subsurface volume of sediment and porous space adjacent to a stream, can lead to high rates of suspended particle deposition in sediment beds, even when the suspended particles are very small and have no appreciable settling velocity. On the other hand, Jamieson et al. (2005) reported that the calibrated settling velocities observed in their study were two orders of magnitude lower than the corresponding Stokes fall velocities. They suggested that the high shear stresses occurring near the bed limited the number of particles that can actually bond to the bed without being reentrained. Recently, modeling of bacteria release from sediment has been included in pathogen fate and transport modeling in streams and lakes (Bai and Lung, 2005; Cho et al. 2010; Jamieson et al., 2005). Modeling showed that an unrealistic surface input of E. coli may be needed to explain the dynamics of E. coli concentrations if the sediment resuspension is ignored (Kim et al., 2010). None of the existing models quantifies E. coli exchange between water and sediment due to sediment resuspension; the rate and impact of this process remain essentially unknown.

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Similarly, modeling of E. coli persistence in sediment has begun only recently (Rehmann and Soupir, 2009). The presence of bottom sediments containing large, unquantified reservoirs of fecal pollution indicator bacteria introduces substantial uncertainty in detection, monitoring, and control of microbiological water quality and stream impairment. 5.2.3. Bank soils Bank soils present yet another reservoir of microorganisms that can affect microbial water quality. Autochthonous (indigenous) E. coli have been found in soils of various environments, first in tropical (Byappanahalli and Fujioka, 1998), then in subtropical (Desmarais et al., 2002; Solo-Gabriele et al., 2000), and finally in temperate regions (Byappanahalli et al., 2006). In the latter work conducted with soils of several coastal Lake Superior watersheds, PCR-based DNA fingerprint analyses indicated that there was a 92% similarity/relatedness in E. coli genotypes that survived over winter in frozen soil. Growth at high temperatures (3035 C) and survival at medium temperatures (25 C) were also observed in this work. The distance from the water edge affects the density of E. coli populations in soils and beaches. Byappanahalli et al. (2003) noted that E. coli concentrations decreased rapidly with distance from the streambed. A similar pattern was reported by Desmarais et al. (2002) who observed one log difference in E. coli concentrations in soil at a distance of 90 cm from the edge of the water. E. coli appears to be able to utilize constituents of the soil organic matter for population support and growth (Tate, 1978). However, the presence of organic litter does not necessarily imply elevated populations of E. coli. Byappanahalli et al. (2003) surveyed banks of a stream in Michigan and found that E. coli was highest in relatively clean moist sands and much lower in litter-laden sandy soils. Soil water content affects the survival and regrowth of E. coli. The greatest survival of coliforms was noted with anaerobically grown cells amended to flooded soil as compared to moist soil in the work of Tate (1978). However, temporary drying did not eliminate the E. coli population in soil taken at 50 cm distance from the edge of water (Desmarais et al., 2002). Laboratory experiments of Solo-Gabriele et al. (2000) confirmed that, upon soil drying, E. coli is capable of multiplying by several orders of magnitude. The authors hypothesized that E. coli can survive at lower soil moisture than its predators, and therefore, upon soil drying, conditions are suitable for E. coli growth. Under this assumption, it is likely that the outer fringes of the channel banks, which experience the most extreme drying conditions, dominate the contribution of E. coli to the water column. All surface sources of irrigation water include bottom sediments and bank soils as reservoirs of microorganisms. These reservoirs affect water

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transported in irrigation canals and ditches. Water from subsurface sources or wastewater treatment plants is often stored in presence of sediments before being dispensed to fields. Available data show that bottom sediments and bank soils may have substantial effect on microbiological quality of irrigation waters. 5.2.4. Aquatic biota Aquatic ecosystems include a host of biota in addition to microorganisms. Some of these, in particular algae and amebae, can affect growth of pathogenic bacteria. Numerous studies suggest that bacteria and algae coexist in an association that benefits both groups of organisms (Carr et al., 2005). Cinotto (2005) studied an impoundment area in a Pennsylvania creek and observed that elevated aquatic growth resulted in sharp increases in E. coli concentrations from upstream to downstream throughout the impoundment area for 2 years in a row. In laboratory experiments by Ksoll et al. (2007), E. coli readily colonized periphyton (the mixture of algae, microbes, and detritus attached to surface) from the Lake Superior shoreline and persisted for several weeks; in addition, cells were released to the overlying water. Field data of these authors showed a significant linear relation between fecal coliform concentrations and periphyton ash-free dry weight. The bacterial pathogens Shigella, Salmonella, Campylobacter, and shiga toxin-producing E. coli (STEC) were recently found to be associated with Cladophora—a green alga growing in southern Lake Michigan (Byappanahalli et al., 2009). Aquatic flora in a sewage-impacted area of the southern Asian Gangetic riverine system exhibited significantly high levels of enterotoxigenic E. coli (Singh et al., 2010). Algae can enhance survival of microorganisms in surface and subsurface water sources in various ways, including the release of carbon substrates (Carr et al., 2005), protection from direct and indirect sunlight (Dewedar and Bahgat, 1995), and attachment to plant surfaces (Karim et al., 2004). Amebae have been shown to host a wide variety of bacterial species including many human pathogens such as Vibrio cholerae, L. monocytogenes, Mycobacterium spp., and Helicobacter pylori (Thomas et al., 2009). In addition to warm blooded hosts, Campylobacter spp. might use water bound organisms as hosts for survival and potentially for replication in the environment (Axelsson-Olsson et al., 2010). Pathogens have been transported by zooplankton (Grossart et al., 2010) and by other aquatic organisms. Although the ability of aquatic organisms to facilitate survival of pathogens and indicator organisms is proven, the relative importance of water biota as the reservoir of pathogen and indicator organisms in irrigation systems is currently unknown. Additional studies are needed to (i) document the occurrence of invertebrate-associated pathogens in relevant field conditions, such as distribution systems; (ii) assess the fate of microorganisms ingested by higher organisms in terms of viability and (or) infectivity; and

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(iii) study the impact of internalization by zooplankton on pathogen resistance to water disinfection processes, including advanced treatments such as UV disinfection (Bichai et al., 2008). 5.2.5. Biofilms in pipe-based irrigation water delivery systems There is currently not a universally recognized definition for biofilms. However, it is commonly agreed that a water distribution system biofilm consists of a complex mixture of microbes, organic, and inorganic material accumulated amidst a microbially produced organic polymer matrix attached to the inner surface of the distribution system (USEPA, 2002). Biofilm formation in irrigation systems is a well-known phenomenon. In particular, there has been a lot of attention paid to biofouling and clogging of drip-irrigation emitters (Cararo et al., 2006; Dehghanisanij et al., 2004; Ravina et al., 1992; Taylor et al., 1995). Several recent studies have revealed the potential for biofilms within drinking water distribution systems to harbor pathogenic bacteria (Septembers et al., 2007) and viruses (Skraber et al., 2005). However, no peer-reviewed research has been reported on biofilms as potential reservoirs of pathogenic microorganisms in irrigation distribution systems. Potential effects of biofilms on irrigation water quality can be inferred from the literature on pathogens in biofilms in drinking water distribution systems and from the literature on food safety. It has been demonstrated for drinking water systems that practically any microbe (including some pathogens) present in water may attach, or become enmeshed, in the biofilm. Pathogens, which cause disease in healthy humans, may persist in the biofilm. However, the survival time for many pathogens in biofilms is uncertain and likely varies depending on the organism. Biofilms may extend the survival of human and zoonotic pathogens by protecting them from disinfectants. These pathogens may be sloughed from the biofilm into the water column due to changes in the flow rate (USEPA, 2002). Biofilms may present a reservoir for pathogenic protozoa in their nonreproductive protective stage. Searcy et al. (2006b) observed the capture and retention of Cryptosporidium parvum oocysts in Pseudomonas aeruginosa biofilms using laboratory flow cells. Given the ability of Cryptosporidium oocysts to adhere to fresh produce (Macarisin et al., 2010) and the common presence of C. parvum oocysts in many sources of irrigation water (Thurston-Enriquez et al., 2002), biofilms as reservoirs of C. parvum oocysts in irrigation systems should be examined. In addition to the well-established water- and foodborne pathogens, infections may also be caused by other common microbes, referred to as opportunistic pathogens. Opportunistic pathogens include P. aeruginosa, Legionella pneumophila, and the M. avium complex (MAC). These microorganisms have attracted attention with respect to biofilms in drinking water system because they are well adapted to the low nutrient level and cool water temperature of water distribution systems (Lau and Ashbolt, 2009).

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These organisms can also survive in produce (Morris and Monier, 2003), and therefore the importance of these microbes populating biofilms in irrigation systems should also be evaluated. One consequence of pathogen accumulation in biofilms in irrigation pipes is the loss of indicator organism utility. Irrigation pipe biofilms may compromise the effectiveness of microbial indicators of water quality in two major ways (Geldreich, 1996). Firstly, incorporation of indicator organisms into pipe biofilms will result in higher concentrations at intake versus the actual irrigation stream. Secondly, because coliforms can grow and detach into the flowing water, concentrations may increase versus the intake. The following factors have been demonstrated to affect pathogen survival and growth in drinking water distribution systems: (a) environmental factors, (b) presence of nutrients, (c) microbial interactions, (d) pipe material, (e) system hydraulics, (d) disinfectant type and residuals, and (e) sediment accumulation (USEPA, 2002). All these factors undoubtedly can affect presence of pathogenic microorganisms in biofilms developing in pipe-based irrigation water delivery and distribution systems. The effects of environmental factors such as temperature and pH are probably organism-specific, and may depend on whether organisms attach and actually form biofilms, or adhere to an existing biofilm. LeChevallier et al. (1987) found a positive correlation of heterotrophic bacteria counts with both increased temperature and pH. Attachment of Salmonella enteritidis to stainless steel surfaces was larger at 20 C compared with 5 C and 37 C (Giaouris et al., 2005). Patel Sharma (2011) observed significantly higher attachment of E. coli O157:H7 on a spinach harvester blade when incubated at simulated temperature of California (30 C-day, 20 C-night) than at 22 C. On the other hand, a temperature increase in the range from 13 C to 42 C led to a decrease in the attachment of C. jejuni to stainless surfaces (Sanders et al., 2008). Temperature affected not only attachment but also the detachment of this bacterium to biofilms (Nguyen et al., 2010). The probability of detachment was significantly higher at 4 C than at 25 C for five of the six strains tested. The presence of nutrients enhances biofilm growth with carbon usually being the limiting factor (Mains, 2009). An influx of nutrients may not be beneficial for survival of a specific organism in a biofilm. Banning et al. (2003) observed reduction in E. coli survival with the increase in available nutrients and attributed that to either enhanced competition for nutrients or enhanced antagonism by the indigenous microbial population. The ability of pathogens to form biofilms may depend on the composition of the microbial population. Klayman et al. (2009) reported that E. coli O157:H7 required P. aeruginosa as a colonizing partner to adhere and persist in a capillary flow cell. Habimana et al. (2009) observed that the initial attachment of L. monocytogenes to biofilms was dependent on the genetic background of bacteria that formed the biofilm; some biofilms reduced the ability of L. monocytogenes to attach.

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Pipe material can be a strong factor controlling biofilm formation. Metal pipes present better substrates compared with PVC pipes (Silhan et al., 2006), although the release of organic components from PVC pipes may contribute to biofilm persistence at later stages. Sediments, or loose deposits, have been claimed to harbor pathogens in drinking water systems (Rubulis et al., 2008). Other components can also support biofilm growth, including materials used in valves, gaskets, washers, pump lubricants, and pipe coatings (Mains, 2009). The effects of hydraulic regime on biofilm development and pathogen survival in water distribution systems vary widely. Extreme flow regimes appear to cause substantial changes, for example, low velocities may result in stagnant water, which facilitates microbial growth (Reynolds et al., 2008; Vo¨lker et al. (2010). High water velocities may increase the level of nutrients in contact with the biofilm, on one hand, and also cause greater shearing of biofilms that may contain pathogens from the pipe surface, on the other hand (O’Toole et al., 2000). The complexity of drinking water delivery systems causes differences in opportunities for biofilm formation in different parts of the pipe network. This also may be true for irrigation water delivery systems, although they presumably have simpler configurations than drinking water systems. Although biofilm growth and shear removal have been simulated in simple pipe rigs (Eisnor and Gagnon, 2003; Maier, 1999), a simple relationship between the hydraulic effects and microbial growth in biofilms has not been reported or does not exist (USEPA, 2002). All factors of biofilm formation and microorganism survival in biofilms found in drinking water distribution systems are represented in irrigation water delivery systems. However, these two types of pipe-based water delivery systems have quite different parameters, including temperature and pH, nutrient contents and proportions, microbial communities, pipe and sediment material, hydraulic regimes, and disinfection techniques. The biofilm formation from creek water has been shown to exhibit seasonality that could not be explained by any measured water quality parameters (Wolyniak et al., 2010). Therefore, the knowledge base on biofilms accumulated in works for drinking systems conditions has to be evaluated with respect to its applicability for irrigation systems. Currently, irrigation systems lack any credible information on biofilm formation and pathogen survival and release in such biofilms.

6. Management and Control of Produce Contamination with Pathogens from Irrigation Waters Several strategies have been proposed to reduce the risk of produce contamination with pathogens during irrigation. Figure 3 provides an

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overview of possible directions for irrigation water quality control. A decrease of pathogen inflow from direct input sources (runoff, direct deposition, infiltration and lateral flow in shallow soils, sewage discharge) and/or from pathogen reservoirs (bottom sediment, bank soils, algae and periphyton) presents a possible strategy for microbiological water quality control. Treating water during storage, between storage and delivery systems, and water while in the delivery systems represents another class of strategies. Changing the irrigation method may affect the pathogen availability to plants. Finally, manipulating irrigation schedules and concurrent use of irrigation waters of different quality can help to reduce the risk of produce contamination with pathogens. Decreasing direct inputs of pathogens to irrigation water sources can present practical ways to improve the irrigation water quality. Estimates show that, at least in some cases, the pathogen input from wildlife can be much smaller than that from cattle and other domestic animals (Kim et al., 2010). In the latter case, improvement of microbiological water quality may occur due to preventing animals from entering streams by fencing (Miller et al., 2010) or using off-stream water sources—setting water troughs in riparian areas (Clawson, 1993), being aware of upstream use and sources of water that are planned to be used in irrigation (Cooley et al., 2007), and preventing manure runoff from fields, pastures, and feedlots to irrigation water sources as a part of good agricultural practices. Constricted wetlands have been shown to be efficient in decreasing fecal indicator concentrations in agricultural return flows that exceeded Californian standards for water discharge into state waterways (Diaz et al., 2010). It has been noted that contradictions may arise between goals of keeping pathogens away from irrigation water by removing buffer strips that harbor wildlife populations and preserving water resources from impairment due to runoff and erosion (Crohn and Bianchi, 2008), and those may need to be reconciled. Animal husbandry practices can affect the amount of pathogens in manure and possibly mitigate the microbial pollution of irrigation waters from runoff. For example, management practices have been recommended that limit the probability that feedlot cattle shed Giardia duodenalis in their feces (Hoar et al., 2009), the duration of Salmonella shedding and dissemination by the manure removal methods (Kabagambe et al., 2000), dietary interventions affecting E. coli O157 shedding in cattle ( Jacob et al., 2009), etc. Unless runoff enters environmental reservoirs suitable for pathogen growth, the decrease in their concentrations in runoff will decrease the risks of produce contamination. Treating water during storage employs inexpensive methods. In warmer regions, waste stabilization ponds, waste storage and treatment reservoirs, on-farm sedimentation ponds, and filtration through sands and soils are widely used (Keraita et al., 2010) and some of them have been

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proven to decrease the populations of pathogens (Maϊga et al., 2009; Mara and Silva, 1986). In temperate regions, a similar technology combined with mixing was shown to be promising—if a shallow reservoir was well mixed to prevent gradients in temperature and dissolved oxygen, storage of irrigation water for a period of at least 2 weeks would reduce bacterial numbers by at least three logs during the growing season in Nova Scotia (Murphy et al., 2010). Eliminating or avoiding environmental reservoirs of pathogens at the irrigation water intake can be very efficient. Since sediments are substantial microbial reservoirs (see Section 5.2.1), water intake without disturbing sediments has the potential to improve the microbiological quality of irrigation waters (Ensink et al., 2006; Keraita et al., 2010). Algae and periphyton can serve as microbial reservoirs (Section 5.2.3); as a result, some good agricultural practices include elimination of litter and algae in ponds used as an irrigation water source (Anonymous, 2010a, 2010b). Several technologies have been recommended for disinfecting water after it is taken from storage and before it is sent through the water delivery system to plants. For example, filtration, oxidation reduction, for example, chlorination, ozonation, exposure to UV light, electronic beam processing, and heat treatment, or “heat pasteurization” all can potentially reduce the levels of microorganisms in irrigation water (Newman, 2004). The costs of the treatments may vary (USEPA, 2010) and can be prohibitive. Greenhouses and nurseries are primary adopters of those technologies. The evaluation of the site-specific applicability of any of those methods should be concerned about maintenance costs, safety, and biological effects on crops and humans, among other things. For example, chlorine is the disinfecting agent most commonly used for combined sewage overflows and it decreases turbidities to a level similar to that of irrigation water. However, the effectiveness of chlorine is reduced in water with high levels of organic matter; chlorine can react with organic matter to yield potential carcinogens, and the long-term effects of chlorine and other disinfectants on crops and soil are unknown (Steele and Odumeru, 2004). Biofilm control in irrigation water delivery systems is currently an uncharted territory. Although little doubt exists about pathogen- and indicator-populated biofilm developments in pipes and other parts of irrigation systems, the efficiency of measures to control biofilms in these systems is unknown. Successful biofilm control programs incorporate multiple approaches, such as system flushing, line replacement, nutrient level reduction, possible combinations of several disinfection methods, corrosion control, and filtration (Mains, 2008). It remains to be seen how these components can be selected and arranged in an efficient program to prevent the persistence of biofilms as pathogen reservoirs in irrigation systems.

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Concurrent use of irrigation waters of different quality appears to be efficient when the availability of good-quality irrigation water is limited. The high-quality water is directed to irrigate produce, whereas the lowquality water is used for forage crops. This approach, sometimes called crop restriction, has been recommended and successfully tried in Canada (Anonymous, 1988), United States (Anonymous, 1992), Mexico (Cifuentes et al., 2000), and Chile (Westcot, 1997). Certification programs have been suggested to provide produce labels indicating that it was produced under safe conditions as a means of avoiding low compliance rates in crop restriction programs (Anonymous, 2002). Steele and Odumeru (2004) suggested that an alternative to crop restriction can be using water of low and high quality in the beginning and at the end of the growing season, respectively. They indicated that caution should be exercised when using this approach, because data regarding survival patterns of pathogens in soils and plants are not definitive. The interval between final irrigation and harvest influences the extent of contamination, as pathogens have been shown to decline with time following cessation of irrigation before harvest. Proposed first for developing countries (Shuval et al., 1986), the practice of irrigation cessation prior to harvest has become a popular recommendation in developed countries. A report to the UK Food Safety Agency indicated that “in the context of this review of food safety issues, the most important factors relating to irrigation scheduling are the amount of water which may be applied to a particular crop, and the interval between the final application and harvest.” The amount of irrigation will influence the potential loading of pathogens on the crop in the event that pathogens are present in the water. The “harvest interval” indicates the time available for natural die-off of pathogens to occur (Groves et al., 2002). The QMRA for irrigation waters (Section 4.3) uses the harvest interval as the essential input variable. However, knowledge about the effect of harvest interval on the produce microbial contamination is far from sufficient and should include several site-specific variables. Keraita et al. (2007) concluded that the measure can significantly reduce fecal contamination of lettuce during the dry season, but it is not suitable for the wet season due to favorable conditions for pathogen survival and recontamination by splashes from contaminated soils. The irrigation method may affect the pathogen availability to plants (Fonseca et al., 2011). In particular, subsurface drip irrigation was shown to have potential in decreasing health risks when microbial-contaminated water was used for irrigation (Enriquez et al. 2003; Song et al., 2006) although it was not always the case (Moyne et al., 2011). These reports relate to studies done in the arid zone, and a comparison of transmission of pathogens from irrigation water to vegetables with different types of irrigation is needed. The use of drip irrigation is limited with irrigation water with high turbidity, and other water delivery methods, for example, surface irrigation, may be of more benefit (Solomon et al., 2002a).

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Overall, a number of strategies have been proposed and tested to control the microbiological quality of irrigation waters. Monitoring of microbiological water quality is the essential part of application of any strategy or combination of strategies for minimizing produce contamination. Definitive site-specific data showing the effectiveness of interventions combined with incentives for their adoption will improve the microbiological quality of produce irrigation.

7. Research and Development Needs Previous chapters demonstrate that, given the current level of the public awareness about the risks of microbiological produce contamination by irrigation water, the state of knowledge and regulations in this field are far from satisfactory. Producers, regulators, and consumers simply do not have the information to make informed decisions about sitespecific risks of microbiological contamination of produce. The sanitary quality of many irrigation water sources is unknown currently (Suslow, 2010). Moreover, how to determine and characterize the microbiological quality of irrigation water sources are subjects of recurrent debate. A monitoring protocol is needed that is based on the actual spatial and temporal variability of pathogen and indicator concentrations in irrigation water sources and in environmental reservoirs. Some research supports monitoring pathogen and indicator organism concentrations in watershed sediments, periphyton and algae based on the adherence of bacteria to flocs that provide nutrients and protection in aquatic systems and migration to the bottom of water columns (Droppo et al., 2009). Irrigation system-wide monitoring has to be designed based on the fact that even water that is clean at the source can become microbiologically polluted as it is stored and distributed. The site-specific value of monitoring indicator organisms accepted currently for assessing risk of encountering key foodborne pathogens (such as E. coli O157:H7 and Salmonella) needs to be reexamined (Suslow, 2010). Suitable indicator organism(s) that relate to presence of pathogens in irrigation water need to be investigated. Where irrigation of leafy greens occur with well water, for example, in Salinas Valley, CA, a monitoring study of suspect wells is needed with large volumes of water tested to identify whether there are dynamic pulses of bacteria, possibly fecal indicators, that reflect recharging of the aquifer with inferior water or some other factor. If pulses occur infrequently, they will be missed when a low volume of water is tested. The current focus on zoonotic pathogens and indicators of their presence appears to be insufficient and may be misguided in some cases.

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In particular, presence of other microorganisms such as opportunistic pathogens, Legionella and Mycobacterium, in irrigation water sources and irrigation systems may present substantial health risks. Currently, little is known about the occurrence of those pathogens in irrigation water and their epidemiology as related to consumption of irrigated produce. Amebas are another class of organisms that have recently been proven to be important for microbial water quality (Bukhari et al., 2010) and are expected to be present in surface water used for irrigation. No methods to measure populations of those organisms have been validated for irrigation water sources. Nothing is published about biofilms being pathogen and indicator microorganism reservoirs in irrigation water delivery systems. This is a critical issue given the role biofilms have been found to play in microbiological contamination of drinking water distributing systems. Biofilms protect embedded microbes against disinfection and most bacteria in water systems are attached to piping and other surfaces in the form of biofilms (Lazarova and Manem, 1995). Methods of biofilm detection in irrigation water delivery systems, practices to minimize their formation, and evaluation of the comparative efficiency of different disinfection technologies with regard to biofilms in irrigation equipment should be developed or adapted from other water system fields and validated. Disinfection methods for irrigation systems have not been rigorously evaluated, and the remediation costs for contaminated water prior to irrigation are listed as a knowledge gap (Suslow, 2010). Simple and inexpensive methods for improving the microbial quality of marginal irrigation water at the farm level need to be developed, tested, and demonstrated. The risk assessment methodology for produce irrigation from sources other than wastewater has not been developed. Whereas QMRA is the accepted technology to evaluate risks of the disease transmission via irrigation wastewaters and produce (Mara, 2010), irrigation from surface water sources relies mostly on indicator-based standards that have hardly any factual support and are not site-specific. Application of QMRA is needed to better understand the impact of low-level pathogen transmission (Gerba, 2009). Current assessments of microbiological quality of irrigation waters do not use any microorganism fate and transport modeling. Modeling can provide valuable insights given the current state of knowledge about irrigation water quality. Process-based models can provide reasonable estimates in absence of site-specific data, screen and evaluate various management practices with respect to their relative efficiency, and estimate the uncertainty in microbial concentrations of source waters that can be used in QMRA. Empirical models of regional and local relationships between weather patterns, pathogen concentrations in waters and disease rates (Haley et al., 2009) can be useful for predictive purposes as it is currently done for climate-sensitive diseases (Rogers et al., 2002).

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Some emerging issues in microbial quality of irrigation water may change the paradigm of preharvest produce safety and require increased attention. The ecology of microorganisms in irrigation water sources and in colonized plants may be relevant to understanding microbial contamination by irrigation of produce (Critzer and Doyle, 2010). Microbial community composition may be a better indicator of pathogen presence and survival in irrigation waters and in environmental reservoirs, as it has been in drinking water (Berry et al., 2006; Bichai et al., 2008). Climate change will affect the water resources availability and structure, and therefore will influence the microbiological safety of produce. A basic understanding of these factors currently is lacking. Overall, there is a recognized need to establish HACCP-based produce safety standard protocols for irrigation of fresh produce. Standards need to be developed for irrigation waters that are meaningful and reduce the risk of produce contamination taking into consideration the means of irrigation and the type of produce (Gerba, 2009). Substantial knowledge gaps exist, and a concerted effort by researchers and practitioners is needed to maintain food safety of fresh produce in an increasingly intensive food production system and limited and declining irrigation water resources.

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C H A P T E R T H R E E

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry of Humification Morris Schnitzer and Carlos M. Monreal Contents 1. Introduction 2. Criticism on Soil HS Research 3. Extraction of SOM 3.1. Extraction with dilute base 3.2. Other extractants 4. Analysis of SOM 4.1. In situ analysis of OM in whole soils 4.2. Analysis of OM in whole soils by 13C NMR 5. Analysis by Py-FIMS 5.1. Analysis of OM in soil extracts and whole soil by Py-FIMS 5.2. Py-FIMS analysis of HA 5.3. Py-FIMS analysis of FA 5.4. Py-FIMS analysis of humin 5.5. Py-FIMS analysis of whole soil 5.6. Summary of compound classes identified 6. Chemical Structure 6.1. The chemical structure of SOM 7. Chemical Characteristics of HS 7.1. Analytical characteristics of HAs and FAs 7.2. Infrared and Fourier transform infrared spectrophotometry 7.3. Oxidative degradation of HAs, FAs, and humins 7.4. Reductive degradation 8. Spectrometric and Spectroscopic Characteristics of HS 8.1. 13C NMR spectrometry of HS 8.2. Effect of hot acid hydrolysis on the 13C NMR spectrum of HA 8.3. Curie-point pyrolysis-gas chromatography-mass spectrometry of HAs 8.4. X-ray analysis of FA

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Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Center, Ottawa, ON, Canada Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00003-8

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8.5. A 2-D structure for HA 8.6. A 3D structure for HA 8.7. Relationships between HA and SOM 8.8. The corrected 2D HA model 8.9. A 3D model structure for SOM plus water 9. Effect of Time on the SOM Structure 9.1. Effect of long-term cultivation on the SOM structure 10. New Concepts on the Chemical and Microbial Synthesis of HAs and SOM 11. Microbial Humification of Small Organic Compounds into Soil PKs 11.1. A complex biological link to the chemistry of humification 11.2. PKs in nature 11.3. Some chemical and functional features of PKs 11.4. Chemical analysis of PKs in soils 11.5. Biosynthesis of PKs 11.6. Ecological function and associated genetic evolution of PKs 11.7. Plant PKs 11.8. Microbial PKs 11.9. PKs in soils 11.10. A microbial PKSs model for studying biotic humification in soils 12. Thermodynamic, Energy, and Kinetic Considerations 13. PKs and the Central Structure of HS and SOM 13.1. PKs as a passive SOM pool 13.2. Biotic humification process forming the CUS of HS and SOM 14. Future Research References

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Abstract Soil organic matter (SOM) is the substrate and habitat of soil microorganisms and fauna. These biotic pools, along with inorganic soil components, contribute to the degradation and synthesis of humic acids (HAs) and SOM through chemical and biochemical reactions. During the past 225 years, most researches on the molecular structure and reaction of HA and SOM were done by soil chemists, with limited contribution from soil microbiologists. This may be one reason why progress in our knowledge of the molecular structure of SOM and HA has been slow. Closer cooperation between chemists and microbiologists would have been certainly beneficial. In this chapter, we summarize current knowledge of 2D and 3D molecular structures and propose new chemical reactions to synthesize HAs and SOM. We indicate how the application of recent advances in microbial biochemistry could assist soil chemists in their work on elucidating the molecular structures of HAs and SOM. We show that the continuous production of complex polyketides (PKs) from small soil oxoacids by soil microorganisms and associated polyketide synthases catalyzes the second biotic stage of humification in soils. The PKs involve complex alkylaromatic, aromatic, polyaromatic,

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phenolic, and polyphenolic structures. Due to their bioactivity, rapid adsorption to clay colloids, and high energy content in their chemical bonds, biologically and chemically formed PK structures represent kinetically passive soil carbon pools, and so lend themselves as carbon skeletons that contribute to the formation of a stable central unit structure in humic substances and SOM. Keywords: Humic acids; soil organic matter; chemical structure; synthesis; polyketides; biotic humification

1. Introduction Soil organic matter (SOM) is a complex dynamic system whose chemical, microbial, and biochemical components change over time and space, depending on several abiotic and biotic factors, organic residue inputs and on their degree of association with inorganic components. Functional outcomes of soil biotic and abiotic factor interactions may be diverse and represented simply as a response to altered nutrient availability for plant uptake or as complex as the chemical communication between individual soil microbial cells and plants. These outcomes may also include the sequestration of atmospheric C in SOM as a result of anabolic reactions during microbial cell growth, or the microbial humification of simple soil organic molecules into alkylaromatic, polyaromatic, or polyphenolic structures and subsequent secretion into soil solutions to antagonize and compete with other species. Soil animals and microorganisms, intracellular and extracellular enzymes, and inorganic surfaces are the catalysts that continuously process, modify, and bind residues into small molecules and metabolites of plants and microorganisms into humic substances (HS) (Huang and Hardie, 2009). Over the years, there have been many definitions of HS. For example, Aiken et al. (1985) defined HS as “a category of naturally occurring, biogenic, heterogeneous organic substances that can be generally characterized as being yellow-to-black in color, of high molecular weight, and refractory.” An alternate definition indicates that HS are “a category of naturally occurring materials found in or extracted from soils, sediments, and natural waters. They result from the decomposition of plant and animal residues” (MacCarthy, 2001). In this article, we define HS as a portion of the total SOM that is extracted and solubilized with dilute alkali (0.1
0.5 M NaOH or KOH). The alkaline extract is usually partitioned into three fractions, which are humic acid (HA), fulvic acid (FA), and humin. Detailed definitions of the three fractions are found in Section 3.1. Several biotic and abiotic factors and processes are involved in the formation of humus from soil organic residues. Briefly, humification

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of soil organic residues proceeds in two stages: the first stage involves physical fragmentation, microbial and enzymatic depolymerization, and decomposition of plant and microbial fragments and polymers into simpler structural or molecular units by microbes and enzymes (Kobayashi et al., 2001; Rehm, 2010). The second humification stage (i.e., synthesis) involves transformation of simple molecular units into large-size molecules through processes catalyzed by biotic agents and mineral surfaces (Huang and Hardie, 2009). Conceptual precursors and ways of HS formation, including the modified lignin pathway, have been presented and discussed by several workers over many decades (Flaig, 1988; Huang and Hardie, 2009; Kononova, 1966; Stevenson, 1994; Waksman, 1936; Wershaw, 2004). Conversely, Piccolo (2001) proposed that HS are supramolecular associations of self-assembling heterogeneous and relatively small molecules, derived from the degradation and decomposition of dead biological material. In this supramolecular association model, weak dispersive forces instead of covalent linkages would stabilize the HS structure. The supramolecular model is proposed as a better representation of the actual state of SOM in soil extracts than the humic polymer models (Wershaw, 2004). Lately, Kleber and Johnson (2010) made an attempt to discredit the scientific concept of organic matter (OM) humification in soils, mostly through opinions and partial interpretations of scientific information on HS published during the last 20 decades. Over the last two decades, research on soil HS and SOM has focused to a large extent on scientific studies dealing with their chemical structure and the effects of inorganic colloids on the polymerization of single organic molecules (Liu and Huang, 2002; Schnitzer and Schulten, 1992, 1998). In spite of all scientific efforts conducted thus far, the basic chemical nature, biosynthetic pathways, and reactivity of HS and SOM are still poorly understood. Further, little information exists on the role played by soil microorganisms and associated enzymatic components and processes in the synthesis of humus and decomposition of humified soil materials. It appears, in part, that the limited existing knowledge on the biology (i.e., microorganisms, enzyme complexes, and gene expression) involved in humification processes of SOM has led some scientists to discredit the existing and past definitions and theoretical pathways of SOM humification (Kleber and Johnson, 2010). The aim of this review article is to demonstrate that: (a) the published information on the chemistry of HS is based on the use of rigorous scientific methods that helps elucidate 2D and 3D molecular structure of HS and SOM; and (b) the biosynthetic processes controlling HS formation occur in soil ecosystems.

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2. Criticism on Soil HS Research In a recent review on SOM, Kleber and Johnson (2010) make two points on which we want to comment: (1) The authors severely criticize the extraction of HS from soils by dilute alkali, mainly 0.1 or 0.5 M NaOH solutions. They quote Waksman (1936) implying that HS obtained by alkaline extraction are chemically and physically different from organic materials actually occurring in soils (p. 92, lines 35
37). On p. 93, they interpret Baldock and Nelson (2000), as well as Waksman(1936), as having postulated that the materials represented by alkaline extraction and commonly defined as HAs do not exist in nature. Over the years, a number of objections have been raised against the use of alkaline solutions. Stevenson (1994) lists the following: (1) silica is dissolved from the minerals; (2) protoplasmic and structural components of fresh organic tissues are dissolved and mixed with humic extracts; (3) auto-oxidation of some organic components may occur when extracts are allowed to stand in contact with air for extended periods of time; and (4) other chemical changes can occur in the alkaline solutions, including condensation between amino acids and CQO groups of reducing sugars and quinones. Criticisms (1) and (2) are of minor importance with respect to the chemical structure of HS. Possible changes (3) and (4) can be minimized by doing the extractions under N2. On p. 93, fourth paragraph, Kleber and Johnson (2010) state that “structural observations on alkaline extracts are not representative of SOM because they are chemically altered organic moieties and no longer in situ components of SOM.” On p. 120, line 33, they write: “the products of the alkaline extraction procedures (HS) are different from natural OM and should not be used as proxies for OM dynamics.” In spite of the objections raised by Kleber and Johnson (2010) and others against the use of alkaline solutions for the extraction of SOM from soils, dilute aqueous alkalis, especially 0.1
0.5 M NaOH solutions, have been, by a wide margin, the most efficient extractants since Archard in 1786 until today. Over 95% of all SOM researchers have used and are still using dilute NaOH solutions for this purpose, and their work has produced several thousand scientific peer-reviewed papers. Are we now going to throw rigorous scientific published articles into the waste paper basket and disregard this huge scientific literature? We hope not, because such action will leave us without any scientific facts and history and may result in a new and false start for SOM research. One objective of our write-up is to show that there is no need for such drastic and unwarranted action. The second point which concerns us about the review by Kleber and Johnson (2010) is the following: these authors have “understanding the molecular structure of SOM” in the title of their review article but fail to

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show any molecular structure in the text although the literature contains a number of partial, two- and three-dimensional structures of HA and SOM (Burdon, 2001; Schnitzer, 2000; Schnitzer and Schulten, 1995; Stevenson, 1994). A chemical or molecular structure tells us more about the elemental composition of a compound, its molecular weight, functional groups, its aliphaticity, aromaticity, and its reactivity, and possible origin and degradation and ecological functions than numerous pages of speculations and wishful thinking. Why are SOM specialists so reluctant to show and discuss chemical structures of HA and SOM? We plan to demonstrate in this write-up that chemical structures are important for a better understanding of HA and SOM chemistry. One of the major objectives of this chapter, however, is to summarize and interpret data obtained by analytical chemical methods, infrared spectrophotometry, chemical degradation, 13C NMR (nuclear magnetic resonance), chemical hydrolysis, X-ray analysis, and pyrolysis-soft-ionization mass spectrometry. With the aid of computational chemistry, the results of the analyses described above were converted to 2D and 3D HA and SOM structures, which are shown in this chapter. A second major objective is to demonstrate that HA, FA, and SOM have similar chemical compositions, therefore, showing that the criticisms of chemical artifacts during SOM extractions are unjustified. During the past 200 and more years, most of the researches on soil HS (HA, FA, and humin) have been done by soil chemists. In spite of the fact that in soils HS are substrates for microbial activity, soil microbiologists have contributed relatively little to structural studies on HS. We believe that this relationship needs to change so that both soil chemists and soil microbiologists work side by side to resolve structural and other problems associated with SOM. It is true that it is often difficult to distinguish between chemical and microbial reactions. For example, the decarboxylation of fatty acids to alkanes may be brought about by chemical or by microbial reaction, and this applies to many other reactions. The input of soil microbiologists will certainly facilitate the work of soil chemists in structural studies on HS.

3. Extraction of SOM 3.1. Extraction with dilute base The SOM content of agricultural soils usually ranges between 1% and 5% (w/w). In the soil, OM and inorganic soil constituents are closely associated so that it is necessary to separate the two before either can be

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investigated in greater detail. Archard (1786) was the first scientist to use dilute base, either 0.1
0.5 M NaOH or KOH, for this purpose. Since that time, dilute NaOH has been found to be the most efficient extractant for OM from soils from all over the earth’s surface. The alkaline extract is usually partitioned into three fractions which are HA, FA, and humin. The definitions of the three humic fractions accepted by most soil scientists are as follows: HA is that fraction of the alkaline extract which coagulates when the extract is acidified to low pH ( , 2.0); FA is the OM fraction that remains in solution when the extract is acidified, that is, it is soluble in both base and acid; while humin is that fraction that remains with the soil, that is, it is insoluble in both base and acid. As mentioned in the Introduction, a number of objections have been raised against dilute alkali as an extractant, but if the extractions are done under N2, some of these objections can be minimized.

3.2. Other extractants Neutral salts of mineral and organic acids have been used for the extraction of SOM, but yields are usually low. Bremner and Lees (1949) proposed the use of 0.1 M Na4P2O7 (sodium pyrophosphate) solution at pH 7. The action of the neutral salt is thought to arise from the ability of the anion to interact with polyvalent cations bound to SOM to form a soluble salt of OM as illustrated by the following reaction: RðCOOÞ4 Ca2 þ Na4 P2 O7 -RðCOONaÞ4 þ Ca2 P2 O7

(1)

where R is the OM without COOH groups. Alexandrova (1960) found that Na4P2O7 solution extracted not only HS but also organo-mineral complexes without breaking up nonsilicate forms of sesquioxides. Kononova (1966) reports that the efficiency of extractions with Na4P2O7 solution can be improved by raising the pH from 7 to 9 and increasing the temperature. Schnitzer et al. (1958) demonstrated that pyrophosphate was difficult to remove from the humic material during the subsequent purification. Other approaches have been employed, without too much success, for the extraction of OM from soils. These were treatments with chelating resins (Levesque and Schnitzer, 1967; Ortiz de Serra and Schnitzer, 1972). Attempts have been made to extract SOM with organic solvents, Hayes (1985) compared the extraction efficiency of 13 reagents which included dipolar aprotic solvents pyridine, ethylenediamine, organic chelating agents, ion exchange resins, Na4P2O7, and dilute NaOH solutions. The latter was found to be the most efficient and reproducible extractant. The danger of using organic solvents containing C and N for extracting

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SOM is that under these conditions C and N may be added irreversibly to the SOM, and so alter its composition and properties.

4. Analysis of SOM 4.1. In situ analysis of OM in whole soils In recent years, we have witnessed the rapid development of two analytical methods based on “state-of-the-art technologies,” which appear to be suitable for the in situ analysis of OM in whole soils. These are solid-state 13 C NMR spectrometry and pyrolysis-field ionization mass spectrometry (Py-FIMS). It was hoped that the use of these methods would tell us whether extraction of soils with dilute alkali would damage or modify the extracted OM as claimed by Kleber and Johnson (2010).

4.2. Analysis of OM in whole soils by 13C NMR One of the first solid-state 13C NMR analyses of whole soils has been described by Wilson (1987). This type of analysis requires that the soil contain at least 3.0% C and that the concentration of paramagnetic ions, for example, Fe31, in the soil be relatively low as not to interfere with the recording of acceptable 13C NMR spectra. According to Arshad et al. (1988), the C/Fe31 w/w ratio is an important indicator for obtaining satisfactory solidstate 13C NMR spectra of whole soils and particle-size fractions separated from them. If the C/Fe31 w/w is c1, the quality of the spectrum will be good; if the ratio is .1, a reasonable spectrum will be obtained, but if the ratio is ,1, the quality of the spectrum will be poor. The quality of the spectrum can be improved by reducing the Fe31 to Fe21 by dithionate and then removing it. Another option is to separate the soil into particle-size fractions and record 13C NMR spectra for each fraction. Another approach is to separate particle-size fractions by flotation or removal of paramagnetic metal ions (Arshad et al., 1988). Figure 1 shows solid-state 13C NMR spectra of: (A) the whole Ap horizon of the Culp soil, a loamy sand from northwestern Alberta, classified as a Gray Luvisol; (B) the silt and clay fraction separated from the Ap horizon of the Culp soil; (C) the nonmagnetic silt and clay fraction separated from the Culp soil; and (D) the magnetic silt and clay fraction separated from the Culp soil. The 13C NMR spectrum of the whole Culp soil (Fig. 1A) shows relatively strong signals due to CH3 and (CH2)n between 0 and 40 ppm, small resonances due to C in methoxyls and in amino acids (41
60 ppm), stronger signals arising from C in carbohydrates and in aliphatic structures

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73

30 173

(A)

129 153 73 32 24

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127 102 73

(B)

30 173

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84

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(C)

(D)

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Figure 1 Solid-state 13C NMR spectra of: (A) whole Culp soil; (B) silt and clay fraction separated from the Culp soil; (C) nonmagnetic silt and clay fraction separated from the Culp soil; and (D) magnetic silt and clay fraction separated from the Culp soil. (Source: From Arshad et al., 1988, Fig. 1, p. 593. With permission of the publisher.)

bearing OH groups (61
104 ppm), signals between 105 and 150 ppm (aromatic C), 151
170 ppm (phenolic C), and a more prominent resonance between 171 and 190 ppm (C in CO2H groups) (Schnitzer and Preston, 1986). The 13C NMR spectrum of the silt and clay fraction separated from the Culp soil (Fig. 1B) is characterized by rounded broad signals although the different components produce resonances with similar chemical shifts as Fig. 1A. The spectrum of the demagnetized silt 1 clay fraction (Fig. 1C) exhibits well-defined chemical shifts in the aliphatic, aromatic, and carboxylic regions. By contrast, the 13C NMR spectrum of the magnetic portion of the silt 1 clay fraction (Fig. 1D) is very poorly resolved and illustrates the adverse effect of paramagnetic Fe3 1 on the development of the spectrum. While at this time, the use of solid-state 13C NMR for the in situ analysis of SOM is somewhat limited by the restrictions outlined above, we believe that with future improvements in 13C NMR technology, these difficulties will be overcome.

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5. Analysis by Py-FIMS 5.1. Analysis of OM in soil extracts and whole soil by Py-FIMS While 13C NMR spectrometry provides information on the types of C present in SOM, Py-FIMS tells us what individual chemical compounds are volatilized under these experimental conditions, that is, it yields data at the molecular level. Py-FIMS is more sensitive than 13C NMR and is not subject to interference by paramagnetic ions and can be used for the in situ analysis of OM in whole soils. Schnitzer and Schulten (1992) investigated whether there were any significant differences in Py-FIMS spectra of OM extracted with 0.5 M NaOH solution and separated by the classical method into HA, FA, and humin and that of the untreated whole soil. Soil samples selected for this very purpose were taken from the Ap horizon (0
15 cm) of the Bainsville soil, a Haplaquoll, on the Central Experimental Farm in Ottawa. This soil had a pH (in water) of 6.3 and contained 2.43 w/w% C and 0.21 w/w% N.

5.2. Py-FIMS analysis of HA The Py-FIMS spectrum of the HA (Fig. 2A) shows that this material is rich in carbohydrates, phenols, monomeric and dimeric lignins, n-fatty acids, and N-compounds. Prominent n-fatty acids range from n-C14 to n-C34, with n-C24 and n-C26 fatty acids dominating. The presence of alkylbenzenes is indicated by m/z 540, 554, 568, 582, 596, 610, and 638 (C6H5  C33H67 to C6H5  C40H81, respectively). Weak signals due to n-alkyl diesters, also shown in the Py-FIMS spectra of the whole soil and the humin, are present. The intense molecular ions at m/z 59, 81, 83, 97, and 99 are N-compounds.

5.3. Py-FIMS analysis of FA The Py-FIMS spectrum of the FA (Fig. 2B) is also dominated by carbohydrates, phenols, and lignins. The signals at m/z 262, 276, and 290 appear to be due to hexa-, hepta-, and octa-methylphenanthrene. The presence of a series of n-fatty acids is indicated by m/z 382 (C26) extending to m/z 564 (C38). The FA is relatively rich in N-compounds as shown by strong signals at m/z 59, 67, 83, 97, and 109.

5.4. Py-FIMS analysis of humin The Py-FIMS spectrum of the humin (Fig. 3A) shows that the major components are carbohydrates, phenols, monomeric lignins, and alkyl esters. Other

153

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

84

59

(A)

100 96

80

126 110

396

60

284 312 67

208

168

40

138

340

368 424

228 256

452

Rel.abundance

20

480 508

50

150

100

200

250

350

300

400

450

540

568 596

550

500

600

638

650

700

750

110

100 84

(B)

124 126

80

138 59

60

96

168 178

40

208

262 280 276

67

20 382

50

100

150

200

250

300

350

564

400

450

500

550

600

650

700

750

m/z

Figure 2 Pyrolysis-field ionization mass spectrum of: (A) the Bainsville HA and (B) the Bainsville FA. (Source: From Schnitzer and Schulten, 1992, Fig. 3, p. 1815. With permission of the publisher.) 100

58

96

(A)

80 82

108 110

60

67 72

40

122

146

132

202 216 180

230

244 258 272

300 286

170

20

316

330

358

372

386 400

344

Rel.abundance

428

50 100

150

100

250

200

300

350

450

400

96

500

(B)

58

80 82

110

60 67

40

124 126 132

72

184 208 180 202 170

20

258 230

286 314

342

394 380

408 422

50

100

150

200

250

300

350

400

450

500

m/z

Figure 3 Pyrolysis-field ionization spectrum of: (A) the Bainsville humin and (b) the whole Bainsville soil. (Source: From Schnitzer and Schulten, 1992, Fig. 4, p. 1815. With permission of the publisher.)

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Morris Schnitzer and Carlos M. Monreal

significant compounds are m/z 330, 344, 358, and 372 which are C6H5  C18H37 to C6H5  C22H45 n-alkylbenzenes. Signals at m/z 386, 400, and 414 are cholesterol, campesterol, and B-sitosterol, respectively. Signals ranging from m/z 202 to 342 appear to be due to n-C10 to n-C20 alkyl diesters, while m/z 242 to m/z 424 is due to n-C15 to n-C28 fatty acids. The spectrum of the humin also shows the presence of methylnaphthalenes and a series starting with m/z 252 and extending to m/z 350 which we have so far not identified. The presence of N-compounds is indicated by m/z 59, 67, 81, and 93.

5.5. Py-FIMS analysis of whole soil This Py-FIMS spectrum (Fig. 3B) is dominated by carbohydrates, phenols, monomeric and dimeric lignins, and alkyl esters. Molecular ions m/z 394 and 408 indicate the presence of small amounts of n-C28 and n-C29 alkanes, whereas the weak signals at m/z 442, 456, and 470 appear to be due to C6H5  C26H53 to C6H5  C28H57 n-alkylbenzenes, respectively. This whole soil contains suberin-derived aromatics esters at m/z 446, 474, 502, and 530 (Hempfling et al., 1985). The signals at m/z 170 and 184 arise most likely from tri- and tetra-methylnaphthalenes, respectively, while m/z 178, 192, 206, 220, and 234 are due to phenanthrene, methyl-, dimethyl-, trimethyl-, and tetra-methylphenanthrene, respectively. The presence of N-compounds is indicated by m/z 59 (acetamide), 67 (pyrrole), 79 (pyridine), 81 (methylpyrrole), 93 (methylpyridine), 103(benzonitrile), 117 (indole), 131 (methylindole), and 167 (N-acetylglucosamine).

5.6. Summary of compound classes identified Table 1 lists a summary of compound classes identified by Py-FIMS in the HA, FA, and humin, all extracted from the Bainsville soil, and in the whole Bainsville soil. The compound classes identified in the four materials are qualitatively identical, although there are some quantitative differences in the data presented in Table 1. The latter data obviate the need for laborious extractions, separations and purifications. At then same time, we can conclude from an inspection of the four mass spectra in Figs. 2 and 3 that extraction with 0.5 M NaOH solution has not caused any measurable losses nor alterations in the different molecular compound classes identified. Py-FIMS is possibly the first and so far the only procedure currently available which allow soil chemists to do comprehensive molecular analyses without any pretreatment, that is, in-situ.

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Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

Table 1 Compound classes identified by Py-FIMS in the HA, FA, and humin fraction, all isolated from the Bainsville soil, and in the untreated whole Bainsville soil

a

Compound classes identified

HA

FA

Humin

Soil

Carbohydrates Phenols Lignin monomers Lignin dimers n-Fatty acids n-Alkylbenzenes Methylnaphthalenes Methylphenanthrenes N-compounds n-Alkanes

11a 11 11 11 1 11 1 1 11 1

11 11 11 11 1 1 1 1 1 1

11 11 11 11 1 11 1 1 11 1

11 11 11 11 1 1 1 1 1 1

Intensity of peak height: 1 1 , 20
40%; 1 , ,20%.

6. Chemical Structure 6.1. The chemical structure of SOM According to Hatcher and Spiker (1988), SOM is hypothesized to be formed by one of the following two pathways: (a) the degradation of plant and microbial biopolymers to form the central core or (b) condensation
polymerization reactions in which plant and microbial biopolymers are first degraded to small molecules which then repolymerize. In 1921, Fisher and Schrader suggested that lignin was the mother substance of SOM. It soon became apparent, however, that microorganisms played an essential part in the synthesis of SOM because the “lignin theory” could not account for its relatively high N content. To solve this problem, Waksman (1936) proposed that microbial-produced protein was chemically linked to microbial-modified lignin to form SOM. Another approach was advocated by Flaig (1964) who suggested that lignin was oxidatively degraded to simpler phenolic monomers, which then underwent oxidative polymerization to produce SOM. Along similar lines, oxidative polymerization of simple phenolic acids to SOM, catalyzed by enzymes, was proposed by Flaig et al. (1975), Sulflita and Bollag (1981), and by abiotic catalysis (Huang, 1990; Wang et al., 1986). Maillard (1913) suggested that SOM originated from interactions of reducing sugars with amino acids and amines, which produced brown to dark-brown polymers rich in N. According to Maillard (1913), SOM was formed by purely chemical reactions in which microorganism did not play a direct role except to produce sugars from carbohydrates and amino acids from proteins.

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Morris Schnitzer and Carlos M. Monreal

Burdon (2001) has criticized the Maillard reaction as a significant source of SOM because: (a) there are not sufficient concentrations of free monosaccharides and of proteins/peptides/amino acids in soils for this reaction to proceed at any great extent; (b) since the Maillard reaction proceeds best at high pH, alkaline soils should contain much more SOM than neutral and acidic soils, but this is not the case; and (c) the Maillard reaction produces aromatic N-heterocyclic compounds but these compounds occur at only low concentrations in arctic soils compared to temperate and tropical soils. If humification proceeded via the Maillard reaction, there should be similar concentrations of aromatic compounds in all soils. Jokic et al. (2005), disagreeing with Burdon (2001), provide evidence that the Maillard reaction catalyzed by δ-MnO2, which is common in soils and sediments, produces aromatic N-heterocyclic compounds which are the dominant N-structures in fossil fuels as well as amides, which are the dominant N-compounds in SOM and sediments. The role of δ-MnO2 as catalyst in the Maillard reaction is very important according to Jokic et al. (2005).

7. Chemical Characteristics of HS 7.1. Analytical characteristics of HAs and FAs The elemental composition and functional group content of a typical HA (extracted from the Ah horizon of a Haploboroll) and of a FA (extracted from the Bh horizon of a Spodosol) are presented in Table 2. A more detailed analysis of the data shows that: (1) the HA contains B10 w/w% more C, but 25 w/w% less O than the FA; (2) there are quantitatively smaller differences between the two acids in H, N, and S contents; (3) the total acidity and the CO2H content of the FA are significantly higher than those of the HA; (4) the FA is per unit weight richer in phenolic and alcoholic OH as well as in ketonic CQO groups than the HA, but the latter is richer in quinonoid CQO groups; (5) both materials contain relatively few OCH3 groups; and (6) the E4/E5 ratio of the FA is almost twice as high as that of the HA, indicating that the FA has a lower molecular weight than the HA.

7.2. Infrared and Fourier transform infrared spectrophotometry Infrared (IR) and Fourier transform infrared (FTIR) spectra of HA and FA show bands at 3400 cm21 (H-bonded OH), 2900 cm21 (aliphatic CaH

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

Table 2

157

Analytical characteristics of a Mollisol HA and a Haplaquod FA HA

FA

21

Element, g kg C H N S O Functional groups, cmol kg21 Total acidity COOH Phenolic OH Alcoholic OH Quinonoid CQO Ketonic CQO OCH3 E4/E6

564 55 41 11 329 660 450 210 280 250 190 30 4.3

509 33 7 3 448 1240 910 330 360 60 250 10 7.1

Source: From Schnitzer and Schulten (1998), Table 8-1, p. 155. With permission of the publisher.

stretches), 1725 cm21 (CQO of CO2H, CQO stretch of ketonic CQO), 1630 cm21 (COO21, CQO of carbonyl and quinone), 1450 cm21 (aliphatic CaH), 1400 cm21 (COO21), 1200 cm21 (CaO stretch of OH deformation of CO2H), and 1050 (SiaO of silicates). The bands are usually broad because of extensive overlapping of individual absorbencies. IR and FTIR spectra of HA and FA reflect the importance of oxygen-containing functional groups, that is, COOH, OH, and CQO in these materials. The latter groups appear to indicate the presence of polyketides in the HA and FA fractions (see section 11 in this Chapter). IR and FTIR provide useful information on the outer surfaces of HA and FA molecules and on reactions that occur at these surfaces with metals, pesticides, and other organics, but they tell us little about the “insides” of these molecules.

7.3. Oxidative degradation of HAs, FAs, and humins One of the most useful methods for obtaining information on the chemical structure of complex organic substances is oxidative degradation by a variety of methods. Oxidative degradations of methylated and unmethylated HS with alkaline KMnO4 solution have been carried out by Schnitzer and coworkers and have been summarized by Schnitzer (1978). The somewhat milder oxidation with alkaline CuO as well the sequential oxidation with CuOaNaOH 1 KMnO4 and with CuOaNaOH 1 KMnO4 1 H2O2 solutions has also been employed. HS have also been oxidized under acidic

158

Morris Schnitzer and Carlos M. Monreal

conditions with peracetic and nitric acids (Schnitzer, 1978). Other oxidants used included alkaline nitrobenzene, sodium hypochlorite, and H2O2 solutions. Degradations with Na2S and phenol have also been done (Hayes and O’Callagham, 1989). Major components produced by the oxidation of methylated and unmethylated HS from widely differing pedological and geographical origins under alkaline as well as under acidic conditions were aliphatic carboxylic, phenolic, and benzenecarboxylic acids (Figs. 4
6) (Schnitzer, 1978). The most prominent oxidation products were aliphatic mono, di-, tri-, and tetra-carboxylic acids, phenolic acids with between 1 and 3 OH groups and between 1 and 5 CO2H groups, and the tri-, tetra-, and penta-benzenecarboxylic acids. From the oxidation products identified, it appeared that in the initial HS the aromatic rings were linked by paraffinic CH3(CH2)14CO2H CH3(CH2)16CO2H CO2H (CH2)n, n = 0 – 8

O

R5

R2

CO2H

CH2 CH CH2

CO2H CO2H

R3

R4

CO2H

Figure 4 Major aliphatic oxidation products of HS. (Source: From Schnitzer, 1978, Fig. 7, p. 28. With permission of the publisher.)

CO2H

CO2H

CO2H CO2H

CO2H

CO2H

CO2H

HO2C

CO2H

CO2H CO2H CO2H

HO2C

CO2H

CO2H

CO2H

HO2C CO2H

CO2H

HO2C

CO2H

CO2H

HO2C

CO2H CO2H

Figure 5 Major benzenecarboxylic acid oxidative degradation products of HS. (Source: From Schnitzer, 1978, Fig. 8, p. 29. With permission of the publisher.)

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Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

chains (Fig. 7). On oxidation, the aliphatic C’s closest to the aromatic rings were converted to C’s of the CO2H groups which remained bonded to the rings, while the C’s in aliphatic chains were oxidized to aliphatic acids. The purpose of methylation prior to oxidation was to protect OH groups against attacks by electrophilic oxidants. This made it possible to isolate and identify phenolic acid in addition to benzenecarboxylic and aliphatic acids. The oxidation products from soil HS separated from soils from all the earth’s surface were remarkably similar. Two conclusions can be drawn from the oxidative degradation experiments: (1) isolated aromatic rings are important structural units in all HS CO2H

CO2H

OH OH

OH

HO

OH

CO2H

CO2H

CO2H

CO2H OH

OH CO2H

HO

CO2H

OH

OH CO2H

HO2C

CO2H

CO2H

HO2C

CO2H

CO2H

CO2H

CO2H

Figure 6 Major phenolic oxidation products of HS. (Source: From Schnitzer, 1978, Fig. 9, p. 29. With permission of the publisher.)

OH

OH

HO

OH OH

COOH

COOH

HOOC

COOH

HO

COOH

OH OH HOOC OH

OH

COOH OH

Figure 7 Chemical structure for HS based on oxidative degradation products. (Source: From Schnitzer, 2000, Fig. 12, p. 23. With permission of the publisher.)

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Morris Schnitzer and Carlos M. Monreal

and in SOM and (2) aliphatic chains are linking aromatic rings to form an alkylaromatic network.

7.4. Reductive degradation Compared to oxidative degradation of HS and SOM, reductive degradation has been less successful in obtaining structural information. The methods most widely used for this purpose were Na-amalgam reduction and Zn-dust distillation (Stevenson, 1994). Reduction of HS with Na-amalgam produced phenols and phenolic acids. On the other hand, Zn-dust distillation and Zn-dust fusion (Hansen and Schnitzer, 1969) yielded small amounts of methyl-substituted naphthalene, anthracene, phenanthrene, pyrene, and perylene. The methyl groups on these polycyclic rings were probably remains of longer alkyl chains which linked the polycyclics in HS and in SOM; or from microbial methylation during polyketide (PK) synthesis (see Section 11).

8. Spectrometric and Spectroscopic Characteristics of HS 8.1.

13

C NMR spectrometry of HS

One of the advantages of 13C NMR is that it indicates the presence in HS a wide variety of C types whose determination by other methods would either be laborious and time consuming or not at all possible. In this sense, the use of 13C NMR offers unique possibilities. Of considerable interest is a comparison of solid-state or CP-MAS (cross-polarization magic angle spinning) 13 C NMR spectra of HA (extracted with 0.5 M NaOH solution from the Ah horizon of a Haploboroll) and FA (extracted with 0.5 M NaOH solution from the Bh horizon of a Spodosol). Both extracts were freeze-dried prior to NMR analysis. The CP-MAS 13C NMR spectrum of HA (Fig. 8A) shows several distinct signals in the aliphatic (0
105 ppm), aromatic (106
150 ppm), phenolic (151
170 ppm), and carboxyl (171
185 ppm) C regions. The signals at 17, 21, 25, 27, and 31 ppm are due to alkyl C. The resonance at 17 ppm is characteristic of terminal CH3 groups and that at 31 ppm of (CH2)n in straight paraffinic chains. The peak at 40 ppm could include contributions from both alkyl and amino acid-C. The broad signal at 53 ppm and the sharper one at 59 ppm may arise from C in OCH3 groups, but amino acid-C may also contribute to signals in this region (Breitmaier and Koelter, 1978). Carbohydrates in HA would be expected to produce signals in the 60
65, 70
80, and 90
104 regions, although other types of aliphatic C bonded to O would also do so. The aromatic region

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

200

100

161

0

(A)

(B)

ppm

200

100

0

Figure 8 13C NMR spectra of: (A) a Mollisol HA and (B) a Haplaquod Bh FA. (Source: From Schnitzer and Schulten, 1998, Fig. 8-4, p. 158. With permission of the publisher.)

exhibits a relatively sharp maximum near 130 ppm, due to alkylaromatics. The signal at 155 ppm shows the presence of O- and N-substituted aromatic C (phenolic OH and/or NH2 bonded to aromatic C). The broad signal near 180 ppm is due to C in CO2H groups, although amides and esters could also contribute to this resonance. The CP-MAS 13C NMR spectrum of the FA (Fig. 8B) consists of a number of aliphatic resonances in the 20
50 ppm region, followed by signals from C in OCH3 groups and amino acids between 50 and 60 ppm and from carbohydrates between 61 and 105 ppm. The broad signal between 130 and 133 ppm indicates the presence of C in alkylaromatics. The strong signal between 170 and 180 ppm shows the presence of C in CO2H groups. In general, fewer sharper signals are observed in the spectrum of the FA than in that of the HA, possibly because of the occurrence of more H-bonding in the FA. The 13C NMR spectra in Fig. 8 show that the HA is slightly more aromatic than the FA, but the FA is significantly richer in CO2H groups, which appears to be the main difference between the two substances and accounts for the water solubility of the FA in contrast to the HA which is insoluble in water. Other differences are that the HA is richer in paraffinic C but poorer in carbohydrate C than the FA. It is noteworthy that the main structural features such as aromaticity and aliphaticity are similar. Little is known about the chemical structure of humin, which is that portion of SOM which is left behind after extraction of the soil with dilute alkali. Preston et al. (1989) deashed a humin fraction separated from the surface horizon of the Bainsville soil and allowed it to stand

162

Morris Schnitzer and Carlos M. Monreal

with an aqueous solution of HClaHF (1.16 M HCl 12.88 M HF) at room temperature for a prolonged period of time, changing the solvent at fixed intervals. With progressive deashing, the humin became more soluble in 0.5 M NaOH solution. After extensive deashing, the CP-MAS 13 C NMR of the residue and its elemental composition were identical to those of the HA extracted with 0.5 M NaOH solution from the same soil. This indicates the humin is HA bound strongly to minerals and metal oxides and hydroxide in the soil, so that instead of three fractions, SOM contains only two fractions, that is, HA and FA. As has been mentioned before, since in most soils FA represents ,10% of the alkaline extract, and because FA is essentially a partial oxidation product of HA, it is clear that HA is the major component of SOM.

8.2. Effect of hot acid hydrolysis on the spectrum of HA

13

C NMR

Figure 9A shows the solution-state 13C NMR spectrum of a HA extracted with 0.5 M NaOH solution from the Ah horizon of a Chernozem from Central Alberta, Canada (Schnitzer and Preston, 1983). To facilitate the analysis of the 13C NMR data, the spectrum was divided into the following regions: 0
40 ppm (C in straight chain, branched and cyclic alkanes, and alkanoic acids); 41
60 ppm (C in branched aliphatics, amino acids, and OCH3 groups); 61
105 ppm (C in carbohydrates and in aliphatics containing C bonded to OH, ether oxygens, or occurring in five- or six-membered rings containing O; 106
150 ppm (aromatic C); 151
170 ppm (phenolic C); and 171
190 ppm (C in CO2H groups). The 13C NMR spectrum of the HA after hydrolysis with hot 6.0 M HCl for 24 h is shown in Fig. 9B. This spectrum is simpler than that in Fig. 9A, with distinct maxima remaining only at 18.7, 25.1, 31.3, 40.0, and 58.7 ppm in the aliphatic region and at 131 and 180 ppm in the aromatic region. The 13 C NMR of the same HA, which had been hydrolyzed first by standing in contact with 12.0 M H2SO4 for 16 h at room temperature, and then refluxed with 0.5 M H2SO4 for 5 h, is shown in Fig. 9C. Note that curves 9B and 9C are very similar. In the aliphatic region of curve 9C, small signals can be seen at 16.5, 25.2, 31.4, 40.1, and 58.4 ppm. The persistence of the distinct signal at 58.7 in (B) and at 58.4 ppm in (C) as well as at 58.5 ppm in (A) suggests that this signal is due to C in OCH3 groups. Resonances at 56.2, 63.5, and 73.4 ppm in Fig. 9A are no longer present in the spectra of the hydrolyzed HA (Fig. 9B and 9C). Chemical analysis for amino-N and carbohydrates indicated that the two acids had removed most of the proteinaceous compounds and carbohydrates from the acid-treated HAs. On the basis of these observations, and from data published in the literature, we could now assign the following bands in Fig. 9A: 56.2 ppm (C in amino

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

200

100

163

0

131.8 31.3 16.5

121.6

63.5 73.4

40.2

25.0

58.5

180.4

(A)

130.6 31.3 25.1

40.0 58.7

179.3

(B)

131.2 31.4 40.1

25.2 16.5

171.0

58.4

178.3

(C)

75.2

ppm

200

100

0

13

Figure 9 C NMR spectra of (A) Chernozem HA; (B) the same HA hydrolyzed with 6 M HCl; (C) the initial HA hydrolyzed first with 12 M H2SO4 and then with 0.5 M H2SO4. (Source: From Schnitzer and Preston, 1983, Fig. 1, p. 204. With permission of the publisher.)

acids), 63.5 and 73.4 ppm (C in carbohydrates). One striking observation was that the relative intensity of the aromatic regions in spectra B and C was greater than that in spectra A, although the position of the maximum remained near 130 ppm. For the initial HA, the HCl-hydrolyzed HA, and the H2SO4-hydrolyzed HA (spectra A, B, and C), aromaticities were 41%, 62%, and 53%, respectively. Aromaticities were computed by expressing areas due to aromatic C (105
165 ppm) as percentage of the total area (0
165 ppm) but omitting contributions from carboxyl carbons (166
185 ppm). Acid hydrolysis also reduces the intensity of the carboxyl region (170
180 ppm). Area integration of the carboxyl regions (170
180 ppm) in spectra A, B, and C showed CO2H contents of 4.5, 3.0, and 3.1 meq g21, respectively. Thus, aside from lowering the CO2H by acid decarboxylation

164

Morris Schnitzer and Carlos M. Monreal

and by removing amino acids and carbohydrates, the hot acids also increase the aromaticity of the HA by removing aliphatic compounds such as amino acids and carbohydrates. To establish whether hot acid hydrolysis would affect other HAs and also FA in the same way as the Chenozemic HA, Schnitzer and Preston (1983) passed a HA extracted with 0.5 M NaOH solution from the Ah horizon of a Humic Gleysol and a FA extracted from the Bh horizon of Spodosol through the same procedure. The results obtained were identical to those described herein. Thus, hot acid hydrolysis removes proteinaceous materials and carbohydrates from HAs and FAs but leaves aliphatic and aromatic compounds intact. These findings may have important structural implications: (1) they suggest that proteinaceous compounds and carbohydrates are not structural compounds of HA, FA, or SOM but are adsorbed on their surfaces and in their voids. If they were structural components, they would have resisted dissolution by the hot acids; (2) the strong signal at 130 ppm in the 13C NMR spectra is due to C in aromatic rings not substituted by electrondonating O and N but by C, as in alkylaromatics, which indicates that the latter are very significant building blocks of HA, FA, and SOM.

8.3. Curie-point pyrolysis-gas chromatography-mass spectrometry of HAs Curie-point pyrolysis-gas chromatography-mass spectrometry (Cp
GC
MS) is a valuable method for structural studies on HS (Schulten and Schnitzer, 1992) because the transfer of thermal energy from the wire to the sample is fast with temperature rises on the order of milliseconds. The resulting thermal shock produces small, stable organic molecules as pyrolysis products whose identification is based on two independent data sets: (1) gas chromatographic retention times and (2) computer-assisted library searches of standard mass spectra libraries. Schulten and Schnitzer (1992) did Cp
GC
MS analyses of two HAs which had previously been examined by Py-FIMS. As shown in Table 3, the major compounds produced from the two HAs were benzene and alkylbenzenes, ranging from C6H6 to C6H5  C22H45 (docosylbenzene). Other compounds produced were naphthalene and alkylated naphthalenes and phenanthrene and alkylated phenanthrenes. The alkylaromatics identified in Table 3 consisted of aromatic rings which were covalently linked to aliphatic groups or chains, and these building blocks were released during pyrolysis from an alkylaromatic structural network that was made up of the constituents listed in Table 3. A chemical structure for a HA skeleton, without functional groups, based on alkylbenzenes is shown in Fig. 10. This structure contains voids of varying dimensions which can trap and bind organic and inorganic components.

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Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

Table 3 Building blocks of Bainsville and Armadale HAs identified by Curie-point pyrolysis GC-MS Intensity Armadale

Bainsville

Compounds

11111 11111 1111 1 11 1 1 1 11 1 11 1 11 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 111 1 11 111 1 11 11 11 11 1 1

111 11111 1111 1 11

Benzene Toluene Ethylbenzene, xylenes Ethylmethylbenzene Propylbenzene Butylbenzene Methylpropylbenzene Tetramethylbenzene Pentylbenzene Hexylbenzene Octylbenzene Methyloctylbenzene Decylbenzene Methylnonylbenzene Undecylbenzene Methyldecylbenzene Dodecylbenzene Methylundecylbenzene Tridecylbenzene Tetradecylbenzene Pentadecylbenzene Hexadecylbenzene Heptadecylbenzene Octadecylbenzene Nonadecylbenzene Eicosylbenzene Hemicosylbenzene Decosylbenzene Styrene Methylstyrene Indene Indane Fluorene Naphthalene Methylnaphthalenes Dimethylnapthalenes Trimethylnaphthalenes Tetramethylnaphthalenes Pentamethylnaphthalene

1 1 1 1 1 1

111 1 1 11 1 1 1 1 1

(Continued)

166 Table 3

Morris Schnitzer and Carlos M. Monreal

(Continued )

Intensity Armadale

1 1 1 1 1

Bainsville

Compounds

Phenanthrene Methylphenanthrene Dimethylphenanthrene Trimethylphenanthrene Tetramethylphenanthrene

Intensity of peak height: 1 1 1 1 1 , 80
100%; 1 1 1 1 , 60
80%; 1 1 1 , 40
60%; 1 1 , 20
40%; 1, observed. Source: From Schulten et al. (1991), Table 1, p. 312. With permission of the publisher.

(CH3)0-4

(CH3)0-5

(CH3)0-5

Figure 10 Chemical structure of HA based on alkylaromatic building blocks without functional groups. (Source: From Schulten et al., 1991, Fig. 1, p. 311. With permission of the publisher.)

An inspection of the data in Table 3 shows that Armadale HA produced a greater variety of alkylaromatics than the Bainsville HA. This may be related to differences in the origins of the two HAs. The Armadale HA was extracted from the Bh horizon of a Spodosol, about 25 cm below the soil surface while the Bainsville HA was extracted from the surface horizon of the Bainsville soil, a Haplaquoll. One of the striking features of a Spodosol Bh horizon is its low microbial activity, which may have led to a better preservation of the alkylaromatics listed in Table 3.

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

167

8.4. X-ray analysis of FA The X-ray diffraction pattern of a nonoriented flat powder specimen of a FA extracted with 0.5 M NaOH from the Bh horizon of a Spodosol exhibited a diffuse band at about 4.1 A˚, accompanied by a few minor humps (Kodama and Schnitzer, 1967). The experimental intensity curve was corrected for polarization and absorption. Figure 11, curve A, shows the corrected intensity curve as a function of (sin θ)/λ over the range 0.02
0.5. Curve A was normalized to the total independent scattering curve B for FA in such a way that curves A and B approached each other. Curve B was obtained by adding the independent coherent scattering curve C to the incoherent scattering curve D. Calculations for the independent coherent scattering curve C and the incoherent scattering curve D were based on C28H23O19 (the molecular formula of FA) and included contributions from all atoms. 8.4.1. Radial distribution analysis The radial distribution analysis of FA is based on the generalized Fourier method for polyatomic substances (Warren et al., 1936). For more details on how this method was used see Kodama and Schnitzer (1967). Figure 12 4 ×103 3

I, (e.u.)

(A)

2 (B) (C) 1

(D) 0

0.2

0.4

0.6

(sin θ)/λ

Figure 11 X-ray intensity curve for FA in electron units per C20H12(CO2H)6(OH)5(CO)2 (the elemental and functional group composition of FA). (A) The corrected intensity curve; (B) the total independent scattering curve; (C) the independent coherent scattering curve; and (D) the incoherent scattering curve. (Source: From Kodama and Schnitzer, 1967, Fig. 1, p. 90. With permission of the publisher.)

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8 ×104

Σ km 4πr 2 ρm (r)

6

4

2 16 700 0

6000 2

4

6

r (Å)

Figure 12 Radial distribution curve for FA. (Source: From Kodama and Schnitzer, 1967, Fig. 2, p. 91. With permission of the publisher.)

shows the existence of two peaks with maxima at 1.6 and 2.9 A˚ and two ˚. shoulders at 4.2 and 5.2 A Since FA resembles low-rank coals in a number of properties, it seemed worthwhile to compare our results with those for carbon black as reported by Warren et al. (1936). His curve showed four distinct maxima at distances of separation at 1.5, 2.7, 4.05, and 5.15 A˚, indicating that carbon black was not amorphous but consisted of graphite-like layers. These four distances are in good agreement with the respective peak positions for FA. The broadness of the peaks and the similarity of their positions as compared to carbon black suggest that FA has a considerable random structure in which carbon atoms are arranged in a manner similar to that in carbon black. More dedicated analysis of each peak is complicated because in addition to carbon atoms, oxygen atoms also form part of the structure of FA. Contributions of hydrogen atoms can be neglected because they constitute less than 10% of the total number of electrons. Interatomic distances of bond pairs according to the Interatomic Tables (Hypercube Inc., 1962) are 1.54
1.40 A˚ and 1.47
1.36 A˚ for a single bond or a partial double bond for carbon
carbon and carbon
oxygen, respectively. Assuming loose packing, the peak at 1.6 A˚ may include contributions from both atom pairs. The area under the peaks corresponds, therefore, to the sum of the electrons from carbon
carbon and carbon
oxygen bond pairs. One number
average molecular weight of FA contains 28 carbon atoms, 13 of which are either in functional groups or linked to the functional groups and may be diatomically bonded to

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19 oxygen atoms (Table 2). Assuming that the effective number of electrons is six per carbon atom and eight per oxygen atom, the average number of electrons due to carbon
oxygen bond pairs will be 2(13 3 6 3 19/ 13 3 8) 5 1824. Subtracting this number from the area of the first peak ( 5 6000 electrons, see Fig. 12) leaves 4176 electrons which can only be due to carbon
carbon bond pairs. The average number (n) of carbon neighbors nearest to each carbon atom can then be estimated from the relation 4176 5 2(28 3 6 3 n 3 6), so that n will be about 2.0. Contrary to carbon black, the number of first neighbors is smaller than 3, possibly indicating the presence of more nonaromatic carbon atoms. ˚ also agrees with this explanation. Analysis of the second peak at 2.9 A ˚ A distance of separation of 2.9 A may include oxygen
oxygen bond pairs as well as second carbon neighbors. The area under the second peak (516,700 electrons) must arise from both contributions. Assuming for purposes of simplification that all consists of carbon atoms arranged as in carbon black, then the average number of second neighbors should be 9, and this relation should require 2(28 3 6 3 9 3 6) 5 18,144 electrons. This value is larger than the observed second area of 16,700, even including contributions from the other bond pairs. This means that the average number of second carbon neighbors must be less than 9. All oxygen atoms in FA are in functional groups (Table 2). Assuming that each of these never meets more than two other oxygen atoms at a distance of ˚ , the contributions due to carbon
carbon bond pairs would range 2.9 A between 11,500 and 14,200 electrons, corresponding to B6 or 7 second carbon neighbors. These data suggest that the carbon skeleton of FA is a broken network rather than a continuous sheet as in graphite. Unlike coal, FA has no distinct 0.02 band which normally occurs in the 3.43
3.75 A˚ region, depending on the carbon content of the coal (Hirsch, 1954). The position of the diffuse band at 4.1 A˚ is close to that of the γ-band (Blayden et al., 1994). Thus, according to the interpretation of the γ-band by Curtz et al. (1956), the diffuse band of FA at 4.1 A˚ tells us that the structure of FA consists of irregularly packed poorly condensed aromatic layers and of appreciable numbers of disordered aliphatic chains or alicyclic rings at edges of the aromatic layers, in which a relatively uniform spacing of about 4 A˚ is maintained between layers of chains and rings. In general, this interpretation is consistent with the structure of FA revealed by chemical and spectrometric studies reported in this chapter. 8.4.2. Proposed structures for FA on the basis of X-ray experiments Since the number
average molecular weight (Mn) and density (d ) of FA are 670 and 1.61 cm3 g21, respectively, the unit molecular volume (V ) when Z 5 1 as calculated from the relation V 5 ZM/dN, where Z 5 the number of

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chemical formulae per unit cell and N is Avogadro’s number (0.602 3 1024), ˚ 3. The spacing of 4 A˚ observed in the X-ray diffraction pattern of is 690 A FA is thought to be equivalent to the average distance between aromatic layers, including aliphatic chains and/or alicyclic rings. Interlamellar adsorp˚ corretion studies of FA on montmorillonite showed that the spacing of 4 A sponded to the thickness of a single layer of FA molecules (Schnitzer and Kodama, 1966). Consequently, the cross-sectional area of one FA molecule ˚ 3/4 A ˚ 5 172.5 A˚2 (or 17.25 nm2). was estimated to be 690 A Possible models for a planar FA molecule must conform to the X-ray, chemical, and spectroscopic data. Thus, the model should contain two aromatic rings, six CO2H and two ketonic CQO groups, and two phenolic OH and three alcoholic OH groups. Allowances must also be made for the presence of aliphatic chains or alicyclic structures and for a limiting cross-sectional size of the order of 175 A˚2. Naturally, one can design a considerable number of models that fit these requirements. Four of the more realistic models are shown in Fig. 13. These models only show carbon skeletons and maximum outlines of the modeled FA molecules. In Fig. 13, two isolated aromatic rings are joined to each other via an acyclic five-membered ring (A); they are linked via two aliphatic chains (B); one long aliphatic chain links two isolated aromatic rings (C), whereas the two aromatic rings are condensed and the aliphatic chains extend form one of the aromatic rings only (D). Although more detailed studies are needed for more definitive conclusions, it is noteworthy that the carbon skeletons of the FA models are in broad agreement with the results of physical and chemical studies that have so far been done on this FA and that the sizes of the models conform to the calculated cross-sectional area. It is also of interest that the model FA structures (B), (C), and (D) in Fig. 13 are alkylaromatics and that structures (B) and (D) contain voids.

8.5. A 2-D structure for HA With the aid of pyrolysis-soft ionization mass spectrometry, Schnitzer and Schulten (1992) demonstrated that carbohydrates, phenols, lignin monomers, lignin dimers, lipids (alkanes, alkenes, fatty acids, n-alkyl esters and alkylaromatics) and N-containing compounds were the major HA components. Curie-point pyrolysis-gas-chromatography/mass spectrometry of HA’s showed the presence of relatively large amounts of alkyl-substituted aromatic hydrocarbons (Schulten et al. 1991). Alkylaromatics consist of aromatic rings covalently linked to aliphatic groups and chains. In a first approach, Schulten et al. (1991) proposed that these alkylaromatics were HA “building blocks”, released during pyrolysis from an alkylaromatic structural network. In the hand-drawn draft structure (Fig. 14) Schulten and Schnitzer (1993) assigned n-alkylaromatics a significant role. Oxygen was present in

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171

(A) (C)

(B)

(D)

0

nm

0.5

Figure 13 Schematic representations of four possible structures for the planar FA molecule. (Source: From Kodama and Schnitzer, 1967, Fig. 3, p. 93. With permission of the publisher.)

the form of carboxyls, phenolic, and alcoholic hydroxyls, esters, ethers, and ketones, whereas nitrogen occurred in nitriles and N-heterocyclic structures. The HA structure in Fig. 14 is in agreement with chemical (Schnitzer and Khan, 1972; Schnitzer, 1978), oxidative and reductive degradation (Schnitzer and Khan, 1972, Schnitzer, 1978), colloid-chemical (Ghosh and Schnitzer, 1980), electron microscopic (Stevenson and Schnitzer, 1982) and 13C NMR and X-ray data (Schnitzer, 1991) obtained over many years, as well as with exhaustive consultations of the voluminous literature on humic substances. The elemental composition of the HA shown in Fig. 14 is C308H328O90 N5, with a molecular weight of 5,539.949 g mol 2 1 and an elemental analysis of 66.78w/w % C, 5.97 w/w % H, 25.99 w/w % O and 1.26 w/w % N. There are diverging views in the literature on SOM as to whether carbohydrates and proteinaceous materials are adsorbed on or loosely retained by HAs or whether they are bonded covalently by HAs (Sowden and Schnitzer, 1967). But regardless of the mechanisms considered, carbohydrates and proteinaceous materials are co-extracted with HAs, and their presence affects the elemental analysis and functional groups content of HAs. Carbohydrates have been reported to make up about 10 w/w% of the HA weight (Lowe, 1978), a similar value has been suggested for proteinaceous materials in HAs (Khan and Sowden, 1971). Thus, Schulten and Schnitzer (1993), accepted these values, and assumed that one molecular weight of HA interacted with 10 w/w% carbohydrates

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HO O

O

O

OH

HO O

O

O

HO

O

OH

OH O

O

OH (CH3)0–2

O

(CH3)0–3

OH

O

OH O HO

OH

O OH O

HO OH

N H

HO HOO OH HO

O

OH

O

HO O

OH

HO

O

OH OH

(CH3)0–2

O

O

O

OH

(CH3)0–4 OH

(CH3)0–2

(CH3O)0–3 O OH

(CH3)0–4 O OH

N H

OH (CH3)0–2

OCH3 HO

O

HO OH

C≡N OH O

O

OH O OH O OH

O O

OH OH

O OH

N

OH O

O

O

OH

O

O O

HO

OH

OH

O

(CH3)0–5

O

O OH

C≡N

O

CH2OH

(CH3)0–5

OH

Figure 14 A 2D chemical structure for HA. (Source: From Schulten and Schnitzer, 1997, Fig. 1, p. 119. With permission of the publisher.)

and 10 w/w% proteinaceous material. The resulting HA had an elemental composition of C342H388O124N12 with a molecular weight of 6,650.831 g mol 2 1 and an elemental analysis of 61.67 w/w% C, 5.88 w/w% H, 29.83 w/w% O, and 2.53 w/w% N. When more carbohydrates and proteinaceous materials were added to the HA, the C content decreased but the O content increased. For the development of the HA structure (Fig. 14), Schulten and Schnitzer (1993) assumed that carbohydrates and proteinaceous materials were not integral structural HA components but were adsorbed electrostatically in internal voids and on external surfaces. One of the most interesting features of the proposed HA structure is the presence of voids of various dimensions that can trap and bind other organics such as carbohydrates and proteinaceous materials, lipids, and biocides as well as inorganics such as clay minerals and hydrous oxides.

8.6. A 3D structure for HA Schulten and Schnitzer (1997) converted the 2D structure for HA (Fig. 14) to a 3D structure with the aid of HyperChem software, using the following four steps: (1) the CaC skeleton was drawn by hand in the

Quo Vadis Soil Organic Matter Research? A Biological Link to the Chemistry

636 637 165

173

95 157 95

248

642 282

586

344 747

746

638

Figure 15 Improved 2D model structure of HA. (Source: From Schulten and Schnitzer, 1997, Fig. 3A, p. 122. With permission of the publisher.)

workspace and O and N were added; (2) H atoms were attached with accurate bond lengths and angles. In addition, seven H atoms were added to obtain a complete chemical compound with the composition C308H335O90N5; (3) the 3D HA structure was constructed with accurate bond distances, bond angles, torsion angles, van der Waal’s forces, and hydrogen bonds; and (4) the 3D stick structure of HA (C308H335O90N5) with 738 atoms is shown in Fig. 15. This structure has a molecular weight of 5547.004 g mol21 and an elemental analysis of 66.69 w/w% C, 6.09 w/w% H, 25.96 w/w% O, and 1.26 w/w% N. Molecular mechanics calculations allowed the geometry optimization of this structure, which resulted in a favorable, energy-minimized conformation with a total energy of 710.70 kJ (0.1 nm)21 mol21 and a convergence gradient of 0.037 kJ (0.1 nm)21 mol21 (Schulten, 1995a,b). It was of interest to explore the capacity of the software (ChemPlus) to determine quantitative structure
activity relationship (QSAR) properties of HAs. Attempts were made, using QSAR calculations, to correlate the HA structure at a higher level of total energy and an optimal display of structural voids (Fig. 15) with properties that are of interest in

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soil- and nanochemistry. The investigated HA conformation had a total energy of 1696.02 kJ (0.1 nm)21 mol21 at a gradient of 0.83 kJ (nm)21 mol21, and the spatial dimensions (smallest rectangular box enclosing the molecule) were: height 5 2.81 nm, width 5 2.26 nm, and depth 5 4.93 nm. Utilizing the ChemPlus software, a (solvent accessible van der Waal’s) surface area of 44.08 nm2 and a volume of 11.50 nm3 were calculated. Also, an estimate for the octanol
water partition coefficient (log P) of 146.88, which is a measure of hydrophobicity, was obtained. In addition, data for molar refractivity (1.37 nm3) and polarizability (0.58 nm3) were also determined. To evaluate to what extent biological molecules could be inserted or trapped in the voids of the 3D HA structure, Schulten (1995a) tested geometrically optimized 3D structures of a sugar and peptide. The sugar was a trisaccharide with 66 atoms, an elemental composition of C18H32O16 with a molecular weight of 504.44 g mol21, and an elemental analysis of 42.9 w/w% C, 6.4 w/w% H, and 50.8 w/w% O. The hexapeptide tested was Asp-Gly-Arg-Glu-Al-Ly, which contained amino acids found in soils and which had an elemental composition of C26H46O10N10, a molecular weight of 658.71 g mol21, and an elemental analysis of 47.4 w/w% C, 7.0 w/w% H, 24.3 w/w% O, and 21.3 w/w% N. Measurements of two voids in the 3D HA structure showed that the voids were of sufficient volume to retain the carbohydrate and the polypeptide. With the merger of the 3D HA with the structures of the two biological materials, the elemental composition of the humic complex (896 atoms) was C352H413O116N15 with an elemental analysis of 63.0 w/w% C, 6.0 w/w% H, 27.7 w/w% O, and 3.1 w/w% N. The molecular weight was 6710.16 g mol21. Compared with the elemental analysis of the 3D HA (Fig. 15), the C content of the complex dropped by 3.7 w/w%, but the H content increased by 0.1 w/w%, O by 1.8 w/w%, and N by 1.9 w/w%. The simulation experiments showed that trapping the biological molecules by the 3D HA alone was insufficient to account for the still existing oxygen deficiency. It appeared, therefore, that a significant portion of the biological molecules was either chemically or physically retained on the HA surface. The net effect of surface-retained biological molecules was to lower the C and to increase the O content of the HA.

8.7. Relationships between HA and SOM In agricultural soils, the bulk of the SOM (about 70 w/w%) consists of HS (HA, FA, humin). In addition, we find smaller amounts of carbohydrates (10 w/w%), N-compounds (10 w/w%), and lipids (10 w/w%) (Schnitzer, 1991). As has been mentioned earlier in this chapter, the analytical characteristics and chemical structure of humin are very similar to those of HA. Humin is so strongly complexed by clays and hydrous oxides that it can no longer be extracted by dilute base or acid.

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As far as FA is concerned, 13C NMR spectra of HA and FA show a similar contents of aliphatic and aromatic C. The major difference between HA and FA is that FA has a lower molecular weight and is richer in CO2H groups and carbohydrates than HA. Thus, FA is lower in C but richer in O than HA, which suggests that FA is partially oxidized HA. In many agricultural soils, except Spodosols, we find the following distribution of humic fractions: HA—23%, FA—7%, humin—70%). This indicates that FA is a relatively small humic fraction so that it may be realistic to model all HS in terms of HA. Thus, an agricultural soil containing 3.0% SOM can be viewed as consisting of 2.5% HA 10.25% carbohydrates 10.25% proteinaceous materials, so that: SOM ¼ HA þ carbohydrates þ proteins

(2)

Another SOM component that needs to be considered is water. The water content of air-dry HA is of the order of 3.0% (Schnitzer, unpublished data). Schulten and Schnitzer (1997) further developed an improved SOM model structure to include 3.0% H2O, so that: SOM ¼ HA þ carbohydrates þ proteins þ H2 O

(3)

8.8. The corrected 2D HA model The 2D model shown in Fig. 14 needed to be corrected because it was too small to accommodate all oxygen-containing functional groups. Also, on the average, the aliphatic (CH2)n chains were too long because the proposed preliminary CaC skeleton was based mainly on data obtained by Py-GC/MS and Py-FIMS, which qualitatively indicated the presence of (CH2)n units ranging from n 5 1 to n 5 20. Essentially, however, the 2D structure shown in Fig. 14 was the basic structure which was adopted. The elemental composition and chemical analysis, as well as the molecular weight of the corrected HA, are listed in Table 4. The corrected 2D HA model structure shown in Fig. 16 has an elemental analysis that is close to that of naturally occurring HAs (Schnitzer, 1978). The sizes of the structural voids are large enough to occlude peptides, polysaccharides, water, etc. The corrected 2D HA model structure shown as a simplified C, H, O, H, and S skeleton in Fig. 16 (sticks) contains 5 aliphatic and 21 aromatic CO2H groups, 17 phenolic OH, 17 alcoholic, 7 quinonoic and ketonic, 3 methoxyl groups, and 1 sulfur function. The aliphatic links between the aromatic rings have shortened to between 1 and 10 CH2 units, with an average of n 5 5. Hydrogen bonds are of particular importance in HAs. The use of HyperChem software can help to confirm favorable conditions for these bonds. Hydrogen bonds are formed if the hydrogen donor distance is less

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Table 4 Elemental composition, elemental analysis, molecular weight, and number of atoms in corrected 2D HA, water-free 3D SOM, and 3D SOM 13% water

Elemental composition

Corrected 2D HA

C305H299O134N16S

Water-free 3D C349H377O161N26S SOM

3D SOM 13% C349H401O173N26S Water

Elemental analysis

57.56% C 4.73% H 33.68% O 3.52% N 0.5% S 55.57% C 5.04% H 34.15% O 4.38% N 0.42% S 54.02% C 5.21% H 35.67% O 4.69% N 0.41% S

Molecular weight (g mol21)

Number of atoms

6364.81

755

7543.00

914

7760.15

950

Source: From Schulten and Schnitzer (1997), Table 1, p. 121. With permission of the publisher.

Figure 16 Geometrically optimized 3D structure of HA. Element colors are carbon (cyan); hydrogen (white); oxygen (red); and nitrogen (blue). (Source: From Schulten and Schnitzer, 1997, Fig. 2, p. 120. With permission of the publisher.)

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than 0.32 nm and the angle made by covalent bonds to the donor and acceptor atoms is less than 120 (HyperChem). In total, seven hydrogen bonds were calculated and displayed for the corrected HA (Fig. 15). The corresponding number of atoms (in brackets) and determined bond lengths (d) were: H (157)aO (195) 5 0.298 nm, H (586)aO (248), d 5 0.264 nm, H (746)aO (344), d 5 0.240 nm, and H (747)aN (638), d 5 0.256 nm if the acceptor H was covalently bound to O. For the three H-bonds for which N was the binding partner of the H, bond lengths increased slightly as might be expected: H (636)aN (165), d 5 0.303 nm, H (642)aN (282), d 5 0.310 nm, and H (637)aN (95), d 5 0.391 nm. A preliminary check of the total energy by single point calculation (without changing the geometry) yielded a relatively high value of 4707.68 kJ (0.1 nm)21 mol21 at a gradient of 36.05 kJ (0.1 nm)21 mol21 and indicates a distorted sheet structure (height 5 4.2 nm, width 5 3.5 nm; depth 5 0.92 nm). Calculations of the QSAR properties gave a volume of 12.67 nm3 and a surface area (grid) of 51.04 nm2. The solvent accessible or van der Waal’s surface area was obtained by the grid method.

8.9. A 3D model structure for SOM plus water The corrected 2D HA model was further developed into a 3D model of SOM that included a water content of 3% (Fig. 17), which is about the moisture content of an air-dried soil. The elemental composition and analysis as well as the molecular weight of the SOM 1 H2O structure are listed in Table 4. Trapped in this structure are a typical soil hexapeptide (C26H46 O11N10) a trisaccharide (C18H32O16), and 12 water molecules (H24O12). In order to find the best conformation for this SOM complex of 15 molecules and 950 atoms within a reasonable calculation time (600 calculation cycles, 1171 points, time about 2 days), the convergence limit was set by the determination gradient of 0.838 kJ (0.1 nm)21 mol21. Geometry optimization (and thus energy minimization) was performed with molecular mechanics calculations which yielded a total energy of 2453.99 kJ (0.1 nm)21 mol21. The CaC skeleton containing H, O, N, S atoms of this geometrically optimized structure is shown in Figs 16 and 17. Particularly interesting is that even after extensive calculations, and with progressing energy minimization, the SOM structure still showed mobility of the side chains and occluded molecules, changes in hydrogen bonding, and formation of voids. Thus, the dynamics and flexibility of the structure appeared to be a decisive structural property; it may be part of a novel definition of SOM.

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Figure 17 Geometrically corrected 3D structure of SOM (HA 1 carbohydrates 1 proteins) 1 wt/wt% water. The element colors are: carbon (cyan); hydrogen (white); oxygen (red); and nitrogen (blue). For interpretation of the references to color in this figure legend, the reader is referred to the web version of this book. (Source: From Schulten and Schnitzer, 1997, Fig. 4B, p. 124. With permission of the publisher.)

Figure 17 shows a 3D color plot for SOM containing 3% water in the disk version, which illustrates the high oxygen density of this macromolecule. At this stage of optimized SOM conformation, a total of 11 hydrogen bonds were determined. In the search for particular molecular properties, empirical calculations using ChemPlus software were employed. The resulting QSAR properties were: (1) surface area (approx.) 5 40.23 nm2; (2) more exactly but time consuming in calculation time, surface area (grid) 5 51.27 nm2; (3) volume 5 14.39 nm3; (4) log P 5 124.80, log of the water
octanol partition coefficient as a measure of hydrophobicity; (5) refractivity 5 1.74 nm3; (6) polarizability 5 0.72 nm3; (7) mass 5 7760.15 g mol21, which is consistent with the values listed in Table 4 obtained by HyperChem (Molinfo) directly; and (7) hydration energy 5 233.43 kJ mol21 of the trapped hexapeptide. It is noteworthy that when comparing the structure of the corrected HA (Fig. 17), the surface area and the volume of the latter increased by B25%, which is similar to increases in molecular weight and atom numbers (see Table 4). Of special interest are the 10 hydrogen bonds which are observed in the SOM 1 H2O conformation obtained after geometry optimization (Figs 15 to 17). The majority (six) of these hydrogen bonds were formed within the corrected HA.

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9. Effect of Time on the SOM Structure 9.1. Effect of long-term cultivation on the SOM structure The influence of long-term cultivation on the chemical quality of SOM was studied by Schulten et al. (1995). These authors employed Py-FIMS to analyze whole soil samples from a Typic Haploboroll under long-term cultivation established in 1910 at Lethbridge, Alberta. One soil sample was collected in 1910 from the Ah horizon after breaking the native grassland was stored. Another soil sample was collected in June 1990 from the A horizon under a wheat-fallow, nonfertilized rotation. This soil sample had been under cultivation for 80 years. The native sample (collected in 1910) contained 3.03 w/w% SOM, but the cultivated sample (collected in 1990) contained only 2.23 w/w% SOM. Percentages of sand, silt, and clay as well as the exchange capacities of the two samples were, however, almost identical. The aggregate stability of the native soil was 65%, whereas that of the cultivated soil was only 42%. There were significant reductions in enzyme activities after 80 years of cultivation. The activity of dehydrogenase dropped by 60%, that of acid phosphatase by 77%, and that of ureases by 82%. The total ion intensities (TIIs) of the Py-FIMS, which are related to carbon concentrations, were dramatically different for the two samples. The TII value per mg of soil for the native sample was 31.25 3 104 counts compared to 3.96 3 104 counts for the cultivated soil. The major signals in the Py-FIMS of the native sample were due to carbohydrates, phenols, lignin monomers, alkylaromatics, N-compounds, peptides, lipids, and lignin dimers, but microbial biomarkers were present in the cultivated sample in much lower concentration, less than 10
15 w/w% or not present at all. The TII for the SOM in the cultivated soil was only about one-sixth of that in the SOM of the native soil. Thus, the SOM in the cultivated soil was significantly more thermostable than that of the native soil. Conversely, the SOM in the native soil was more volatile and had a lower molecular weight than that in the cultivated soil. This means that cultivation depleted the easily metabolizable substrates and increased the resistance to pyrolysis of the major SOM components, leading to the formation of larger molecules with higher molecular weights and complexity. Increases in molecular weight, microbial biomarkers, and size of the SOM in cultivated soils may also explain the observed decreases in enzyme activities, involving the C, N, and P cycles. While the effects of anthropogenic disturbances through cultivation to bring about significant changes in the quality, chemical composition, and molecular size of SOM have been discussed here in terms of 80 years of cultivation, one wonders how profound these effects would be after 300 or 500 years of continuous cultivation.

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10. New Concepts on the Chemical and Microbial Synthesis of HAs and SOM According to Burdon (2001), the following plant and microbial constituents in various stages of degradation contribute to the formation of HS: plant carbohydrates and microbial carbohydrates, plant proteins and microbial proteins, plant lipids and microbial lipids, lignins and tannins, and microbial substances such as melanins and other polypeptides. This is an enormously complex mixture in which it is difficult to find well-defined chemical structures, and this has been the experience of many SOM chemists during the past 225 years since Archad’s first paper in 1786. We have now come to a point in time at which we have to further enhance structural research on HA and its formation process. In our opinion, it is possible and we are presenting a number of new ideas in order to demonstrate this point: The experimental research on the chemistry of HS and SOM which we are presenting in this chapter shows that the complex mixture of plant and microbial products, degraded to small organic molecules which we have described above, is consistent with the first stage of HS formation. The second stage of HS formation is through the selective chemical and microbial synthesis. Chemical synthesis of HAs occurs from two major components of the plant and microbial polymer mixture, that is, lignins and lipids (n-fatty acids, n-alkyl mono- and diesters, and n-alkanes, see Table 1). Major HA “building blocks” are aromatic lignin degradation products such as benzene, naphthalene, and phenanthrene, while the major lipid degradation products participating in the synthesis are alkanes, which originate from decarboxylated fatty acids, released by lipids. The chemical covalent bonding of these two building blocks produces alkylaromatic structures. Chemical synthesis of HA can also occur by the alkylation of the aromatic rings, which means that alkyl chains occupy positions on the aromatic rings, substituting hydrogens. This reaction may be brought about by free radicals, which are present in all HS (Schnitzer, 1978). The alkylaromatic structures so formed by covalent bonds are chemically stable in that they resist degradation by strong hot acids. They can readily be seen in 13C NMR spectra of HAs sampled from soils from all over the earth’s surface. They also show in soft ionization mass spectra of all HAs and SOM samples so far analyzed. Carbohydrates and proteinaceous materials in the mixture are not structural components of the chemically and microbially synthesized HA structures. Instead, they are physically adsorbed in the voids and electrostatically adsorbed on the surfaces of the alkylaromatic structural network, which now becomes SOM. They are the main sources of carbon, nitrogen, and energy for the microbes performing HA synthesis. Finally, the microbial synthesis of HS occurs through the synthesis of complex and diverse PKs from simple organic molecules residing in

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soil solutions. The sequence of events in the microbial synthesis of HAs, which we describe in the subsequent section, is based on published experimental data which we are presenting in this chapter.

11. Microbial Humification of Small Organic Compounds into Soil PKs 11.1. A complex biological link to the chemistry of humification One important aspect of this review article deals with the microbial and enzymatic transformation (i.e., humification) of simple organic compounds (i.e., organic acids, amino acids) into large, complex, and diverse PK molecules (i.e., alkylaromatic, polyaromatic, and polyphenolic structures) in soils (Fig. 18). This biotic process is consistent with the Plant tissues/ polymers

Microbial tissues/ polymers

Soil solutioni.e., organic acids

CO2

CO2

Stage II — step 3 abiotic polymerization

Adsorbed/ fixed PKT

Polymerization (inorganic colloids)

ea

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Decomposition

Humification

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Stage I — fragmentation, depolymerization

Root exudates

Carbohydrates proteins, heterocyclic-N

ea

CUS-PKT in HS & SOM

Stage II — step 2 abiotic synthesis

Figure 18 A pathway involving biotic and abiotic processes for the humification of oxoacids into PKs in soils. PKSs, PK synthases; MCoA, malonyl coenzyme A; ACoA, acetyl coenzyme A; ea, electrostatic attraction. (Source: From Monreal (2011), unpublished.)

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second stage of OM humification (i.e., synthesis of complex soil organic molecules) as defined by Huang and Hardie (2009). Soil PKs have their origin through the excretion of secondary metabolites synthesized by crops and soil organisms (microorganisms and protozoa included) into their surrounding soil environment. The concept of microbial humification of soil oxoacids will be tested with published information on the synthesis of microbial PKs from simple organic acids by complex and gigantic modules of polyketide synthases (PKSs). This chapter demonstrates that biotic humification occurs in soil and that much remains to be investigated for establishing the most important biontic pathways of humification in soil
plant systems.

11.2. PKs in nature This section reviews published information of PKs, an important class of natural compounds that have been studied and reported in the scientific literature over the last four decades. Nature produces an amazing variety and number of biosynthesis products, especially secondary metabolites possessing diverse chemical structures. About 100,000 secondary metabolites of molecular weight less than 2500 have been characterized (Roessner and Scott, 1996); and some 50,000 are of microbial origin (Berdy, 1995; Fenical and Jensen, 1993). Figure 19 shows a few selected examples of PK compounds including simple and complex aromatic, polyaromatic, polyphenolic, and alkylaromatic compounds that are secondary metabolites synthesized by PKSs in microbial, protozoa, and plant cells. PKs are one of the largest and most structurally diverse classes of naturally occurring compounds, which include many important compounds that have antihelmintic, insecticidal, antibiotic, and antienzymic properties even at low concentration (Demain, 1999; O’Hagan, 1991). The rich chemical diversity of PKs results in part from an evolutionary process driven by selection for acquisition of improved defense against microbial attack or insect/animal predation (Dixon, 2001). Many important PK antibiotics are of medical or agricultural use, such as polyenoics, antibiotics, and macrolides. About 1% of all known organic molecules used in medicine are natural products, the rest are manmade, yet it has been recognized that more than a third of all drugs currently in use are at least in part derived from a natural source (von Nussbaum et al., 2006). PKs appear to play important functions in ecosystems and comprise a significant fraction of the total number of microbial metabolites with secondary physiological activities (Cane, 1997). In this article and for simplicity, we use the terms alkylaromatics and PK alkylaromatics interchangeably.

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CH3 O

OH

H N

HO H3C

O

CH3

CH3

HO

O

HO OH O

O

OH

O H3C OH

O

OH

(A) Benastatin A

O

(B) Fredericamycin A Sug O O

OH

H3C O

NO2

CH3 CH3

O

O

O

OH

OH

OH

O OH

COOH OH

(C) Candicidin/FR-008

Figure 19 Example of PKs: (A) polyphenolic, (B) polyaromatic, and (C) alkylaromatics synthesized by PK synthases in microorganisms, plants, and other living organisms. (Source: Redrawn from Hertweck, 2009, Scheme 8, p. 4694. Copyright Wiley-VCH Verlag GmbH & Co. KGaA; and from Das and Khosla, 2009, Fig. 1, p.632. Reproduced with permission from publishers.)

11.3. Some chemical and functional features of PKs Many PKs produced by living organisms are water soluble, may be synthesized in high yield, and are excreted into the growth media. From a chemical viewpoint, PKs comprise aromatic, polyaromatic, alkylaromatic, and alkylated molecular structures such as polyphenols, macrolides, polyenes, enediynes, and polyethers (Hertweck, 2009). Plants, animals, and soil microorganisms synthesize polyphenols that include compounds having a moderate solubility in water, and a molecular weight of 500
4000 Da, .12 phenolic hydroxyl groups, and 5
7 aromatic rings per 1000 Da (Quideau et al., 2011). A macrolide is a large macrocyclic lactone ring to which one or more deoxy sugars, usually cladinose and desosamine, may be attached. The lactone rings are usually 14-, 15-, or 16-membered. Polyenes consist of long alkylated-aromatic moieties that contain one or more sequences of alternating double and single carbonacarbon bonds. Polyenes and macrolides are biosynthesized by several strains of Streptomyces as antibiotics mostly targeting not only fungi but also bacteria (Dixon and Walsh, 1996). Enediynes have nine- and ten-membered rings containing two triple bonds separated by a double bond (Nicolaou et al., 1993). Much of the published information existing on PKs relate to organic compounds that provide ecological protection

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and advantages to soil microorganisms and plants during periods of environmental and abiotic stress. From a functional viewpoint, PKs include a large class of diverse compounds, including antibiotics, pigments, and regulatory substances associated with sporulation and cell differentiation processes. The biomolecular activity of PKs may depend on the particular chemical structure rather than its concentration. For example, an alkylaromatic occurring in large amounts may not be as significant as an end product, but it might be important antagonistic intermediates during their decomposition (Firn and Jones, 2003). The latter needs special consideration when conducting ecological studies of the metabolism and dynamics of PKs in soil systems. The energy content stored in the bonds of PKs is high due to the aromatic and alkylaromatic nature of their molecular structures. Thermodynamically, excreted PKs appear not to be the best substrate sources to provide carbon and energy during microbial growth in soils.

11.4. Chemical analysis of PKs in soils The chemical analysis of highly diverse soil alkylaromatic structures of microbial origin presents a significant challenge to soil and other scientists. Environmental chemists are continuously challenged in the analysis of bioactive substances in soils because these compounds present strong sorption to SOM and clay minerals, and the inherent complexities of soil ecosystem properties that vary from region to region (O’Connor and Aga, 2007). Depending on the molecular properties of PKs, such as solubility, polarity, or amphiphilicity, it is necessary to use different chemical extractants, and methods for sample clean up, separation, and quantification (O’Connor and Aga, 2007; Stoob et al., 2006; Turiel et al., 2006). For example, compound separation involves various techniques including liquid chromatography coupled with detection by UV, diode array detection, and techniques of mass spectrometry (Hamscher et al., 2002). Thiele-Bruhn (2003) has reported and summarized analytical methods for analyzing soil antibiotics using various techniques of mass spectrometry, which were developed for natural and synthetic PK antibiotics. State-ofthe-art Py-FIMS and field desorption mass spectrometry (FD-MS) have been used in pioneering the identification of several aromatic, phenolic, and alkylaromatics in HS and SOM, although the origin of these soil chemical structures remains unknown.

11.5. Biosynthesis of PKs The large chemical diversity of PKs is derived from the biotic polymerization of some of the simplest building blocks available in nature, such as acetic

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acid or propionic acid and, occasionally, butyric acid (Hertweck, 2009). Many organisms including, among other, protists, plants, and bacteria produce PKs by PKSs. A set of β-ketoacylsynthase (KS), acyltransferase (AT), and acyl carrier protein (ACP) domains, as well as optional β-keto processing domains, constitute a PKS module, and generally each module is responsible for only one elongation cycle (Fig. 20) (Hopwood, 1997). The domains of a complete PKS include the starting or loading module: AT-ACP-; the elongation or extending modules: -KS-AT-[DH-ER-KR]-ACP-; and the termination or releasing module: -TE, where DH 5 dehydratase, ER 5 enoyl reductase, KR 5 keto reductase. The PK chain and the starter groups are bound with their carboxy group to the SH groups of the ACP and the KS domain through a thioester linkage: RaC(QO)OH 1 HS protein , 5 . RaC (QO)S-protein 1 H2O. The growing chain is handed over from one SH group to the next by transacylations and is released at the end by hydrolysis or by cyclization (http://www.chemistrydaily.com/chemistry/Polyketide). The chemical steps of chain extension and corresponding enzymatic catalysis are strikingly similar to those of fatty acid synthases (FASs) (Cane et al., 1998; Hopwood and Sherman, 1990). All PKs are the result of successive rounds of PKS activities catalyzing the Claisen condensation of a starter coenzyme A (CoA) ester, such as acetyl-CoA, with extender CoA esters, such as malonyl-CoA (Fig. 21) (Hopwood, 1997). The unraveling of the biosynthetic origins of PKs started when radioactive isotopes of C and H and 13C NMR became widely available and were used to conduct the first microbial in vitro studies in the early 1970s. In the 1980s and 1990s, there were many published studies resulting in the identification of the fundamental precursors of these secondary metabolites and the first detailed information on the manner in which the individual building blocks were assembled and subsequently modified inside microbial cells (Hopwood, 1997; Robinson, 1991). The PKS-catalyzed reactions involve chain elongation or β-ketoacyl synthesis, followed by some or all of a sequence of ketoreduction, dehydration, and reduction (Thomas, 2001), although the reduction steps are optional (Hopwood, 1997). The PKSs can use a broad range of building blocks and are also involved in the formation of various chain lengths (Hertweck, 2009). Most aromatic PK backbones are synthesized by the minimal PKS involving four dissociated enzymes acting mainly through decarboxylation of a malonyl building block, although other PKs may be derived from nonacetate sources (Tang et al., 2004). Different types of multifunctional PKSs are classified as type I, type II, type III, and hybrid modules. These PKSs produce the complex and diverse PK molecules based on their protein architecture (Chopra et al., 2008; Hertweck, 2009). Type I PKSs are multifunctional enzymes that provide a construction plan for the assembly of complex structures from simple carbon building blocks (i.e., acetic acid). A minimal PKS module contains at least

O

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CO2H

KS = Ketosynthase ACP = Acyl carrier protein

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D O H3C

O

O

O

n

Polyketide

SX

O

OH

O

O SX

R Reduced polyketide

R

n

OH

Fatty acid

Figure 20 Basic mechanisms involved in PK and fatty acid synthesis by PKSs and FASs, respectively. (A
D) represent the alternative versions of the reductive cycle that lead to keto, hydroxy enoyl, or methylene functionality, respectively, at specific β-carbons during the assembly of reduced PKs. Note that the starter unit may be acetyl CoA (where R 5aCH3) or an alternative CoA ester for all classes of synthases, while the chain extender unit is malonyl-CoA for the synthesis of fatty acids and aromatic PKs, but varies for reduced PKs. (Source: From Hopwood, 1997, Fig. 13, p. 2465. Reproduced with permission of the American Chemical Society.)

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C

A

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Loading

Module 2 Module 1

At ACP KS

AT

KR ACP KS

S

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AT ACP KS

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HO

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mMCoA

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KR KS AT

HO mMCoA

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KR ACP TE

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OH

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ACP KS

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S O

TE

Module 5

ER

KR AT

Module 6

Module 4 Module 3

O HO

O

HO

HO

mMCoA

OH

O O

OH 6-dEB (1)

HO mMCoA

Figure 21 The deoxyerythronolide-B-synthase, giant multimodular megasynthases, required for erythromycin biosynthesis as an example of a modular type I PKSs in prokaryotes. (Source: From Hertweck, 2009, Scheme 2, p. 4691. Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission from the publisher.)

ketosynthase (KS), AT, and ACP domain and β-keto acyl synthase (β-KS) activities. Type I PKSs are giant megasynthases subgrouped as iterative or modular; found mostly in bacteria but also in protozoa and fungi (Hertweck, 2009; Moore and Hopke, 2001; Moss et al., 2004). The active sites of type I PKSs are organized linearly into modules, such that each module catalyzes one cycle in the assembly and modification of PK elongation. Once a module has acted on a nascent acyl chain (i.e., removed one or more OH groups from an oxoacid), it is passed to the next downstream module. Type II PKSs is a system of individual enzymes that carry a single set of iteratively acting activities to synthesize aromatic PKs (Olano, 2011). A minimal set consists of two ketosynthase units (α- and β-KS) and an ACP, which serves as an anchor for the growing PK chain. Additional PKS subunits such as ketoreductases, cyclases, or aromatases define the folding pattern of the polyketo intermediate and further post-PKS modifications, such as oxidations, reductions, or glycosylations, are added to the PK (Hertweck et al., 2007; Rix et al., 2002). The only known group of organism that employs type II PKS systems for PK biosynthesis is bacteria (Flores-Sa´nchez and Verpoorte, 2009; Hertweck, 2009; Seow et al., 1997). Type III PKSs are the condensing enzymes that catalyze the synthesis of aromatic PKs mainly in plants, but also in fungi and bacteria (Austin and Noel, 2003; Austin et al., 2004; Funa et al., 2007; Seshime et al., 2005). Type III PKSs are condensing enzymes that lack ACP and act directly on acyl-CoA substrates. The specificity of AT for malonyl-CoA, methylmalonyl-CoA, or other α-alkylmalonyl-CoAs determines which

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carbon extender is used. Since the latter two substrates have a chiral center, their incorporation gives different stereoisomers of the prolonged PK chain. After condensation of an activated acyl starter unit with malonyl-CoA-derived extender units, the oxidation state of the β-carbon is either kept as a keto group or modified to a hydroxyl, methine, or methylene group by the optional activity of ketoreductase (KR), dehydratase (DH), and enoylreductase (ER) domains ( Jenke-Kodama et al., 2006). Further molecular variability of PKs comes from the existence of two types of KR domains that create different stereoisomers regarding the chiral β-carbon (Caffrey, 2003). So far, most of the type III PKSs have not been characterized fully. Hybrid PKSs have also been identified as one of the types of PKSs, where modules from type I PKSs are linked, for example, to nonribosomal peptide synthetase (NRPS) modules, which result in the production of PK-peptide hybrid metabolites, such as phenolic lipids that appear to play an important role as minor components in the biological membrane in various bacteria. Other hybrid systems include type III/type I, type I/type II, and FAS/PKS hybrids (Hertweck, 2009). For example, the aromatic moieties of the phenolic lipids in Azotobacter vinelandii are synthesized by two type III PKSs, ArsB and ArsC, which are encoded by the ars operon. In turn, the aliphatic moieties arise from C22
C26 fatty acids that are synthesized from malonyl-CoA by two type I FASs, ArsA and ArsD, which are members of the ars operon. Miyanaga et al. (2008) showed through in vivo and in vitro reconstitution of phenolic lipid synthesis systems with the Ars enzymes that the C22
C26 fatty acids produced by ArsA and ArsD remained attached to the ACP domain of ArsA and were transferred hand-to-hand to the active-site cysteine residues of ArsB and ArsC. The type III PKSs then used the fatty acids as starter substrates and carried out two or three extensions with malonyl-CoA to yield the phenolic lipids. The phenolic lipids in A. vinelandii were found to be synthesized solely from malonyl-CoA by the four members of the ars operon. Miyanaga et al. (2008) were the first to demonstrate that a type I FAS interacts directly with a type III PKS through substrate transfer. In some cases, the hybrid enzymes could be either inactive or catalytically inefficient (Kennedy and Hutchison, 1999). The genes encoding for the biologically active PKs are important because their presence express the capacity of various soil microorganisms to synthesize PKs that are excreted into the surrounding soil, thus contributing to the structure of SOM. The encoding genes for active PKs are located within DNA stretches of 20 kb to more than 100 kb. The genes for antibiotic biosynthesis are frequently located near one or more genes mediating resistance to the corresponding antibiotic (Dworkin, 2006). It has been revealed that genes ArsB and ArsC express type III PKSs in A. vinelandii, capable of catalyzing the synthesis of alkylresorcinols and

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alkylpyrones, respectively, which are essential for encystment as the major lipids in the bacterial cyst membrane (Funa et al., 2006). In Streptomyces griseus, the srs operon consisting of srsA, srsB, and srsC is responsible for the synthesis of methylated phenolic lipids derived from alkylresorcinols and alkylpyrones (Funabashi et al., 2008). The srsAB- and srsABC-like operons are distributed in both gram-positive and -negative bacteria (Nakano et al., 2009). The phenolic lipids synthesized by the complex of srs enzymes confer resistance to β-lactam antibiotics (Funabashi et al., 2008). The microbial alkylaromatic structures have longer alkyl chains than those in Fig. 4. Noteworthy, Mu¨ller (2004) indicated that the classification of PKSs into the four types is too limiting because a growing body of evidence showing nature realized limitless transitional stages between differently classified biosynthetic systems during evolution. Although there are only four known module architectures, which are classified as type A, B, C, and D (Fig. 22), the potential permutation of combining the different structural variants gives an enormous diversity of PK molecular structures (Fig. 23). Theoretically, a PKS system comprising six elongation modules could produce more than 100,000 possible PK chemical structures (Gonzalez-Lergier et al., 2005).

Module type A

KS

AT

B

KS

AT

C

KS

AT

D

KS

110

AT

320

DH 70

320

DH

KR 90

ER

KR

KR

ACP

ACP

ACP

ACP

Determines

R

R

R

R

S

O

O

OH (L or D configuration)

Figure 22 Different module types of modular PKSs and their influence on the structure of the PK backbone. The numbers written between domains give the typical length of the respective interdomain region in terms of amino acid residues. (Source: Adapted from Jenke-Kodama et al., 2006, Fig. 1, p. 1211. No permission required to reproduce, www.ploscompbiol.org.)

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CH3

H

O

O

Sug H

O HO

CH3

O H

CH3 CH3 CH3 OH

CH3 H H3C

O

H3C

OH

CH3

CH3

CH3 OH

O H3C

OH H3C

OH

O H

N

O

OH

O

O

H

OH

OH CH3 O

CH3

H3C

OH CH3 O

O CH3

O O

Cl

HO OH

O

O

H3C O

CH3 O

CH3

H3C

OH O

H3C

CH3

CH3

CH3

CH3

CH3 CH3

CH3

CH3

CH3

H3C

O

COOH

HO H 3C

HO

NH

NH H3C O

H3C

CH3 HC3

C CH3 O H3O

O

O O

CH3

CH3

(CH2)8COOH

CH3 H3C

O

OH H3C O

H 3C

O

Cl

O CH3

Cl

OH O

O

H 3C

O

CH3

NO2

OH

OH

O H3C

NH O

H3C COOH

H

HO

CH3

O

O

OH

OH CH3 CH3

H3C CH2

S

O

N

CH3

O

CH3

H3C

Figure 23 A few selected examples of the chemical diversity of PKs. (Source: Redrawn from Hertweck, 2009. Cover page, p. 4688. Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Adapted with permission from the publisher.)

11.6. Ecological function and associated genetic evolution of PKs The diversity of plants and microorganisms in soil ecosystems is enormous; however, only a minor proportion of soil bacteria, fungi, and actinomycetes have thus far been cultured and examined for secondary metabolite production. Only 5% of the total number of fungal species has been described, and of those described (69,000), only 16% (11,500) have been cultured (Hawksworth, 1991). Differences of opinions have existed with respect to the potential ecological functions of secondary metabolites in living organisms. Some scientists have been of the opinion that most microbial natural products had no function in enhancing the fitness of the producer organism and they were considered as waste products or accidents of metabolism (Bennett, 1995). Other chemical ecologists adhere to the view that natural product diversity is associated with processes of genetic selection allowing chemical protection against the attack by other living organisms (Fraenkel, 1959). Fungi in particular, but also actinomycetes, have successfully used their chemical arsenal against bacterial competitors for millions of years (Firn and Jones, 2003). As cited by Hartman (2008), Ernst Stahl, an early pioneer of chemical ecology, who in 1888, suggested that plants use

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chemical protective means to control competitors and aggressors. Lately, it has been reported that bacteria can differentiate between surrounding microorganisms and tailor their responses to the presence of competing microorganisms, as well as a variety of responses targeted to specific bacteria (van Ooij, 2011). Molecules that kill their living neighbors or prohibit their replication have also coevolved with their producers and their resistance strategies. Based on the warfare view for the ecological role of natural products, diverse soil PKs represent key weapons of living organisms in the continuous struggle for ensuring vital space, and access to carbon, nutrients, and energy resources in terrestrial ecosystems. From an evolutionary viewpoint of the molecular diversity of PKs and SOM, it is important to know whether repetitive PKS modules within a single genome has an impact on the diversification of the PK products synthesized by microorganisms and other living cells in soil ecosystems. Data obtained from phylogenetic studies of Streptomyces avermitilis shows that homologous recombination of DNA has led to exchange, loss, and gain of domains and domain fragments and hence to a natural “reprogramming” of the PKS assembly lines ( Jenke-Kodama et al., 2006). It is also plausible to suggest that the chemical arsenal of synthesized PKs excreted by soil microbial cells and other living organisms into their surrounding milieu has had an important effect on the humification processes and chemical quality of SOM during the thousands of years of pedogenesis.

11.7. Plant PKs Plants have evolved adaptive processes that involve the production and use of many secondary metabolites to perceive and translate that perception into an adaptive response to repel attacks from fungi, bacteria, nematodes, and insects (Dangl and Jones, 2001; Dixon, 2001). Plants produce five groups of secondary metabolites: PKs, isoprenoids (e.g., terpenoids), alkaloids, phenylpropanoids, and flavonoids, many in response to environmental stress (Flores-Sa´nchez and Verpoorte, 2009; Pankewitz and Hilker, 2008). Rice, corn, soya bean, Arabidopsis, and the model legume Medicago truncatula are rich sources of antimicrobial indole, terpenoid, benzoxazinone, and flavonoid and isoflavonoids (Dangl and Jones, 2001). Flavonoids, which are polyphenols, are synthesized in plants by the combination of PKs and phenylpropanoids (Verpoorte and Memelink, 2002). The compounds of the phenylpropanoids family are synthesized predominantly by plants and also by endophytic Penicillum brasilianum (Pacheco et al., 2010). Chalcone synthase (CHS) catalyzes the first step in the synthesis of flavonoids which uses malonyl-CoA and 4-coumaroyl CoA as substrates. CHS is a family of plant PKSs that accumulates transcripts and produces chalcones (i.e., aromatic ketones) in response to stimuli such as pathogen

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attack (Arias et al., 1993; Meier et al., 1993), light and UV light (Batschauer et al., 1991; Knogge et al., 1986), wounding (Lawson et al., 1994), elicitor treatment ( Junghans et al., 1993), and symbionts (Harrison and Dixon, 1993). A few PKs have physiological functions in plants (i.e., spatial pattern of cell development in plant embryos) (Seigler, 1998). Attack by pathogens to plants causes accumulation of flavonoids and isoflavonoids, and their importance as antibiotic phytoalexins is well established (van Etten and Pueppke, 1976; van Etten et al., 1989). The flavonoid phytoalexins, known to be produced in response to invasion by fungi and bacteria, have been found and described predominantly in legumes, but have also been identified as major phytoalexins in cereals like wheat, sorghum, and rice (Feng and McDonald, 1989; Hipskind et al., 1990; Ioset et al., 2007; Kodama et al., 1992; Seigler, 1998). In barley, flavonoids are present at constitutive levels in the epidermis and mesophyll of leaves (Fro¨st et al., 1977; Seikel et al., 1962), and, as seen in other species, illumination with UV light prompts the accumulation of flavonoids in barley and rye leaves (Christensen et al., 1998; Haussuehl et al., 1996; Lanz et al., 1991; Reuber et al., 1996). The presence of gchs2, indicated that CHS-related enzymes are involved in the biosynthesis of pyrone (a heterocyclic with five carbon and one O atom and two double bonds), that confer plants resistance to insects and pathogens (Eckermann et al., 1998). Pankewitz and Hilker (2008) published an extensive review article on the ecological role and evolutionary aspects of PK biogenesis in insects. The chemical structures of plant PKs and flavonoids have structural and bond similarities with respect to their aromatic and alkyl moieties, and bioactivities against other living organisms. The input of the latter two types of natural products into soils may contribute with basic carbon skeletons to the formation of a central unit structure (CUS) in HS and SOM. Little is known, however, about the ecological effects on soil biota by plant secondary metabolites, especially once plant residues containing PKs on their tissue surfaces are incorporated into the soil. The potential microbial utilization or decomposition of plant PKs in soils is also unknown, as well as pathways for incorporating plant PKs into soil HS and SOM (Fig. 18).

11.8. Microbial PKs Prokaryotic microorganisms are the largest reservoir of genetic diversity on earth, yet to date only a small fraction (maybe 0.1
1%) of all microbes have been cultured and rigorously described (Wawrik et al., 2005). Of the 12,000-PK antibiotics compounds known in 1995, 55% were produced by soil actinomycetes of the genus Streptomyces, 12% from nonfilamentous bacteria, 11% from other actinomycetes, and 22% from fungi (Berdy, 1995; Strohl, 1997). In general, the accumulation of inducible antagonistic microbial compounds is often orchestrated through signal-transduction pathways linked to perception of the pathogen by receptors encoded by

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host resistance genes (Dangl and Jones, 2001). To exert an effect, most of the antimicrobial compounds must penetrate cell membranes and attack at least one adversarial molecular target (Nikaido, 2003). Most PKs when excreted into their surrounding disrupt protein synthesis, DNA replication, and the integrity of cell membranes of microorganisms lacking resistance (Hertweck, 2009). Microbial PKs including several polyaromatic compounds such as anthracyclines, with medicinal application, are synthesized primarily by Streptomyces peucetius, a soil actinomycete (Alekhova and Novozhilova, 2001; Das and Khosla, 2009). PKs are also synthesized by fungal and bacteria species commonly found in soil systems (i.e., Penicillum sp., Aspergillus sp., Mycobacterium sp.). The capacity to produce PKs in vitro varies and depends on the species, strain type, and other abiotic factors, and may range from a few μg mL21 to thousands μg mL21. For example, Streptomyces globisporus may produce from 1.3 to 14 μg mL21 of a nine-membered ring enediyne over 5 days of growth (Horsman et al., 2010). Relative to other Streptomyces species and strains, the strain N2 of Streptomyces chrysomallus displayed an increased capacity to biosynthesize PK antibiotics during several days of growth in vitro. S. chrysomallus was able to produce 730 μg mL21 of actinomycin after 5 days of growth; and the production of actinomycin by several Streptomyces strains, their mutants, and genetically transformed strains ranged from 25 to 1120 μg mL21. The minimum inhibiting concentration of actinomycin was 5000 μg mL21 for the same species. Other PK compounds, however, inhibit the growth of S. chrysomallus at concentrations ranging from 15 to 50 μg mL21 (Alekhova and Novozhilova, 2001). Microbial synthesized alkylaromatics are also named phenolic lipids in the published literature. For example, alkylresorcinols and alkylpyrones which have a polar aromatic ring and a hydrophobic alkyl chain, are found in fungi, bacteria, and plants (Miyanaga et al., 2008). Su et al. (1981) identified the synthesis of 10 phenolic lipids in A. vinelandii during encystment because of carbon substrate-induced stress. Encystment and sporulation is a frequent process carried out by bacteria and other microorganisms under adverse nutrient and environmental conditions in soils. During encystment, the phospholipids of cell membranes are replaced by a family of 5-n-alkylresorcinols and 6-n-alkylpyrones (Reusch and Sadoff, 1983). The encystment of A. vinelandii, a gram-negative, free-living nitrogen fixing soil inhabitant, can be induced by transferring exponentially growing cells from glucose to 0.2% β-hydroxybutyrate. The encystment is completed within 4
6 days and results in the conversion of more that 90% of the vegetative cells into cysts (Lin and Sadoff, 1968). Lipid phenols are synthesized at a time in the differentiation process when lipids are turning over (Reusch and Sadoff, 1979, 1981). Su et al. (1981) identified various phenolic lipids in A. vinelandii including 6-n-eneicosylresorcylic acid methyl ester and 6-n-tricosylresorcylic acid methyl ester,

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5-n-(2-hydroxy)heneicosylresorcinol and 5-n-(2-hydroxy)tricosylresorcinol, 5-n-heneicosyl-4-acetylresorcinol and 5-n-tricosyl-4-acetylresorcinol, 6-nheneicosyl-4-hydroxypyran-2-one and 6-n-tricosyl-4-hydroxypyran-2-one, and 6-(2-oxotricosyl)-4-hydroxy-pyran-2-one and 6-(2-oxopentacosyl)-4hydroxypyran-2-one. The polymethylene chains of the cyst lipids average 2.7 nm in length and 57 nm3 in volume, which are larger in size than the phospholipids of vegetative cells (1.9 nm length and 40 nm3 volume); and the proportion of lipids allocated into the cyst membrane was equivalent to 95% of the total lipids found in cysts (Reusch and Sadoff, 1983).

11.9. PKs in soils This section presents and discusses published information on the production of soil PKs, the effects of antibiotics on soil microbial activity, and the effect of soil management and the importance of adsorption reactions on soil PKs. In spite of their natural occurrence, the biochemistry and chemistry of microbial PKs in vivo soils has received little attention. Most research on PKs and the microorganisms responsible for their syntheses has been conducted in vitro in the laboratory. Soils are a source of many and diverse bioactive compounds involving PK structures that are produced by indigenous microorganisms (Gottlieb, 1976; Tomashow et al., 1997). About 30
50% of actinomycetes isolated from soil are capable of synthesizing antimicrobial compounds (Topp, 1981). In soil
plant ecosystems, the interactions of living organisms with their surroundings are complex and involve plants, microorganisms, insects, animals, and inorganic colloids. In soils, the microbial-synthesized PK antibiotics help the microbial producer to gain access to nutrients and the suppression of plant root pathogens, among other benefits. For example, isoepoxydon is an alkylaromatic compound synthesized by fungi and actinomycetes that causes growth interference between Poronia punctata, a late colonizer of animal dung and earlier colonizer species of Ascobolus furfuraceous and Sordaria fimicola (Gloer and Truckenbrod, 1988). Harris and Woodbine (1967) examined a total of 560 bacterial isolates from four rhizosphere and eight nonrhizosphere soils and tested them for resistance to seven different antibiotics. They found marked differences between the overall levels of antibiotic resistance in the different soils. The bacteria isolates inhabiting the rhizosphere were more susceptible to antibiotics than the corresponding bacteria from nonrhizosphere soils. The latter authors also showed that only 2
10% of the total soil bacterial isolates showed resistance to the seven antibiotics. In comparison, the bacteria populations in the rhizosphere of 10 plant species were shown to have a greater resistance to streptomycin than the comparable bacteria from nonrhizosphere soil when assessed by a plate count technique (Brown, 1961). Penicillin activity added to unsterilized soil at 1000 μg g21 completely

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disappeared after 2 weeks (Pramer and Starkey, 1951). The latter study, however, did not determine the potential effects of biotic decomposition and adsorption reactions by soil colloids on the disappearance of streptomycin. Other studies show that microbial populations in various soils differ in their reactivity to antimicrobial compounds (Harris and Woodbine, 1967; Williams and Davies, 1965); and there is no universal antibiotic or set of antibiotics capable of inhibiting all the soil heterotrophic activity. The optimum concentration of PK antibiotics and their degradation susceptibility need to be investigated before conducting CO2 soil respiration studies (Anderson and Domsch, 1973). Soil respiration studies show a dose response to added antibiotics after 24 and 48 h of incubation in selected soil samples (Thiele and Beck, 2001). There is little and inconclusive evidence demonstrating the proportion and type of soil microorganisms that are susceptible or resistant to microbial PKs in situ. Published information clearly indicates, however, that some soil organisms and bacteria isolates have resistance genes to protect themselves from PKs secreted by other living organisms in soils. The synthesis and stabilization of various PK antibiotics were studied in sterilized and unsterilized soil samples amended with organic residues and inoculated with Streptomyces aureofaciens and Streptomyces rimosus. The maximum amount of aureomycin found in the soybean-amended sterilized soil samples was 0.15 μg g21 soil, and that in unsterile-amended soil samples was 0.03 μg g21 soil after 16 days of incubation. In comparison, the amount of terramycin in the same soil treatments was about six times greater. After 30 days of incubation, the amount of aureomycin was reduced by about 50% in sterile soil and by nearly 90% in unsterile soil (Soulides, 1965). The antibiotic glycotoxin produced by the soil saprophyte fungus, Gliocladium virens, to control soilborne pathogens was detected at concentrations between 0.2 and 0.37 μg cm23 soil in clay and composted mineral soil (Lumsden et al., 1992). Several antibiotic compounds persist in the environment and are not transformed (Winckler and Grafe, 2001). Antibiotic residues given to animals for treatment of illness or growth promotion are found in soils, after manure application, at concentrations of 0.02 μg g21 soil (Aga et al., 2005; De Liguoro et al., 2003; Hamscher et al., 2002). In a different study, the concentrations of residual tetracyclines in soils ranged from 0.025 to 0.105 μg g21, but sulfonamide antibiotics were not detected (Cengiz et al., 2010). PKs are strongly adsorbed to organic and inorganic soil particles. Strongly adsorbed antibiotics to soil colloids may be leached from soils through preferential transport in macropores (Thiele-Bruhn, 2003). The strong adsorption of PK antibiotics to OM and clay colloids in soils is associated with charge transfer and ionic interactions and not to hydrophobic partitioning. The distribution coefficient for the adsorption of antibiotics to soil materials varies widely depending on soil type and on the chemical structure and amount of the compound, and as expected,

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sorption is stronger in clay than in sand particle-size fraction (Thiele et al. 2002; Thiele-Bruhn, 2003). Aminoglycosides, tetracyclines, and tylosin are more strongly adsorbed to expandable than to nonexpandable clay minerals while desorption of aminoglycosides was not observed (Bewick, 1979; Pinck et al., 1961a, 1961b). Effects of antibiotic addition on soil organisms include shifts in microbial communities and development of resistance under frequent and high addition of these compounds to soils via animal manures. The reviewed published information clearly indicates that indigenous PK antibiotics and those of added to soil through manures or other animal residues results in rapid adsorption and fixation by soil organic and inorganic components, and thus their biological utilization and decomposition is largely prevented in soil ecosystems.

11.10. A microbial PKSs model for studying biotic humification in soils The soil solution in native and cultivated amended and unamended soils is a key central and fast cycling SOM pool (Monreal and McGill 1989a, 1989b). In the path for enhancing our knowledge on the biotic humification of soil organic molecules residing in soil solution, it will be extremely useful to identify and establish a model or a few models of representative soil microorganisms and PKSs. Actinomycetes and fungi in general, and the genera Streptomyces in particular, are active in synthesizing PKs in soils compounds that also serve as biocontrol agents against fungi in crop rhizospheres (Blaak and Schrempf, 1995; Yuan and Crawford, 1995). Streptomyces constitute the most abundant ( . 90%) soil streptomycete, and soils rich in SOM such as grasslands contain the largest number of these filamentous bacteria that live under various soil conditions including different pH (Schrempf, 2006). Soil streptomycetes produce compounds with antibacterial and antifungal activities and are active during the initial decomposition of OM (Kutzner, 1981; Dworkin, 2006). Soil is the most important habitat for streptomycetes, and most soils contain 104 to 107colony forming units g21 soil, representing 1
20% or more of the total viable counts. Over 20 genera of soil actinomycetes were isolated and reported by Lechevalier and Lechevalier (1967). Other important genera of soil actinomycetes appearing in culture media include Micromonospora, Rhodococcus, and Streptosporangium (Williams and Wellington, 1982). The rhizosphere of soil
crop systems is a location with high microbial diversity and activity, and carbon, energy, and nutrient fluxes; and it is an ideal place to study the increased synthesis of PKs by Streptomyces and PKSs. The scientific community has been improving the standard cultivation techniques of soil microbes by developing better methods for the efficient identification and screening of pure culture actinomycetes, and also of soils containing PKSs and secondary metabolites potentially useful for industrial

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application. For example, molecular methods using DNA tools are useful to detect Streptomyces strains colonizing and producing streptomycin in the rhizosphere of soybeans (Huddleston et al., 1997). The streptomycin biosynthetic gene cluster of two species, S. griseus and S. glaucescens, has been well studied (Distler et al., 1992). Genes for resistance and biosynthesis have also been cloned and characterized (Piepersberg, 1995). For streptomycin resistance, the gene cluster comprised over 30 genes, including an aminoglycoside phosphotransferase gene, strA (Huddleston et al., 1997). Metsa¨-Ketela¨ et al. (1999) obtained the partial sequences of PKS genes from six known and 29 unidentified soil actinomycete isolates using degenerate polymerase chain reaction (PCR) primers. Pigment- and antibioticproducing PKS gene clusters could be clearly distinguished based on their KSα sequences, indicating that functional information may be derived from PKS sequence data. A different study by Metsa¨-Ketela¨ et al. (2002) found that the phylogenies of 16S rRNA and PKS genes in actinomycete soil isolates were not congruent, indicating that the phylogenetic grouping of actinomycetes is an inadequate predictor for the type of secondary metabolites these strains produce. A study conducted to directly access PKS gene soil diversity by terminal restriction fragment length polymorphism (TRFLP) patterns found that some soil samples contained a particularly rich and unique actinomycetes community; and that the same soil samples also contained the greatest diversity of KSα genes as determined by TRFLP analysis of KSα PCR products (Wawrik et al., 2005). These workers identified seven novel clades of KSα genes by vector cloning. Greatest sequence of diversity was observed in a sample containing a moderate number of peaks in its KSα TRFLP. Cluster of sequences were most similar to the KSα involved in the production of several antibiotics such as ardacin, angucycline, simocyclinone, pradimicin, and jasomycin. Work conducted by several scientists on phenolic lipids has involved characterization of phenol lipids and PKSs associated in cysts of A. vinelandii (Miyanaga et al., 2008). A molecular technique approach that helps confirm the occurrence of given soil biotic process is the characterization of soil mRNA to measure the gene expression controlling the synthesis of specific proteins (Nannipieri et al., 2010). The confirmation of the humification pathway for the biontic production of specific soil PKs as intermediate or end products may be confirmed by characterizing the expression of genes associated with the complex PKS systems in model soil fungi or actinomycetes. Important progress has been made on the development of methods for the characterization of mRNA and so detecting the expression of gene sequences in soil (Krsek et al., 2006). Accordingly, A. vinelandii, Streptomyces sp., associated PKSs complexes and PK products, such as phenol lipids, may be used as microbial
gene
enzyme
product models to elucidate the role of PKSs in the formation of alkyaromatic or polyaromatic structures from simple soil oxoacids (first step of

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humification synthesis, Fig. 18), and to elucidate their role as basic backbone structures contributing to the formation of the CUS of HS and SOM, and complementary physicochemical reactions between PKs with colloidal surfaces and other organic molecules (second step of humification synthesis, Fig. 18).

12. Thermodynamic, Energy, and Kinetic Considerations Determining the energy, kinetic, and thermodynamic relationships of soil microbial metabolism is basic to the understanding of interactions between the activities of microbial communities, available substrates, and the humification processes associated with PK synthesis and decomposition in their geochemical environments. Microbial intracellular enzymes capture the free energy in chemical bonds of substrates to conduct work for the maintenance, growth, and transport of organic and inorganic species across the cell membrane during anabolic and catabolic processes (Russell and Cook, 1995). Adenosine triphosphate (ATP) is the cell’s energy currency to conduct work, and it is mostly produced through oxidative phosphorylation during catabolic cell respiration processes in aerobic and anaerobic cells. To a lesser extent, ATP is also produced by substrate-level phosphorylation occurring during glycolysis and the Krebs cycle. Part of the energy produced is conserved by translocating protons across the membrane and synthesizing ATP from adenosine diphosphate and inorganic phosphorus (Senez, 1962). The complete microbial oxidation of glucose in vitro produces 38 ATP mol per mol (Brock and Madigan, 1991) and releases 2881 kJ mol21 of free energy. Conversely, the anaerobic fermentation of glucose produces two molecules of ATP and releases 180 kJ mol21. Bacteria require about 70 kJ mol21 of free energy to conserve 1 mol of ATP under the physiological conditions of a cell (Jackson and McInemey, 2002). Depending on the intracellular pH, between 210 and 212 kcal (241.8 and 250.2 kJ) is required for the synthesis of 1 mol of ATP from adenosine diphosphate (ADP) and Pi in anaerobic bacteria (Thauer et al., 1977). The average heat of combustion (energy content) of whole Pseudomonas fluorescens cells was 22.3 kcal (93.2 kJ) g21; and this average energy content is similar to values obtained for other heterotrophs (Powers et al., 1973). The biontic humification pathways for soil PK synthesis from simple oxoacids residing in soil solution require consistency with the laws of thermodynamics and kinetic principles. In general, soils are characterized by having small amounts of available carbon sources, and thus the production of chemical free energy for biological work is limited, thus deviating

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little from thermodynamic equilibrium ( Jin and Bethke, 2007). A minimum quantum of free energy (12.7 6 2 kJ mol21) sustains microbial metabolism of acetate and butyrate by the synthropic fermentative bacteria Synthrophomonas wolfei and Desulfovibrio strain G11; and surprisingly, this free energy value was lower than the 20 kJ mol21 for thermodynamic limits predicted by Jackson and McInemey (2002). The latter authors concluded that substrate and bacterial metabolism can proceed near thermodynamic equilibrium—a condition that is often thought to be a biological improbability. Under conditions of limited free energy, metabolic reactions must proceed in the forward and reverse (or branched) directions to have consistency with thermodynamics, such as data reported from physicochemical studies (Pekaˇr, 2005; Wolfe, 2005). Most microbial respiration studies conducted in vitro only show forward directions with high rate of reactions because abundant substrate is used to produce high levels of chemical free energy (Monod, 1949). With low free energy levels, however, the rate of reaction for soil microbial metabolism is defined by the difference between the forward and reverse rates ( Jin and Bethke, 2007). As a contribution to the study of evolution of SOM, Tardy et al. (2005) indicated that the thermodynamic properties and stability of soil HS may be delineated as a function of polymerization indexes. Others have examined the thermodynamic and free energy relations for the sorption onto clay colloids of aromatic, soil HS and contaminants (Chin and Weber, 1989; Faria and Young, 2010; Ghabbour et al., 2004); but no work has been associated with the thermodynamics of secondary metabolites in soils. There are several articles and books published with information on thermodynamic properties including enthalpies of reaction and formation for single organic compounds (Rossini et al., 1952; Wagman et al., 1982; Yaws, 1999), but not for natural compounds such as PKs. The authors of this article used the modeling approach of Banfalvi (1999) to estimate the relative energy contents (kJ mol21) for two PKs, tetracenomycin and a triketide pyrone, both biosynthesized from acetic acid. Tetracenomycin (C20H12O8; MW 5 366 g mol21) is a four-membered polyaromatic ring structure with eight double bonds synthesized by S. glaucescens, a grampositive soil bacterium (Tang et al., 2004). The triketide pyrone is an alkyaromatic PK (C21H43; MW 5 295 g mol21) produced in vegetative cells and cysts of A. vinelandii (Miyanaga et al., 2008). The estimated energy content for tetracenomycin is 7420 kJ mol21 and for the triketide pyrone is 15,240 kJ mol21. Acetic acid has a MW 5 60.05 g mol21 and an energy content of 869.4 kJ mol21 (Fig. 24). Thus, the humification of acetic acid by PKSs modules in S. glaucescens produced tetracenomycin, a humified compound whose molecular weight is five times larger than acetic acid, and contains 8.5 times more energy than that stored in the chemical bonds of the parent compound. The

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OH

O

OH

CH3 COOH

PKS1

HO

O

OH Tetracenomycin (6100 kJ mol–1)

H3C OH Acetic Acid (869.4 kJ mol–1)

PKS2

R

O

O

R = C21H43 OH Triketide pyrone (15,240 kJ mol–1)

Figure 24 The estimated change in molecular bond energy content during the biotic humification of acetic acid to tetracenomycin and a triketide pyrone in soils. (Source: From Monreal, 2011, unpublished.)

biotic production of the triketide pyrone resulted in a humified PK that is six times larger, but contains 16.5 times more energy than that stored in the chemical bonds of acetic acid. In comparison, the 3D modeling work done by Schulten and Schnitzer (1997) indicates that the total energy stored in the chemical bonds of a thermodynamically stable HA molecule (31.3 nm3) is 169,600 kJ mol21 (see Section 8.6), an energy content that is 11
23 times greater than the energy stored in the bonds of the previous two PK compounds. The difference in energy content and molecular size of biosynthesized PKs and HAs may be explained by either the small size of PKs used in the previous example or by additional bond energy attained by PKs during polymerization catalyzed by the surface of inorganic soil colloids. These estimated energy and molecular size data are consistent with the conventional definition for humification of SOM. The latter data also suggests that during the humification synthesis of simple soil organic compounds, soil microorganisms have to produce the additional energy stored in the chemical bonds of PKs through branched metabolic pathways and are likely working near the thermodynamic equilibrium in non-rhizosphere soils. In other cases of soil PKs enediynes or polyphenol biosynthesis, the differences in molecular size and bond energy content between the resulting humified products and the parent soil oxoacids will be much greater. The aromatic, polyaromatic, alkylaromatic, and polyphenolic biosynthesized structures are also characterized by high chemical stability (Krygowski and Cyranski, 2001). The biosynthesis of soil PKs is associated with the production of free energy by soil microorganisms, which in turn depends on the availability

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1400

19

1200

14 1000

9

800

4 –1

600

–6

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–11

δ13C (‰)

Organic-C in soil solution (μg C mL–1)

and chemical quality of soil substrates available for metabolism. The half saturation value for limiting microbial growth on glucose and several other C sources is between 10 and 100 μM or 10
100 mg C L21 (assuming 1000 g C mol2 I) (Pirt, 1975). Soil microorganisms can use a multicity of simple and complex organic and inorganic substrates that may also serve as electron acceptors or donors in the cell’s metabolic network. In order to satisfy their energy needs, soil microorganisms can also switch between pathways in the metabolic network when the free energy content of certain metabolites in a given pathway is not favorable (Banfalvi, 1994; Wolfe, 2005), and thus, the magnitude of the reverse (or branched) metabolic processes may be quite significant for soil microorganisms due to limited capacity of a soil to supply C. For example, the microbial mineralization of SOM was limited in an Andept of Chile supplying 38 mg C L21, but it was not limited in a Mollisol of Canada supplying 220 mg C L21 (Monreal et al., 1981). In soil rhizospheres, the production of PKs will be associated with conditions of substrate availability, especially when crop photosynthesis and root exudation supplies adequate amounts of organic substrates for microorganisms during 3
8 weeks of crop growth after seeding (Fig. 25); or after the incorporation of crop residues into soils at harvest. Supply and availability of soluble-C sources also occurs through cell excretion and the lysis of soil microbial cells. Research is warranted to conduct studies for establishing the relations

–16 200 0

–21 16

24

35

42

56

–26

Time (days after seeding)

Figure 25 The dynamics of soluble organic carbon (dark cyan) and the δ 13C in the soil solution (light yellow) of an in vivo soil
canola system labeled for 2 hours with 13CO2 in a closed chamber under continuous flow at different growth stages during the growing season of canola (Brassica napus L.). The δ 13C values in soil solution indicate significant production and supply of small organic compounds through root exudation, such as oxoacids, for the potential biosynthetic humification of soil PKs. (Source: From Monreal, 2011, unpublished experimental data.)

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between free energy production by soil microorganisms and soil C supply in relation to the synthesis and decomposition of soil PKs. Thus far, the question on how the energy, kinetic, and thermodynamic relationships operate in soil systems, especially those relations associated with the biontic humification of simple organic compounds into PKs, has not been answered. Within this context, a thermodynamic analysis based on the modeling approach reported by Jin and Bethke (2007) may be used to examine the relationships associated with the biotic humification of simple oxoacids into PKs in soils. The latter authors used a new and rigorous way to explain how the rate law can account quantitatively for the thermodynamic driving force of kinetic reactions in oligotrophic and eutrophic conditions. An understanding of the energy, kinetic, and thermodynamic relationships of biotic humification will also benefit from the following additional information: (a) the intramolecular energy of chemical bonds in PKs, (b) the intermolecular energy between PK moieties and other classes of soil organic compounds, and the surface of soil inorganic colloids, (c) characterize the free energy, kinetic, and thermodynamics for all the biosynthesis pathways and decomposition of excreted microbial PKs, and their incorporation into the CUS of HS and SOM, as shown in Fig. 18.

13. PKs and the Central Structure of HS and SOM 13.1. PKs as a passive SOM pool Published information shows that PKs are adsorbed strongly and rapidly by soil inorganic colloids once released into soils. The latter abiotic process together with the antimicrobial properties of the biosynthesized PKs and the high-energy content in their bonds appear to prevent their metabolism by many soil microorganisms, protozoa, nematodes, and fauna. These biotic and abiotic factors and processes would favor the long-term stabilization of PKs in soils, contributing to the large turnover time of soil carbon ( .1000 years) in soil HS and microaggregates ( .265 years) (Campbell et al., 1967; Monreal et al., 1997). The chemical analysis of SOM by Py-FIMS provides indirect information of soil putative PKs as represented by alkylaromatic, aromatic, phenolic, and lipid pyrolytic products during soil sample analyses. Recently, the analysis of SOM by Py-FIMS showed that putative PKs represented by alkylaromatic, aromatic, phenolic, and lipid structures make up the great majority of SOM residing in the clay and nanosize soil fractions where the age of carbon is .1000 years (Monreal et al., 2010).

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Diverse studies conducted in microorganisms have shown that the amounts of native and residual PK antibiotics in soil samples obtained from incubation or field trials range from as little as 0.01 to about 0.1 μg g21 (or 0.05
0.5 μg mL21 soil solution, assuming a soil moisture content of 20 w/w% at field capacity). S. globisporus produced even higher amounts, from 1.3 to 14 μg mL21, of a nine-membered ring enediyne produced in a fermenter during 5 days of growth (Horsman et al., 2010). The amounts of PK antibiotics produced in in vitro studies are somewhat similar and within an order of magnitude of those amounts reported for indigenous and residual PKs measured in soils (Aga et al., 2005; Cengiz et al., 2010; Soulides, 1965). The authors of this article used selected published data to make some general inferences about the net flow of PK through soils during pedogenesis. The production and accumulation of PKs in soil
plant systems appears to have occurred continuously during pedogenesis. On a long-term basis, a glaciated Chernozemic soil with a content of 2% organic C (48,000 kg SOM ha21 20 cm21, assuming a bulk density of 1.2 g cm23) would need a net accretion of 0.1 μg g21 week21 of PKs for 16 week year21 (or 1.92 kg PK ha21 year21) to produce a kinetic passive and chemically diverse soil putative PKs pool of 19,200 kg ha21 during 10,000 years of soil formation. This passive putative PK pool would represent 40% of the total SOM content; and its chemical composition is similar to the relative proportion of lignin monomers, phenols, alkylaromatics, lipids (i.e., n-alkanes, alkenes, and n-alkylesters), and fatty acids, as determined by Py-FIMS analysis of the SOM residing in a clay and nanosize fraction of a Chernozemic soil having a carbon turnover time .1000 years (Monreal et al., 2010). Using the specified SOM content at the end of pedogenesis (i.e., 48,000 Mg ha21 20 cm21), the accumulation of putative PKs in A horizons during pedogenesis was represented by the exponential function of Fig. 26 after using the Century model of Parton et al. (1987) to describe the dynamics of the passive SOM pool. It appears that soil microbial PKs constitute an important chemical pool of SOM with turnover time .1000 years.

13.2. Biotic humification process forming the CUS of HS and SOM Figure 18 shows that humification consists of stages I (i.e., depolymerization) and II (i.e., synthesis). Stage II in turn involves three steps, step 1, which is a biocatalytic step, and steps 2 and 3, which are abiotic catalytic steps. The first step in stage II humification is a key and complex biosynthetic process catalyzed by soil microorganisms having PKSs modules types I, II, II, and hybrids, and malonyl-CoA and acetyl-CoA to synthesize PKs. The synthesized PKs are adsorbed rapidly and strongly onto inorganic soil colloidal surfaces, thereby minimizing their potential decomposition by resistant soil organisms. The biosynthesized PKs, such as alkylaromatics, may contribute

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Soil organic matter (Mg/ha/20 cm)

50,000 Total SOM

40,000

30,000

20,000 Putative polyketides y = 21,228.34 (1–e–0.000378t)

10,000

0 0

2000

4000 6000 Time (years)

8000

10,000

Figure 26 An exponential model for the hypothetical accumulation of putative soil PKs in the 0
20 cm depth of a Black Chernozem growing a G3 grass in Canada during 10,000 years of pedogenesis. The exponential function describing the accumulation of putative PKs was derived from data obtained for a passive SOM pool after a simulation run with the Century model. (Source: From Monreal, 2011, unpublished data.)

thus to the formation of the CUH of HS and SOM. The CUS derived through microbial biosynthesis reacts electrostatically with carbohydrates, proteins, and N-heterocyclic to produce macromolecular assemblies (Stage II—step 2 of humification, Fig. 18). In parallel, a third abiotic process (step 3) involves the polymerization of adsorbed PKs and macromolecules of HS and SOM by catalytic surfaces of soil inorganic colloids into even largersize humified macromolecules in soil (step 3 in Stage II of humification, Fig. 18). Steps 2 and 3 in Fig. 18 appear to be the abiotic processes contributing to a pool containing large and highly diverse soil chemical macromolecules such as those HS structures modeled by Schnitzer and Schulten (1998) and those described earlier in this chapter. In order to act as a central architectural unit in HS and SOM, the CUS needs to have the following associated characteristics: (1) It is a microbialsynthesized PK core involving polyphenolic, alkylaromatic, polyaromatic, and aromatic rings bonded to alkyl moieties of different C lengths; (2) it is kinetically stable due to the high energy content in chemical bonds with predominance of covalent bonds in and between adjacent aromatic rings and between aromatic rings and aliphatic moieties; (3) it is devoid of protein-N and carbohydrates; (4) it has moieties allowing H-bonding, electrostatic attractions to other macromolecules such as carbohydrate and proteins; (5) it continuously generates a random molecular structure variance to HS and SOM by binding with other molecules.

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The net accumulation of soil PKs appears to be a function of the rates of adsorption and desorption; the prevalence of microbial populations with the ability of PKSs expression and PK resistance; type of PK; and associated pedogenetic processes. The biotic and abiotic soil factors that influence the residence time of soil microbial PKs, and the amounts and frequency of synthesis that occurs at an ecological significant level under field conditions in soil
plant systems during humification of soil oxoacids awaits further elucidation.

14. Future Research As we move forward, further work is warranted to better understand the synthesis and decomposition of soil PKs in relation to soil microorganisms, PKSs, and catalytic inorganic surfaces in in vivo soil
plant systems. Within this context, it will be necessary to demonstrate the consistency of biohumification processes with the rate laws of thermodynamics. Information from future studies will be obtained from soil microcosm studies involving microbial
gene
enzyme
product models to determine the factors controlling the humification of small soil organic molecules into PKs, and their subsequent incorporation into the CUS in HS and SOM. Such work may also result in the discovery of new soil PKs, PKSs, and their decomposition metabolites. Future studies on the structure of SOM will benefit from including sensitive biosynthetic DNA and RNA molecular approaches that may detect either the potential for soil PK biosynthesis as inferred from the presence or expression of biosynthetic genes or an activity attributable to gene expression through the direct presence of the metabolite of interest. The biosynthetic molecular techniques are important and complementary tools to the use of isotopes and direct techniques of mass spectrometry to allow for the elucidation and quantification of humification via secondary PK pathways for the formation of the CUS of SOM in situ. New specific and standardized tests of soil microbiology, chemistry, and biochemistry are needed to further clarify and characterize the exact role of PKs and PKSs in soil environments under the auspices of multidisciplinary scientific research teams. Modern tools of chemistry, thermodynamics, molecular biology, nanoscience and nanotechnology, simulation modeling, and microscopy will enable soil scientists and others to further and better characterize natural occurring substances produced by microbial humification processes in vivo soil
plant systems. These tools include, among other, DNA and RNA transcription and expression for proteins, Py-FIMS, Py-FDMS, ESI-MS, LC-ESI-MS/MS, electrophoresis-MS, 1H-, 15N-, and 31P NMR,

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and solid 13C-CP/MAS-NMR. SOM chemists should benefit from the rapid advances in these fields, which should enable them to develop more and more precise knowledge of the chemical structure of HAs. Some of these tools will support carrying out kinetic studies to characterize the microbial and enzymatic transformation of isotopically labeled substrates into intermediates and end products to better determine the turnover of soil PKs. The chemical techniques need to be complemented by studies involving atomic force microscopy (AFM), transmission electron microscopy (TEM), scanning electron microscopy (SEM), NanoSIMS (secondary ion mass spectometry) and Raman confocal microscopy to better characterize the spatial disposition of PKs, trace elements and isotopes in particle-size fractions, soil organic domains, and in OM
mineral complexes at the nano- and microscales. Such interdisciplinary studies will provide scientific information on: (a) the humification of small organic molecules, and on the soil ecological significance of microbial- and plant-synthesized/excreted PKs in soils, (b) the formation of a CUS in HS and SOM, (c) the chemical diversity and dynamics of PKs at soil nano- and microscales, and (d) the chemical structure of SOM and HS. For many decades, research on the molecular structures of SOM and HS has taken place in almost entirely different scientific worlds, with little cross-fertilization between laboratories or individual researchers working on the chemistry, microbiology, and biochemistry. We hope this article will stimulate the exchange of such disciplines, ideas and concepts, and result in effective collaboration and healthy competition for enhancing human knowledge. In the future, multidisciplinary approaches will be required to continue making progress on elucidating the chemical structure of SOM. But the most important recommendation for the future is closer cooperation between soil chemists, soil microbiologists, and soil biochemists in research on HS and SOM. We have provided scientific evidence showing that PKs are a category of some 100,000 secondary plant and microbial metabolites of molecular weight less than 2500, of which some 50,000 have origin in soil microorganisms. PKs are produced by complex PK synthases (PKSs) modules and are expressed as active compounds against other living organisms even at low concentrations in soil
plant systems. Theoretically, the potential permutation of the presently known four existing PKSs modules could produce more than 100,000 possible alkylaromatic, polyaromatic, and polyphenolic structures from simple soil oxoacids. The synthesis and excretion processes of soil microbial PKs constitute an example of the first step of HS synthesis, being consistent with the scientific definition already established for the second stage of SOM humification. Due to their bioactivity, strong and rapid adsorption to clay colloids, and high energy content of their bonds, the microbial excreted PK structures represent kinetically passive carbon pools, and so lend themselves as basic carbon skeletons that contribute to the formation of a CUS in HS and

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SOM. Moieties of the CUS would then adsorb carbohydrates, proteins, lipids, and N-heterocyclic molecular associations that can be further modified and polymerized by catalytic soil inorganic surfaces to form large soil humified macromolecules of diverse chemical composition.

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C H A P T E R F O U R

Zeolites and Their Potential Uses in Agriculture Kulasekaran Ramesh, and Dendi Damodar Reddy† Contents 1. 2. 3. 4. 5. 6.

Origin and History of Zeolites Classification of Zeolites Structure and Nomenclature of Zeolites Physical and Chemical Properties of Zeolites Major Natural Zeolites of Agricultural Importance Zeolite Nutrient Interactions 6.1. Soil urease adsorption 6.2. Nitrate leaching 6.3. Ammonium trapping 6.4. Rock phosphate dissolution 7. Agricultural Applications 8. Researchable Issues 9. Conclusions References

221 222 223 224 227 227 228 228 228 229 229 235 235 235

Abstract Zeolites are natural crystalline aluminosilicates. They are among the most common minerals present in sedimentary rocks. Zeolites occur in rocks of diverse age, lithology, and geologic setting, and represent valuable indicators of the depositional and postdepositional (diagenetic) environments of the host rocks. It was reported that, of the 40 naturally occurring zeolites studied by research groups, the most well-known ones are clinoptilolite, erionite, chabazite, heulandite, mordenite, stilbite, and phillipsite. Structurally, zeolites are tectosilicates exhibiting an open three-dimensional structure containing cations needed to balance the electrostatic charge of the framework of silica and alumina tetrahedral units. Pores and voids are the key characteristics of zeolite

 †

Indian Institute of Soil Science (ICAR), Nabibagh, Bhopal, Madhya Pradesh, India Central Tobacco Research Institute (ICAR), Rajamundry, Andhra Pradesh, India

Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00004-X

© 2011 Elsevier Inc. All rights reserved.

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materials. The pores and interconnected voids are occupied by cations and water molecules. The internal surface area of these channels are reported to reach as much as several hundred square meters per gram of zeolite, making zeolites an extremely effective ion exchangers. The Si/Al ratio is an important characteristic of zeolites. The charge imbalance due to the presence of aluminum in the zeolite framework determines the ion-exchange property of zeolites and is expected to induce potential acidic sites. The Si/Al ratio is inversely proportional to the cation content, however directly proportional to the thermal stability. Cations can be exchanged by ion exchange and water can be removed reversibly by application of heat. The unique physical and chemical properties of zeolites, coupled with their abundance in sedimentary deposits and in rocks derived from volcanic parent materials, have made them useful in many agricultural applications. Most of the initial research on the use of zeolites in agriculture took place in the 1960s in Japan. A brief review of the literature has pointed out that Japanese farmers have used zeolite rock over years to control the moisture content and to increase the pH of acidic volcanic soils. Ion-exchange properties of zeolites can be utilized in agriculture because of their large porosity and high cation-exchange capacity. They can be used as both carriers of nutrients and a medium to free nutrients. Zeolites are important materials with very broad applications in agriculture and environmental engineering. Zeolite incorporation in soil was found to increase crop yields and to promote nutrient use efficiency. Other possible uses being investigated include applications as a carrier of slow-release fertilizers, insecticides, fungicides, and herbicides, and as a trap for heavy metals in soils. Keywords: Zeolites; clinoptilolite; alumino silicates; slow-release fertilizers; ammonium trapping; rock phosphate; nutrient use efficiency

With the population pressure on the land resources increasing, world food security is dwindling due to declining quality and/or quantity of the soil resource base. One of the fundamental root causes for falling per capita food grain production is supposed to be the soil resource degradation resulting from depletion of nutrient reserves, declining soil organic carbon stocks, heavy metal contamination etc. Most agricultural soils are either inherently low in fertility or made less fertile due to the continuous removal of nutrients without adequate replenishment. Coupled with low native soil fertility is the problem of low use efficiency of inputs, particularly chemical fertilizers and water. Owing to nutrient losses through various loss mechanisms, the use efficiency of applied nutrients continues to be low in most tropical agroecosystems. It is in this context that the use of zeolites in agriculture/soil fertility management assumes greater significance. Zeolites are crystalline aluminosilicates. They are among the most common minerals in sedimentary rocks and are reported to be especially common in tuffaceous rocks. They have been found in rocks of diverse

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age, lithology, and geologic setting, and are valuable indicators of the depositional and postdepositional (diagenetic) environments of the host rocks. They are tectosilicates exhibiting an open three-dimensional structure containing cations needed to balance the electrostatic charge of the framework of silica and alumina tetrahedra and containing water. Different connection of SiO4 and AlO4 tetrahedra leads to the formation of three-dimensional framework with pores and voids of molecular dimension. Shape, dimensions, and linkage of zeolite pores and voids are the key characteristics of zeolite materials. The pores and interconnected voids are occupied with cations and water molecules. The internal surface area of these channels is reported to reach as much as several hundred square meters per gram of zeolite, making zeolites an extremely effective ion exchangers. Cations can be changed by ion exchange and water can be removed reversibly by application of heat. Other useful chemical and physical properties include high void volume, low density, excellent molecular sieve properties, and high cation-exchange capacity. Although both clays and zeolites are alumino-silicate materials, they are dissimilar in structure. While clays can easily break down into tiny particles or soil colloids, zeolites have three-dimensional rigid crystalline structures that have much greater control over molecule movement. This means special care must be taken in testing to provide enough time for the ion exchange to occur (Shaw and Andrews, 2001). The unique physical and chemical properties of zeolites, coupled with their abundance in sedimentary deposits and in rocks derived from volcanic parent materials, have made them useful in many agricultural applications.

1. Origin and History of Zeolites Identification of zeolite as a mineral goes back to 1756, when a Swedish mineralogist, Alex Fredrik Cronstedt, collected some crystals from a copper mine in Sweden. He found that upon rapidly heating the material stilbite, it produced large amounts of steam from water that had been adsorbed by the material. Based on this, he called the material zeolite, from the Greek words meaning “boiling stones,” because of ability to froth when heated to about 200 C. Following Cronstedt’s findings, zeolites were considered as minerals found in volcanic rocks for a period of 200 years. Natural zeolites form where volcanic rocks and ash layers react with alkaline groundwater. Zeolites also crystallize in postdepositional environments over periods ranging from thousands to millions of years in shallow marine basins. Naturally occurring zeolites are rarely pure and are contaminated to varying degrees by other minerals, metals, quartz, etc. For this reason,

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naturally occurring zeolites are excluded from many important commercial applications where uniformity and purity are essential. Most of the initial research on the use of zeolites in agriculture took place in the 1960s in Japan. A brief review of the literature has pointed out that Japanese farmers have used zeolite rock over years to control the moisture content and to increase the pH of acidic volcanic soils. Ion-exchange properties of zeolites can be utilized in agriculture because of their large porosity and high cation-exchange capacity. They can be used as both carriers of nutrients and a medium to free nutrients. According to Coombs et al. (1997) “a zeolite mineral is a crystalline substance with a structure characterized by a framework of linked tetrahedra, each consisting of four oxygen atoms surrounding a cation. This framework contains open cavities in the form of channels and cages. These are usually occupied by H2O molecules and extra-framework cations that are commonly exchangeable. The channels are large enough to allow the passage of guest species. In the hydrated phases, dehydration occurs at temperatures mostly below about 400 C and is largely reversible. The framework may be interrupted by (OH, F) groups; these occupy a tetrahedron apex that is not shared with adjacent tetrahedra.”

2. Classification of Zeolites More than 50 different species of this mineral group have been identified and still more to be identified. Zeolites have been classified on the basis of their morphological characteristics, crystal structure, chemical composition, effective pore diameter, natural occurrence, etc. In the year 1997, the subcommittee on zeolites of the International Mineralogical Association Commission on New Minerals and Mineral Names has recommended nomenclature for zeolite minerals. The report suggested that zeolite species are not to be distinguished solely on the ratio of Si to Al, except for heulandite (Si:Al,4.0) and clinoptilolite (Si:Al $ 4.0). Dehydration, partial hydration and over-hydration are not sufficient grounds for the recognition of separate species of zeolites (Coombs et al., 1997). The Si/Al ratio is an important characteristic of zeolites. The charge imbalance due to the presence of aluminum in the zeolite framework determines the ion-exchange characters of zeolites and is expected to induce potential acidic sites. The Si/Al ratio is inversely proportional to the cation content, however directly proportional to the thermal stability. The surface selectivity changes from hydrophilic to hydrophobic when the ratio increases. Silica molecular sieves (silicalite-1) have a neutral

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framework; are hydrophobic in nature, and have no ion-exchange or catalytic properties. Zeolites are classified on the basis of silica:alumina ratio as follows: 1. Zeolites with low Si:Al ratio (1.0 to 1.5) 2. Zeolites with intermediate Si:Al ratio (2 to 5) 3. Zeolites with high Si:Al ratio (10 to several thousands). As the Si to Al ratio continues to increase, the catalytic activity often tends to pass through a maximum because of two opposing effects: increasing effectiveness of each acid center on the one hand, and decreasing number of acid centers on the other. The aluminous zeolites are excellent desiccants whereas the most siliceous zeolites tend to be organophilic nonpolar sorbents (Barrer, 1986). Flanigen (1980) considered that “low silica” zeolites or aluminum-rich zeolites contain the maximum number of cation-exchange sites balancing the framework aluminum, and thus the highest cation contents; “intermediate silica” zeolites exhibit a common characteristic in terms of improved stability over the “low silica” zeolites and “high silica” zeolites representing heterogeneous hydrophilic surfaces within a porous crystal. The surface of the high silica zeolites approaches a more homogeneous characteristic with an organophilic hydrophobic selectivity and exchange capacities Flanigen (2001) has classified zeolites based on pore diameter, namely ,small-pore zeolites, medium-pore zeolites, large-pore zeolites, and extra-large-pore zeolites: a. b. c. d.

Small-pore zeolites (8-rings) with free pore diameter of 0.3 0.45 nm Medium-pore zeolites (10-rings) with free pore diameter of 0.45 0.6 nm Large-pore zeolites (12-rings) with free pore diameter of 0.6 0.8 nm Extra-large-pore zeolites (14-rings) with free pore diameter of 0.8 1.0 nm.

3. Structure and Nomenclature of Zeolites Zeolites are a volcanogenic sedimentary mineral composed primarily of aluminosilicates. The mineral has a three-dimensional crystal lattice, with loosely bound cations, capable of hydrating and dehydrating without altering the crystal structure (Holmes, 1994). Zeolites are generally formed in nature when water of high pH and high salt content interacts with volcanic ash causing a rapid crystal formation (Oste et al., 2002). The structure of zeolite can be split into two regions: columns of fused rings that expand with temperature and the inter column regions that tend to contract on heating. These competing changes combine to produce a material that contracts parallel to the crystallographic “a” and “b” axes and expands in the “c” direction (Villaescusa et al., 2001). In the

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structure of natural zeolite, the water and cations can be reversibly removed or replaced by other cations (Rehakova et al., 2004). The maximum size of any ionic species that can enter the pores of a zeolite is a function of the dimensions of the channels. These are defined by the ring size of the aperture. “8-Ring” structure refers to a closed loop that is built from 8 tetrahedral coordinated silicon (or aluminum) atoms with equivalent oxygen atoms. These rings are not always perfectly symmetrical due to a variety of effects, including strain induced by the bonding between units that are needed to produce the overall structure, or coordination of some of the oxygen atoms of the rings to cations within the structure. Therefore, the pores in many zeolites are not cylindrical (Beitollah and Sadr, 2009). Mumpton (1960) suggested a compositional gap between the minerals heulandite (Si/AI ratio 2.75 3.25) and clinoptilolite (Si/A1 ratio 4.25 5.25), clinoptilolite being temperature-stable to above 600 C. Recent zeolite research has focused on widening the scope of synthetic procedures, on further exploiting zeolites in commercial processes, and on applying modern characterization techniques to unraveling the complexities of zeolite structural properties. Recent zeolite research has focused on widening the scope of synthetic procedures, on further exploiting zeolites in commercial processes, and on applying modern characterization techniques to unraveling the complexities of zeolite structural properties (Newsam, 1986). The higher the average ionic potential of the extra-framework cations, the larger the hydration capacity of the clinoptilolite. This trend may be attributed to the small size as well as the efficient water-cation packing of high field strength cations in the zeolite structure (Yang et al. 2001).

4. Physical and Chemical Properties of Zeolites Two major processes have been identified as kinetics of ion-exchange process in zeolites, namely, particle diffusion and film diffusion. Zeolites are one of the greatest cationic interchangers and their cationic interchange capacity is two to three times greater than other types of minerals found in soils. Zeolites are potential adsorbents due to the ability of their microporous structures to adsorb molecules at relatively low pressure (Kamarudin et al., 2003). There is a wide variation in the cation-exchange capacity of zeolites because of the differing nature of various zeolite cage structures, natural structural defects, adsorbed ions and their associated minerals. Thus in short, zeolites are natural materials with the ability to exchange ions, absorb gases and vapors, act as molecular-scale sieves, and catalyze reactions owing to fixed pore sizes and active sites in the crystal

Table 1 Chemical composition of clinoptilolite/zeolites Elemental composition (%) SiO2 to CEC Al2O3 ratio

Zeolite origin/place

SiO2

CaO

K2O

Indonesia (Idrus et al., 2008) Hungary clinoptilolite (Kallo, 1993) Parnaı´ba Basin, Brazilian (Monte et al., 2009) Turkish clinoptilolite—Go¨rdes (Gedik and Imamoglu, 2006) Turkish clinoptilolite—Bigadic¸ (Gedik and Imamoglu, 2006) ˇ Mu¨hlbachova´ and Simon (2003) Argentina clinoptilolite (Milla´n et al., 2008) Oregon clinoptilolite (MacKown and Tucker, 1985) Iran zeolite (Khodaeijoghan et al., 2010) Indian zeolite (Gadekar et al., 2008) Agoura clinoptilolite (Wise et al., 1969) Slovakian clinoptilolite (Pawelczyk and Popowicz, 2006)

72.00 69.50

3.3 4.50 1.83

0.7 1.0 9 11 4.44 11.65

1.1 1.5 1.60 0.44 1.06

08 1.2 0.59

6.55 5.97

64.7

3.10

0.97

12.7

0.80

3.30

1.50

5.09

74.36

1.95

4.07

11.87

0.59

1.03

0.85

6.26

72.76

4.16

3.13

11.93

0.10

1.26

1.26

6.09

66.6 62.7

5.00 0.40

3.84 1.20

12.30 12.50

0.19 6.40

1.89 0.60

1.40 0.60

59.8

4.94

0.15

12.17

0.11

0.89

0.89

65.0

2.30

3.00

12.02

1.08

1.50

0.10

56.7

4.30

13.23

0.48

1.12

69.2

1.09

11.76

2.63

0.30

3.13

Al2O3

65 71 2.70 5.20 2.2 3.4 11.50

Na2O

13.10

Fe2O3 MgO

175

5.41 5.02 4.91

200

5.41 4.29

0.39

5.88 4.8 5.4

(Continued)

Table 1 (Continued ) Elemental composition (%) Zeolite origin/place

SiO2

Japanese clinoptilolite (Minato 61.8 and Morimoto, 2006) 52.15 Neapolitan Yellow Tuff (Buondonno et al., 2006) 78.31 Turkey clinoptilolite (C ¸ a˘gn et al., 2006) Beli plast deposit, Eastern Rhodopes, Bulgaria (Chakalov et al., 2006) 

CEC, cation-exchange capacity (cmol (p 1) kg21).

CaO

K2O

Al2O3

Na2O

Fe2O3 MgO

SiO2 to CEC Al2O3 ratio

1.67

3.01

12.60

2.20

1.86

1.02

148

4.90

2.35

7.54

18.56

3.30

0.20

0.20

212

2.81

4.23

3.37

11.44

0.26

1.93

0.48

6.85 125

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lattice. The size of clinoptilolite channels controls the size of the molecules or ions that can pass through them and therefore a zeolite like clinoptilolite can act as a chemical sieve allowing some ions to pass through while blocking others (Mumpton, 1999). Their internal areas mostly fall in the range of 400 850 m2 g21 for zeolites (Barrer, 1986). Zeolites vary widely in their chemical composition, particularly with respect to contents of SiO2, CaO, K2O, Al2O3, Na2O, and Fe2O3 as shown in Table 1. Dixon and Ming (1987) outlined the techniques for separation of clinoptilolites from soil by combining the low specific gravity and fine particle-size characteristics of clinoptilolite in soils. Clinoptilolite was separated from the silt fractions of the chemically treated and untreated samples using a heavy liquid.

5. Major Natural Zeolites of Agricultural Importance Of more than 48 natural zeolites species known, clinoptilolite is the most abundant in soils and sediments. Among the natural zeolites, clinoptilolite (Abadzic and Ryan 2001). is most commonly used in agricultural practices as a soil amendment and for promoting nitrogen retention in soils (He et al., 2002; MacKown and Tucker, 1985; Nus and Brauen, 1991; Polat et al., 2004; Wehtje et al., 2003). Clinoptilolite is a member of the heulandite group of natural zeolites, a temperaturestable heulandite seems to be the most abundant zeolite in soils over a wide variety of pH conditions, from slightly acidic to strongly alkaline (Ming and Dixon, 1986b). They are the most well-known and one of the most useful zeolites. Extensive deposits of clinoptilolite are found in Western United States, Bulgaria, Hungary, Japan, Australia, and Iran (Mumpton, 1999). Clinoptilolite has a high cationic interchange capacity and a great affinity for NH1 4 ions (Inglezakis, 2004). It was reported by Polat et al. (2004) that, of the 40 naturally occurring zeolites studied by research groups, the most well-known ones are clinoptilolite, erionite, chabazite, heulandite, mordenite, stilbite, and phillipsite.

6. Zeolite Nutrient Interactions Some of the characteristics of zeolites that potentially make them desirable for improving the properties of soils are a large internal porosity that results in water retention, a uniform particle-size distribution that allows them to be easily incorporated, and high cation-exchange capacity that retains nutrients (Ok et al., 2003). The addition of zeolite has

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improved the nutrient status of sand-based root zones, especially selective 1 retention of NH1 ions (Nus and Brauen, 1991; Petrovic, 1993). 4 and K

6.1. Soil urease adsorption Urease adsorption on zeolite and the various properties of adsorbed urease were investigated to find out the influence of zeolite on activity and properties of urease by Choi and Park (1988). Free urease in solution was adsorbed on zeolite until maximum adsorption, and the amount of maximum adsorption was 11.3 mg urease/100 mg zeolite at pH 7.0. It is apparent that free urease was adsorbed on the outer surface of zeolite by cation-exchange reaction, and more than 70% of urease was adsorbed within 30 min. The activity of adsorbed urease decreased by 89.6%, whereas Km value increased to 34.4 mM, which is higher than that of free urease. The optimum pH of adsorbed urease widened 6.5 7.0, compared to that of free urease 7.0. Results of Bernanrdi et al. (2010) have shown the potential for urea and zeolite to improve the efficiency of nitrogen use, since the use of the mineral provided similar effects to the treatments with lower volatility losses and were inferior only to those sources that do not exhibit volatility losses and to that with urease inhibitor. Addition of zeolite to soil reduced soil urease activity through urease adsorption on the zeolite (Ramesh et al., 2010b) and the prediction equation followed exponential function Y 5 40.975e23.6848x, where Y is the soil urease activity and x is the soil:zeolite ratio, R2 5 0.95 (Ramesh et al., 2010c).

6.2. Nitrate leaching Huang and Petrovic (1994) advocated the application of zeolites in order to reduce the leaching of nitrates in golf courses located on sandy soils. Amendment of clinoptilolite zeolite to sandy soils has been reported to lower nitrogen concentration in the leachate and to increase moisture and nutrients in the soil due to increased soil surface area and cation-exchange capacity (He et al., 2002). MacKown and Tucker (1985) found that zeolite applications decreased nitrification and leaching losses, NH4-clinoptilolite decreased nitrification by about 11%. The decrease resulted from retention of NH1 4 by clinoptilolite in places where nitrifying bacteria could not oxidize NH1 4 .

6.3. Ammonium trapping The small internal tunnels of clinoptilolite zeolite as an example have been found to physically protect ammonium ions from too much nitrification by microorganisms (Ferguson and Pepper, 1987). Composting experiments have shown that by incorporating clinoptilolitic tuff with animal waste,

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ammonia can be retained by the zeolite (Witter and Lopez-Real, 1988). The slow retention and liberation capacity of NH1 4 ions that have been incorporated in the channels forming crystalline structure is generally attributed to zeolites, and particularly clinoptilolites (Allen et al., 1996; Kithome et al., 1998; MacKown and Tucker, 1985; Lewis et al., 1984). The availability of internal space volume is another interesting characteristic of zeolites for separation/purification applications (Kamarudin et al., 2003). Zeolite may initially immobilize NH1 4 N in the soil when it is applied, reducing N availability to the crop and resulting in the negative effects on growth (Wiedenfeld, 2003). Besides retaining large quantities of ammonium ion, these minerals also interfere with the process of nitrification (Bartz and Jones, 1983; Fergunson and Pepper, 1987). Zeolites have reduced ammonia emissions from animal manures (Amon et al., 1997). Zeolite reduced total ammonia loss by 16% (Witter and Kirchmann, 1989). Ahmed et al. (2002) have found that zeolite mixtures significantly reduced NH3 loss by between 32 and 61% compared with straight urea (46% N) and zeolite (0.75 and 1 g kg 21 of soil).

6.4. Rock phosphate dissolution A glasshouse study undertaken by Pickering et al. (2002) who showed that clinoptilolite in combination with rock phosphate significantly enhanced uptake of hosphorus by sunflowers. The zeolite/rock phosphate combination possibly acted as an exchange fertilizer, with Ca2 1 exchanging onto the zeo1 lite in response to plant uptake of nutrient cations (NH1 4 or K ) enhancing the dissolution of the rock phosphate. The experiment clearly demonstrated greatly enhanced plant uptake of P from rock phosphate when applied in combination with NH4-zeolite, though the P uptake was lower than that from the soluble P source. The zeolite/RP system offers the considerable advantage of P release in response to plant demand and is unique in this regard. Studies on ion-exchange kinetics are very important for the development of technologies of the natural zeolite utilization (Yuan et al. 2008).

7. Agricultural Applications Zeolites are important materials with very broad applications in refineries as catalysts, sorption and separation processes, and also in agriculture and environmental engineering. Olczyk (2005) found that incorporation of zeolite in soil increased tomato yield but had no positive effect on sweet corn. Other possible uses being investigated include applications as a carrier of slow-release fertilizers, insecticides, fungicides, and herbicides, and as a

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trap for heavy metals in soils (Ming and Dixon, 1986). Zeolites are reported to have potential applications in many ways in agriculture (Ramesh et al., 2010a). Improving soil physico-chemical and microbial properties: Natural zeolites are extensively used to improve soil physical environment, particularly in sandy and clay poor soils (Abdi et al., 2006). Application of zeolite to the tune of one-fifth of the soil weight was found to be the best medium for tomato plants (Markovic et al., 1995; Unlu et al., 2004). Bansiwal et al. (2006) emphasized that zeolites were commonly used as soil conditioners. Khan et al. (2008) demonstrated that zeolite application at soybean planting time encouraged the initiation of vegetative phenology on allophanic soil. Zeolite has an effect to mitigate the salt damage to plants and that the leaching of CaCl2 substitutes adsorbed Na in zeolite for Ca. Substituted zeolite gives high productivity to sand. Zeolite amendment is an effective way to improve soil condition in an arid and semiarid environment (Yasuda et al., 1998). Application of natural zeolite increased the available nitrogen, phosphorus, calcium, and magnesium of the medium (Abdi et al., 2006). A study by Wiedenfeld (2003) concluded that the slight effect of zeolite application observed in a study suggests that its potential benefit might be realized only under poorer conditions where the needs for improvement in nutrient retention and moisture holding capacity are greater. Farmers add the zeolites to the soil to control soil pH and to improve ammonium retention (Dwyer and Dyer, 1984). The CEC of soil may be increased by using zeolites as soil amendments (DeSutter and Pierzynski, 2005). Chander and Joergensen (2002) found an increase in soil microbial biomass and incorporation of added 14C into microbial biomass after zeolite amendment. The microbial populations could respond to zeolite amendment in different ways (Mu¨hlbachova´ ˇ and Simon, 2003). Koˇc´ı (1997), while studying the possible toxic effects of water extracts of some cation zeolite forms on some water organisms, concluded that zeolites were not usually toxic to organisms. Enhancing nitrogen use efficiency: There are several reports in the literature showing that the addition of zeolite to the source of N can improve the nitrogen use efficiency (Gruener et al., 2003; McGilloway et al., 2003; Ming and Mumpton, 1989; Rehakova et al., 2004). Surface-modified zeolites offer a great promise as anion carriers for slow release of nutrients (Bansiwal et al., 2006) The high potential of zeolites as nitrogen fertilizers has been demonstrated. Their use would diminish environmental problems and increase fertilizer efficiency (Milla´n et al., 2008). It has been verified that when mixed with nitrogen, phosphorus, and potassium compounds, zeolite enhances the action of such compounds as slow-release fertilizers, both in horticultural and extensive crops (Dwairi, 1998a, 1998b). Natural zeolites have high tendency of ammonium selective properties (Kithome et al., 1998). The main use of zeolites in agriculture

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is for nitrogen capture, storage, and slow release. It has been shown that zeolites, with their specific selectivity for ammonium (NH1 4 ), can take up this specific cation from either farmyard manure, composts, or ammonium-bearing fertilizers, thereby reducing losses of nitrogen to the environment. There is a new possibility, which is the addition of zeolite to the organic substrate (Legge, 2000). Natural zeolites, due to their structure and properties, inert and nontoxic material can be used as a slowly releasing carrier of fertilizer (Reha´kova´ et al., 2004). It is possible to obtain an increase in the efficiency of nitrogen fertilizer in forage crops when nitrogenated clinoptilolites are used in comparison with the use of urea (Milla´n et al., 2008). Ferguson and Pepper (1987) suggested that the effects of zeolite on N uptake and plant growth would vary with soil type, and that maximum benefit would be expected on coarse-textured low cation-exchange capacity soils. The development of a slow nitrogen release system to be used as fertilization technology could simultaneously contribute to the reduction of contamination and the improvement of crop yields. Use of soluble N fertilizers is one of the major causes for groundwater contamination. Nitrogen liberation dynamics of the occluded form (in zeolites) are much slower than for the ionic one (Milla´n et al., 2008). The nitrogen molecules are retained by electrostatic attraction, and modifications of molecular angles and single and double bonds occur in it (Costa, 2000). There are reports of urea-impregnated zeolite chips, which can be used as slow-release nitrogen fertilizers. Li (2003) demonstrated the feasibility of using surfactant-modified zeolite (SMZ) using hexa decyltrimethylammonium as fertilizer carrier to control nitrate release and concluded that SMZ is a good sorbent for nitrate, while slow release of nitrate is achievable. These dual properties suggest that SMZ has a great potential as fertilizer carriers to control the release of nitrate and other anions. Ammonium (NH1 4 ) occupying the internal channels of clinoptilolite should be slowly set free, allowing the progressive absorption by the crop which results in a higher dry matter production of crops (Milla´n et al., 2008).Tiny pores in the clinoptilolite framework are large enough for small cations like ammonium and potassium to enter, but too small for nitrifying bacteria to enter. This means that once ammonium is held internally on the cation-exchange sites within clinoptilolite, it is not likely to be leached out easily as water passes through. It is more likely that it will move out slowly and be taken up in small amounts by the turfgrass plant, similar to the way a slow-release fertilizer works. Nitrification (conversion of ammonium to nitrate) was substantially reduced (Petrovic, 1990). Not only does clinoptilolite improve nitrogen fertilization efficiencies, it also reduces nitrate leaching by inhibiting the nitrification of ammonium to nitrate (Perrin et al., 1998). Several reports have suggested that increased N use efficiency occurs on zeolite

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amended soils (Ferguson and Pepper, 1987; Ferguson et al., 1986; MacKown and Tucker, 1985). Kavoosi (2007) found increased nitrogen use efficiency in rice due to application of zeolites. Nitrogen dynamics in soil air water systems is of great interest in agriculture for its rational use. Higher nitrogen efficiency is an essential factor for reducing environmental contamination. Surface and groundwater contamination as a result of nitrogen fertilization has been demonstrated in Argentina (Andriulo et al. 2000; Costa, 2000). The pronounced selectivity of clinoptilolite to NH4 ion was exploited in slow release of chemi-cal fertilizers (Minato, 1968). Soil clinoptilolite nitrogen dynamics are variable, depending on the physico-chemical soil characteristics, clinoptilolite and nitrogen doses applied and crop management (Ando et al., 1996; Babaririck and Pirela, 1984; Kolyagin and Karasev, 1999; Lewis et al., 1984; Postnikov et al., 1996). The high potential of the clinoptilolite has been demonstrated as a vehicle for nitrogen fertilizers, with the aims of decreasing negative impacts on the environment and increasing fertilizer efficiency (Milla´n et al., 2008). Crespo (1989) showed, in a pot experiment with clinoptilolite, an increase of around 130% in N use efficiency, N uptake, and dry matter yield of Brachiaria decumbens. Improving phosphorus use efficiency: Zeolites have been used to control release of fertilizer components. Ammonium-charged zeolites have been shown their ability to increase the solubilization of phosphate minerals, leading to improved phosphorus uptake and yields of crops. Studies conducted to examine solubility and cation-exchange relationships in mixtures of rock phosphate and NH1 4 - and K-saturated clinoptilolite revealed that mixtures of zeolite and phosphate rock have the potential to provide slow-release fertilization of plants in synthetic soils by dissolution and ion-exchange reactions (Allen et al., 1993). The power-function equation has been found to describe the transport kinetics of the nutrient release process (Allen et al., 1996) in these systems. Investigation conducted to study the effects of the potassium- and ammonium-saturated clinoptilolite on P availability in Ferrosols revealed that potassium- and ammonium-saturated clinoptilolite can increase P solubility while providing K and NH1 4 to the soil, a concurrent positive effect for plant growth (Hua et al., 2006). Enhanced organic manure efficiency: It has been reported that zeolites, with their specific selectivity for ammonium (NH1 4 ), can take up this specific cation from either farmyard manure, composts, or ammonium-bearing fertilizers, thereby reducing losses of nitrogen to the environment. Ammonium-charged zeolites have also been tested successfully for their ability to increase the solubilization of phosphate minerals. Rodrı´guez et al. (1994) confirmed that zeolite mixed with manure increases the effectiveness of organic fertilizers on meadowland soils. Most of the

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manure-ammonia sequestered in the zeolite is unavailable to nitrifying bacteria because of the small (4 5 A ) pore size of the crystal lattice structure (Mumpton, 1999). Experiments of Leggo (2000) revealed that zeolite incorporated with poultry manure served as an effective fertilizer and soil conditioner. Chuprova et al. (2004) found the beneficial effect of zeolite fertilizers on mobile humus substances of Chernozem and on biological productivity of maize. Natural zeolites are able to bind humic acid through the action of the surface extra-framework cations and that this ability was markedly enhanced if the zeolitic material was enriched by divalent cations, especially Ca21 (Capasso et al., 2005). Improving herbicide use efficiency: Controlled release of inputs is being employed extensively in agriculture to deliver active substances like pesticides and herbicides. Porous materials with well-ordered structures are attractive candidates for storage and release of organic guest molecules. Controlled release of paraquat using zeolite has been reported by Zhang et al. (2006). The release of paraquat from zeolites can occur only via ion-exchange because of charge neutrality of the zeolite. Improving water use efficiency: Zeolites possess high water holding capacities without reducing air filled pore space (Huang and Petrovic, 1994). The results of field studies clearly showed that zeolites act as soil amendments for crop production improve available water to the plants (Olczyk, 2005). Zeolite increased the water use efficiency of strawberry plants (Abdi et al., 2006). Improving crop yields: Addition of clinoptilolite increased yields of barley, potato, clover, and wheat after adding 15 ton ha21 in a sandy loam soil (Mazur et al., 1986). Pierla et al. (1984) found that in the field, the zeolite clinoptilolite reduced corn yields, while in the greenhouse this material appeared to act as a slow-release fertilizer increasing the growth of radish after three successive harvests. Bouzo et al. (1994) found increased productivity of sugar cane with utilization of 6 ton ha21 of zeolite in an Oxisol. Carrion et al. (1994) observed that the application of 150 kg ha21 of urea coated with 5 10% of zeolite increased productivity of rice and tomato crops. Wiedenfeld (2003) found that zeolite application did not affect cabbage yields, but pepper yields showed a quadratic response to zeolite application rate, primarily as an initial decrease than an increase in fruit size as rate increased. Highest green herbage yield of Alfalafa was obtained by Turk et al. (2006) when 20% zeolite180% soil was used. Remediation of heavy metal contaminated soils: Reducing the plant availability of heavy metals (Cd, Pb, Cr, Zn, Cu, etc.) in soils is critical for optimizing agricultural production in areas with heavy metal contaminated soils. Phytoavailability of heavy metals correlates best with their concentrations in soil solution rather with their total content in soil (Kabata-Pendias and Bru¨mmer, 1992). The removal of heavy metals in polluted areas is very difficult because they persist in soils for very long periods. However, the fixation of heavy metals in a nonavailable form could be

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a useful method for soils that are already contaminated by heavy metals. Heavy metal phytoavailability may be reduced if the metals are sorbed or precipitated from the soil solution. One of the ways to heavy metal immobilization may be the application of zeolites. Zeolites in general have large cation-exchange capacity (Table 1) and expectedly attract positive-charged ions and, therefore, are widely used for sequestration of cationic pollutants like heavy metals (Kumar et al., 2007). Natural and artificial zeolites increase ion-exchange sites in soils in addition to offering absorption sites for small molecules, due to their porous structure. Consequently, zeolites ˇ are able to retain heavy metals in soil (Mu¨hlbachova´ and Simon, 2003). Some zeolites, for example, clinoptilolite, are stable in acid conditions up to pH 2 (Ming and Mumpton, 1989). High affinity of zeolites to heavy metals has been demonstrated (Barrer, 1978; Tsadilas, 2000). Alexander and Christos (2003) studied Pb adsorption in soil and zeolite, and reported that clinoptilolite zeolite sorbed 20 30 times more Pb than the soil. Application of zeolite leads to a decrease of Pb concentrations in soil solution retaining the metal in the solid phase, where it should be less available for plants. Calculations based on the value of maximal sorption capacity of zeolite reveal that 1% added zeolite can retain 3.6 mmol Pb kg21 or 750 mg kg21 soil. Advantage of zeolite for soil remediation is its high efficiency independent of soil pH in the pH range 3 5 (Alexander and Christos, 2003). Leggo et al. (2006) showed that organo-zeolitic soil systems offer an opportunity to revegetate land made barren by metal pollution and as a consequence reduction of erosion and dissemination of contaminants. Wastewater treatment: A promising alternative option to remove specific contaminants from aqueous solution could be the use of low-cost sorbent materials. Among the different minerals, which possess sorbent properties, zeolites appear to be one of the most promising sorbents for this purpose (Tashauoei et al., 2010). Different kinds of natural zeolites are most frequently suggested as ammonium exchangers for wastewater treatment applications (Hedstrom, 2001). It is well known that aluminosilicate molecular sieves (zeolites) are considered the best sorbents which are used in technological processes of division and deep clearing of liquid and gas mixtures due to their chemical nature and particularities of their porous structure (Nesterenko, 2007). Clinoptilolite is known for its ability to remove ammonium from polluted waters (Rahmani and Mahvi, 2006). Matulova´ and Klokoˇcnı´kova´ (1994) reported 50% inhibition of algae growth after zeolite addition to water. Haggerty and Bowman (1994) reported that SMZ, a type of inexpensive anion exchanger, removes anionic contaminants from water. Results of Erdem et al. (2004) indicate that natural zeolites hold great potential to remove cationic heavy metal species from industrial wastewater.

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8. Researchable Issues There exists a wide scope for zeolites use in agriculture. Rational and profitable use of these zeolites, however, requires a systematic and comprehensive research effort. The most important aspects of future research include: (a) Characterization of the available zeolite deposits in each country, (b) determining physical stability of zeolites in a variety of soil environments, (c) development of methodologies for low-cost and effective organo-zeolitic manure/fertilizers, (d) assessment of nutrient release pattern from organo-zeolites, (e) assessing long-term impact of zeolites on soil biological functions, (f ) understanding mechanisms of zeolite mediated heavy metal stabilization in contaminated soil, and (g) development of zeolitic herbicides to minimize the herbicidal residues in soil plant systems.

9. Conclusions Over the years, there has been a growing interest in and recognition to zeolites use in agriculture because of public concerns about quality and sustainability of soil resources under intensive production systems. Zeolites find a large number of potential applications in agriculture, particularly in soil management. They can be used as either carriers of nutrients or medium to free nutrients to promote nutrient use efficiency. Zeolites are effective as soil ameliorants and in remediation of heavy metal contaminated soils. Research efforts are underway in many countries to exploit the potential of zeolites for the maintenance of soil productivity in perpetuity.

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C H A P T E R F I V E

Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time R.A. Viscarra Rossel,
V.I. Adamchuk,† K.A. Sudduth,‡ N.J. McKenzie,
and C. Lobsey
Contents 1. Introduction 1.1. Proximal soil sensing 1.2. The sampling dilema - Where to measure using proximal soil sensors? 2. Proximal Soil Sensing Techniques 2.1. γ-rays 2.2. X-rays 2.3. Ultraviolet, visible, and infrared reflectance spectroscopy 2.4. Laser-induced breakdown spectroscopy 2.5. Microwaves 2.6. Radio waves 2.7. Magnetic, gravimetric, and seismic sensors 2.8. Contact electrodes 2.9. Mechanical sensors 2.10. Telemetry—Wireless sensing 2.11. Geographic positioning and elevation 2.12. Multisensor systems 2.13. Core scanning and down-borehole technologies 3. Proximal Sensors Used to Measure Soil Properties 3.1. Soil water and related properties 3.2. Nutrients and elements 3.3. Cation exchange capacity 3.4. Carbon 3.5. pH 3.6. Clay, silt, and sand


† ‡

245 246 250 251 251 253 254 256 256 257 261 262 264 266 267 268 269 270 270 271 272 272 272 273

CSIRO Land and Water, Bruce E. Butler Laboratory, Canberra, ACT, Australia Bioresource Engineering Department, McGill University, Ste-Anne-de-Bellevue, QC, Canada USDA Agricultural Research Service, Cropping Systems and Water Quality Research, Columbia, MO

Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00005-1

© 2011 Elsevier Inc. All rights reserved.

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3.7. Soil mineralogy 3.8. Soil strength, bulk density, and related properties 4. Summary 5. General Discussion and Future Aspects Acknowledgments References

273 273 274 274 281 281

Abstract This chapter reviews proximal soil sensing (PSS). Our intent is for it to be a source of up-to-date information on PSS, the technologies that are currently available and their use for measuring soil properties. We first define PSS and discuss the sampling dilemma. Using the range of frequencies in the electromagnetic spectrum as a framework, we describe a large range of technologies that can be used for PSS, including electrochemical and mechanical sensors, telemetry, geographic positioning and elevation, multisensor platforms, and core measuring and down-borehole sensors. Because soil properties can be measured with different proximal soil sensors, we provide examples of the alternative techniques that are available for measuring soil properties. We also indicate the developmental stage of technologies for PSS and the current approximate cost of commercial sensors. Our discussion focuses on the development of PSS over the past 30 years and on its current state. Finally, we provide a short list of general considerations for future work and suggest that we need research and development to: (i) improve soil sampling designs for PSS, (ii) define the most suitable technique or combination of techniques for measuring key soil properties, (iii) better understand the interactions between soil and sensor signals, (iv) derive theoretical sensor calibrations, (v) understand the basis for local versus global sensor calibrations, (vi) improve signal processing, analysis, and reconstruction techniques, (vii) derive and improve methods for sensor data fusion, and (viii) explore the many and varied soil, agricultural, and environmental applications where proximal soil sensors could be used. PSS provides soil scientists with an effective approach to learn more about soils. Proximal soil sensors allow rapid and inexpensive collection of precise, quantitative, high-resolution data, which can be used to better understand soil spatial and temporal variability. We hope that this review raises awareness about PSS to further its research and development and to encourage the use of proximal soil sensors in different applications. PSS can help provide sustainable solutions to the global issues that we face: food, water, and energy security and climate change. Keywords: Proximal soil sensing; geophysics; soil spectroscopy; gamma radiometrics; electromagnetic induction; electrical conductivity; electrochemical sensing; mechanical sensors; multisensor platform; mobile soil sensors; sensor data fusion; soil measurement; soil analysis; soil sampling

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1. Introduction Our scientific understanding of the unique qualities and functions of soils has been gained through long and arduous soil surveys complemented by careful chemical, physical, mineralogical, and biological laboratory analysis. These conventional methods continue to serve us well, but they can be expensive, complex, time consuming, and some are only qualitative. The growing demand for good quality, inexpensive soil information underlines these shortcomings. We need better soil information to solve pressing problems such as how to monitor the effects of climate change on soil, how to populate models of key processes, how to use precision agriculture for improving the sustainability and efficiency of food production, and how to assess and remediate contaminated land. These applications have prompted the development of sensors to measure soil properties and complement, or replace, the more conventional laboratory techniques used for their analyses. Sensors provide quantitative results and can be more time- and costeffective than conventional laboratory analyses. They are becoming smaller, faster, more accurate, more energy efficient, wireless, and more intelligent. Many such devices can be used for proximal soil sensing (PSS), for example, ion-sensitive field effect transistors (ISFETs) to measure soil pH and soil nutrients, or portable visiblenear-infrared (visNIR) spectrometers to measure soil properties like organic carbon content and mineral composition. Worldwide, a vast amount of research is being conducted to develop proximal soil sensors and techniques for their use in various applications. The research includes investigations on the use of frequencies across the electromagnetic (EM) spectrum (Fig. 1). For example, some are using γ-radiometrics to gain an understanding of soil patial variability and the underlying parent material; X-ray fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS) to measure soil

Figure 1 The electromagnetic (EM) spectrum. For color version of this figure, the reader is referred to the web version of this book.

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elemental analysis; visNIR and mid-infrared (mid-IR) energies to measure soil carbon and mineralogy; ground-penetrating radar (GPR) to measure soil water content; and electromagnetic induction (EMI) to measure soil electrical conductivity. Research on the use of ion-selective electrodes (ISEs) and ISFETs to measure soil ion activities and mechanical systems to measure soil strength is also progressing. Many of these sensors are currently in a developmental phase and are used primarily in research, while others are available commercially. The most common techniques, which account for a large portion of the PSS literature, relate to the use of EMI and soil visNIR spectroscopy. With rapid ongoing technological developments, we expect many more explorations to find new soil sensing solutions to help improve our understanding of soil processes. The aim of this review article is to discuss the current state of PSS and the potential benefits and opportunities that it presents for soil science. In it, we define PSS; provide a comprehensive review of current techniques, including multisensor platforms; report their development status and estimates of their cost; discuss the soil properties that we are interested in measuring using proximal soil sensors; finally, we provide a synthesis and discuss the future of PSS.

1.1. Proximal soil sensing We define PSS as the use of field-based sensors to obtain signals from the soil when the sensor’s detector is in contact with or close to (within 2 m) the soil (Viscarra Rossel and McBratney, 1998; Viscarra Rossel et al., 2010a). The sensors provide soil information because the signals correspond to physical measures, which can be related to soils and their properties. Our definition of PSS therefore precludes remote sensing and also laboratory measurements of soil properties with benchtop instruments. We do however, acknowledge that the development of many proximal soil sensors starts in the laboratory, and that some (e.g. vis-NIR sensors) use calibrations derived from laboratory measurements. Proximal soil sensors may be described by the manner in which they measure (invasive [in situ or ex situ] or noninvasive) the source of their energy (active or passive), how they operate (stationary or mobile), and the inference used in the measurement of the target soil property (direct or indirect) (Fig. 2). If there is sensor-to-soil contact during measurement, then the proximal soil sensor is invasive. It is noninvasive if there is no contact between the sensor and the soil. Measurements with invasive proximal soil sensors may be made in situ (i.e., the measurements are made within the soil) or ex situ (i.e., the measurements are made on excavated soil, e.g., measurements on soil cores). A proximal soil sensor is active if for the measurements it produces its own energy from an artificial source. It is

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Measurement

Energy

Operation

Inference

Non-invasive

Passive

Mobile

Indirect

PROXIMAL SOIL SENSING Invasive In situ

Active

Stationary

Direct

Ex situ

Figure 2 Proximal soil sensors may be described by their measurement being invasive and in situ or ex situ, or noninvasive, their energy source being active or passive, their operation being stationary or mobile, and their method for inferring soil properties being direct or indirect.

passive if it uses naturally occurring radiation from the sun or earth. A stationary sensor acquires measurements in a fixed manner. Mobile proximal soil sensors are those that measure soil properties while moving or “onthe-go.” Usually, mobile proximal soil sensors are used for fine-resolution soil mapping. Adamchuk et al. (2004) reviewed “on-the-go” proximal soil sensors for precision agriculture. If the measurement of the target soil property is based on a physical process, then the proximal soil sensor is said to be direct. However, when the measurement is of a proxy and inference is with a pedotransfer function, then the proximal soil sensor is indirect. Table 1 describes the different techniques for PSS and the ways in which they function. For example, from Table 1 and Fig. 2, measurements with a tine-mounted visNIR proximal soil sensor are invasive and in situ, the sensor uses an active source of energy, it has mobile operation and depending on the soil property inference might be either direct (e.g., clay mineralogy) or indirect (e.g., cation exchange capacity or CEC). Measurements of an extracted soil core with a portable visNIR proximal soil sensor might be, depending on the measurement setup, invasive and ex situ or noninvasive, the sensor might use an active (halogen bulb) or passive (the sun) source of energy, it has stationary operation and inference might be either direct or indirect. Measurements with a γ-radiometer are noninvasive, use a passive source (naturally occurring radioisotopes of Cs, K, U, Th), operation is often mobile, although stationary measurements are also possible and inference is mostly indirect. The rationale for the use of proximal soil sensors is that, although their results may not be as accurate per individual measurement as for conventional laboratory analysis (i.e., their results may be more biased and/or imprecise), proximal soil sensors facilitate the collection of larger amounts of (spatial) data using cheaper, simpler, and less laborious techniques which, as an ensemble, are greatly informative. Moreover, the measurements are made at field conditions, they are

Table 1 Proximal soil sensors are described by their measurement being invasive (In) and in situ or ex situ, or noninvasive (N), their energy source being active (A) or passive (P), their operation being stationary (S) or mobile (M), and their method for inferring soil properties being direct (D) or indirect (I) Measurement EM range wavelength (m) 212

γ-rays (10

)

X-rays (10210) UVvisIR (1028 to 1024)

Microwaves (1022) Radio waves (101 to 106)

Energy

Operation

Inference

Technique

Invasive/noninvasive Active/passive Stationary/mobile Direct/indirect

INS TNM Active γ Passive γ XRF XRD UV Vis NIR MIR LIBS Microwave TDR

N In N N N N N In In In In N In

(in situ)

(in/ex (in/ex (in/ex (in/ex

situ)/N situ)/N situ) situ)

(in situ)

A A A P A A A A/P A/P A A A A

S/M S S S/M S S S S/M S/M S S S S

D D D D/I D D D/I D/I D/I D/I D I I

Electrical resistivity Electrochemical Mechanical

FDR/capacitance GPR NMR EMI ER Gypsum/granular ISE/ISFET Implement draft Mechanical resistance Fluid permeability Acoustic

In N N N In In In In In In In

(in situ)

(in situ) (in situ) (in/ex situ) (in situ) (in situ) (in situ) (in situ)

A A A A A P P P P A A

S/M S/M S M M S S/M M S/M S/M S/M

I D/I D I I D D D D I I

Note: INS, inelastic neutron scattering; TNM, thermalized neutron methods; XRF, X-ray fluorescence; XRD, X-ray diffractometry; UV, ultraviolet; vis, visible; NIR, near infrared; MIR, mid infrared; LIBS, laser-induced breakdown spectroscopy; TDR, time-domain reflectometry; FDR, frequency-domain reflectometry; GPR, ground-penetrating radar; NMR, nuclear magnetic resonance; EMI, electromagnetic induction; ER, electrical resistivity; ISE, ion-selective electrode; ISFET, ion-sensitive field effect transistor.

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acquired from the surface or within the soil profile and information is produced in a timely manner; that is, almost instantly. Therefore, PSS offers advantages to soil measurement that cannot be achieved by remote sensing or destructive sampling and laboratory analyses.

1.2. The sampling dilema - Where to measure using proximal soil sensors? The decision of where to measure using proximal soil sensors will depend on whether the proximal sensor is described as direct/indirect and stationary/mobile. If the sensor measures the target soil property directly and at fixed locations, the sampling problem will be the same as conventional spatial soil sampling because it requires optimization of the geographical coverage of the measurements. If the sensor measurements are direct and are made with a mobile, “on-the-go” system, then the sampling problem might relate to the frequency (or resolution) of the measurements so as to optimize the amount of information collected. If the measurements are made indirectly, a calibration will need to be developed (using the sensor’s measurements and soil samples collected and analyzed in the laboratory) to predict the target property from the sensor measurements. In this case, a calibration sampling design that optimizes coverage of property (or feature) space will be required. Ideally, the sampling should also cover geographic space so that landscape position and other location-induced phenomena are included in the calibrations. Most of the published literature considers either geographical space sampling or property space sampling designs, not both (de Gruijter, 2002; de Gruijter et al., 2006; van Groenigen and Stein, 1998). In agricultural land, it will also be important to consider field boundaries and other transition zones to prevent situations in which the samples do not represent the same soil as the nearest measurements obtained using the proximal soil sensors. Designs for calibration sampling have been proposed by Lesch (2005) and Minasny and McBratney (2006). Christy (2008) proposes a sampling design for the calibration of visNIR spectra collected using an on-the-go system. The approach covers the range of variability in property space and considers the location of field boundaries and transition zones but only indirectly considers geographical coverage. De Gruijter et al. (2010) describe geographical and property space sampling with proximal soil sensors for fine-resolution soil mapping, and Adamchuk et al. (2008, 2011) compare designs for mobile PSS that consider geographic and property space and field boundaries and other transition zones. Further research is needed to develop optimal sampling designs for the different types of proximal soil sensors.

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2. Proximal Soil Sensing Techniques Presently, proximal soil sensors can measure the soil’s ability to accumulate and conduct electrical charge, to absorb, reflect, and/or emit EM energy, to release ions, and to resist mechanical distortion. Using energies in the EM spectrum as the framework, below we describe currently available technologies for PSS.

2.1. γ-rays In the EM spectrum, γ-rays occur at quantum energies between 1 MeV and 124 keV and have frequencies of 10201024 Hz and short wavelengths of less than 10212 m (Fig. 1). They contain a very large amount of energy and are the most penetrating radiation from natural or man-made sources. 2.1.1. γ-ray spectrometers A γ-ray spectrometer is an instrument that measures the distribution of the intensity of γ radiation versus the energy of each photon. Most soil γ-ray spectrometers use scintillators with either thallium-doped sodium iodide or thallium-doped cesium iodide crystals, although other materials are also available (International Atomic Energy Agency (IAEA), 2003). When these are hit by the ionizing radiation, they fluoresce and a photomultiplier tube is used to measure the light from the crystal. The photomultiplier tube is attached to an electronic amplifier, which quantifies the signal. Active γ-ray sensors use a radioactive source (e.g., 137Cs) to emit photons of energy that can then be detected using a γ-ray spectrometer. The theory of operation for measuring soil properties such as water, or bulk density using active γ-rays, indicates that when these are emitted and pass through the soil, photons are transmitted following the BeerLambert law (Wang et al., 1975). The attenuation of the signal is determined by the thickness of the material, its density, and its mass attenuation coefficient. Passive γ-ray sensors (Fig. 3A) measure the energy of photons emitted from naturally occurring radioactive isotopes of the element from which they originate. While many naturally occurring elements have radioactive isotopes, only potassium (40K) and the decay series of uranium (238U and 235U and their daughters) and thorium (232Th and its daughters) have long half-lives, are abundant in the environment, and produce γ-rays of sufficient energy and intensity to be measured. The result is a γ-ray energy spectrum (Fig. 3B). Gamma-ray sensors have been more commonly used from remote sensing platforms (Minty, 1997); however, the techniques are also used to measure soil properties proximally (Viscarra Rossel et al., 2007; Wong et al., 2009). The advantage of proximal γ-ray sensing over

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remote sensing is that it is more directly related to soil materials and less prone to the effects of surface cover and geometry. Soil mineralogy, particle size, and the effects of attenuating materials such as water and density control the γ-ray signal. Soil parent material, the intensity of weathering, and the geometry of near-surface soil layers are therefore also important. 2.1.2. Neutron scattering methods Neutron scattering may be categorized into elastic and inelastic techniques. The most common soil sensor that uses elastic neutron scattering is the neutron probe for measuring soil water (Gardner and Kirkham, 1952). Neutrons emitted from a radioactive source into the soil are slowed by elastic collision (i.e., the emitted neutrons have the same energy as those that are injected) with the nuclei of atoms of low atomic weight, such as hydrogen. Hydrogen can slow fast neutrons more effectively than any other element present in soil and the density of the resulting cloud of slow neutrons is a function of the amount of water in the soil. Schrader and Stinner (1961) proposed inelastic neutron scattering (INS) as a technique for elemental analysis of surfaces. Wielopolski et al. (2008) proposed it for the measurement of soil carbon and other elements. INS relies on the detection of γ-rays that are emitted following the capture and reemission of fast neutrons as the sample is bombarded with neutrons from a pulsed neutron generator. The emitted γ-rays are characteristic of the excited nuclide and the γ-rays intensity is directly related to the elemental content of the sample. The detectors used are the same as those used in γ-ray spectroscopy (see above). Zreda et al. (2008) described a passive technique where the neutron source is derived from the naturally occurring neutrons generated through

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cosmic-ray interaction with the atmosphere. The sensor’s detector counts neutrons that are back scattered out of the soil and these can be correlated to soil water content. Since the method relies on a natural source, low counts are obtained and significant integration is required. As such, it is suited to stationary measurements (e.g., for monitoring changes in soil water).

2.2. X-rays X-rays have quantum energies between 124 keV and 124 eV and occur in the EM spectrum at a frequency range of 10171020 Hz and wavelengths of about 102121029 m (Fig. 1). They contain a large amount of energy and have been classified as either “hard” (shorter wavelengths) or “soft” (longer wavelengths). 2.2.1. X-ray fluorescence XRF is used to measure elements in soil samples. The technique relies on the fluorescence at specific energies of atoms that are excited when irradiated with X-rays. Detection of the specific fluorescent photons enables the qualitative and quantitative analysis of the elements in a sample. XRF spectroscopy has been used in the laboratory for many years. Kalnicky and Singhvi (2001) provide a comprehensive overview of XRF for environmental analysis. Portable, handheld XRF technology has gained acceptance as an analytical approach in the environmental community, particularly for rapid measurement of metal contaminants. 2.2.2. X-ray diffractometry X-ray diffraction (XRD) is a nondestructive technique used to acquire detailed information on the mineral composition of the soil. Moore and Reynolds (1997) provide details of the laboratory technique. The use of portable XRD systems and research prototypes are starting to appear in the literature because many of the strict hardware requirements, such as reproducible alignment of the X-ray detectors and long acquisition times, are being overcome (Gianoncelli et al., 2008). Sarrazin et al. (2005) describe the development and testing of a portable combined XRD/ XRF system that can be used in the field. The system was constructed for remote planetary exploration, but has also been used as a field tool for geological research. Gianoncelli et al. (2008) also describe the development of a portable XRD/XRF system for simultaneous elemental analysis and phase identification of inorganic materials.

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2.3. Ultraviolet, visible, and infrared reflectance spectroscopy Diffuse reflectance spectroscopy has been used in soil science research since the 1950s and 1960s (Bowers and Hanks, 1965; Brooks, 1952). However, it is only in around the past 20 years, most likely coinciding with the establishment of chemometrics and multivariate statistical techniques in analytical chemistry, that its usefulness and importance in soil science has been realized. Interest in using reflectance spectroscopy to measure soil properties is widespread because the techniques are rapid, relatively inexpensive, require minimal sample preparation, are nondestructive, require no hazardous chemicals, and several soil properties can be measured from a single scan (Viscarra Rossel et al., 2006a). Ultraviolet radiation possesses quantum energies of 1242.1 eV, has a frequency range of about 10151017 Hz, and wavelengths of around 10291027 m (Fig. 1). There is little in the literature on the use of ultraviolet radiation for PSS, and often the technique is combined with visible or infrared spectroscopy (Islam et al., 2003). Visible light has energies between 2.1 and 1.65 eV, frequency in the range 4 3 10147 3 1014 Hz, and wavelengths of 7 3 10274 3 1027 m (Fig. 1). Absorptions of ultraviolet and visible radiation occur under high energies due to the excitation of outer electrons. Absorption of energy by an atom or molecule involves the promotion of electrons from their ground state to an excited state. Absorptions in organic molecules are restricted to certain functional groups (chromophores) that contain valance electrons of low excitation energy. Many inorganic species, such as iron oxides in soil, show charge transfer absorptions (also called charge transfer complexes) (Schwertmann and Taylor, 1989). For a complex to demonstrate charge transfer behavior, one of its components must be able to donate electrons and another must be able to accept them. Thus, absorptions involve transfer of an electron from the donor to an orbital associated with the acceptor. A soil spectrum in the visible range is shown in Fig. 4A. The near-infrared (NIR) portion of the EM spectrum has a frequency range of 1.2 3 10144 3 1014 Hz, with wavelengths of 7 3 10272.5 3 1026 m, while the mid-IR has a frequency range of 3 3 10121.2 3 1014 Hz and wavelengths of 2.5 3 10262.5 3 1025 m (Fig. 1). Energies in the NIR range between 1.65 eV and 124 meV, while those in the mid-IR range from 124 to 12.4 meV. The farIR has an energy range of 12.41.24 meV, a frequency range of 3 3 10123 3 1011 Hz, and wavelengths between 2.5 3 1025 and 5 3 1025 m (Fig. 1); however, there are no publications on the use of farIR for PSS. Conversely, there is a vast amount of literature on the use of visNIR and mid-IR for soil analysis (Stenberg et al., 2010) and, increasingly, on the use of these techniques for PSS (Ben-Dor et al., 2008; Christy, 2008; Reeves et al., 2010; Viscarra

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Rossel et al., 2009; Waiser et al., 2007). Infrared radiation does not have enough energy to induce electronic transitions as with ultraviolet and visible light. Their absorptions are restricted to compounds with smaller energy differences in the possible vibrational states. For a molecule to absorb infrared energy, the vibrations within a molecule must cause a net charge in the dipole moment of the molecule. The alternating electrical field of the radiation interacts with fluctuations in the dipole moment of the molecule. If the frequency of the radiation matches the vibrational frequency of the molecule, then radiation will be absorbed, causing a change in the amplitude of the molecular vibration. The positions of the molecules are not fixed and are subject to different stretching or bending vibrations. The mid-IR contains more information on soil mineral and organic composition than the visNIR, and its multivariate calibrations are generally more robust (Viscarra Rossel et al., 2006a). The reason is that the fundamental molecular vibrations of soil components occur in the mid-IR, while only their overtones and combinations are detected in the NIR (Stenberg et al., 2010). Hence, soil NIR spectra display fewer and much broader absorption features compared to mid-IR spectra (Fig. 4B and C, respectively). The adaptation of visNIR spectrometers for PSS has been ongoing for the past two decades, with the first field prototype mobile systems developed by Shonk et al. (1991) and Sudduth and Hummel (1993). Since then, other prototype mobile systems have been developed by Shibusawa et al. (2001), Mouazen et al. (2005), Stenberg et al. (2007), and Christy (2008), who described a commercially available mobile visNIR system. Alternatively, stationary PSS of visNIR reflectance has been implemented using portable instruments (Ben-Dor et al., 2008; Kusumo et al., 2011; Viscarra Rossel et al., 2009; Waiser et al., 2007). There are

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fewer reports of portable, mid-IR systems for PSS ( Jahn and Upadhyaya, 2010; Reeves et al., 2010).

2.4. Laser-induced breakdown spectroscopy LIBS is made possible because of lasers. The technology uses an optically focused short-pulsed laser to heat the surface of the soil sample to the point of volatilization and ablation. This results in the generation of a hightemperature plasma on the surface of the sample. It is important to note that the plasma forms over a limited area so that only a very small amount of sample is measured during each event. As it cools, the excited atomic, ionic, and molecular fragments produced in the plasma emit radiation characteristic of the elemental composition within the volatilized material. A spectrometer capable of resolving spectra in the range 200900 nm is used to detect the emitted radiation. A sample spectrum is shown in Fig. 5. LIBS has been used for elemental analysis in geochemical exploration (Mosier-Boss et al., 2002), for the analysis of soil carbon (Cremers et al., 2001) and other elements (Hilbk-Kortenbruck et al., 2001). Fiber optic technology has made it possible to develop portable (Harmon et al., 2005) and mobile LIBS systems (Bousquet et al., 2008).

2.5. Microwaves The quantum energy of microwaves ranges between B12.4 and 12.4 μeV, near frequencies of 3 3 10113 3 109 Hz, and wavelengths of 1 3 1023 5 3 1025 m (Fig. 1). Microwave sensors are typically used for remote sensing

Figure 5 Typical soil laser-induced breakdown spectroscopy (LIBS) spectrum.

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( Jackson et al., 1984), but sensors have been constructed for measuring soil water proximally (Whalley, 1991). They measure either changes in the emissivity of the soil or changes in microwave attenuation caused by changes in water content. This dependence of the soil’s emissivity on its water content is due to the large contrast between the dielectric properties of free water (k0 5 80), dry soil (k0 5 25 depending on its bulk density), and air (k0 5 1). The large dielectric constant for water results from the alignment of the electric dipole moment of the water molecule in response to an applied field. As the water content of a soil increases, its dielectric constant and attenuation increase, and changes in soil emissivity are produced. Therefore, microwave sensors measure the thermal radiation emitted by the soil, which is generated within the volume of the soil and is dependent on the water content (i.e., dielectric properties) and temperature of the soil.

2.6. Radio waves Radio waves occur in the EM spectrum at frequencies less than 3 3 109 Hz with wavelengths greater than 1 3 1023 μ and energies less than 12.4 μeV (Fig. 1). 2.6.1. Time- and frequency-domain reflectometry and capacitance Time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and capacitance sensors use the dielectric properties of soils, that is, their permittivity, to measure water content. TDR instruments consist of a transmission line (TL) and a waveguide made up of two or three parallel metal rods that are inserted into the soil. The instrument produces a series of precisely timed electrical pulses with frequencies of 2 3 1073 3 109 Hz, which travel along the TLs and waveguide. These frequencies provide a response that is less dependent on soil-specific properties like texture, salt content, and temperature. Impedance along the waveguide varies with the dielectric constant of the bulk soil. The soil bulk dielectric constant is determined by measuring the time it takes for the electromagnetic pulse to propagate along the TL and waveguide surrounded by the soil. Since the propagation velocity is a function of the soil bulk dielectric constant, the latter is proportional to the square of the transit time out and back along the TL and waveguide. Because the dielectric constant of soil depends on the amount of water present, soil volumetric water content can be inferred from the reflected measurements. Noborio (2001) provides a good overview of the use of TDR for the measurement of soil water content and electrical conductivity. FDR and capacitance probes consist of two or more capacitors (rods, plates, or rings) that are inserted into the soil. Plates are usually annuli arranged concentrically to facilitate borehole measurements (Dean et al., 1987). These capacitors use the soil as a dielectric and hence depend on the

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soil water content. When the capacitor is connected to an oscillator to form an electric circuit, changes in soil water can be detected by changes in the circuit’s operating frequency. In FDR, the oscillator frequency is controlled within a certain range to determine the resonant frequency at which the amplitude is greatest, which is a measure of the soil water content. In capacitance, a measure of the soil’s permittivity is determined by measuring the charge time of the capacitor in the soil. There are three basic parts to a capacitance sensor (Dally et al., 1993): the target plate, the air space, and the sensor head. The target plate accumulates the voltage that will eventually dissipate across the air space. The air space is the gap between the target plate and the sensor head. This space can be filled with soil to increase or decrease the voltage being accumulated on the target plate. The sensor head measures the voltage accumulated by the target plate and dissipated through the soil. Whalley et al. (1992) developed one such sensor, which was also found to be sensitive to fluctuations in soil bulk density. Liu et al. (1996) and later Andrade-Sanchez et al. (2007) and Adamchuk et al. (2009) also evaluated a dielectric-based moisture sensor under dynamic conditions by incorporating it into a nylon block attached to an instrumented tine (Fig. 6). A series of studies demonstrated that salinity, texture, and temperature also affected measurements. Soil-specific calibrations are recommended because the operating frequency of these devices is generally below 1 3 108 Hz. At these frequencies, the bulk permittivity of the soil may change and measurements are more affected by texture, salinity, and bulk density.

Figure 6

A capacitance soil water content sensor prototype.

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2.6.2. Nuclear magnetic resonance Nuclear magnetic resonance (NMR) is based on the interaction between nuclear magnetic moments and applied static and radiofrequency magnetic fields (Matzkanin and Paetzotd, 1982). Paetzold et al. (1985) developed a tractor-mounted NMR instrument and used the technique to measure soil water content. The instrument detected and measured the NMR signal from the hydrogen in water. By adjusting the strength of the radiofrequency and static magnetic fields, the researchers were able to measure soil water content at depths of 38, 51, and 63 mm. Their findings suggested that the NMR signal is a linear function of volumetric water content and is not affected by clay mineralogy, soil organic matter, or texture. Furthermore, the technique can distinguish between water that is bound by clay particles and not available to plants, and that which is available for plant use. Magnetic resonance sounding (MRS) uses the NMR principle that is used in medical brain scanning (i.e., MRI or magnetic resonance imaging) to measure subsurface free water and hydraulic properties (Lubczynski and Roy, 2003). It is also known as surface NMR and can be used to measure water content and porosity to depths up to 1500 m. 2.6.3. Ground-penetrating radar GPR uses the transmission and reflection of high-frequency (106109 Hz) electromagnetic waves in the soil. They have transmitter and receiver antennas that can be moved across the soil surface (Fig. 7). Much like with TDR sensors, the primary control on the transmission and reflection of the electromagnetic energy is the dielectric constant. Because of the large contrast between the dielectric constants of water, air, and minerals, GPR can be used to measure variations in soil water content (Lambot et al., 2004). Unlike TDR, however, GPR measurements are non invasive and the sensors can measure soil water content of relatively large volumes of soil. The resolution of GPR images can be varied through the use of different antennae frequencies. Typically, higher frequencies increase the resolution at the expense of depth of penetration. Daniels et al. (1988) describe the fundamental principles of GPR. Knight (2001) provides an overview of GPR in environmental applications and Huisman et al. (2003) review its use for soil water determinations. The penetration depth of GPR measurements is affected by the electrical conductivity of the soil. Good penetration depth of up to about 15 m can be achieved in dry sandy soils or massive dry materials such as granite and limestone. As the conductivity increases, penetration depth decreases because the electromagnetic energy is more quickly dissipated into heat, causing a loss in signal strength at depth. In highly conductive soils, such as those with large amounts of clay, water and/or salt, penetration depth can be only a few centimeters. Slowly

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Figure 7

A ground-penetrating radar (GPR) system.

changing water contents are also difficult to detect with GPR, and water profiling is generally not possible with most types of instruments. More abrupt changes, such as wetting fronts, are easier to detect and this use of GPR is more appropriately applied in irrigated regions. 2.6.4. Electromagnetic induction EMI is a highly adaptable noninvasive technique that measures the apparent bulk electrical conductivity of soil (ECa). The instruments commonly have a transmitter and a receiver. Using a varying magnetic field of relatively low frequency (kHz), the technique induces currents in the ground in a way that ensures their amplitude is linearly related to the conductivity of the soil. The magnitude of these currents is determined by measuring the magnetic field that they generate. McNeill (1980) provides a good account. EMI has been used extensively in mapping soils since De Jong et al. (1979) first reported it. It has been particularly useful for mapping saline soils (Rhoades, 1993) and for precision agriculture (Corwin and Lesch, 2003). EMI coupled with a global positioning system (GPS) provides a rapid soil-mapping tool and, until now, may be the most commonly used proximal soil sensor (Fig. 8). Because most soil and rock minerals are very good insulators, the electrical conductivity sensed by an EMI unit is electrolytic and it takes place through the porewater system. The following factors are therefore important: shape, size, and connectivity of the pore system; water content; concentration of dissolved electrolytes in the soil water; temperature and phase of the pore water; and amount and composition of colloids (Rhoades et al., 1989).

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An on-the-go soil electromagnetic induction (EM-38) system.

While clay content, electrical conductivity of the soil solution, and water content are often recognized as the controlling factors that must be accounted for when calibrating EMI measurements (Williams and Baker, 1982; Williams and Hoey, 1987), it is not that simple. It is the pore system and its contents rather than the clay content per se that should be considered. Soils with significant clay content usually have a pore geometry dominated by finer-sized pores. In comparison to a sandy soil, greater proportions of these pores are filled and connected at comparable water contents, and this gives rise to their larger electrical conductivity. The bulk density of the soil should also be considered because it determines total porosity. Clay soils in most cropping areas usually have a substantial CEC, and cations in solution are in equilibrium with the charged clay surface—these cations also contribute to the electrolyte concentration. Finally, colloids—particularly those associated with organic matter—may also contribute to the measured conductivity. Effective measurement depth is a function of coil spacing and, under some conditions, frequency. For commercial EMI sensors, the depth of measurement can range from B0.37 m to more than 60 m. There are a number of operational issues with EMI sensors, including temperature effects (Robinson et al., 2004; Sudduth et al., 2001) and spurious signals due to nearby metal objects (Lamb et al., 2005).

2.7. Magnetic, gravimetric, and seismic sensors 2.7.1. Magnetics Magnetic sensors, or magnetometers, measure variations in the strength of the earth’s magnetic field and the data reflect the spatial distribution of

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magnetization in the ground. Magnetization of naturally occurring materials and rocks is determined by the quantity of magnetic minerals and by the strength and direction of the permanent magnetization carried by those minerals (Hansen et al., 2005). Typically, magnetics has been used for the detection of geological bodies; however, there is increasing use of the technique for near-surface applications. For example, for mapping field drainage for hydrologic modeling (Rogers et al., 2005); to better understand soil genesis and formation (Mathe and Leveque, 2003); to detect anthropogenic pollution on topsoils; through their associations with iron oxides (Schibler et al., 2002); and for rapid identification and mapping of soil heavy metal contamination ( Jordanovaa et al., 2008). 2.7.2. Gravity Gravity data can be collected using gravimeters (or gravitometers) and provide information on the local gravitational field. There are two types of gravimeter: relative and absolute. A relative gravimeter measures relative differences in the vertical component of the earth’s gravitational field based on variations in the extension of an internal spring in the gravimeter. The technique has typically been used to determine the subsurface configuration of structural basins, aquifer thickness, and geological composition. An absolute gravimeter measures the acceleration of free fall of a control mass. Absolute gravimetry can be used to measure mass water balances at regional or local scales (Nabighian et al., 2005). 2.7.3. Seismology Seismic reflection methods are sensitive to the speed of propagation of various kinds of elastic waves. The elastic properties and mass density of the medium in which the waves travel control the velocity of the waves and can be used to infer properties of the earth’s subsurface. Reflection seismology is used in exploration for hydrocarbons, coal, ores, minerals, and geothermal energy. It is also used for basic research into the nature and origin of rocks that make up the Earth’s crust (McCarthy and Thompson, 1988). It can be used in near-surface applications for engineering, groundwater and environmental surveying (Harry et al., 2005). A method similar to reflection seismology, which uses electromagnetic instead of elastic waves, is GPR.

2.8. Contact electrodes This section refers to techniques for measuring electrical properties of soils, such as their resistivity and dielectric, using direct injection of current into the soil using electrodes.

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2.8.1. Electrical resistivity Electrical resistivity (ER) can be used to determine the resistivity distribution of the measured soil volume. Measurements of ER usually require four electrodes: two to inject the current (current electrodes) and two to measure the resulting potential difference (potential electrodes). The ER of the soil is determined from this and measurements of the apparent electrical conductivity (ECa) are possible because resistivity is the reciprocal of conductivity. The technique has long been used in geophysics, and various configurations of electrodes can be used to control the volume and depth of measurement. The soil properties that affect measurements of soil with EMI instruments (see above) also affect resistivity measurements. Samoue¨lian et al. (2005) provide a good review on the use of ER in soil science. 2.8.2. Induced polarization Induced polarization (IP) measurements are essentially an extension of the four-electrode resistivity technique. IP operates by first injecting an electric current between a current electrode pair and the resulting voltage induced in the soil is measured between a potential electrode pair. However, IP captures both the charge loss (conduction) and the charge storage (polarization) characteristics of the soil. IP instruments have been used in hydrogeophysical applications, for example, to look at hydraulic properties of soil in the vadose zone (Binley et al., 2005; Bo¨rner et al., 1996). 2.8.3. Electrochemical sensors Electrochemical sensors have been developed to measure specific ions in solution. The most common of these are electrodes to measure pH; however, their uses for measuring various other ions are increasing in environmental applications. Their durability, portability, fast response, and ability to measure in unfiltered soil slurries are key advantages allowing direct measurements of soil chemical properties. PSS using electrochemical sensors is currently an active area of research with particular focus on the development of mobile soil pH, lime requirements, and nutrient sensing (Adamchuk et al., 1999; Adsett and Zoerb, 1991; Birrell and Hummel, 2001; Sibley et al., 2009; Viscarra Rossel and McBratney, 1997; Viscarra Rossel et al., 2005). Kim et al. (2009a) provide a recent review. 2.8.4. Ion-selective electrodes ISEs are potentiometric sensors that use ion-selective membranes to measure the concentration of the target species. When submerged in the solution to be analyzed, an electromotive force is generated at the sensing surface proportional to the log of the ion activity. The electromotive force

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can then be measured using a suitable reference system (e.g., a reference electrode). ISEs selective for many useful soil nutrients (nitrate, sodium, potassium, calcium) are commercially available and phosphate-selective electrodes for soil phosphorus are also being developed (Kim et al., 2007a). 2.8.5. Ion-sensitive field effect transistors ISFETs combine ISE technology with that of the field effect transistor (FET). The construction of the ISFET is as for the standard FET; however, the gate is replaced with a separate electrode (in contact with the analysis electrolyte) and the exposed insulating oxide (commonly SiO2 but also Al2O3, Ta2O5) is also left in contact with the electrolyte being analyzed. The charge developed on the oxide surface (due to proton interaction) now controls the sourcedrain current of the FET, which is then indicative of the electrolyte. Key advantages of pH ISFETs over standard glass pH electrodes are small size, increased durability, fast response, and the ability to mass produce using microelectronic manufacturing techniques. They have been used for proximal sensing of soil pH (Viscarra Rossel and Walter, 2004) and lime requirement (Viscarra Rossel et al., 2005). ISFETs can be chemically modified by depositing membrane layers on the oxide surface to produce CHEMFETs selective for other ionic species. CHEMFETs selective for nitrate, calcium, and potassium have been developed and evaluated for use in soil nutrient sensing (Artigas et al., 2001; Birrell and Hummel, 2000, 2001). 2.8.6. Metal electrodes Metal electrodes are also being explored for PSS applications to address a need for increased physical durability. Antimony electrodes are being researched as a durable alternative to glass electrodes in direct contact soil pH measurement (Adamchuk et al., 2009; Viscarra Rossel and McBratney, 1997). Kim et al. (2007a) explored the use of cobolt rod-based ISEs for measuring soil phosphates.

2.9. Mechanical sensors Another family of proximal soil sensors quantify soil properties by measuring the mechanical interaction between the sensor and the soil. Although there are no widely used commercial systems, a number of prototypes are being developed and include mechanical, acoustic, and fluid permeability sensors. 2.9.1. Integrated draft Soil strength, or mechanical resistance to failure, has been widely used to estimate the degree of soil compaction. Soil compaction and soil strength

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can be measured using tine-based sensors (Hayhoe et al., 2002; Lapen et al., 2002). A method to determine soil physical properties using specific draft measurements was proposed by Van Bergeijk et al. (2001). In their study, information gathered automatically during plowing was used to predict the spatial distribution of topsoil clay content. Sirjacobs et al. (2002) developed a soil strength sensor that was later evaluated by Hanquet et al. (2004). It consisted of a single chisel shank pulled through the soil at constant speed and a depth of 30 cm. In addition to the integrated (bulk) measure of draft, sensors have also been used to measure vertical variation in soils to identify hardpan layers. These sensors measure by moving the tool up and down while traveling across a field (Hall and Raper, 2005; Manor and Clark, 2001; Pitla et al., 2009; Stafford and Hendrick, 1988). 2.9.2. Mechanical resistance Soil penetrability is a measure of the effort required to force an object through the soil. Penetration resistance of soil is relatively easy to measure and is governed by several soil properties, including shear strength, compressibility, and friction between the soil and the metal. Numerous tip-based penetrometers have been developed (Fig. 9), including the standardized vertically operating cone penetrometer (ASABE, 2009), the single-tip horizontal soil impedance sensor (Alihamsyah et al., 1990), the multiple-tip horizontal soil impedance sensor (Chukwu and Bowers, 2005; Chung and Sudduth, 2006; Chung et al., 2006, 2008), and the vertically oscillating shank with a horizontal single-wedge sensor (Hall and Raper, 2005). While the vertically operated sensor provides the conventional means for measuring soil strength, horizontally operated tip-based sensors have been used for mobile, on-the-go sensing. In addition to multiple-tip soil impedance sensors, several attempts have been made to use tine-based sensors to perform mobile measurements of the entire profile. There are two approaches: (i) using an array of strain gauges mounted on a rigid tine (Adamchuk et al., 2001a, 2001b; Glancey et al., 1989) and (ii) multiple active cutting edges supported by independent load cells (Andrade-Sanchez et al., 2007, 2008; Khalilian et al., 2002). Hemmat and Adamchuk (2008) reviewed proximal soil sensor prototypes to measure compaction. 2.9.3. Fluid permeability Many soil processes depend on the effects of soil structure indirectly through hydraulic conductivity, air porosity, bulk density, and other relevant properties. Therefore, measuring the pressure required to inject a constant flow of air into the soil, as an indication of the relative soil pore space and the continuity of the pores, can provide a measure for soil compaction (Clement and Stombaugh, 2000). Air was forced into the soil

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Figure 9

A five-probe soil penetrometer system.

at a depth of 30 cm using a subsoiler shank and the measured pressure resistance was related to the air permeability of the subsoil. The sensor could detect changes in soil structure/compaction, moisture content, and soil type. Later, Koostra and Stombaugh (2003) redesigned the first version of the air permeability sensor to minimize the soil disturbance induced by the wide point of its shank. 2.9.4. Acoustic sensors The interaction between an implement and the soil creates noise. Thus, Liu et al. (1993) tested an acoustic method for determining soil texture. A shank with a rough surface and hollow cavity was equipped with a microphone that recorded the sound produced through the interaction of soil and shank. The frequency of the resulting sound was used to distinguish different types of soil. In a system developed by Tekeste et al. (2002), sound waves were used to detect compaction layers. A small microphone installed inside a horizontal cone attached to a tine was pulled through the soil. The amplitude of sound in a selected frequency range was compared to the cone index obtained at different depths in the soil profile. The instrument could successfully detect a prepared hardpan at a particular depth; however, in both studies, the authors needed it was necessary to account for background noise.

2.10. Telemetry—Wireless sensing Wireless sensor networks can be used for continuous and real-time monitoring of soil properties such as soil water and nutrients for irrigation. Commercial systems for monitoring soil water using wireless telemetry are currently available, for example, capacitance probes linked to mobile

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telephone systems or radio networks are being used in irrigated agriculture (Vellidis et al., 2008). Wireless sensor technologies are increasingly being used to monitor the condition of the environment (Zerger et al., 2010). With wireless systems, it is possible to obtain information about soil matric potential, water content, temperature, and other properties from remote locations in real time (Kim et al., 2009b; Vellidis et al., 2008). This improves the accuracy and convenience of monitoring soil water content. Irrigation systems manager can then use the data collected to optimize the use of resources in response to dynamic changes in soil condition and reduce the risk of water stress in crops (Han et al., 2009; Lamm and Aiken, 2008; Miranda et al., 2005). Ramanathan et al. (2006) describe a series of wireless networking case studies for monitoring soil CO2, temperature, and moisture. These systems also incorporate ISEs selective for ammonium, calcium, carbonate, chloride, pH, reductionoxidation, and nitrate. Lemos et al. (2004) describe a system that uses potassium ISEs along a PVC tube at various depths with real-time data relayed wirelessly to a base station. The main problems with wireless sensing using ISEs are durability, large sensor drift, and difficulties with in situ calibration. An alternative wireless technique that provides greater spatial coverage and reduced cost is ad hoc wireless networking or “mesh” networking. It is suited to situations where small rates and volumes of data exchange are required. It is based on the deployment of a large number of sensor “nodes” that are battery or solar powered and equipped with low power and low cost radio systems. Various configurations are possible in a network (with each node measuring one or more soil properties) and nodes can be used for relaying information to extend their range through self-configuring ad hoc networks. An example of such a system is the farm-based wireless sensor network developed by Sikka et al. (2006), which is part of a wider network and contains 12 soil moisture nodes using up to five gypsum blocks to measure soil moisture through the profile (to 1 m depth).

2.11. Geographic positioning and elevation In addition to locating sensor measurements on the landscape, the availability of differential global positioning systems (DGPS) and real-time kinematic (RTK) GPS systems make it possible to collect low cost, accurate digital elevation data. This data can then be used to create a digital elevation model (DEM) and provides information on surface geometry (e.g., slope, aspect, various curvatures, and wetness indices), which is an important descriptor of soil. Local variations in terrain control the movement of sediments, water, and solutes in the landscape. Soil formation is strongly influenced by these processes and the DEM and related attributes can be used to help characterize the spatial distribution of

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soil properties (Moore et al., 1993). A DEM also provides the landscape framework for interpreting results from other sensors (e.g., EM, γ-ray survey, and GPR) (Gish et al., 2005). Global positioning and the collection of elevation data are imperative for PSS, particularly for mobile and multisensor systems.

2.12. Multisensor systems As every soil-sensing technology has strengths and weaknesses and no single sensor can measure all soil properties, the selection of a complementary set of sensors to measure the required suite of soil properties is important. Integrating multiple proximal soil sensors in a single multisensor platform can provide a number of operational benefits over single-sensor systems, such as: robust operational performance increased confidence as independent measurements are made on the same soil extended attribute coverage increased dimensionality of the measurement space (e.g., different sensors measuring various portions of the EM spectrum). There are few reports of multisensor systems directed at PSS in the literature. For example, Christy et al. (2004) reported the use of a mobile sensor platform that simultaneously measures soil pH and ECa. An NIR sensor has also been recently added to this multisensor platform (Christy, 2008). Taylor et al. (2006) reported the development of a multisensor platform consisting of two EMI instruments, ER and pH sensors, a γ-radiometer, and a DGPS (Fig. 10A). Adamchuk and Christenson (2007) described a system that simultaneously measured soil mechanical resistance, optical reflectance, and capacitance (Fig. 10B). Yurui et al. (2008) reported the development of a multisensor technique for measuring soil physical properties (soil water, mechanical strength, and electrical conductivity). Other sophisticated integrated sensor systems have been developed for various applications. For example, the United States Army’s site characterization and analysis penetrometer system (SCAPS) is mounted on a 20-ton truck. Down-hole determinations are made to 50 m using realtime video; γ-ray spectrometers to detect radioactive waste; sensors to measure water content, pore water pressure, liquid and gas samplers; laser-induced fluorescence sensors to detect hydrocarbons; mass spectrometers to detect volatile organic compounds; LIBS to measure various metals; and XRF for measuring heavy metals. Eight SCAPS trucks are operated by three federal agencies in the United States and millions of dollars have been saved in site investigation and cleanup costs (USAEC, 2000).

269

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(A)

(B)

Figure 10 Multisensor platforms. (A) A multisensor platform with EMI, passive γ, electrical resistivity, and pH sensors and (B) one with mechanical, electrical, and optical sensors.

2.13. Core scanning and down-borehole technologies Core scanning and borehole sensors can be used to measure soil profiles, for example, to measure soil carbon stocks, determine subsoil constraints to root growth (e.g., subsurface acidity), or to characterize the soilwater regime. Some of the PSS technologies described above provide bulk measurements to a specific depth. For example, EMI sensors provide a depth-weighted ECa reading to a depth proportional to their coil spacing (Sudduth et al., 2010). However, there is still a need to develop core scanning and down-borehole technologies that can characterize the entire soil profile layer by layer to at least 1.5 m. Undisturbed soil cores can be readily collected with small drill rigs using either push-tubes or core samplers. There is an excellent opportunity to apply many of the methods considered above to an automated scanning system for soil cores. Commercial units have been developed for sediment and rock cores (Geotek, 2001) that include active γ-ray attenuation for measuring water content and bulk density, ER, magnetic susceptibility, and digital photography (Fig. 11). Research prototypes that allow core scanning for ECa (Myers et al., 2010) and for visNIR reflectance (Kusumo et al., 2011) have also been reported. Use of such rapid core measurement systems would allow soil surveys to be undertaken in a far more efficient manner and would be a natural complement to vehicle-mounted sensor systems. Down-borehole sensor systems also provide a means for characterizing soil profiles. Measurements of electrical conductivity in particular can be made at a well-defined depth and the sensor can integrate over a realistic volume of soil to reduce the effects of short-range variation (Myers et al., 2010). Cone penetrometers or other specialized probes can also be modified to contain sensors or fiber optic probes for visNIR spectroscopy (Ben-Dor et al., 2008; Hummel et al., 2004; Kweon et al., 2009), XRF (Elam et al., 1998) and LIBS (Mosier-Boss et al., 2002).

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Figure 11

A core scanning multisensor system.

3. Proximal Sensors Used to Measure Soil Properties Many soil properties can be measured with different proximal soil sensors. This section describes and gives examples of alternative techniques that are available for measuring soil properties.

3.1. Soil water and related properties Several sensor systems for measuring water content have been developed. Soil water content has been measured using active γ-ray attenuation (Pires et al., 2005), visNIR (Sudduth and Hummel, 1993; Whiting et al., 2004) and mid-IR spectroscopy ( Janik et al., 2007a), tine-mounted microwave sensors (Whalley, 1991), TDR and FDR, capacitance (Paltineanu and Starr, 1997), GPR (Huisman et al., 2003), and EMI and ER (Sudduth et al., 2005). Although total soil water content as measured by these sensors is useful, measurements of plant-available water capacity (PAWC) are more important for agriculture. PAWC is determined in the field by measuring differences between volumetric water content at the drained upper and lower limits after complete extraction of water by the plants. Whalley et al. (1992) evaluated multisensor capacitance probes in the nontraffic interrows of agricultural fields to monitor soil water dynamics over the growing season. Wireless sensor networks (Vellidis et al., 2008) can also be used for

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this. EMI surveys measured at different times may be used to approximate PAWC ( Jiang et al., 2007; Wong et al., 2006), but local calibration and careful interpretation are imperative. Rapid measurements of bulk density are needed to convert water content measurements to a volumetric basis (see below).

3.2. Nutrients and elements Soil nutrients are important for healthy plant growth. The macronutrients (nitrogen, potassium and phosphorus) are required in large quantities and are therefore managed and replaced as fertilizer on a crop-by-crop basis. They represent a significant input cost of food production both financially and environmentally: excessive nitrogen fertilizer can subsequently leach soil nitrates into waterways and have direct consequences on human and environmental health and water quality. There are various options for proximal sensing of plant nutrients and elements in soil (Kim et al., 2009a); however, their measurement is not straightforward because these properties show large variability in both space and time. This is particularly true for nitratenitrogen, which has been measured using mid-IR spectroscopy ( Jahn et al., 2006), LIBS (Harmon et al., 2005), and electrochemical techniques using ISEs and ISFETs (Adsett and Zoerb, 1991; Artigas et al., 2001; Birrell and Hummel, 2001; Davenport and Jabro, 2001; Kim et al., 2006; Sethuramasamyraja et al., 2008; Sibley et al., 2009). Measurement of soil phosphorus is difficult. Most indices estimate readily available (or labile) phosphates that occur in soil solution. These occur as freshly precipitated forms or as anions that can be readily removed from positively charged sites on clay and organic surfaces. However, most of the phosphorus in soil is very slowly available (or less labile). Apart from electrochemical methods, proximal soil sensors for measuring soil phosphorus are indirect and return variable results, although good correlations using visNIR spectroscopy have been reported in the literature (Bogrekci and Lee, 2005). Janik et al. (1998) also reported good results for phosphorus sorption using mid-IR spectroscopy, but not for available phosphorus. Kim et al. (2007a, 2007b) evaluated the ability of ion-selective membranes and cobalt-rod electrodes to quantify available phosphorus and reported relatively good success with cobalt electrodes. Potassium can be measured using passive γ-radiometry (Wong and Harper, 1999) and electrochemically (Kim et al., 2006; Sethuramasamyraja et al., 2008). Measurements of potassium using visNIR and mid-IR spectroscopy have also been reported but with variable results. Other major nutrients such as calcium and magnesium, however, appear to correlate well with both visNIR and mid-IR spectra (Lee et al., 2009; Viscarra

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Rossel and McBratney, 2008). Minor nutrients and elements can be measured directly using XRF (Kalnicky and Singhvi, 2001) and LIBS (Hussain et al., 2007) and electrochemically using ISEs or ISFETs (Artigas et al., 2001; Davenport and Jabro, 2001). Heavy metal contamination in soils can be measured using XRF, visNIR and mid-IR spectroscopy (Bray et al., 2009), and LIBS (Hilbk-Kortenbruck et al., 2001). Salinity and sodicity can be measured electrochemically with ISEs or ISFETs (Artigas et al., 2001; Davenport and Jabro, 2001) as well as with EMI and ER (Corwin et al., 2003).

3.3. Cation exchange capacity CEC determines the nutrient supply in soils, with cation nutrients in higher CEC soils generally more available to plants. CEC increases with increasing pH, clay, and organic matter in the soil. It also varies with the type of clay, with smectites having the highest CEC, followed by illites and kaolinites. CEC can be inferred using visNIR and mid-IR spectra (Sudduth and Hummel, 1993; Viscarra Rossel et al., 2006b). Reports of good correlations using EMI and ER instruments do exist (Sudduth et al., 2005). Since CEC is affected by soil texture, mineralogy, and organic matter content, it may be more accurately measured by combining measurements from different proximal sensors.

3.4. Carbon Carbon plays a key role in improving soil physical properties, increasing CEC and water-holding capacity, and improving soil structure. Soil carbon is thus considered important in assessing soil quality (Andrews et al., 2004). Furthermore, the ability of soils to sequester carbon is of increasing interest as a potential way to mitigate greenhouse gases in the atmosphere. Soil carbon can be measured using charge-coupled devices (Viscarra Rossel et al., 2008), visNIR and mid-IR (Viscarra Rossel et al., 2006b), LIBS (Cremers et al., 2001), and INS (Wielopolski et al., 2008). Carbon fractions can also be measured using visNIR (Cozzolino and Moron, 2006), but measurements appear to be more accurate using mid-IR spectroscopy ( Janik et al., 2007b).

3.5. pH As a measure of acidity, the level of soil pH is important in many processes, including availability of plant nutrients and efficacy of herbicides. Soil pH, buffering capacity, and lime requirement can be measured using ISE or ISFET systems (Adamchuk et al., 1999; Viscarra Rossel and McBratney, 1997; Viscarra Rossel and Walter, 2004; Viscarra Rossel et al., 2005).

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These properties can also be inferred using visNIR and mid-IR spectroscopy, the latter producing more accurate results (Viscarra Rossel and McBratney, 2008). Because the relationship between soil pH and lime requirement in different types of soil is not constant, measurements of lime requirement can be made using either a lime requirement buffer (Viscarra Rossel et al., 2005) or by combining sensors that account for the buffering capacity of the soil and pH itself.

3.6. Clay, silt, and sand Soil texture (clay, silt, and sand content) has been measured using γ-radiometrics (Viscarra Rossel et al., 2007), visNIR and mid-IR spectroscopy (Lee et al., 2009; Viscarra Rossel et al., 2006b, 2009), and EMI and ER ( James et al., 2003; Sudduth et al., 2005). However, measurements of silt are often less accurate than for clay. Sand content has also been measured with mid-IR spectroscopy (Viscarra Rossel et al., 2006b), where strong fundamental vibrations of siliconoxygen bonds exist. There is no response to sand in the visNIR spectrum as quartz is insensitive in this region, although sand may affect soil albedo and might also show some response due to iron oxide coatings on sand grains. EMI and ER have also been used to measure sand and determine soil textural boundaries (Carrol and Oliver, 2005; James et al., 2003).

3.7. Soil mineralogy Mineralogy strongly affects the physicochemical processes occurring in soils. In particular, the mineralogy of soil clays relates to soil fertility through CEC and also to soil water dynamics by virtue of the shrinkswell nature of the various clays. Clay mineralogy can be measured in situ by portable XRD and XRF instruments (Sarrazin et al., 2005), visNIR spectroscopy (Viscarra Rossel et al., 2006a, 2009), and mid-IR spectroscopy (Nguyen et al., 1991). Iron and its oxides have been measured using γ-radiometrics (Viscarra Rossel et al., 2007), XRF (Kalnicky and Singhvi, 2001), ultraviolet and visNIR spectroscopy (Viscarra Rossel et al., 2010b), and LIBS (Hussain et al., 2007).

3.8. Soil strength, bulk density, and related properties Soil strength can be determined using mechanical sensors for measuring soil mechanical resistance (Adamchuk and Christenson, 2007; AndradeSanchez et al., 2007; Chung et al., 2006; Hemmat et al., 2008). Simultaneous measurements of soil water content are often necessary to account for its relationship to soil strength. Measurements of soil bulk density using conventional techniques are slow and subject to large

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measurement error. The development of proximal sensors to measure bulk density is important because it makes more sense to provide measurements of the soil profile in a volumetric rather than gravimetric basis, for example, for reporting organic carbon, water content, and lime requirements. Bulk density and compaction can be inferred using active γ-ray attenuation measurements (Oliveira et al., 1998) and mechanically by measuring draught, depth, and soil water content (Mouazen and Ramon, 2006).

4. Summary Table 2 provides a summary of this review, showing the approximate frequency, energy, and wavelengths at which these sensors operate and whether the measurement is direct or indirect. For most soil properties, multiple sensing options can be used. For example, soil pH can be measured directly using ISFETs or indirectly using visNIR spectroscopy. There is widespread interest in the use of diffuse reflectance spectroscopy for PSS because several soil properties can be measured from a spectrum. Largely, however, the techniques are indirect (Table 2) and to be useful quantitatively, spectra must be related to a set of known reference samples through calibration. Successful generalization of indirect proximal soil sensor calibrations will depend on the type of soil: its mineralogy, particle-size distribution, presence of segregations (e.g., iron oxides and oxyhydroxides), soluble salts, water content, and the abundance and composition of organic matter. Inference using indirect techniques may be strong or weak (Table 2), but their measurements are invariably less accurate than direct methods. However, indirect methods are generally less expensive, technologically and methodologically better developed and more readily available to users. The different proximal soil sensors described in this article are in various stages of development, with some relying on expensive instruments designed for the laboratory, and others on purpose-built, lowercost, portable sensors designed for field application. Table 3 indicates the developmental status of various proximal soil sensors and their approximate costs.

5. General Discussion and Future Aspects PSS is not entirely new, although its development and that of new technologies is ongoing. The earliest reported use of a proximal soil

Table 2

Proximal soil sensors used to measure soil attributes

EM Range

γ-rays

X-rays

Frequency (Hz)

1022

10218

1016 1015 1013

Wavelength (m)

10212

10210

1028 XRD UV

INS TNM Active γ

Passive γ

XRF

Total carbon

D

i

D

Organic carbon

I

Technique

UVvisibleinfrared

Micro and radio waves

1012

1010

108

107

106

102

1026

1025

1024

1022

101

102

103

106

Vis

NIR

mid-IR

LIBS Micro WSN TDR

FDR Capac

GPR

EMI

ER

ECh Mech

I

D

D

D

I

D

D

I

D

D

I

I

D

D

i

I

i

i

D

Biochemical

Inorganic carbon I Total nitrogen

D

D

D

Nitratenitrogen Total phosphorus D

D

I

Extractable

I

I

D

phosphorus Total potassium

D

Extractable

D

D

I

I

I

I

I

I

D

D

i

I

D

D

i

I

I

D

D

D

D

i

I

I

I

I

I

D D

potassium Other major

D

D

D

D

nutrients Micronutrients,

D

elements Total iron

D

Iron oxides Heavy metals CEC

i

D

i D

D D

I

D i

i

(Continued)

Table 2

(Continued )

EM Range

γ-rays

X-rays

Frequency (Hz)

1022

10218

1016 1015 1013

Wavelength (m)

10212

10210

1028

XRF

XRD UV

Technique

INS TNM Active γ

Soil pH

Passive γ

UVvisibleinfrared

Micro and radio waves

1012

1010

108

107

106

102

1026

1025

1024

1022

101

102

103

106

Vis

NIR

mid-IR

FDR Capac

GPR

EMI

I

Buffering

I

I

I

I

LIBS Micro WSN TDR

ER

D

ECh Mech

D I

capacity and LR Salinity and

D

D

D

sodicity Physical Color Water content

D D

D

D

I

i

D

D

D

D

D

D

D

D

I

Matric potential Clay

I

Silt

I

Sand

I

Clay minerals

I

i

I

D

i

I

I

I

I

I

I

i

i

I

D

I

I

D

D

i

i

I

I

I

Soil strength Bulk density

D/I

D I

I

D

I

I

I

Porosity Rooting depth

I D

D

We denote the measurement as either physically based and direct (D) or correlative and indirect (I). Lower case “i” indicates weak inference. Note: INS, inelastic neutron scattering; TNM, thermalized neutron methods; XRF, X-ray fluorescence; XRD, X-ray diffractometry; UV, ultraviolet; vis, visible; NIR, near infrared; mid-IR, mid infrared; LIBS, laser-induced breakdown spectroscopy; Micro, microwaves; WSN, wireless sensor networks; TDR, time-domain reflectometry; FDR, frequency-domain reflectometry; Capac, capacitance; GPR, ground-penetrating radar; EMI, electromagnetic induction; ER, electrical resistivity; ECh, electrochemical; Mech, mechanical.

Table 3

Current development status of proximal soil sensors and approximate costs in US dollars (USD)

EM range wavelength (m) 212

γ-rays (10

)

X-rays (10210 m) UVvisR (1028 to 1024)

Microwave (1022) Radiowave (101 to 106)

Technique

Development status a

Approximate costs (USD)

INS TNM Active γ Passive γ

Research Commercial/research Commercial/research Commercial

XRF

Commercial/research

XRD UV Vis NIR

Commercial/research Commercial Commercial Commercial/research

MIR LIBS

Commercial/research Commercial/research

Microwave TDR FDR and capacitance GPR NMR EMI

Research Commercial Commercial

800,0001,500,00 10,00015,000 10,000 10,00070,000 (depends on crystal size and sensitivity) 800040,000 (OEM to commercial handheld units) 75,000 (portable combined XRF/XRD) 3000 (combined UVvisNIR 250900 nm) 10005000 (combined visNIR 4001000 nm) 10,000100,000 (visNIR depends on range and portability) 10,00075,000 (field and laboratory instruments) 15,00040,000 (dependent on the number of channels)  5001500 (sensor with display) 100500 (sensor only)

Commercial Research Commercial

80,000  10,00040,000 (depends on number of coils) (Continued)

Table 3

(Continued )

EM range wavelength (m)

Technique

Development status

Approximate costs (USD)

Electrical resistivity

ER

Commercial

Gypsum

Commercial

Electrochemical

Electrochemical

Commercial/research

Mechanical

Tillage Penetrometers

Research Commercial

Acoustic Pneumatic

Research Research

2003000 (for handheld/portable sensor); 7000 (for on-the-go sensor) 5100 (single sensor—dependent on type and quality) 501000 (with data logger—depends on sensor capabilities) 1001000 (single sensor—depends on ion, quality, reference electrode) 20010,000 (with logger/interface, e.g., interfacing multiple sensors to a computer)  15005000 (hand-operated device with digital data storage)  

Note: INS, inelastic neutron scattering; TNM, thermalized neutron methods; XRF, X-ray fluorescence; XRD, X-ray diffractometry; UV, ultraviolet; vis, visible; NIR, near infrared; MIR, mid infrared; LIBS, laser-induced breakdown spectroscopy; TDR, time-domain reflectometry; FDR, frequency-domain reflectometry; GPR, ground-penetrating radar; NMR, nuclear magnetic resonance; EMI, electromagnetic induction; ER, electrical resistivity; ISE, ion-selective electrode; ISFET, ion-sensitive field effect transistor. a Commercial INS systems are likely to appear in 23 years. Their cost will be determined largely by the cost of the neutron generator and the number of detectors used.

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sensor was in the 1920s when an instrumented drawbar dynamometer was used to discern spatial variation in soil compaction (reported in McBratney and Minasny, 2010). PSS gained prominence in soil science in around the past 30 years because of the realization that sensed data could provide good quality soil information more efficiently than laboratory methods of soil analysis, which can be expensive and time consuming. Some of the earlier reports using sensors to measure soil properties were given by Bowers and Bowen (1975), who measured electrical resistance to detect drying fronts; Rhoades and Corwin (1981), who used EMI to detect soil salinity; Perumpral (1987), who used standardized penetrometers for measuring soil compaction; and Dean et al. (1987), who used capacitance for measuring soil water. In the 1990s, the development and use of sensors for soil measurement gained momentum and various technologies were being reported, for example, GPR (Whalley et al., 1992), microwave sensing (Whalley, 1991), visible and NIR reflectance (Ben-Dor and Banin, 1995; Shonk et al., 1991; Sudduth and Hummel, 1993; Viscarra Rossel and McBratney, 1998), ISEs (Adsett and Zoerb, 1991), ISFETs (Birrell and Hummel, 1997; Viscarra Rossel and McBratney, 1997), mobile penetrometers (Alihamsyah and Humphries, 1991), acoustic sensors (Sabatier et al., 1990), and odor sensors to determine soil air composition (Persaud and Talou, 1996). Recognizing the increasing interest in soil sensing, Viscarra Rossel and McBratney (1998) used “proximal soil sensing” to describe measurement of soil properties with ground-based sensors. The development of PSS coincided with that of precision agriculture, which for some time appeared to be the application most suited to the use of proximal soil sensors. Interest in PSS is now more widespread (Viscarra Rossel et al., 2010a), and currently a wide range of technologies can be used for it. By its own merit, PSS is becoming a new discipline and is a topic of considerable interest in the soil, agricultural and environmental sciences, and engineering communities. The efficiency with which PSS can obtain soil data makes it naturally suited to many situations that require large amounts of quantitative data at fine spatial and/or temporal resolutions, for example, digital soil mapping, soil monitoring, precision agriculture, the assessment of contaminated sites, and measurement of subsurface hydrology. Although the fundamental scientific principles of the sensors that were reported early on remain the same, for the most part we understand them better and therefore are better placed to use them. For some sensors, technology has improved considerably, for example, with visNIR array-based detectors that increase instrument portability and ruggedness. Research has also refined our understanding of how we can best apply these sensors to measure soils and their properties. Our ability to extract useful information from the sensed data and to analyze large spatial

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datasets (Cressie and Kang, 2010) has improved because of advances in mathematical and statistical methods. Improvements in electronics and mobile computing, fueled by the consumer and automotive sectors, have made it feasible (and often relatively inexpensive) to interface with sensors in a user-friendly manner. The recently established International Union of Soil Sciences (IUSS) Working Group on Proximal Soil Sensing (www.proximalsoilsensing.org) aims to provide the framework for greater interaction between scientists and engineers with a common interest in developing proximal sensing technologies and mathematical and statistical techniques to better understand soil processes and spatiotemporal soil variability. Two large ongoing multinational European projects—the iSoil (Werban et al., (2010)) and Digisoil (Grandjean et al., 2010) projects that aim to develop PSS for digital soil mapping—present a step in the right direction and are the largest current investment in PSS research. The future of PSS lies in such interactions and multidisciplinary collaborations. Below, we short-list general considerations for future work. Development of soil sampling (measuring) designs for PSS—considering both geographic and property spaces. Research to define the most suitable technique or combination of techniques for measuring key soil properties, for example, bulk density, plant-available water, soil carbon, and carbon fractions. Research the often-complex interactions between the soil matrix and sensor signals. Research the underlying mechanisms that allow prediction of soil properties from indirect proximal soil sensors to develop theoretical calibrations that use soil knowledge. This will lead to improved accuracy, robustness, and applicability. Research the use of local versus global sensor calibrations. This might be soil property specific. Develop better signal processing and signal reconstruction methods. Often the methods used to process data from a proximal soil sensor are chosen ad hoc based on the experience of the particular investigator. Better, more widely applicable methods that could lead to standardization would help advance collaborative research and PSS. Develop data fusion methods that combine data from multiple sensors to produce useful soil information. Research the application of proximal soil sensors for diverse applications, for example, the use of multisensor platforms for digital soil mapping, soil monitoring, assessment of soil carbon, contaminated site assessment, and soilplant relationships. PSS provides soil scientists with an effective approach that can be used to learn more about soils. Proximal soil sensors allow rapid and

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inexpensive collection of precise, quantitative data at fine (spatial and temporal) resolutions, which can be used in more meaningful analyses to better understand soils and the spatiotemporal variability of their properties. Soil science needs PSS to device sustainable solutions to the global issues that we face today: food, water, and energy security and climate change. Our intent here is to raise awareness about PSS to further its research and development and to encourage the use of proximal soil sensors in different applications.

ACKNOWLEDGMENTS Dr. Viscarra Rossel would like to thank the CSIRO Division of Land and Water Capability Development Fund—“Which soil sensors do we use where?” for supporting this work.

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C H A P T E R S I X

The Role of Knowledge When Studying Innovation and the Associated Wicked Sustainability Problems in Agriculture J. Bouma, A.C. van Altvorst, R. Eweg,† P.J.A.M. Smeets,‡ and H.C. van Latesteijn† Contents 1. 2. 3. 4.

Introduction 294 Current Problems in Dutch Agriculture 299 The Flow of Knowledge When Studying Sustainable Development 300 Case Studies 301 4.1. Case 1: Northern Frisian Woods: Cradle-to-Cradle dairy farming 301 4.2. Case 2: The new mixed farm: an example of industrial ecology 305 4.3. Green care: health care on the farm 309 4.4. Case 4: Developing the new “Rondeel” chicken housing system 315 5. Discussion and Conclusions 319 Acknowledgments 321 References 321

Abstract Scientific institutions all over the world emphasize the importance of effective links between science and society when pursuing sustainable development thereby linking science and development. Unfortunately, the knowledge paradox implies that too much research is not applied, partly because the research community is still rather inward looking, creating a gap between what is written and what is achieved in practice. The Dutch government initiated, therefore, the large 6-year TransForum program to enhance innovation in agriculture, not allowing the regular research circuit to set the agenda. TransForum emphasized the relevance of connected value development when dealing with wicked problems associated with sustainable development, requiring a balance between  † ‡

Professor of Soil Science, Wageningen University, The Netherlands TransForum Innovation Program, The Netherlands Alterra, Wageningen University and Research Center, The Netherlands

Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00006-3

© 2011 Elsevier Inc. All rights reserved.

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the well-known people, planet, and profit aspects of sustainable development. Thus new and innovative 3P businesses were created through the sequence of value proposition, -creation, and -capture, together constituting connected value development. This required transdisciplinary interaction between knowledge institutions, entrepreneurs, nongovernmental organizations (NGOs), and governmental bodies working together on innovation (KENGi partners), each of these partners contributing different opinions, goals, and values. In this chapter, four case studies are used to illustrate that innovation was achieved by successively working together on value proposition, -creation, and -capture. Only the committed persistence of entrepreneurs supported by knowledge brokers and development of new business and organizational models ultimately led to the desired connected value development, representing a successful link between knowledge creation and societal appreciation. The process involved mobilization and strategic injection of various forms of tacit and scientific knowledge in the overall interaction process that often took more than 10 years to mature, requiring an important role for knowledge brokers with hard knowledge and social intelligence (“T-shaped skills”) as well as long-term funding. The development of value propositions needed much more attention than is usually provided. Research planning and management procedures as well as judgement procedures need to be adapted to fit transdisciplinary requirements. The cases demonstrate that the process of connected value development is unique for each project; there is no standard recipe. Track records of case studies, as presented, can be used in education as a learning tool to create awareness for possible opportunities as well as pitfalls in transdisciplinary studies. Keywords: Communities of Scientific Practice; KENGi partners; NGO; 3P’s; Connected Value Development

1. Introduction There is general agreement that inventions generated by scientific research are an essential but not exclusive ingredient to achieve innovation in the context of sustainable development. Inventions are seen as the result of a creative process with outcomes beyond what is currently known ( Jacobsen et al., 2010). But what role do inventions play in realizing a more sustainable development through system innovations? In innovation sciences, innovation is nowadays perceived as “an interactive, nonlinear process in which multiple actors (e.g., firms, research institutes, intermediaries, customers, authorities, financial organizations and possibly others) depend on each other in realizing innovation” (Van Mierlo et al., 2010). To achieve a more sustainable development, all these actors are needed to come up with new modes of production and new institutional and organizational arrangements to allow for these

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new modes of production. Therefore, we speak of “system innovations,” leading to new ways of doing things that can be commercialized leading to new and improved configurations with the surrounding physical and social environment. The latter two elements are important for sustainable development, since this type of development not only considers economic but also environmental and social aspects (the 3P’s) (Van Latesteijn and Andeweg, 2010). When describing the significance of science, strategic reports of science academies, research organizations, and universities all over the world emphasize the importance of sustainable development and the need for effective communication between science and society. The International Council for Science (ICSU) advocates “Strengthening international science for the benefit of society.” This implies de facto integration of science and development, which is not agreeable to at least some members of the scientific community. It also represents a view from the perspective of science with an implicit plea for increased funding that is not necessarily embraced by society. The statement can also be read in reverse in terms of a view from society raising questions as to how society judges benefits gained from science, acknowledging the “knowledge paradox,” indicating that too much research hardly contributes to innovations and thus to societal development (Bouma, 2010). One reason for this paradox is the rather self-centered nature of the scientific community where quality is still mainly being judged by (“vertical”) peer review and by the number of refereed publications in international journals. Here, emphasis is on science, while development is seen as the responsibility of others. Social scientists have thoroughly analyzed these phenomena and propose a new transdisciplinary approach where (“horizontal”) interaction with various stakeholders, entrepreneurs, and policy makers plays a more important role. In fact, transdisciplinary implies integration of science and development (Bunders et al., 2010; Gibbons et al., 1994; Hessels and Lente, 2008). True transdisciplinarity can only be achieved if various stakeholders (with often strongly contrasting views and visions) somehow work together. To underline the importance of this collaboration, the acronym KENGi partners is used here to represent the major partners in the transdisciplinary debate, where K stands for the knowledge community, E for enterprises and business, N for NGOs and civil-society organizations, G for government at different levels, and i for system innovation that can only be reached when these stakeholders work together. Wenger et al. (2002) proposed Communities of Practice (CoPs) in which scientists work together with ENGi partners. Bouma et al. (2008) strongly supported this but suggests that the scientific community is as yet ill prepared to face the ENGi partners and that the scientific community would be well advised to first get their own act together in what he calls Communities of Scientific Practice (CSPs) before embarking on working in CoPs.

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The traditional “vertical” research paradigms are persistent with good reason because the scientific community is aware of the possible risk of loosing its precious independence and scientific vigor when either entrepreneurs, NGOs, or governmental funding agencies obtain major influence in establishing research agendas. The classic notion of research states that the research community operates best when the intellect is allowed to freely roam and this may be questioned when linking science and development. To break this apparent deadlock, three observations are made. Distinction between different fields of science allows different approaches. When studying basic sciences such as, for example, astronomy or theoretical physics, emphasis on cutting-edge, curiosity-driven basic science subject to peer review is the only way forward. “Horizontal” interaction with stakeholders and policy makers implies at most popularizing results. However, many other fields of science are focused on societal problems dealing with sustainable development and covering intertwined economic, social, and environmental issues. The epistemological term to denote these types of societal problems is “wicked,” expressing their complexity and messiness. Moreover, wicked problems have no clear and undisputed definition of the problem. As a result, these types of problems do not have a single solution. Constraints and available resources change over time and often quite vocal participants in the discussions have different frames of reference, ideas, and interests as to how the problems should be solved (Rittel and Webber, 1973). This chapter intends to address the latter field of scientific activities, exploring the “horizontal” approach linking science and development, while acknowledging the intrinsic characteristics of basic science. Whatever research activities are realized within a transdisciplinary context, the scientific quality of the work should be beyond question. For this reason, this specific type of research requires additional value indicators as compared with the classical “vertical” approach (Bouma et al., 2008). Because these specific value indicators have been proposed (Spaapen et al., 2007) but are not yet applied, value criteria for basic research are still indiscriminately applied for “horizontal” approaches, thereby hampering its development. The KENGi partners are increasingly well informed following the profound information and communication revolution of the last decade. Besides, many of them are yesterday’s students. Each partner is likely to have a quite different perspective as to the character and possible solutions of problems being considered. In any case, the traditional image of knowledgeable, generous scientists informing ignorant, and grateful third parties become increasingly outdated. When studying “wicked” problems of sustainable development, many scientific disciplines are involved. Combining essentially separate “vertical” research efforts of different disciplines, which is still the prevalent procedure, is quite difficult. Moreover, problems are so complex

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and involve so many different players with contrasting ideas and desires that in any case undisputed, single solutions to such “wicked” problems cannot be defined. At most, there are a large number of options each of which with different trade-offs between economic, social, and environmental considerations. The traditional linear image of a problem first being identified by researchers, then studied, and “solved” after which the result is applied in the real world, becomes irrelevant when working on wicked problems like sustainable development. Here, political decisions determine the course being taken. Scientists advise but do not decide. Still, they can have a major impact on decisions ultimately being made because knowledge (when properly formulated and injected in a timely manner into interactive KENGi processes) is still the most powerful tool to distinguish facts from beliefs and to clarify opinions. The case studies to be discussed in this chapter will illustrate this. The scientific community studying sustainable development has to make a clear choice now because the discrepancy between what is written in strategic plans in terms of societal relevance of research and what happens in reality is beginning to strain the credibility of science. Clearer distinctions are needed between science and development. For good reasons, the traditional “vertical” approach should be maintained for basic science, cherishing its independence and judging its quality by peer review. However, engaging science in transdisciplinary endeavors, managing wicked problems, is a different game altogether that implies active knowledge development together with other KENGi partners. This chapter intends to focus on a critical discussion of the alternative “horizontal” approach, realizing that many questions remain because while the theory of transdisciplinarity is by now rather well developed (cf. Bunders et al., 2010), more practical applications in the real world, as presented in this chapter, are still needed. We will use experiences from the public private funded “TransForum” innovation program (2004 2010) aimed at stimulating a more sustainable development of the agricultural sector in the Netherlands with the objective to discuss applications in practice of the proposed “horizontal” approach to research. The TransForum group followed an innovative approach based on the widespread feeling that the existing form of knowledge generation and dissemination could not possibly result in the type of innovation needed in agriculture to confront the societal challenges of the twenty-first century. The government therefore provided substantial funds to invite collaborative programs for areas considered to be important for the future development of the country. Agriculture was one of the areas, and the collective TransForum proposal of knowledge institutes, entrepreneurs, NGOs, and (regional) government bodies received 30 million euros, provided that the other partners would come up with the same amount. The Royal Academy of Sciences, Arts and Letters was invited to judge the

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scientific quality of the submitted proposals and the central planning bureau judged societal relevance for economic development. This procedure bypassed established funding mechanisms for research by, for example, the national science foundation, governmental agencies, and industry. In summary, TransForum proceeded by:  Allowing practitioners to define projects, requiring 50% cofinancing which tends to cement commitment. Overall, some 80 practical, scientific, and learning projects were executed.  Forming a lean management structure of the program, including a managing director, a board with representatives of the research community, industry, societal groups, and governments, accompanied by international advisory councils.  Analyzing research needs, after approval of proposed projects, by four scientific directors, initiating research where relevant.  Emphasizing the role of “knowledge brokers” in the overall management of each project. They were either prominent KENGi members or TransForum staff members. Their function was to keep interaction going between the different partners in the project, using various forms of knowledge as a major lubricant (Andeweg and van Latesteijn, 2010; Peterson and Mager, 2010). The objective of this chapter is to analyze the role of knowledge and research in four selected Transforum case studies and discuss desirable education and research practices in future. A successful link of science and society is only established when both parties consider results obtained to be valuable, leading to specific results. As different KENGi partners were involved in each case study, each with quite contrasting visions, opinions, and values, a common framework, was developed in TransForum on the basis of connected value development, following from consecutive value proposition, -creation, and -capture (Andeweg and van Latesteijn, 2010). A project was only considered successful when connected value development resulted in a tangible new 3P business case and this, in turn, was interpreted as support for the “horizontal” procedure as discussed. In summary, the objective of this study is to: 1. Analyze four representative case studies by constructing a track record as a function of time in terms of the role of various forms of knowledge used by knowledge brokers interacting with the KENGi partners in achieving project objectives, defined in terms of connected value development. 2. Analyze the specific role of the scientific community in the process of connected value development in terms of value proposition, -creation, and -capture vis-a-vis the various KENGi partners. 3. Discuss possible implications for scientific education and knowledge infrastructure.

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2. Current Problems in Dutch Agriculture Dutch agriculture has reestablished itself very well after the devastation of World War II and research linked with an effective extension service has made major contributions to this revival. Until the 1970s, problems were mainly production oriented and corresponding research resulted in spectacular increases in production levels of crops and animals. Research produced more productive crops and animals by breeding, effective fertilization and crop protection procedures, drainage, and irrigation practices and mechanization. Extension services were very effective in this particular setting as objectives were clear in terms of an exclusive focus on production increase. However, since the late 1970s, conditions gradually changed and environmental problems were increasingly recognized in terms of water, soil, and air pollution caused by agricultural practices. Agriculture became the major cause of the degradation of nature. Moreover, increasing urbanization changed the classical differences between urban and rural areas that tend to disappear in a densely populated country as the Netherlands (Smeets, 2011). The term “metropolitan agriculture” has been coined to reflect this development (Van Latesteijn and Andeweg, 2010). Animal welfare became an issue as well as was the changing character and quality of the agricultural landscape that was appreciated by city dwellers for its recreational potential and its significance in terms of cultural heritage and biodiversity. As the concept of sustainable development was ever more broadly embraced, next to economics, environmental and social aspects of land use became important and were increasingly reflected in environmental laws and regulations at national and at the European policy levels. In other words, land users were confronted with environmental and societal requirements for sustainable development involving a highly diverse group of stakeholders with quite contrasting visions and demands and ever more governmental rules and regulations. Rather than the single goal of increasing levels and efficiency of production, research objectives became multigoal oriented requiring different, innovative approaches. Privatized extension services were ill prepared to face up to the new challenge, but also the research community is still struggling to cope with this new broad perspective. As mentioned, social science literature provides valuable suggestions for different (“horizontal”) approaches to research, but they still need to be tested in practice. Also in the ecological literature and in the new landscape science, these issues of inter- and transdisciplinarity were extensively discussed (Jacobs, 2006; Tress et al., 2001, 2004). The TransForum program based its work on the key questions articulated

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by practitioners and knowledge workers and by taking a broad view on the utilization and generation of knowledge including tacit knowledge by stakeholders.

3. The Flow of Knowledge When Studying Sustainable Development Knowledge is the major product of scientific labor. Five forms of knowledge can be distinguished (Fig. 1 from Bouma et al., 2008) in terms of two ranges of characteristics: from qualitative to quantitative and from empirical to mechanistic. The term mechanistic has been chosen to emphasize understanding of the mechanisms of relevant processes rather than empirical observation of the effects of these processes. K1 represents user expertise, sometimes referred to as tacit knowledge. K2 is expert knowledge from practitioners or applied scientists and still fits the concept of tacit knowledge. The still essentially empirical character of this knowledge is, however, supported by a better mechanistic understanding of the underlying processes and can be highly effective in practice. K3 K5 represents increasingly specific scientific knowledge. K3 represents, for example, empirical statistical relationships, while the underlying processes are increasingly expressed in quantitative terms in K4 and K5, the latter representing new cutting-edge science in terms of instrumentation and modeling. The case studies to be discussed in this chapter covered periods of up to 15 years and the cutting-edge K5 knowledge of, say, 10 years

Figure 1 Knowledge diagram with five knowledge types on the basis of four defining characteristics.

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ago may now be K3 or K2 knowledge, illustrating the shifting character when characterizing knowledge as a function of time. But this is, of course, not true for all K4 or K5 knowledge. Ideally, such knowledge is transformed to the K3 or K2 level when it is internalized by its users. But often this transformation does not occur and K4 and K5 knowledge catches dust after publication, thus contributing to the knowledge paradox. The K1 K5 classification of knowledge is focused on the character of knowledge itself rather than on the way in which it is used and is more specific than the one proposed by Peterson (2009) who distinguishes tacit knowledge from explicit knowledge and introduces the category of cocreated “new knowledge,” needed for innovation. When characterizing TransForum projects over periods of up to 15 years, different forms of knowledge, originating from different KENGi partners, turned out to play a crucial part in different phases of the various projects. The four selected case studies were therefore characterized in terms of a track record of the flow of different types of knowledge as a function of time. This was expected to result in conclusions as to the most effective knowledge management procedures for any given case study and in suggestions for future education and knowledge management.

4. Case Studies 4.1. Case 1: Northern Frisian Woods: Cradle-to-Cradle dairy farming 4.1.1. Problem and objectives In 1991, the EU nitrate guideline was introduced, limiting N applications in manure to 170 kg ha21 with the objective to reduce the nitrate content of groundwater. In 1994, a law was passed requiring farmers to inject liquid manure into the soil rather than spread it at the surface. This was intended to reduce ammonia volatilization that, in turn, was expected to result in less deposition in adjacent nature areas. Many dairy farmers did not like these (“vertically imposed”) limitations, particularly the second one because they felt that injection would harm their soils. But more importantly, they hated the infringement on their entrepreneurial independence that was particularly evident as farmers refusing to inject manure received substantial fines. They suggested alternative technical procedures, based on the Cradle-to-Cradle (C2C) concept (McDonough and Braungart, 2002), reducing nutrient cycles and ammonia volatilization by changing the feeding regime, producing manure with less N (Sonneveld et al., 2008). This would also be favorable for water and

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landscape quality and biodiversity by reducing nutrient loads (Bouma et al., 2008). Farmers wished that such alternative procedures would be allowed by rules and regulations. In addition, they suggested a “horizontal” self-governance approach requiring clear and transparent environmental guidelines. To this purpose, they established a farmers’ cooperative also exploring introduction of various product market combinations leading to higher future farmer’s income. 4.1.2. The players The problem raised is a “wicked” one: the KENGi partners had at first different visions as to what constituted sustainable development and the proper role of environmental legislation as they operated in different contexts. Researchers (K) were primarily focusing on various forms of disciplinary research. Their contacts with the Ministry of Agriculture (G) were close and their funding was assured leading to limited intellectual curiosity. The Ministry was clearly risk averse as they had to face European regulators when satisfying European environmental guidelines. The farmers (E) expected that the mentioned problems could only be solved when they would be allowed to apply C2C methodology in a self-governing context and NGOs (N) considered agriculture as a negative threat to environmental quality. But lobbying by farmers, supported by a number of politicians, changed the scene. A farmer’s cooperative (E) was initially established in 1997 and has now 850 members (representing 40,000 ha). In 2005, an agreement was signed, indicating support for the cooperative by provincial and local government (G), environmental NGOs (N), the Ministries of the Environment and Agriculture (G), and researchers of Wageningen University and Research Center (K). The support of this group not only covered the manure problem being discussed but also other issues such as landscape quality and cultural heritage preservation. TransForum financed a substantial part of the program in the period 2005 2009 and provided knowledge brokerage in the form of a project director and several monitors. 4.1.3. Track record of the storyline (Fig. 2) As mentioned above, the EU-inspired nitrate guideline was introduced in 1991 (box 1, with no supporting research; the guideline was based on K2 knowledge). The injection legislation of 1994 (box 3) was, however, based on K5 research at the time (box 2: Huijsmans et al., 2001). Later, farmers refusing to inject manure were taken to court where substantial fines were issued as represented in Fig. 2 by a horizontal arrow between the years 1994 and 2006. In 1997, farmers organized into a small environmental cooperative, that was later expanded (box 4). They were inspired by scientist Jaap van Bruchem (box 5), not a member of the regular research circuit and a strong advocate of C2C agriculture (even long

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Track record of case study 1: NFW (see text).

before the term was widely introduced), pointing out (at K3 level) that N-use efficiency was very low on farm level and could be increased significantly by changing feeding practices of cattle and by lower application rates of chemical fertilizers. The environmental cooperative organized many meetings among its farmers to share experiences (at K1 level). In 1998, Dutch researchers devised a mineral accounting system (MINAS) for the government (boxes 6 and 7) to satisfy the nitrate guideline in a major research effort applying K3 level knowledge (box 6). The system, allowing farmers to estimate the difference between N entering and N leaving the farm, was successful in creating awareness about nutrient balances but yielded data only indirectly related to water quality. MINAS was, therefore, rejected by the European Court of Justice in 2003 (box 9), implying that the original guideline be maintained (Schroder and Neeteson, 2008). In 2001, an interdisciplinary research project was started, ending in 2008, initiated by the Department of Sociology of Wageningen University and joined by other Departments in close cooperation with the farmers to support development of a system-C2C approach (box 8), and this was instrumental in mobilizing broad societal support for the program by KENGi partners in 2005 (box 10) as mentioned above. In 2006, two actions occurred. First, Dutch researchers, using a K3 approach, presented a so-called derogation plan to the EU allowing farmers to temporarily apply 240 kgN ha21 from manure rather than the required

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170 kgN. This was accepted through 2009 and was recently extended to 2013 (not shown in Fig. 2). Second, the national government allowed 29 farmers in the Northern Frisian Woods (NFW) area, supported by scientists, to freely experiment with the C2C approach by temporarily canceling all legal manure requirements until 2009 (box 11). A final extension of this cancelation was granted until the Spring of 2012 and resulted in interactive research (box 14). In 2007 (not shown in Fig. 2), the Dutch Parliament approved the Polderman proposal requesting the government to investigate C2C dairy farming and, when successful, to create a certification system for this particular practice. Research contributions (at K3 level) in this period covered research on the effect of manure injection on soil biology (Van Vliet and de Goede, 2006) and on compaction (box 12). Integrative systems research using K5 life cycle analysis (Thomasson et al., 2008, 2009), started in 2010 (box 13) was essential to document the particular advantages of C2C dairy farming including a business plan (B in box 14) and is expected to result in the acceptance by the government of the principle of self-government and therefore, implicitly, in completing connected value development. This is, however, not yet clear at this time. Flow of knowledge The track record of Fig. 2 spans a period of 20 years, indicating that short-term projects dealing with certain aspects only are unlikely to achieve connected value development. Ultimately, government will probably accept the principle of self-governance provided that clear environmental guidelines corresponding to those of the EU are adhered to. But strict controls will be enforced to check the control mechanisms of the farmer cooperative. Figure 2 illustrates that this “wicked” problem had no simple, straightforward answer nor that only researchers possessed the required knowledge. In fact, the arrows in Fig. 2 represent most often transfer of K1, K2, or K3 knowledge, and K5 knowledge was restricted to the relatively sophisticated boxes 2 and 12 using state-of-the-art but available techniques and models. The key factor of connected value development was the perseverance of the board of the farmer’s cooperative that persisted in its activities despite of many problems encountered. But effective interaction with researchers and with the knowledge broker of TransForum was crucial as was TransForum funding. Governmental agencies were strictly enforcing the rules but ultimately open to change as evidenced by the experimental permits provided in the period 2006 2012. Two lines are presented in Fig. 2 for knowledge (K) because two research cultures clearly prevailed. The upper (K) line represents more traditional, rather monodisciplinary research that certainly made significant contributions even though some (MINAS in boxes 6 and 7) did not work out. Note that upward arrows from the upper K line toward G and E are one way. The lower (K) line presents more systems-oriented,

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inter- and transdisciplinary research, starting conceptually and inspiringly in box 5, moving toward comprehensive but still rather disciplinary research in close cooperation with the farmers (box 8) to, finally, a truly systems approach by introducing life cycle analysis, including economics (box 13), allowing formulation of a business plan in the experimental period granted by the government (box 14). Arrows toward G and E in the lower K line now point into two directions. In the end, the government (G) is likely to accept the advantages of the C2C approach and will also move from a “vertical” command-and-control mode into a “horizontal” participatory mode, expressed by permitting controlled self-regulation. But, as stated, this is not clear yet. Lessons for knowledge management The following lessons can be learned when analyzing the flow of knowledge in Fig. 2: 1. The need for emphasis on comprehensive systems analyses, such as the life cycle assessment. Separate and disconnected disciplinary studies prevailed for too long. 2. More attention should have been paid to connected value development in the early proposition phase of the project. Now, KENGi partners followed their own, separate views for too long. 3. Communication was hampered by unclear and nontransparent environmental rules and regulations. More emphasis should have been given to environmental monitoring providing specific data, using, for example, new automatic sensors that are available now (Bouma, 2011). 4. Developing management plans to address wicked problems takes a very long time. Short-term projects cannot possibly result in connected value development. 5. The roles of committed entrepreneurs and knowledge brokers are crucial to keep the process moving and so is long-term financing.

4.2. Case 2: The new mixed farm: an example of industrial ecology 4.2.1. Problem and objectives Economic rationalization of agricultural production processes (the industrialization of agriculture) has resulted in ever higher productivity in intensive livestock farming. This has particularly occurred in the sandy areas in the southern and eastern part of the Netherlands. From 1960 until 1980, this resulted in large quantities of excess manure and associated pollution of water, soil, and air. From 1980 onward, strict environmental laws were directed toward control of the mineral flows and they are increasingly successful. Also, animal-welfare NGOs objected to what

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they considered to be unacceptable living conditions for animals. The outbreak of a large-scale foot-and-mouth disease gave rise to a governmental program that aimed at the spatial concentration of intensive livestock farms. A regional or landscape dialogue was started in the late 1990s by the provincial Limburg government to guide this process in the North of the Province, involving local government and a number of local entrepreneurs to explore integrated regional solutions for this spatial reconstruction of the intensive livestock sector. By 2001, major support was provided by different experts of Wageningen University and Research Center (UR) in developing a plan for an agropark that became known as “New Mixed Farm.” This integrated system combined pig, chicken, and mushroom production with a concentrated greenhouse area of 200 ha and a power plant based on codigestion of manure and other organic restproducts. The New Mixed Farm was designed on the basis of principles of industrial ecology, optimizing energy, CO2, and nutrient flows between the units (Smeets, 2011). This way, the problem of environmental pollution by excess manure could not only be solved, but what used to be a negative cost factor in the farmers budget would change into positive income. At the same time, innovative construction of the pig and chicken farms would enhance animal welfare. 4.2.2. The players Initially, entrepreneurs and local government initiated a Regional Dialogue in the 1990s and researchers of Wageningen UR became involved in analyzing the problems associated with intensive chicken and pig farming. In 2001, Wageningen UR invested in KnowHouse, a public private liaison organization acting as knowledge broker between local entrepreneurs and knowledge suppliers. The agropark received their prime attention, and major support was provided by the National Ministry of Agriculture when in 2004 they assigned a separate status to the agropark ensuring that existing legislation would not inhibit the development of innovations in the agropark. In the period 2004 2009, Transforum funded the project, supporting research and activities of KnowHouse and also provided input by a knowledge broker. Increasingly, objections by animal-welfare NGOs and local action (NIMBY) groups that underlined the human health hazards inhibited progress. Environmental impact statements were prepared and the local city council finally approved construction in 2008. Still a large number of environmental permits had to be obtained from national and local government which again causes delays. This hurdle is expected to be cleared in the end resulting in a start of construction of the agropark after 2011. Thus, all KENGi partners were involved in different ways and times in the overall process that will at least cover about 14 years.

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4.2.3. Track record of the storyline (Fig. 3) The design process that was initiated by the Regional Dialogue (Fig. 3) involved four entrepreneurs (E) (a pig, chicken, mushroom farmer, and a greenhouse grower) and local government (G) supported by different researchers from Wageningen UR (K). This culminated in a first design for an agropark in 2004 (box 1) where knowledge management by KnowHouse played an important role. Also a business plan was made (B). The Ministry of Agriculture assigned the project special status in 2004 (box 2), while local government provided continuing support. Until 2008, progress in realizing the agropark slowed down. The greenhouse grower withdrew from the team as he worried about the possible negative spin-off for his operation of the poor public image of intensive livestock farming. The mushroom farmer went bankrupt in 2006. The remaining entrepreneurs decided to proceed with a slimmed-down plan consisting of building a robust, open structure, which consisted of a chicken and pig business and a manure processing facility. They found a new building location. As plans developed, local inhabitants protested as they feared negative effects of livestock farming (increasing traffic, smell, health hazards, and degradation of landscape quality). Some political parties and a national NGO, focusing on animal-welfare and environmental issues, joined the protests. Apparently, there was a gap in perception between the KENGi partners that wanted to develop an agropark and the protesters that only saw the potential threats of very large pig and chicken farms. In the

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Track record of case study 2: New Mixed Farm (see text).

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agropark, manure is processed in a facility producing energy and dried organic fertilizer, thus solving the excess manure problem. Besides, in the agropark, particular attention was paid to installing scrubbers, strongly reducing ammonia and dust emissions. Individual farms would not be able to afford such installations. Local government became increasingly aware of local resistance and, being strongly risk averse, its support decreased. Therefore, Fig. 3 shows no arrows from local government after 2004 except again in 2010 when permits for construction had to be granted. Ultimately, the municipal council approved the new location with only a one-vote margin in 2008. An operation like this requires an independent environmental impact statement, which was delivered in 2010 (box 3). The initial reaction to this report was mildly positive (box 4), but the national committee judging these reports demanded additional information. Once this revised report has been presented to the committee it is expected that there will be a final positive outcome (box 5). The realization of the agropark might then begin, be it that there are still several hurdles to overcome with respect to individual permits. And there is always the possibility that the opponents of the agropark will fight the decisions all the way up to the highest court in the Netherlands. That may take another several years. 4.2.4. Flow of knowledge Establishing the concept of the agropark and defining its characteristics in a continuous and iterative research and design process from 2001 onward involved input of K5 knowledge by different research groups from Wageningen UR and K2 tacit knowledge by the local entrepreneurs and government. The process of knowledge exchange was interesting (Hoes et al., 2008). The entrepreneurs worked as a group as did the scientists. Contact was only established when specific questions arose. This procedure resulted in team building on both sides, while mutual trust was slowly built as contacts proved productive. Joint meetings right from the start would most probably have been less effective. In addition, KnowHouse, connecting the E, G, and K partners, successfully facilitated the interaction process. This result would, most likely, not have materialized without KnowHouse input. The support by the national government in 2004 and participation of TransForum accelerated progress and a business plan was prepared. After 2004, the value creation phase was cumbersome because of public resistance to the plans. Two arrows appear in Fig. 3 for N. One represents the negative feelings of local citizens, expressed in the municipal council and the other the impact of national NGOs, both reflecting K1 knowledge. Although KnowHouse and TransForum contributed to local discussions, one of the weaker points in the KENGi approach was the relative absence of NGOs and societal groups in the core team. It would have been better to reward the protests

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of local groups by inviting them over and help the connected value development by bringing their design criteria to the table. This certainly would not have been an easy task, but the alternative—polarization between the entrepreneurs and the (local) NGOs—is worse. Unfortunately, the discussions were on one side dominated by the perceived benefits of the agropark and on the other side by the perceived negative consequences of large-scale intensive livestock farming. This division was not bridged in the process; so on this issue there was no real connected value development. Still, lobbying on the basis of scientifically based arguments led ultimately to narrow acceptance by the local municipal council. They required yet another independent environmental impact statement (box 3), representing K4 knowledge. Also, several permits had to be obtained from national and local governments before construction could start. Some local legal requirements contradict national ones, and the reverse was also true. It remains to be seen whether realization of the agropark will be within reach the coming year. There is progression although it is very slow. 4.2.5. Lessons for knowledge management The initial development of the agropark concept in this particular context was quite successful as K, E, and G partners worked well together being facilitated by knowledge brokers of KnowHouse. The mistake was made to wait too long with involving local citizens and national NGOs. They should have been involved earlier as codevelopers of ideas being generated. Now, a conflict situation arose that was difficult to overcome. K5 knowledge from research (K) was crucial to establish and realize the agropark concept for this particular location. The E and G partners made their K2 contributions and they clearly enhanced the science-based concepts, as reflected by the plans materialized by 2004. Discussions in the period 2004 2008 were dominated by K1 and K2 knowledge of local citizens and politicians. Attempts of KnowHouse and Wageningen UR scientists to inject K3 K5 knowledge in local discussions has contributed to the ultimate acceptance of the agropark plans by the local municipality. The impulse of an independent environmental impact statement (K4) in 2010 played an important role in what may ultimately result in acceptance of the plans. This case study illustrates that the whole range of K1 K5 knowledge is important in achieving connected value development.

4.3. Green care: health care on the farm 4.3.1. Problem and objectives New approaches in the health-care sector started during the second half of the previous century by emphasizing that elderly people, drug addicts, and psychiatric patients should be dehospitalized and more integrated in society.

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New forms of independent living groups and labor therapies were introduced. At the same time, farmers, government, and knowledge institutions were developing new approaches of rural development in threatened smallscale landscapes by introducing multifunctionality and by stimulating societal interest in local and regional products. In line with these developments, small groups of innovative farmers started experimenting with receiving elderly people and (former) psychiatric patients on their farms as a new source of income. On their “green care farms,” they offered structure, tranquillity, nature and labor (Hassink et al., 2007). In 2003, the sector received a strong incentive when the “Personal Budget” was officially introduced, which is a budget provided by public insurance, which can—after being approved—be spent freely by the client to obtain the necessary care depending on the clients’ specific needs. The number of care farms increased remarkably since then numbering 620 in the Netherlands in 2008. Farmers, however, are, despite of their good intentions, ill equipped to deal with various types of patients and the potential problem of failing care systems is real. Farmers in the area of “Waterland” in the north of Dutch capital, Amsterdam, were under pressure by the urbanization of the area: urban activities were spreading to the north, making it difficult for the farmers to scale up. Also, the area was declared a “national landscape” because of its cultural historical value as a traditional Dutch agricultural landscape, posing additional restrictions on farming. The “Landzijde” foundation was therefore established to connect farmers to the urban market of clients interested in care farming in order to create additional income enabling them to keep on farming. The regional and local governments were interested because in this way small-scale farmers would stay in business and keep on maintaining the typical small-scale agricultural national landscape. When TransForum joined this initiative, they underlined the importance of supporting the farmers in professionalizing their business proposal on the care market and therefore support them in competence development and administrative skills. The ultimate objective of this program was therefore to create a commercial system in which “green” care could provide essential functions for a variety of patients while contributing toward multifunctional agriculture providing benefits to both farmers and city dwellers. The Waterland example should become a pilot for other areas in the Netherlands. In the description of this case in this chapter, attention will be focused on knowledge generation and transfer. A detailed analysis of the overall project is provided by van Altvorst et al. (2011). 4.3.2. The players Quite a number of diverse players are involved: (i) the initiator of Landzijde, was a farmer with a background in education (E). He made

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contact with (ii) the regional government, the province of North Holland and the municipalities (G). The national government (iii) was not directly involved, but played a decisive role in the development of care farming, by introducing the “Personal Budget.” The national health-care insurance agency (iv) provided the quality mark needed to spend insurance money for the care clients (v). Various research institutes were involved of Wageningen UR and of the Free University of Amsterdam (K). Wageningen UR’s main interest was the development of new sources of farm income by providing new services to the urban community. The Free University of Amsterdam performed participatory research into the knowledge development resulting from collaboration of entrepreneurs, governments, and knowledge institutes (Regeer, 2010). (vi) The Athena Institute and the Medical Centre of the Free University investigated effects of green care therapies on depressions and anxieties (Hassink et al., 2010), addressing the “wicked” problem of therapeutical effects of care farms, and (vi) care institutions, such as hospitals, are considered as “social entrepreneurs” (E). In contrast to the other case studies discussed in this chapter, no players were involved in the N category of NGOs. The reason is simple: the concept of green care is attractive and noncontroversial. However, its implementation creates many wicked problems as interests involved are quite diverse, originating in different worlds of perception. For example, the medical and agricultural communities have widely different perspectives on their own and on society’s problems. Linking good health care with financially attractive multifunctional agriculture presents, therefore, a huge challenge. 4.3.3. Track record of the storyline (Fig. 4) The “Personal Budget,” as discussed above, was introduced in 2003 (box 1). In the same year, the National Authority for Health Insurances approved Landzijde as an organization where health insurance money could be spent (box 2). In 2006, the TransForum project “Green Care” started connecting researchers of Wageningen UR and the Free University to the entrepreneur. Together they drew up the most important knowledge questions which had to be investigated during the project (box 3) (Fig. 4):  What are the unique qualities represented by care farms and how can they distinguish themselves from regular forms of care? How can rural entrepreneurs effectively make use of these qualities?  What is the societal impact of care farming (on a social, ecological, and economic field and on micro-, meso, and macro scale level?) What is the present state of the art, what are the developments, and what is the potential?  Which type of agricultural enterprises may fulfill the potential demand for care, now and in the future?

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Figure 4 Track record of case study 3: Green Care (see text).

 What are the most appropriate organizational forms resulting in an integral approach to develop the new value chains between rural areas and cities?  What is the impact of these new chains between city and rural areas for the spatial organization of rural areas?  How can new, unambiguous marketing strategies be developed to promote health-care products and services from rural areas? The challenge is to connect farming and care, healthy recreation, healthy food, education, inspiration, etc. The project started with a care experiment on two farms (box 4). The province of North Holland and the Municipality of Amsterdam got actively involved (box 5) as, respectively, sponsor of the project and in terms of the personal commitment of an aldermen. Three working groups (“CoPs”) were started in which farmers, care workers, civil servants, and researchers addressed the knowledge questions in a network approach (box 6). The researchers shared experiences elsewhere in the Netherlands and Europe. In the meantime, the Wageningen researcher connected the project with a National Initiative on care farming and an European network on care farming, “SoFar.” Through his contacts, the researcher succeeded in providing the Landzijde project the status of “European pilot on care farming,” which provided publicity, status, knowledge, and funds. Based on its research activities, Wageningen University and the Free

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University published papers and articles on care farming and its societal impact in the following years. A researcher of the Free University continuously monitored the interaction processes and intervened during sessions, focusing on the effectiveness of collaboration between the stakeholders with different interests. In the end, the researcher drew up a “learning history” of the Landzijde foundation (Regeer, 2010). In 2007, basic K5 scientific research was initiated to investigate the effects of agriculture and health care on depressions and anxieties (box 7). This was based on collaboration between two groups at the Free University: the Athena Institute and the Medical Centre (Psychiatry) and resulted in several leading publications. Connections of Landside with regional and municipal health-care organizations were formalized by giving these organizations a place in the supervising board (box 8). Furthermore, connections were established with banks, health insurance companies, the national farmers organisation, and the national youth care platform (box 9). In 2008, TransForum directed attention toward the business case of Landzijde and the possibilities to scale up or translate the Landzijde concept to other regions and thus become a contribution to a real systems innovation. For this a new scientific project, a collaboration, was started between the scientific partners which were already involved (Wageningen University, Free University) and new scientists on policy science (University of Amsterdam) and entrepreneurship (Erasmus University, Rotterdam) (box 10) to analyze green care initiatives from a viewpoint of entrepreneurship and systems innovation. Meanwhile, Landzijde and the municipality developed a professional relationship, in which Landzijde started to offer services to the urban community in 2009 (box 11). The first project that Landzijde carried out for the Amsterdam city was the “Farm force project,” in collaboration with the welfare organization “Streetcornerwork.” In this successful project, homeless people in Amsterdam were offered daily activities on farms. The work on the farm offered the clients a structured life, steady work, and development of self-esteem, which supports their reintegration in society. This point can be seen as the achievement of value capture. In 2010, the TransForum project ended but research projects at Wageningen UR and Free University continue. Clearly, the TransForum period from 2006 to 2010 has been particularly productive. At this moment, Landzijde is a full-grown, professional organization with 102 care farmers, 421 clients, 7 full-time employees (director, administration and regional coordinators), and a turnover of 4 million euros. Clients and farmers are represented in the advisory board. Clients, farmers, and care institutions form the board of commissioners (van Altvorst et al., 2011).

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4.3.4. Flow of knowledge The Green Care project was a project about learning and system change. The challenge was the development and professionalization of a new agricultural sector. In 2006, an entrepreneur with an innovative idea (K2) was through TransForum connected to the knowledge institutions Wageningen UR and the Free University of Amsterdam (K3 K5). This started new developments and dynamics. New partnerships were set up, new connections were made, and new attitudes and views were introduced on the crossroads of existing sectors. The focus on learning can be recognized on different levels: primarily by the clients of the projects, who develop new attitudes, competences and structure at K1 level by working on the farms. This learning process reflects interaction between clients, farmers, and health-care workers. On a second level, learning by the farmers and health-care institutions at K2 and K3 level, who have to develop environments that enable clients to work and develop themselves on the farms. This puts new demands on farmers and health-care workers, both on their competences, mode of operation and organization. These new demands were developed in working groups in which farmers, health-care workers, and researchers interacted. Here, K2 knowledge was dominant but occasionally researchers injected results of the scientific program at K3 level. The Landzijde foundation forms the anchorage of the new knowledge resulting from these groups. On a third level, new (financing) structures, quality control systems, and legislation have to be developed, by governments, health insurance companies, and health organizations. This involved new arrangements of existing structures and rules at K3 level. The knowledge institutes played an important role in this process. By bilateral discussions, workshops, symposia, and publications, they contribute to the necessary changes in the system. Finally, on a fourth level, the system change addresses the development of new paradigm’s and views: new views on care and clients and new views on the agricultural sector. This requires evidence-based research into the effects of care on farms and the economic impact on agricultural enterprises and was successful because knowledge from the entire knowledge chain ranging from K1 to K5 was injected at appropriate times in the working groups. 4.3.5. Lessons for knowledge management 1. Partners had different roles in the different phases of the program. When developing the value proposition, scientists collaborated closely in mixed working groups with entrepreneurs, health-care workers, and civil servants to design new (3P) business cases. These working groups had a crucial function, and scientists acted here in two ways

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by contributing knowledge and by acting as knowledge broker able to understand and communicate with representatives of the practical, institutional, and scientific world, thus creating meaningful connections between them. When achieving value creation, the partners had an important function to connect with government and health-care organizations to develop a business plan, including new institutional structures in order to achieve value capture in the end. 2. The innovative process required flexibility of all partners. They should not necessarily adhere to familiar methods and instruments or even to procedures agreed upon at the start of the program, but they should be flexible with an open attitude to learning. 3. Cutting-edge basic medical research originated from this project and represents an interesting reversal of the knowledge chain, as shown in Fig. 1. Traditionally, curiosity-driven K5 research is communicated down the chain. Here, K1 K2 experiences inspired to specific K5 research that most likely would not have materialized without this program. Focusing K5 research this way is likely to result in more applicable results than the classical top-down procedure while it does not in any way infringe on academic freedom.

4.4. Case 4: Developing the new “Rondeel” chicken housing system 4.4.1. Problem and objectives The present crowded housing systems for egg laying hens are unsustainable as they are associated with animal and human health risks, environmental pollution and, in general terms, with a perceived lack of animal welfare. Problems culminated in 2003 with an outbreak of chicken flue requiring the killing of millions of chickens and resulting in a major public backlash as this was perceived by the general public as resulting from an unethical production system primarily focused on generating output. Next, the Ministry of Agriculture initiated research to develop innovative production systems to be focused on animal welfare. The outlook was for such systems to be built in future. 4.4.2. The players Many players were involved in developing innovative housing systems (named “Rondeel” after 2005) for chickens: (i) the national Ministry of Agriculture initiated a research project—“The Keeping of Hens” (2004 2006) became involved in providing financial guarantees; (ii) local government was involved in providing environmental and building permits for the experimental demonstration site in 2009; (iii) researchers representing different disciplines from Wageningen UR were involved in different ways during the entire process; (iv) local entrepreneurs (chicken farmers)

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were involved during the entire process and so was the farmers’ trade union; (v) a large cooperative, showing commercial interest in the new innovative design; (vi) animal-welfare and environmental NGOs were also involved during the entire project; (vii) TransForum financed important activities in the period 2007 2010 and provided continuous support by a knowledge broker who was not a member of the scientific community but had worked for an animal-protection NGO, creating a favorable position to work from. The knowledge broker maintained and initiated interaction among the various players and guarded overall progress. 4.4.3. Track record of the storyline (Fig. 5) As discussed, the Ministry of Agriculture commissioned research to design an innovative system in 2003 (box 1). In 2004, the project “The Keeping of Hens” was started (box 2) and research until 2006 involved a multistakeholder approach with entrepreneurs (E) and right from the start interaction with NGOs (N). Two-way arrows are therefore shown in Fig. 5. The design process evolved into a truly innovative open and accessible design, including: (i) an animal-friendly environment with a relatively low stocking density, a dustbath and a woodland rim for chickens to roam into; (ii) a 50% reduction of ammonia emissions and 50% reduction of energy consumption as compared with traditional systems; (iii) a visitor’s section where the production process can be observed, allowing direct

Figure 5

Track record of case study 4: the Rondeel (see text).

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contact with consumers; (iv) environmentally friendly packaging of the eggs, and (v) a design that blends harmoniously into the landscape (van Altvorst et al., 2011). A Rondeel stable houses 30,000 chickens and 150,000 eggs will be produced per week. In 2006 (box 3), a large manufacturer of poultry systems decided to embrace the design and take part in its development. But more was needed than the technical design. The ultimate success required effective marketing, also internationally, and of an innovative chain design from production to consumption where eggs were to be sold directly to a major supermarket concern. To realize all of this, much interaction and development occurred in the period 2007 2010 (box 4) during the TransForum-funded phase of the project: “The quest for the golden egg.” A local entrepreneur volunteered in 2008 to lead the first of—it was hoped—many future Rondeel stables and he received support from local government. In this period, the Rondeel concept received the highest three-star quality award from the Animal Protection Fund and an environmental label (“Milieukeur”) which was helpful in engaging the supermarket concern and to convince the public that buying Rondeel eggs (30% more expensive than the regular ones) was a means to express awareness in terms of the environment and about animal care. Still, funding of the first Rondeel building remained problematic. Government and banks were unwilling to provide funds and funds were ultimately provided by the manufacturer himself as a future investment (Box 5). The first Rondeel stable was built and eggs are now for sale in a major supermarket chain. After this, the government ultimately approved guarantee funding for the second and third Rondeel stable to be constructed elsewhere. In 2011, the second stable was opened and three more are under development. 4.4.4. Flow of knowledge Figure 5 illustrates that the wicked problem being considered had no simple straightforward answer and that relevant knowledge was scattered among many disciplines and experts. Creating an innovative and sustainable egg production system requires multi- and interdisciplinary K5 research (box 2) involving animal scientists, construction engineers, energy experts, ethics specialists and K1 K2 interaction with various stakeholders, including animal activist and environmental groups who are highly suspicious of what they perceive as yet another industrial project. Certainly, the Rondeel concept as presented (box 3) represents a true interdisciplinary invention. But Fig. 5 also illustrates that an invention, as such, does not necessarily lead to value capture. Involvement of TransForum (box 4) included four major workshops where a wide range of participants discussed practical implementation in terms of marketing, also internationally, contacts with the media,

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creation of short, innovative producer consumer chains, regulatory requirements, and finances. Here, the knowledge broker, the project director of TransForum, and the director of Rondeel played a crucial role arranging contacts at appropriate times with different specialists, entrepreneurs, and representatives of NGOs. The exchange of knowledge was dominantly of the K2 type and was technical, administrative, and entrepreneurial by nature. Simply focusing on processes and social intelligence would not have been adequate. The large group of participants did have a joint objective, but their frames of reference differed widely, making facilitation by the knowledge broker essential. Value capture was only achieved (box 5) because the manufacturer of poultry systems decided to make a strategic investment (box 4). It is highly unlikely that value capture would have been achieved without the actions represented in box 4. 4.4.5. Lessons for knowledge management  Rondeel is a fine example of successful interdisciplinary research, and it also shows that an invention, as such, is not enough to achieve connected value development. The research community could argue that organizational, financial, and commercial aspects are not part of a scientific exercise. Still, without value capture, the invention of the Rondeel concept would most likely have remained academic and would not have represented a successful link between science and society as it presently does. Thus, continued involvement with the value creation and capture process is important for the scientific community.  It is essential to assemble the right parties at the start of the value proposition process. Combination of various types of knowledge is essential for solving complex, wicked problems. In this project, researchers were important for contributing scientific knowledge, the manufacturer of poultry systems for technical and financial know-how, the NGOs for public support and the entrepreneurs for their understanding of the commercial and technical feasibility of the project. Progress is only maintained when the overall objective, which can be reached in different ways, remains in focus. This requires a clear strategy and an open mind for different frames of reference, opinions, and societal processes, taking all partners seriously.  Financial requirements for not only project funding but also implementation need to be satisfied before effective action can take place. TransForum has performed a crucial function in this project. Government agencies, notorious for short-time change and being risk averse, are often not a reliable source for long-term financial support that is needed.

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5. Discussion and Conclusions 1. Four case studies from the TransForum portfolio demonstrate that system innovation in agriculture can be achieved by managing “wicked” problems associated with sustainable development, applying the method of connected value development to develop new modes of 3P agricultural production in three phases of value proposition, -creation, and -capture. This transdisciplinary approach involves all KENGi partners (knowledge workers, entrepreneurs, NGOs, and government), each with different interests, goals, and value judgments. The value proposition phase serves to define a common solution to the different goals and requires much more time than is usually provided. Value creation involves integration of different types of hard and soft data and information into a coherent, operational design supported by a solid business plan and investment strategy. The value capture phase represents successful completion and can serve as an illustration of science contributing to societal developments, supporting claims being made in many strategic research reports. Knowledge-driven inventions by research, when presented in isolation, often do not contribute to societal developments because there is no cocreation of system innovations that lead to new modes of agricultural production. This may explain part of the knowledge paradox. 2. Persistent entrepreneurs and knowledge brokers played a crucial role in connected value development in the four case studies. The latter are most effective when they possess both “hard” knowledge and considerable social intelligence (so-called T-shaped skills) as they function as intermediaries between the various KENGi partners. They can be regarded as a new type of extension workers of the twenty-first century (Extension 2.0). But there are major differences with the classical extension agents of the agricultural era who basically interpreted and communicated research knowledge to farmers in a linear process. Extension 2.0 requires initiation and facilitation of complicated interaction processes, as illustrated in the four case studies, involving all KENGi partners in unpredictable ways. Also, interaction has a different focus in the different phases of systems innovation. To support the independent role of knowledge brokers, Extension 2.0 should preferably be independent with separate financing, just like the traditional extension services in the agricultural era. Aside from knowledge brokers acting in the overall KENGi context, each KENGi partner itself needs members with T-shaped skills that can effectively represent the partner in the overall discussion. They act as knowledge brokers within their own group and not all members of the group are able or willing to interact with third parties.

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3. Focusing attention on the scientific community, they would be well advised to actively participate in case studies on sustainable development, as presented in this chapter, with the objective to demonstrate the importance of various forms of knowledge when cocreating systems innovations. This has major consequences for research organization and planning as it implies:  Active participation of all participants, including scientists, in the entire connected value development process, which often takes years requiring long-term commitment and funding. This contrasts with current practices of short-term research projects with limited funding often focusing on disciplinary knowledge creation only.  Educating knowledge brokers, who may be seen as extension agents of the twenty-first century and presenting them with good job perspectives. As discussed above, this aspect has two dimensions for the science community. First, knowledge brokers are needed to facilitate connected value development among the KENGi partners. Second, within the group of scientists also, some researchers with T-shaped skills should be selected to interact with the other partners on behalf of the group. These knowledge brokers deserve a solid position next to their traditional colleagues in the scientific community being engaged with basic, strategic, and applied research as they are all part of a CSP (Bouma et al., 2008).  Expanding the way scientific research is judged. Publishing refereed papers in international journals is still relevant for basic, strategic, and even applied research, but broader criteria have to be applied to judge research in a transdisciplinary context as exemplified in this chapter. As stated, knowledge brokers perform an important function in this context and they should be judged and rewarded accordingly. Judging criteria have been proposed but have so far not been implemented (Spaapen et al., 2007). 4. The four case studies show that innovation rarely starts with cuttingedge K5 inventions in the value proposition phase that result in innovation and value capture. Progress in the innovation process depends on joint learning, obtaining business plans, and organizational arrangements based on mainly tacit K2 knowledge of various stakeholders, with occasional injections of focused K3 K5 research to solve particular problems where tacit knowledge is inadequate. This is shown in the four track records of the case studies. Achieving innovation requires, therefore, activation of the entire knowledge chain from K1 to K5. The green care case showed that innovative medical K5 research resulted from practical experiences earlier in the project. Thus, basic research is linked to practical problems and this potentially increases its applicability as compared with a purely curiosity-driven approach.

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5. There are no simple recipes to successfully manage wicked problems associated with innovation and sustainable development. The track records of the four case studies are quite different. However, the case studies illustrate a successful approach following the connected value development approach. Detailed case studies, as reported in this chapter, may serve to point out potential problems and opportunities and may be used in teaching and education to impart “T-shaped skills.” Showing developments as a function of time as track records is important because aggregate characteristics of each project cannot adequately reflect knowledge transfer and questions as to what happened when and why. Along these lines, the case-study method is followed successfully in business schools.

ACKNOWLEDGMENTS The commitment and hard work of all participants in the case studies is gratefully acknowledged.

REFERENCES Andeweg, K., & van Latesteijn, H. C. (2010). Transforming agriculture: A new approach to stimulate sustainable development. In Van Latesteijn & Andeweg (Eds.), The Transforum model: Transforming agro innovation toward sustainable development. Dordrecht, Heidelberg, London, New York: Springer Verlag (Chapter 7). Bouma, J. (2010). Implications of the knowledge paradox for soil science. Advances in Agronomy, 106, 143 171. Bouma, J. (2011). Applying indicators, threshold values and proxies in environmental legislation: A case study for Dutch dairy farming. Journal of Environmental Science and Policy, 14, 231 238 (doi:10.1016/j.envsc 2010.12.00). Bouma, J., de Vos, J. A., Sonneveld, M. P. W., Heuvelink, G. B. M., & Stoorvogel, J. J. (2008). The role of scientists in multiscale land use analysis: Lessons learned from Dutch communities of practice. Advances in Agronomy, 97, 177 239. Bunders, J. F. G., Broerse, J. E. W., Keil, F., Pohl, C., Scholz, R. W., & Zweekhorst, M. B. M. (2010). How can transdisciplinary research contribute to knowledge democracy? In R. J. in ‘t Veld (Ed.), Knowledge democracy. Consequences for science, politics and media. Heidelberg, Dordrecht, London, New York: Springer-Verlag. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, H. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: SAGE. Hassink, J., Elings, M., Zweekhorst, M., van den Nieuwenhuizen, N., & Smit, A. (2010). Care farms: Attractive empowerment-oriented and strengths-based practices in the community. Health and Place, 16, 423 430. Hassink, J., Zwartbol, C., Agricola, H. J., Elings, M., & Thissen, J. T. N. M. (2007). Current status and potential of care farms in the Netherlands. Netherlands Journal of Life Sciences, 55(1), 21 36. Hessels, L. K., & Lente, H. (2008). Re-thinking new knowledge production: A literature review and a research agenda. Research Policy, 37, 740 760.

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Hoes, A. C., Regeer, B. J., & Bunders, J. F. G. (2008). Transformers in knowledge production: Building science practice collaborations. Action Learning: Research and Practice, 5, 207 220. Huijsmans, J. F. M., Hol, J. M. G., & Hendriks, W. M. M. B. (2001). Effect of application technique, manure characteristics, weather and field conditions on ammonia volatilization from manure applied to grassland. Netherlands Journal of Agricultural Science, 49, 323 342. Jacobs M. (2006). The production of mindscapes. A comprehensive theory of landscape experience. Thesis Mansholt Graduate School, Wageningen University and Research Centre, Wageningen. Jacobsen, E., Beers, P. J., & Fischer, R. H. (2010). Inventions for future sustainable development in agriculture. In H van Latesteijn, & K. Andeweg (Eds.), The Transforum model: Transforming agro innovation towards sustainable development. Dordrecht, Heidelberg, London, New York: Springer-Verlag. McDonough, W., & Braungart, M. (2002). Cradle to Cradle: Remaking the way we make things. New York, NY: North Point Press. Peterson, H. C. (2009). Transformational supply chains and the “wicked problems” of sustainability: Aligning knowledge, innovation, entrepreneurship and leadership. Journal on Chain and Network Science, 9(2), 71 82. Peterson, H. C., & Mager, S. E. (2010). From motivating assumptions to a practical innovation model. In Van Latesteijn & Andeweg (Eds.), The Transforum model: Transforming agro innovation toward sustainable development. Dordrecht, Heidelberg, London, New York: Springer-Verlag (Chapter 6). Regeer, B.J. (2010). Making the invisible visible. Analysing the development of strategies and changes in knowledge production to deal with persistent problems in sustainable development. PhD Thesis, Free University of Amsterdam. Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155 169. Schroder, J. J., & Neeteson, J. J. (2008). Nutrient management regulations in the Netherlands. Geoderma, 144, 418 425. Smeets, P. J. A. M. (2011). Expedition agroparks. Research by design into sustainable development and agriculture in the network society. Wageningen, The Netherlands: Wageningen Academic Publishers. Sonneveld, M. P. W., Schroder, J. J., De Vos, J. A., Monteny, G. J., Musquera, J., Hol, J., et al. (2008). A whole-farm strategy to reduce environmental impacts of nitrogen. Journal of Environmental Quality, 37(3), 333 337. Spaapen, J., Dijstelbloem, H., & Wamelink, F. (2007). Evaluating research in context. A method for comprehensive assessment. The Hague, The Netherlands: Consultative Committee of Sector Councils for Research and Development (COS). Thomasson, M. A., Dalgaard, R., Heijungs, R., & de Boer, I. J. M. (2008). Attributional and consequential LCA of milk production. International Journal of Life Cycle Assessment, 13, 339 349. Thomasson, M. A., Dolman, M. A., van Calker, K. J., & de Boer, I. J. M. (2009). Relating life cycle assessment indicators to gross value added for dutch dairy farms. Ecological Economics, 68, 2278 2284. Tress, B., Tress, G., De´camps, H., & d’Hauteserre, A. (2001). Bridging human and natural sciences in landscape research. Landscape and Urban Planning, 57, 137 141. Tress, B., Tress, G., & Fry, G. (2004). Integrative studies on rural landscapes: Policy expectations and research practice. Landscape and Urban Planning. van Altvorst, A. C., Eweg, R., van Latesteijn, H., Mager, S., and Spaans, L., (2011). Sustainable agricultural enterprises. The metropolitan area as an engine for new entrepreneurial activities. Transforum, Zoetermeer, the Netherlands. 180p. (in press).

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Van Latesteijn, H. C., & Andeweg, K. (2010). The need for a new agro innovation system. In Van Latesteijn & Andeweg (Eds.), The TransForum model: Transforming agro innovation toward sustainable development. Dordrecht, Heidelberg, London, New York: SpringerVerlag (Chapter 1). Van Mierlo, B., Leeuwis, C., Smits, R., & Woolthuis, R. K. (2010). Learning towards system innovation: Evaluating a systemic instrument. Technological Forecasting and Social Change, 77(2), 318 334. Van Vliet, P. C. J., & de Goede, R. G. M. (2006). Effects of slurry application methods on soil faunal communities in permanent grassland. European Journal of Soil Biology, 42, 348 353. Wenger, E., Mc Dermott, R., & Snyder, W. M. (2002). Cultivating communities of practice—A guide to managing knowledge. Boston, MA: Harvard Business School Press.

C H A P T E R S E V E N

Crops Yield Increase Under Water-Limited Conditions: Review of Recent Physiological Advances for Soybean Genetic Improvement Walid Sadok,
and Thomas R. Sinclair† Contents 1. Introduction 2. Crop Water Use and Yield: A Framework for Trait Identification 2.1. Transpiration coefficient k 2.2. Harvest index 2.3. Vapor pressure deficit 3. Traits Influencing Water Conservation 3.1. Limited TR and slow-wilting 3.2. Timing of stomatal closure in the soil drying cycle 3.3. Slow leaf area development 3.4. Other possible water conservation traits 4. Traits Influencing Water Access 4.1. Increased rooting depth 4.2. Increased rooting rate 4.3. Decreased root hydraulic conductance 4.4. Other rooting traits 5. Traits for Special Sensitivities: Nitrogen Fixation Tolerance to Drought 6. Concluding Remarks Acknowledgment References

326 327 329 330 331 331 331 336 337 338 339 339 340 341 342 342 344 345 345

Abstract Due to future requirements for more crop production, there will be greater needs to increase yields for crops subjected to water deficits. In recent years, substantial progress has been made with soybean (Glycine max (L.) Merr.) in understanding the water-deficit limitation on yield using model assessments,
†

Earth and Life Institute, Universite´ Catholique de Louvain, Louvain-la-Neuve, Belgium Crop Science Department, North Carolina State University, Raleigh, NC

Advances in Agronomy, Volume 113 ISSN 0065-2113, DOI: 10.1016/B978-0-12-386473-4.00007-5

© 2011 Elsevier Inc. All rights reserved.

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physiological investigations, and plant breeding. This knowledge has been applied in developing higher yielding genotypes. This review examines physiological options and genetic advances made with soybean as possible guides for studies with other crops. Three approaches exist for minimizing the negative impact of water deficit on crop production: (1) conserve soil water, (2) access more water, and (3) overcome special water-deficit sensitivities. Water conservation in soybean has been achieved by exploiting a genotype that has limited hydraulic conductance in its leaves. A consequence of this trait is that transpiration rate is limited at times of high vapor pressure deficit resulting in soil water conservation for use later in the season. Acquisition of more water is most likely to be achieved by greater depth of rooting or greater root length density deep in the soil. Although promising genetic variability has been identified, breeding efforts for these rooting traits are still required. A special sensitivity in soybean that results in a major limitation in yield is a decrease in symbiotic nitrogen fixation rate with only modest soil drying. Germplasm has now been released that results in increased yields due to a capacity for sustained nitrogen fixation with drying soil. This review highlights that soybean investigations combining physiological investigations, simulations studies, and field-based phenotyping of traits have resulted in the identification of genotypes with increased yield potential in water-deficit environments. Keywords: breeding strategy; nitrogen fixation; phenotyping; simulation; soybean; transpiration; water deficit; yield

1. Introduction To meet food demands of a growing human population, current soybean worldwide production (about 220 million tons) must increase by a staggering 140% by 2050 according to FAO estimates (Bruinsma, 2009). With decreasing availability of well-watered agricultural areas, attempts to reach such future production levels will require the use of existing or new cropping areas having limited water supply (Sinclair et al., 2010). Serraj et al. (2009) reviewed for rice (Oryza sativa L.) the challenges of increasing the land area and yields in view of water limitations. For soybean (Glycine max (L.) Merr.), current intensive production systems in major producing areas in the United States, South America, and China are already experiencing a decrease in precipitation amounts, which is expected to reach 20% in some regions by the end of the century as a consequence of climate change (IPCC, 2007). While accurate projections of future precipitation patterns and their quantitative impact on crop yield is still lacking (Sinclair, 2010), it is critical to the sustainability of crop production systems to have drought-tolerant genotypes that can maintain economically viable yields under water-deficit conditions.

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Since 2005, significant progress has been achieved with soybean in (i) identifying the physiological and genetic basis of traits for water-limited environments, and (ii) quantifying their impact on yields. These efforts have resulted for the first time in the release of nitrogen fixation droughttolerant cultivars (Sinclair et al., 2007) and the use of new genetic sources of “slow-wilting” in soybean in breeding programs (Charlson et al., 2009; Hufstetler et al., 2007). In each case, progress has been the result of an interdisciplinary approach involving field-based phenotyping and physiological and genetic dissections. A useful contributor to this progress was the results of simulation studies based on a soybean crop model incorporating the direct link between crop yield and available water for transpiration (Sinclair, 1986; Sinclair et al., 2010; Tanner and Sinclair, 1983). Sinclair et al. (2005) with sorghum (Sorghum bicolor L.) and Serraj et al. (2009) with rice also demonstrated the importance of model assessments in guiding experimental investigations. In this review, an agroeconomic perspective is presented so that plant traits resulting in increased crop survival, for example, are not considered. Under extreme levels of drought threatening crop survival, water availability will be so low that even with crop survival yield will be very low and the grower will be economically devastated. Therefore, this review will address plant traits that can be genetically altered to improve economic sustainability of cropping systems in water-limited environments. The selection criteria for these traits for water-deficit conditions include the existence of a quantitative assessment of the possible yield benefits from a trait, an established physiological basis, and a genetic variability for the trait. The perspective of this review is an examination of drought tolerance traits in soybean from an agrophysiology standpoint rather than from a molecular physiology view. The relevant drought-tolerant traits can be broadly classified into three strategies that attempt to match key plant physiological functions to water supply. These are (i) improved conservation of soil water, (ii) increased crop access to water, and (iii) limited special sensitivities that negatively affect yield under water-deficit conditions (e.g., nitrogen fixation tolerance to drought). In the following sections, traits pertaining to each of these strategies are presented and discussed based on their potential benefits on yields under water-limited conditions, their physiological basis, and breeding considerations such as the extent of their genetic variability (Table 1).

2. Crop Water Use and Yield: A Framework for Trait Identification The early analyses of crop water use by deWit (1958), Fisher and Turner (1978), and Tanner and Sinclair (1983) established the existence of

Table 1 Selection of traits for improving drought tolerance in soybean Approach

Strategy

Trait

Objective

Benefit in simulationa

Genetic variabilitye

Matching physiology to water supply

Conserving water loss

Maximum TR Early stomatal closure Delayed stomatal closure

Save water for later drought Save water for later drought Aggressive water use in anticipation of rain or irrigation Save water for later drought

111 (Soybean)b 111 (Soybean)b 2 (Maize)c

Yesf Unknown Unknown

1 (Soybean)b

Unknown

Adjusting water access

Slow leaf area development Increase in root depth

Increase in rooting rate Adjusting special sensitivities

a

Decrease in root hydraulic conductance NFDT

11 (Maizec, Sorghumd, Wheatd) Quickly accessing deeper water 1 (Soybean)b before onset of drought Save water for later drought NA Access deeply stored water during drought

Improve HI

Reported yield benefits resulting from those traits in simulation analysis carried out on soybean. Sinclair et al. (2010) or other crops such as maize. c Sinclair and Muchow (2001), sorghum and wheat. d Jordan et al. (1983). The levels of benefits are qualitatively indicated by the (1) and (2) operators. e Reported genetic variability for these traits in soybean. f Sadok and Sinclair (2009a, 2009b). g Cortes and Sinclair (1986). h Kaspar et al. (1984). i Rincon et al. (2003). j Sinclair et al. (2007). b

1111 (Soybean)b

Yesg Yesh Yesi Yesj

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a robust link between the amount of water used by a crop for transpiration and its growth and yield. Such a relationship is the consequence of the inextricable link between the physical and physiological processes involved in carbon dioxide (CO2) uptake by plants for mass production and water loss by transpiration. The key link is gas diffusion through stomata, which are the same portals for CO2 diffusion into the leaf and for water vapor diffusion from the leaf. In 1983, Tanner and Sinclair undertook a theoretical derivation of crop gas exchange in crops to better understand crop water use for growth. Considering a few simplifying assumptions, they were able to derive a relationship between yield (Y) and water loss through transpiration (T) that was superficially simple: Y ¼ T
HI
k=VPD

(1)

where Y 5 grain yield (g m22) T 5water lost by transpiration (g m22) HI 5 harvest index, a ratio of grain yield to total growth k 5 transpiration coefficient dependent on crop physiology (Pa) VPD 5 vapor pressure deficit (Pa). Below is an overview of some of the complexities of each component in Eq. (1) in defining yield limits with a particular focus on the relevance of each component as a lever for breeding for drought tolerance in soybean.

2.1. Transpiration coefficient k Coefficient k is a mechanistic transpiration coefficient that is dependent on a number of parameters, all of which are fairly stable for each crop species. An environmental parameter that determines k is the atmospheric CO2 concentration, resulting in increasing k with increasing CO2. There are two physiological parameters that cause k to be conserved within a species but vary among crop species. One physiological parameter is dependent on the photosynthetic pathway of the plants (i.e., C3 or C4). For C4 species, the value of k is much greater than for C3 species, but little variation of k exists within a given crop. A second physiological parameter is the biochemical composition of the plant mass synthesized by the crop. Plants that synthesize large fractions of carbohydrates have much larger values of k than those synthesizing proteins and lipids. For soybean, k has a value of approximately 5 Pa (Sinclair, 2010; Tanner and Sinclair, 1983). According to Ludlow and Muchow (1990) and Sinclair (2007), there seems to be little prospect for modifying either of the two major plant

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parameters determining the value of k. Indeed, modification of soybean—or any other crops—photosynthesis to include the C4 precursor pathway in carbon dioxide fixation would require a large number of modifications in both plant biochemical and anatomical traits. Recent reviews offer a number of speculations for modifications that can have an impact on photosynthetic activity and k including short-circuiting photorespiration, increased or optimized RuBP regeneration, developing a CO2 pump with Kranz anatomy, Rubisco without oxygenase, and high catalytic turnover rate (Parry et al., 2011). Development of these options will need to rely heavily on innovative and complex transgenics. Also, the base germplasm for any transgenics will likely need to have exceptional photosynthetic capacity and efficiency, and such germplasm remains to be identified. In soybean, recent evidence indicates that there is little variability in photosynthesis among genetically distant soybean lines that included commercial cultivars, breeding lines, and plant introductions (Gilbert et al., 2011). Therefore, although very desirable, it seems that a very significant and sustained effort has yet to be undertaken before it is possible to achieve any substantial progress in increasing photosynthetic activity (Godfray et al., 2010) and in soybean in particular. In contrast, the second plant factor of plant biochemical composition influencing k can likely be readily adjusted by genetic selection. However, pursuit of these modifications seems highly unlikely since plant composition is dictated by the marketplace. Soybean seeds, for example, are sold for their high seed protein and oil composition, and any decrease in these components to achieve a higher value of k will result in a major economic penalty in the marketplace (Sinclair, 2007). Under drought conditions, however, conflicting reports exist regarding the relevance of k as a trait for breeding for drought tolerance. For example, there seems to be no consistent link between intragenotypic variation in several physiological components of k such as internal leaf CO2 (Ci) and improved yield under drought (Blum, 2009; Purcell and Specht, 2004). Also, it seems that conditions under which k will vary among genotypes are those conditions prevailing during survival conditions, which are not considered in this review. Therefore, it appears that k is approximately stable across soybean cultivars unless the photosynthetic capacity of an individual genotype is especially low.

2.2. Harvest index HI determines the proportion of whole plant mass that is dedicated to grain. Over the last century, raising crop HI was a significant accomplishment in many crops, including soybean, resulting in increased yield potential (Passioura and Angus, 2010). In soybean, there is significant variability across genotypes (B0.3 to B0.6), but most high-yielding soybean cultivars have HI . 0.45. There appears to be little room for further

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major increases in HI simply because crops need nongrain tissue (roots, stems, leaves) to produce seed yield (Sinclair, 2007). The response of HI to different levels of drought has been extensively documented in soybean. However, while some studies determined that HI was stable for a given genotype across treatments involving different timings of severe drought stress (Spaeth et al., 1984), others reported seemingly opposite results (reviewed in Purcell and Specht, 2004). Nevertheless, one common ground on which many studies agree either implicitly or explicitly is that the sensitivity of HI to drought appears to be dependent on the sensitivity of soybean nitrogen fixation to soil drying (Serraj et al., 1999a; Sinclair and Serraj, 1995). This occurs since grain filling in soybean requires mobilization of fixed N when nitrogen fixation is inadequate to meet the nitrogen demands of the growing seeds (Sinclair and deWit, 1975). The special sensitivity of nitrogen fixation by soybean to drought will be discussed later.

2.3. Vapor pressure deficit As shown in Eq. (1), vapor pressure deficit (VPD) is a critical variable in determining yield under water-limited conditions. VPD is the difference in the saturated vapor pressure of the atmosphere at a given temperature and the actual vapor pressure. An increase in temperature will result in an increase in saturated vapor pressure, and hence an increase in VPD. The value of VPD is considered by some as an estimation of “atmospheric drought” experienced by the plant. The VPD is the driving force for transpiration rate (TR) in plants, including soybean (Bunce, 1984; Fletcher et al., 2007; Sadok and Sinclair, 2009a, 2009b). Given that k is approximately constant for soybean at 5 Pa and that HI is approaching its maximum value in commercial soybean, variation in maximum yield in water-limited environments is closely related to variation in VPD and on water available for transpiration (Fig. 1). Therefore, one direct interpretation of Eq. (1) is that improving soybean yield under water-limited conditions can only be achieved by either reducing the impact of high VPD on transpiration or accessing more water through the root system so that the crop can transpire greater amounts of water.

3. Traits Influencing Water Conservation 3.1. Limited TR and slow-wilting Until 2007, the existence of a maximum-TR trait was not documented in soybean, although it has been reported in other species such as maize

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100

Gain yield (g m–2)

VPD = 1.5 kPa VPD = 2.2 kPa

75

VPD = 3.5 kPa 50

25

0 0

10

20

30

40

50

Transpirable water (mm)

Figure 1 Theoretical relationship between maximum grain yield and transpirable water available in the growing season as a function of three different VPD environments (1.5, 2.2, and 3.5 kPa). (Source: Adapted from Tanner and Sinclair, 1983).

(Zea mays L.) (Ray et al., 2002) and sorghum (Sorghum bicolor L.) (Bunce, 2003). In soybean, the study by Fletcher et al. (2007) revealed that a plant introduction from Japan, PI 416937, which was observed to be “slow-wilting” in the field (King et al., 2009; Sloane et al., 1990), exhibited a limitation on its TR which remained constant above a breakpoint VPD of 2.1 kPa (Fig. 1A). A follow-up study by Sinclair et al. (2008) confirmed the response and demonstrated that such capability is likely to be the result of a limited hydraulic conductance located in the leaves between the xylem and into the guard cells. This discovery was the basis of a series of later studies that attempted to: (i) investigate the extent of genetic variability of TR response to VPD in soybean, and in particular whether there was variation in the VPD breakpoint at which maximal TR was reached, and (ii) resolve the physiological basis behind the observed leaf-based hydraulic limitation in the slow-wilting line PI 416937. Studies by Sadok and Sinclair (2009a, 2009b) confirmed the earlier observations of Bunce (1984) and Fletcher et al. (2007) that indicated the possible existence of a genetic variability of TR response to VPD in soybean. The results of Sadok and Sinclair (2009a, 2009b) showed an unexpectedly high variation in the VPD response among a relatively small group of 31 lines consisting of commercial cultivars and recombinant inbred lines that resulted from a cross between Benning and PI 416937 (Fig. 2). In particular, among genotypes, the maximum TR values ranged from B30 to B70 mg H2O m22 s21 and the VPD breakpoints varied from B1.5 to B2.4 kPa (Fig. 2CF). A genotype with a low-maximum-TR and/or low VPD breakpoint was more restrictive of water use and would be more desirable for water-limited environments. For example, a genotype-like G00BP-59 in which TR reaches

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(A)

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PI 416937 r 2 = 0.97 S1 = 30.8

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4

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Figure 2 Genetic variability of TR response to VPD in soybean. S: slope of the linear response of transpiration to VPD; S1: first slope of the bilinear response of transpiration to VPD. The unit of the slopes is mg H2O m22 s21 kPa21. (Source: (A) and (B) (From Fletcher et al., 2007). (C), (D), and (H) (From Sadok and Sinclair, 2009b). (E) and (G) (From Sadok and Sinclair, 2009a). (F) Sadok and Sinclair, unpublished).

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a low-maximum value (B30 mg H2O m22 s21) at a low VPD breakpoint of B1.5 kPa could hypothetically conserve substantial amounts of soil water in advance of late-season drought conditions. A possible negative impact of the maximum-TR trait is that under high VPD conditions, decreased water loss also results in decreased photosynthetic rates and decreased crop growth. Therefore, a consequence of such a trade-off between water conservation and decreased growth is that there would be yield penalties resulting from the maximum-TR trait if significant drought did not develop later in the growing season. The overall impact of the positive and negative consequences of the maximum-TR traits for the United States was examined in the simulation study of Sinclair et al. (2010). They used a mechanistic, soybean growth model (Sinclair, 1986) to simulate yield for 2655 grid locations (30 km 3 30 km wide) across the United States using 50 years of weather data for each grid location. The maximum-TR trait was examined on an hourly basis during the daily cycle. When hourly VPD exceeded 2 kPa, transpiration and growth were restricted. The results showed a probability for yield increase in at least 80% of the years in virtually all regions of the United States. This result was achieved in the simulations for two major reasons. First, of course, decreased stomatal conductance at high VPD resulted in water conservation for use later in the growing season when water deficit was likely to develop. Second, by restricting TR during the daily cycle when VPD was large, the “effective VPD” for transpiration in Eq. (1) was decreased contributing to an increased yield potential for the limited amount of available water. The existence of genotypic variability in TR response to VPD in soybean is very desirable for breeding programs and also indicates that the trait is likely to be quantitative, under the control of a high number of genes. Among other practical consequences, this means that breeding options that aim at introducing the trait in other genetic backgrounds may require checking for the maximum-TR trait at every generation. Ultimately, a quantitative trait loci (QTL) analysis that is carried out on an entire soybean population will help identify and quantify the effect of genetic regions that are associated with this trait. The identification of those QTLs could lead to the development of new commercial lines with the desired TR response to VPD under the target conditions. High throughput phenotyping platforms that are able to characterize TR response to VPD for a high number of genotypes will be particularly useful for such analysis. Such platforms helped identify the genetic basis of growth responses to VPD in maize (Sadok et al., 2007) and Arabidopsis thaliana (Granier et al., 2006) and have been recommended by Munns et al. (2010) for wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) based on the genetic variability reported for soybean. To facilitate breeding for maximum TR in soybean, the underlying genes or molecular markers need to be identified by resolving the

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physiological basis of this trait to the level of candidate genes. Sadok and Sinclair (2010) tested the hypothesis that the leaf-based limitation of the hydraulic flow in the leaves of the maximum-TR line PI 416937 might be localized in the symplastic water flow in leaves, hypothetically involving plasma membrane water channels or aquaporins (AQPs). Transpiring leaves of PI 416937 and those of a commercial line that increases its TR with increasing VPD (N01-11136, Fig. 2G) were fed three AQP inhibitors (silver, mercury, and cycloheximide). Under high VPD conditions, silver (as AgNO3) did not decrease TR of the leaves of PI 416937 while it dramatically decreased TR of the line N01-11136 in all tested concentrations (Fig. 3). Such genotypic difference was not observed with the other AQP inhibitor treatments which caused decreased TR of both lines similarly. It was concluded that the silver insensitivity of PI 416937 is not linked to a lack of solute delivery to the xylem of the leaves but probably to a lack of a silver-sensitive protein-mediated water pathway from the xylem into the stomata of PI 416937. Since silver has been reported to inhibit the activity of several AQPs, it was speculated that the limited hydraulic conductance in the leaves of the maximum-TR line PI 416937 may involve the lack of a population of silver-sensitive AQPs. Such a hypothesis echoes the recent findings of Charlson et al. (2009) which undertook a QTL analysis of canopy wilting in soybean that revealed that one of the detected QTLs mapped closely to a previously reported AQP gene.

Drop in tranapiration rate (%)

100 Closed: N01-11136 Open: PI 416937 75

HgCl2 CHX

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* AgNO3

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** 10

* 20

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Figure 3 Doseresponse curves of the drop in transpiration (DTR) induced by different AQP inhibitors for soybean genotypes N01-11136 and PI 416937. Circles, squares, and inverted triangles correspond to HgCl2, CHX, and AgNO3 treatments, respectively. Data are the mean of 36 observations and error bars represent standard error of the mean. Error bars for some points are invisible when they are smaller than the size of the symbols (
,

, and


: P , 0.05, P , 0.02, and P , 0.005, respectively). (Source: From Sadok and Sinclair, 2010).

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3.2. Timing of stomatal closure in the soil drying cycle When water deficit starts to be sensed by the plant, its water use progressively shifts from a free transpiration phase, where plant TR is independent from soil water content (Phase I) to a second phase where TR and therefore yield is strongly dependent on availability of soil water (Phase II). As the soil water deficit develops further, stomatal conductance and transpiration eventually decrease to the point where the plant is merely surviving drought (Phase III, Sinclair and Ludlow, 1986). These three phases in the soil drying cycle can be modeled by plotting TR against the fraction of transpirable soil water (FTSW) remaining in the soil, as shown in Fig. 4. The transition from Phase II to Phase III occurs when all the soil water that can be used effectively to support transpiration and plant growth has been exhausted. Early stomatal closure in the drying cycle, that is, the transition point from Phase I to Phase II, would be a water conservation strategy similar to the maximum-TR trait. An early stomatal-closure trait is illustrated in Fig. 4 in the dashed line as compared to an average genotype with “normal” closure (Fig. 4, solid line). Given that the restrictions on gas exchange of an early stomatal-closure trait would also limit CO2 intake, its yield benefits are expected to be greater under lengthened periods of drought. The simulation analysis for soybean in the United States by Sinclair et al. (2010) demonstrated that genotypes closing their stomata early in the soil drying cycle as compared to standard cultivars will increase yield with a probability $ 79% of the Free transpiration

Stomatal closure

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1 S u r v i v a l

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1/2

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Phase II

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0 1

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Figure 4 Relationship between relative plant transpiration and soil water available for transpiration as a function of the sensitivity of stomatal closure to drought. (Source: Adapted from Sinclair and Ludlow, 1986).

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years for three quarters of the US soybean production regions. Even in well-watered areas, the simulation results indicated that there was little yield penalty as a result of this trait. Such benefit was hypothesized to result from the plant conserving water so that the severity of drought is minimized later in the season allowing for sustained higher nitrogen fixation rates. In contrast to early stomatal closure, delayed stomatal closure at lower FTSW assumes that the crop can continue extracting water for transpiration at a high rate until the amount of water available for transpiration becomes very low (Fig. 4, dotted line). Success with delayed stomata closure places an urgency on the crop receiving rain or irrigation before the soil water is exhausted and the crop is subjected to survival conditions. The system analysis of maize by Sinclair and Muchow (2001) indicated that delayed stomatal closure was not beneficial. Their study considered only one location (Columbia, MO) and 20 years of weather data. Overall, both increased and decreased sensitivity of plant response to soil drying could be useful, but in the context of soybean breeding, they face two challenges. A first difficulty is the unclear physiological basis for such putative traits. For example, it is hypothesized that osmotic adjustment (i.e., active accumulation of solutes within cells which helps maintain turgor) may result in the maintenance of stomatal aperture under drought (Ludlow and Muchow, 1990), but such a possibility appears to be unsubstantiated (Serraj and Sinclair, 2002). A mechanism behind early stomatal closure remains elusive and there is ongoing speculation about the possible involvement of applied behavior analysis (ABA) in the response (Ludlow and Muchow, 1990; Manavalan et al., 2009). A second difficulty is the apparent absence of genetic variation within soybean germplasm displaying any of these behaviors (Table 1). However, significant genetic variation has been identified in rice (Serraj et al., 2008) and in peanut (Arachis hypogea L.) (Devi et al., 2009) indicating that screening of more “exotic” glycine germplasm might result in the identification of variability for this trait.

3.3. Slow leaf area development Hypothetically, another leaf-based water conservation strategy is to limit plant leaf area, which would result in a decrease in the total water lost by transpiration (Sinclair and Muchow, 2001; Tardieu, 2005). Saved water could then be used later in the season when canopy achieves full closure and soil water evaporation is minimal. Increasing evidence in other crops also indicates that leaf growth rate and area can be decreased by high VPD conditions, even under well-watered conditions (Reymond et al., 2003; Sadok et al., 2007). In soybean, the simulation study of Sinclair et al. (2010) demonstrated that the probability of yield gains resulting from this trait was very low or

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nonexistent in wet environments. Yield benefits were simulated only in the driest areas as a result of a slow leaf area development. Although this trait can result in large yield penalties in well-watered conditions— contrary to other leaf traits such as limited TR and early stomatal closure—it appears that it is desirable for environments where dry conditions are dominant. Unfortunately, it seems that no information is available regarding the existence of a genetic variability for this trait in soybean (Table 1), but it has been reported in other species such as sorghum (Salih et al., 1999). Further studies are required to investigate this possibility in soybean by considering the impact of key environmental variables such as VPD.

3.4. Other possible water conservation traits Several other leaf traits for which genetic variability exists in soybean have been reported to be potentially useful in breeding for drought tolerance. These include leaf traits such as movement, reflectance, pubescence, and epidermal conductance (Ludlow and Muchow, 1990; Purcell and Specht, 2004; Sinclair, 2000). It has been suggested, however, that the benefit of these traits may only occur under severe, survival conditions (Ludlow and Muchow, 1990; Sinclair, 2000). Resistance to xylem embolism may be another survival trait that could be beneficial under severe drought. The benefit from such a resistance results from the idea that under drought conditions, water transport through the xylem may be impeded by cavitation and embolism, impairing water transport to the leaves, thereby reducing transpiration (T in Eq.(1)) and photosynthesis. Such conditions are often encountered in woody plants in natural and seminatural conditions, but according to Sperry et al. (2003) it is very likely that the phenomenon also occurs in annual crops, under agronomic conditions. In a recent study, Li et al. (2009) found that the stems of a drought-tolerant maize cultivar had greater resistance to cavitation. Such resistance was thought to be the result of special anatomical features in the xylem of the shoots of the drought-tolerant cultivar, involving a lower loss of functional elements in the central bundles. However, embolism resistance of the drought-tolerant maize cultivars did not appear until the plants were under severe stress with a water potential of less than21.0 MPa. In soybean, the studies of Blizzard and Boyer (1980) and Sperry (2000) indicated that the observed root-to-shoot decrease in hydraulic conductance that occurs during a soil water potential decrease to 21.2 MPa parallels an increase in xylem cavitation. Still, more direct evidence of the contribution of xylem cavitation in the decrease of plant hydraulic conductance remains to be established and the question persists whether such contribution is significant at less negative water potentials that exist before survival-level water deficit. The study of Buchard et al. (1999) established the existence of

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xylem cavitation and refilling for soybean that varied over the course of the day for well-watered plants. On a typical sunny day in which leaf water potentials ranged from 20.3 (morning) to 21.1 MPa (midday), 60% of soybean xylem vessels with diameters larger than 25 µm experienced embolism from mid-morning to late afternoon. But as the authors stated, the study failed to show a substantial decrease in TR as a response to those embolisms.

4. Traits Influencing Water Access Access to more soil water requires alteration of root architecture and/or physiological activity. Since roots grow unseen in the soil, phenotyping for such traits is experimentally very challenging (Draye et al., 2010). The relevance of many of these traits cannot be fully estimated without explicitly considering the root physical (e.g., texture, structure, compaction, and depth), chemical (e.g., Al and Na toxicities), and biotic (e.g., nematodes and fungi) environments, together with the extent of competition between shoots and roots in terms of water and carbon allocation (Passioura, 1983). Such complexity may explain why so far no commercial soybean cultivar has been identified explicitly relating its drought tolerance capability to root traits, other than nitrogen fixation tolerance to drought (see Section 5).

4.1. Increased rooting depth Traditionally, increasing rooting depth is one of the most studied root traits for improving drought tolerance in crops (Ludlow and Muchow, 1990). Deeper roots could help increase the amount of water transpired (T in Eq. (1)), by accessing drainage water that is unavailable to a “standard” plant. Simulation analyses carried out on crops such as maize (Sinclair and Muchow, 2001), sorghum (Sorghum bicolor L.), and wheat ( Jordan et al., 1983) clearly confirmed such benefit, and in soybean, differential drought tolerance which was the result of a difference in rooting depth has been reported in the study of Cortes and Sinclair (1986). In the simulation study of Sinclair and Muchow (2001), increasing rooting depth dramatically minimized the risks of catastrophic yield loss (i.e., long and severe drought) and increased yields in all 20 years of their simulations of maize. The study of Jordan et al. (1983) showed that deeper sorghum and wheat roots increased yields by at least 20% in a simulation which considered locations in Texas and Kansas. However, in the later study, such increase was observed in about 40% of the 30 years

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considered, because the simulation also included years where water was not limiting and very dry years where there was no water available at depth. Therefore, it seems that deeper rooting will be advantageous only in situations where soil moisture is available at the extra depth accessed by deeper roots. If water has not been replenished at the deeper depths, then the strategy of having deeper roots can be risky for the plant because it would mean that the water and carbon invested in growing deep roots will not contribute to yield (Passioura, 1983). In any case, the existence of a genetic variability for this trait in soybean and the availability of ready-to-use simulation tools (Sinclair et al., 2010) will be crucial to assessing the possibility of breeding for deeper rooting in soybean.

4.2. Increased rooting rate Sinclair et al. (2010) hypothesized that a more rapid exploration of the soil water reservoir by soybean could improve its tolerance to drought. However, the results of their simulation analysis showed that in more than 70% of the years, the trait resulted in negative or nonsignificant impacts on yield across the United States. The authors interpreted this result as being the consequence of the rapid exploration and use of soil moisture, which left the crop more vulnerable to drought later in the season. This strategy is therefore very risky in areas where significant drought is expected to be sustained over long periods. However, in environments where (i) irrigation or rain is expected to occur reliably shortly after an early drought, and/or (ii) important levels of soil moisture are available deeper and can be reached early enough by the crop (i.e., before the onset of significant drought), such aggressive depletion can help maintain levels of nitrogen fixation rates and T (Eq. (1)) optimal for the crop. Variation among soybean cultivars exists for rooting rate. In a study carried out on 105 soybean genotypes belonging to different maturity groups, Kaspar et al. (1984) observed that variation in taproot elongation rates was highly variable, with growth rate differences reaching up to B1.3 cm day21 among genotypes. That study also revealed a positive correlation between taproot elongation and rooting depth, but this correlation disappeared 72 days after sowing when both slow and fast growing roots reached the same depth. A similar result was obtained in the recent study of Manavalan et al. (2010), which showed that differences in taproot length observed 21 days after sowing had disappeared 14 days later among nine soybean genotypes. Both studies clearly indicated that there are soybean genotypes with root traits that hypothetically will allow them to be more drought tolerant in the scenarios exemplified above. However,

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further studies are needed to better characterize root elongation rate under different soil biotic and abiotic characteristics and to investigate the heritability of this trait.

4.3. Decreased root hydraulic conductance As a strategy to save water under drought conditions, Passioura (1972) proposed that increased hydraulic resistance in the seminal roots of wheat would result in plant use of soil water more slowly under water-limited conditions. In wet seasons, however, the nodal root system would proliferate and water uptake will not be impaired. By breeding wheat cultivars that had decreased their xylem vessel diameter from 65 to B55 µm, Richards and Passioura (1989) were able to link the resulting decrease in root hydraulic conductance to an increase in yield that ranged from 3% to 11% in a 5-year field study carried out over four dry Australian locations. Those yield differences became nonsignificant under well-watered conditions (Richards and Passioura, 1989). Unfortunately, the higher yielding genotypes were never developed into commercial lines. In sorghum, Salih et al. (1999) associated the drought tolerance of one cultivar to fewer late metaxylem vessels per nodal root, among other traits that included smaller leaf area and lower TR. In soybean, recent evidence indicates the possibility of the existence of sources of water flow restriction in the roots and that there is a genetic variability for these resistances among soybean genotypes. Rincon et al. (2003) found that both radial and axial hydraulic conductances were significantly variable among a selection of six lines which consisted of five plant introductions and one commercial cultivar. A major outcome of that study was that the root hydraulic conductance of a Nepalese slow-wilting line, PI 471938, had the lowest conductivity among the six lines. The study of Fletcher et al. (2007) clearly showed, however, that PI 471938 does not display the limited TR trait at high VPD (Fig. 2) that would be expected as a result of a limiting hydraulic conductance under high demand for water flow. The study of Manavalan et al. (2010) indicated that PI 471938 had an average taproot length among a selection of 38 soybean genotypes. In the study of Rincon et al. (2003), however, no direct link was made between the slow-wilting phenotype of that line and the limited root hydraulic conductance, since other slow-wilting plant introductions were tested as well. Since the experiment of Rincon et al. (2003) was carried out on detached roots, experiments that will allow measurements under fluctuating transpiration conditions of root and leaf conductances of whole plants may help to establish a link between slow-wilting in PI 471938 and limited root conductance.

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4.4. Other rooting traits Other architectural, hydraulic, and metabolic root-related traits have the potential to improve drought tolerance in soybean, but such benefits are not well documented. Genetic variability in architectural traits such as root density, surface areas, and volume has been examined in soybean. Raper and Barber (1970) reported large differences in root surface area, volume, and length among soybean genotypes, but this variability was not studied in water-limited conditions. The maximum-TR line PI 416937 is reported to have a prolific lateral rooting (Hudak and Patterson, 1995), but the study of King et al. (2009) indicated that this trait cannot account for the slowwilting capability of that line. Such a finding is consistent with the report of Taylor (1980) which showed that increasing root length density (cm of roots per cm3 of soil) in the surface soil is less important for postponing the onset of water stress in soybean than an increase in root depth. Putative metabolic traits are the most challenging to study because they are extremely dynamic and under a complex control exerted by the outside environment and by layers of constantly interacting physiological processes. Among these metabolic traits, maintenance of root growth under drought is probably one of the most promising. In their review, Serraj and Sinclair (2002) suggested that osmotic adjustment in stressed root tips could lead to drought tolerance by allowing the roots to maintain significant growth to reach water that might be available deeper in the soil. However, questions remain whether the level of osmotic adjustment in soybean (between 0.3 and 1 MPa, according to Purcell and Specht, 2004) is enough to result in significant growth maintenance and the existence of genetic variability of osmotic adjustment specifically in root tips. Other physiological mechanisms can also contribute to the soybean root growth maintenance under low water potentials such as the modification of root wall extension properties, growth sustaining effect of ABA, and protection from oxidative damage (reviewed by Yamaguchi and Sharp, 2010). Once their interactions with the field environment are well characterized, these complex mechanisms may have the potential to be very useful in breeding for drought tolerance, provided that they are readily heritable.

5. Traits for Special Sensitivities: Nitrogen Fixation Tolerance to Drought In soybean, nitrogen (N2) fixation is very sensitive to drought and N2 fixation rates decrease earlier in the soil drying cycle than other processes in the plant such as gas exchange (Purcell and King, 1996; Sinclair,

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1986; Sinclair and Serraj, 1995). The impact of N2 fixation sensitivity was demonstrated in an N fertilization experiment on water-limited plots where up to a 20% increase in yield was achieved in comparison with plots dependent on N2 fixation (Purcell and King, 1996; Ray et al., 2006). The simulation study of Sinclair et al. (2010) found that yield benefits resulting from enhanced N2 fixation drought tolerance (NFDT) were greater than all other tested soybean drought traits including early stomatal-closure, slow-wilting, and faster root elongation. The probability of yield increase resulting from the NFDT trait was found in more than 85% of the years in all US regions where soybean is grown, indicating substantial potential economic benefits from an NFDT. The physiological basis of NFDT is associated with accumulation of ureides, which are the main N2 fixation transport products from nodules to the shoot in soybean. Ureides accumulate during soil drying which in turn is associated with a decrease in N2 fixation rates (deSilva et al., 1996; Serraj et al., 1999a; Vadez et al., 2000). The first genotype identified with NFDT, the cultivar “Jackson,” has the ability to rapidly metabolize ureides in the leaves, which allows it to maintain higher N2 fixation rate at lower soil water content, compared to other soybean cultivars (Vadez and Sinclair, 2002). The genetic variability for the NFDT trait is large in soybean (Purcell et al., 2000; Sall and Sinclair, 1991; Serraj and Sinclair, 1997), but it was not until 2007 that a multidisciplinary breeding program released two genotypes descended from a cross between KS 4895, a high yielding line, and Jackson (Chen et al., 2007). These genotypes, R01-416F and R01-581F, had greater yields in modest drought environments than commercial checks (Sinclair et al., 2007). As shown in Fig. 5, genotypes R01-416F and R01-581F

% N2 Fixation increase

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45 R01-416F 30 R01-581F 15

0 0.3

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Figure 5 N2 fixation activity advantage measured in the zone of soil water deficit for R01-416F and R01-581F relative to the N2 fixation measured for the parental line KS 4895. (Source: From Sinclair et al., 2007).

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displayed much higher N2 fixation rates than parent KS 4895 when soil dried to low levels, that is, low FTSW. Likely, there is still more potential to improve NFDT in soybean. Sinclair et al. (2000) identified in a screen of 3500 plant introduction lines that eight genotypes exhibited substantial NFDT, exceeding that of Jackson, which is the parent of the already released germplasm. Also, mechanisms other than ureide metabolism are involved in NFDT (reviewed in Serraj et al., 1999b) and can be potentially exploited, including carbon flux in nodules (Ladrera et al., 2007), nodule permeability to oxygen (King and Purcell, 2001), or feedback regulation (King and Purcell, 2005).

6. Concluding Remarks In this review, it is suggested that traits improving drought tolerance in soybean can be one of three types: improving access to water, limiting water loss, and overcoming certain sensitivities that are critical to productivity such as NFDT. All of these fall within the limits of the concept of matching physiology to water supply, a generalization of one of the trait-concepts offered by Ludlow and Muchow (1990). For this concept to work as a framework for breeding for drought tolerance in crops, the following considerations have to be taken into account. 1. As indicated by Eq. (1) and Fig. 4, it is critical to keep in mind that the highest yields can only be achieved in nonwater-limited environments (Phase I in Fig. 4) and that survival situations (Phase III in Fig. 4) are often not economically viable. This narrows the window of opportunities where it is still possible to manipulate traits pertaining to one of the three above subcategories while guaranteeing yields that are economically meaningful to the grower. It is suggested that in breeding efforts or trait discovery enterprises, such economic considerations must be considered. 2. A key consideration in any approach aimed at breeding for drought tolerance is development of a full understanding of the physiological traits involved in response to water deficits. Through a set of specific experiments, physiological studies should help in at least identifying how traits operate with respect to water saving, accessing more water, or in terms of overcoming some special sensitivities. The results of the physiological studies would also be very beneficial in implementing models to quantitatively assess trait impact on yield in different environments. 3. Increased probability of improving drought tolerance is likely to result from combining the above effort with other interdisciplinary approaches involving: (i) the dissection of the genetic and—when relevant—the

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molecular basis of these traits and (ii) high throughput phenotyping methods in the both field and laboratory. It is such interdisciplinary effort that has led recently to the identification of new drought tolerance traits in soybean such as “slow-wilting” (Charlson et al., 2009; King et al., 2009), limited TR (Fletcher et al., 2007; Sinclair et al., 2008), and NFDT (Chen et al., 2007; Sinclair et al., 2007), some of which having led to the development of new cultivars.

ACKNOWLEDGMENT The authors are thankful for the financial support during the preparation of this review from the United Soybean Board.

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Index

A ABA. See Applied behavior analysis Absolute gravimetry, 262 Acoustic sensors, 266 Active γ-ray sensors, 251 Adenosine triphosphate (ATP), 198 Ad hoc wireless networking, 267 Aeromonas in bottom sediments, 107 108 in treated wastewater plants, 101 102 Aeromonas hydrophila, 107 108 Agricultural applications, of zeolites, 229 234 crop yields, improving, 233 heavy metal contaminated soils, remediation of, 233 234 herbicide use efficiency, improving, 233 nitrogen use efficiency, enhancing, 230 231 organic manure efficiency, enhancing, 232 233 phosphorus use efficiency, improving, 232 soil physico-chemical and microbial properties, improving, 230 wastewater treatment, 234 water use efficiency, improving, 233 Amebae, 114 Ammonium trapping, 228 229 rock phosphate dissolution, 229 Animal husbandry practices microbial pollution in irrigation waters, 118 Antibiotics effects, on soil microbial activity, 194 196 Antimony electrodes, 264 Applied behavior analysis (ABA), 337, 342 Aquaporins (AQPs), 334 335 Aquatic biota, 114 115 Arabidopsis thaliana, 334 Arachis hypogea L., 337 ATP. See Adenosine triphosphate (ATP) Australia, study sites in, 25, 26, 50t, 61t

B Bacillus cereus, 76 77 Bank soils, 113 114

Barley, VPD in, 334 Biofilms control of, in irrigation water, 119 environmental microbial reservoirs, 115 117 Biota, 17 Biotic humification, 196 198, 203 205 Bioturbation, 24 30 faunaturbation, 27 29 floraturbation, 29 30 horizonation and haplodization, 27 Macquarie school research, 25 pedology, 26 stone layer formation, 27 Biphasic decay, 103, 104f Bottom sediments, 112 113 environmental microbial reservoirs, 107 111 Brachiaria decumbens, 232

C Campylobacter inactivation patterns, 106 107 in aquatic biota, 114, 114 in bottom sediments, 107 111 in irrigation water, 76 77, 79, 82 83 Capacitance sensors, 257, 258 Carbon, measurement of, 272 Cation exchange capacity (CEC), 272 CEC. See Cation exchange capacity Central unit structure (CUS), 192, 203 205 Chabazite, 227 Chalcone synthase (CHS), 191 192 CHEMFETs, 264 Chemical weathering Bega Valley, 21 of bedrock, 18 22 box plot distributions of, 23f field sites, 21 22 laboratory studies, 18 rates (CWR), 46t saprolite, 20 21 soil mantle, 17 18

351

352 Chemical weathering (Continued) soil mixing rates and, 32f soil residence time, 21 supply limited weathering, 20 21 and total denudation rate, 50t watershed studies, 19 weathering indices, 20 ChemPlus, 173 174, 178 CHS. See Chalcone synthase Clinoptilolite, 222, 224, 227 agricultural importance of, 227 chemical composition of, 225t “Clorpt” model, 8 Clostridium botulinum, 107 108 Clostridium perfringens, 88 13 C NMR spectra CP-MAS, 161 hot acid hydrolysis, 162 164 of humic acids, 160 161, 162 164 of humic substances, 160 162 of whole soils, 150 151, 151f Communities of Practice (CoPs), 295 Communities of Scientific Practice (CSPs), 295 Community Supported Agriculture (CSA), 77 Connected value development, 298, 304 305, 308 309, 318, 319 Contact electrodes, 262 264 electrical resistivity, 263 electrochemical sensors, 263 induced polarization, 263 ion-selective electrodes, 263 264 ion-selective field effect transistors, 264 metal electrodes, 264 Contamination risks, management of, 101f, 117 121 CoPs. See Communities of Practice Core scanning and borehole sensors, 269, 270f Cp GC Ms. See Curie-point pyrolysis-gas chromatography-mass spectrometry CP-MAS. See Cross polarization magic angle spinning Cradle-to-Cradle (C2C) concept, 301 302 Critical zone, 2, 3f, 4 definition of, 4 Cronstedt, Alex Fredrik, 221 222 Crop restriction, 120 Crop water use and yield, 327 331 NFDT genetic variability, 343 344

Index

physiological basis, 343 R01-416F and R01-581F, 343 344 nitrogen fixation tolerance to drought, 342 344 trait identification harvest index, 330 331 transpiration coefficient k, 329 330 vapor pressure deficit, 331 water access, 339 342 decreased root hydraulic conductance, 341 increased rooting depth, 339 340 increased rooting rate, 340 341 rooting traits, 342 water conservation, 331 339 limited TR and slow-wilting, 331 335 slow leaf area development, 337 338 stomatal closure timing, in soil drying cycle, 336 337 water conservation traits, 338 339 Cross polarization magic angle spinning (CPMAS), 160 161 Cryptosporidium in bottom sediments, 107 108 fate and transport, 102 food poisoning outbreaks and, 85 86 in irrigation water, 76 77, 79, 82 83 CSA. See Community Supported Agriculture CSPs. See Communities of Scientific Practice Culp soil, 150, 151f Curie-point pyrolysis-gas chromatography-mass spectrometry (Cp GC Ms), 164 166 CUS. See Central unit structure Cyclospora cayetanensis, 76 77, 85 86

D Darwin, Charles, 26 DEM. See Digital elevation model Deoxyerythronolide-B-synthase, 187f Desulfovibrio, 198 199 DGPS. See Differential global positioning systems Differential global positioning systems (DGPS), 267 268 Diffuse reflectance spectroscopy, 254 Digisoil, 280 Digital elevation model (DEM), 267 268 Dokuchaev, V.V., 6 7 Down-borehole sensor systems, 269 Drinking water distribution systems, 116 Drop in transpiration, 335f Dutch agriculture, current problems in, 299 300

353

Index

E Earthworms, bioturbation rates for, 25 26 Earth’s surface, definition of, 2 3 “8-Ring” structure, 224 Electrical conductivity of soil, 260, 261 Electrical resistivity (ER), of soil, 263 Electrochemical sensors, 263 Electromagnetic (EM) spectrum, 245f Electromagnetic induction (EMI), 260 261 EMI. See Electromagnetic induction Endopedonic agents, 27 Energy, kinetic, and thermodynamic relationships, in soil systems, 198 202 Entamoeba histolytica, 76 77 Enteric communicable diseases, 89 90 Environmental microbial reservoirs aquatic biota, 114 115 bank soils, 113 114 biofilms, 115 117 bottom sediments, 107 111 resuspension of sediments, 111 113 Erionite, 227 Escherichia coli adherence to plants, 88 in aquatic biota, 114 bank soils, 113 in bottom sediments, 108, 109 111 fate and transport, 102 food poisoning outbreaks and, 85 86, 87 inactivation phase, 103, 104f, 106 107 in irrigation water, 76 77, 78 79, 82 nutrients and, 105 pathways into plants, 87 88 pH and, 105 resuspension of sediments, 111 112 sunlight and, 106 Exopedonic agents, 27 Exponential soil production model, 15 16 Exposure model, QMRA, 98 99 Extra-large-pore zeolites, 223

F FA. See Fluvic acid Faunaturbation, 27 29 FC. See Fecal coliform FDR. See Frequency-domain reflectometry Fecal coliform (FC), 79, 91 in sediment, 108, 109 111 FET. See Field effect transistor Field effect transistor (FET), 264

Flavonoids, 191 192 Floraturbation, 29 30 Foodborne diseases, irrigation waters and, 84 90 “cause-effect” relationships, 84 epidemiological investigations, 85 86 increased incidence with high concentrations of pathogens, 89 90 pathogens in produce irrigated with contaminated water, 87 89 adherence to plants, 88 concentration, effect of, 88 89 pathogen pathways into plants, 87 88 Fraction of transpirable soil water (FTSW), 336 Frequency-domain reflectometry (FDR), 257 258 FTSW. See Fraction of transpirable soil water Fulvic acid (FA) definition of, 148 149 Infrared and Fourier transform infrared spectrophotometry, 156 157 oxidative degradation of, 157 160 Py-FIMS spectrum of, 152, 153f X-ray analysis of, 167 170 proposed structures based on, 169 170 radial distribution analysis, 167 169 γ-ray spectrometer, 251 253 active, 251 passive, 251 252, 252f

G Geographic positioning and elevation, 267 268 Giardia in bottom sediments, 107 108 fate and transport, 102 in irrigation water, 76 77, 79, 82 83 Glicotoxin, 195 Gliocladium virens, 195 Glycine max (L.) Merr., 326 GPR. See Ground-penetrating radar Gravimetric sensors, 262 Green care, case study, 309 315 flow of knowledge, 314 knowledge management, 314 315 players involvement, 310 311 problems and objectives, 309 310 track record of, 311 313, 312f Ground-penetrating radar (GPR), 259 260, 260f

354

Index

H HA. See Humic acids Haploidization, 27 Harvest index (HI), 330 331 Harvest interval, 120 Helicobacter pylori, 114 Heulandite, 224, 227 HI. See Harvest index Hordeum vulgare L., 334 Horizonation, 27 HS. See Humic substances Humic acids (HAs) 13 C NMR spectra of, 160 161, 161f effect of hot acid hydrolysis on, 162 164 analytical characteristics of, 156 chemical and microbial synthesis of, 180 181 chemical structure of, 166f corrected 2D model, 175 177, 176f Curie-point pyrolysis-gas chromatographymass spectrometry (Cp GC MS) of, 164 166 definition of, 148 149 Infrared (IR) and Fourier transform infrared (FTIR) spectrophotometry, 156 157 oxidative degradation of, 157 160 Py-FIMS analysis of, 152, 153f and SOM relationships between, 174 175 3D structure for, 172 174 2D structure for, 170 172, 176f X-ray analysis of, 167 170 Humic substances (HS) 13 C NMR spectrometry of, 160 162 effect of hot acid hydrolysis on, 162 164 benzenecarboxylic acid oxidative degradation products of, 158f chemical characteristics of, 156 160 analytical characteristics, 156 Infrared and Fourier transform infrared spectrophotometry, 156 157 oxidative degradation of, 157 160 reductive degradation, 160 chemical structure for, 159f corrected 2D HA model, 175 177 criticism on soil HS research, 147 148 Curie-point pyrolysis-gas chromatographymass spectrometry, 164 166 definition of, 145 phenolic oxidation products of, 159f relationships between HA and SOM, 174 175

3D model structure for SOM plus water, 177 178 3D structure, 172 174 2D structure, 170 172 X-ray analysis, 167 170 Humification stages, 145 146 Humin, 148 149 oxidative degradation of, 157 160 Humped soil production model, 16 18

I ICSU. See International Council for Science Indicator organisms, in irrigation waters, 78 83 environmental microbial reservoirs, 107 117 aquatic biota, 114 115 bank soils, 113 114 biofilms, 115 117 bottom sediments, 107 111 resuspension of sediments, 111 113 fate and transport of, 101 117 inactivation patterns, 106 107 survival of, 103 107 Indigenous biota, 106 Induced polarization (IP) measurements, 263 Inelastic neutron scattering (INS), 252 Infectivity model, QMRA, 98 99 Infrared reflectance spectroscopy, 254 256 Innovation, in agriculture case studies, 301 318 Dutch agriculture, current problems in, 299 300 “knowledge paradox”, 295 and sustainable development knowledge flow role, 300 301 transdisciplinary approach, 295 INS. See Inelastic neutron scattering International Council for Science (ICSU), 295 Ion-selective electrodes (ISEs), 263 264 Ion-selective field effect transistors (ISFETs), 264 Irrigation water foodborne diseases, spread of, 84 90 “cause-effect” relationships, 84 epidemiological investigations, 85 86 increased incidence with high concentrations of pathogens, 89 90 pathogens in produce irrigated with contaminated water, 87 89

355

Index

pathogenic microorganisms in concentration, 78 83 contamination risks, management and control of, 101f, 117 121 developing vs. developed countries, 80 environmental microbial reservoirs, 107 117 fate and transport of, 101 117 inactivation patterns, 106 107 indicator organism, 78 83 (see also Indicator organisms, in irrigation waters) local differences, 80 82 overview, 76 78 regional differences, 80 82 research and development, need for, 121 123 risk assessment, 98 100 spatial variabilities, 82 83 standards/guidelines, 91 100 survey, 79 survival of, 103 107 temporal variabilities, 82 83 ISEs. See Ion-selective electrodes (ISEs) ISFETs. See Ion-selective field effect transistors iSoil, 280

K KENGi partners, 295, 296, 298 connected value development, 298 transdisciplinary approach in, 319, 320 Knowledge brokers, 298 Knowledge types, 300 301, 300f L Landscape evolution models, 34 35 Large-pore zeolites, 223 Laser-induced breakdown spectroscopy (LIBS), 256, 256f Legionella, 101 102 LIBS. See Laser-induced breakdown spectroscopy Listeria monocytogenes, 76 77

M Macquarie School of “bioturbation”, 25 Magnetic resonance sounding (Mrs), 259 Magnetic sensors, 261 262 Maize, 331 332, 334 Mass balance model, 37

Mechanical sensors, 264 266 acoustic sensors, 266 fluid permeability, 265 266 integrated draft, 264 265 mechanical resistance, 265 Medium-pore zeolites, 223 “Mesh” networking, 267 Metal electrodes, 264 Metropolitan agriculture, 299 Microbial PKSs model, for studying biotic humification in soils, 196 198 Microbial polyketides, 192 194, 206 207 Microwave sensors, 256 257 Mid-IR systems, 255 Mineralogy, measurement of, 273 Mordenite, 227 Mrs. See Magnetic resonance sounding Multisensor systems, 268, 269f Mycobacterium, 114 Mycobacterium avium, 107 108

N N2 fixation drought tolerance (NFDT), 342 344, 343f Near-infrared (NIR) reflectance spectroscopy, 254 255 Neutron scattering, 252 253 New Mixed Farm and industrial ecology, case study, 305 309 flow of knowledge, 308 309 knowledge management, 309 players involvement, 306 problems and objectives, 305 306 track record of, 307 308, 307f NFDT. See N2 fixation drought tolerance NGOs. See Nongovernmental organizations Nitrate leaching, 228 Nitrification, 231 232 NMR. See Nuclear magnetic resonance Nongovernmental organizations ( NGOs), 295 Northern Frisian Woods, case study, 301 305 Cradle-to-Cradle (C2C) concept, 301 302 flow of knowledge, 304 305 knowledge management, 305 players involvement, 302 problems and objectives, 301 302 track record of, 302 305, 303f NRC. See National Research Council Nuclear magnetic resonance (NMR), 259

356

Index

Nutrients bacteria survival and, 105 measurement of, 271 272

O On-the-go proximal soil sensors, 251, 265 On-the-go soil electromagnetic induction (EM-38) system, 261f Optically stimulated luminescence (OSL), 30, 43 Oryza sativa L., 326 OSL. See Optically stimulated luminescence

P Passive γ-ray sensors, 251 252 Pathogenic microorganisms in irrigation waters. See Irrigation water, pathogenic microorganisms in Pathogen pathways into plants, 87 88 PAWC. See Plant-available water capacity Pedogenesis, quantifying processes of conceptual models of soil formation, 5 12 energy, 10 12 factors, 5 8 pathways, 9 10 processes, 8 9 models of soil formation, based on concept of mass balance, 33 39 in landscape, 35 39 landscape evolution models, 34 35 soil mixing, 22 33 bioturbation, 24 30 faunaturbation, 27 29 floraturbation, 29 30 pedoturbation, 31 rain splash, 30 31 soil creep, 30 soil production, 12 22 exponential soil production model, 15 16 from parent materials, 13 18 humped soil production model, 16 18 soil weathering, 12 22 chemical weathering, of bedrock, 18 22 Pedoturbation, 13 forms and agents of, 24t modeling, 31 Penetration resistance of soil, 265 pH bacteria survival and, 105 measurement of, 272 273 Phillipsite, 227

Phosphorus measurement, 271 PKs. See Polyketides PKSs. See Polyketide synthases Plant-available water capacity (PAWC), 270 271 Polyketides (PKs) biosynthesis of, 184 189 and central structure of HS and SOM, 202 205 biotic humification process forming CUS of HS and SOM, 203 205 PKs as a passive SOM pool, 202 203 chemical analysis of, in soils, 184 chemical and functional features of, 183 184 chemical diversity of, 190f complex biological link to the chemistry of humification, 181 182 ecological function and associated genetic evolution of, 190 191 example of, 183f future research, 205 207 microbial PKs, 192 194 microbial PKSs model, for studying biotic humification in soils, 196 198 nature, 182 plant PKs, 191 192 in soils, 194 196 Polyketide synthases (PKSs), 185, 206 classification of, 185 188 microbial PKSs model for studying biotic humification in soils, 196 198 Potassium measurement, 271 272 Process water, 78 79 Proximal soil sensing (PSS) definition, 246 250 development status and approximate costs, 277t future aspects, 274 281 measurements, location of, 250 “on-the-go” system, 246 247 soil properties, measurement of, 270 274 carbon, 272 cation exchange capacity, 272 clay, silt, and sand, 273 mineralogy, 273 nutrients and elements, 271 272 pH, 272 273 soil strength, bulk density, and related properties, 273 274 soil water and related properties, 270 271

357

Index

technologies, 248t, 251 269 contact electrodes, 262 264 core scanning and borehole sensors, 269, 270f electromagnetic induction (EMI), 260 261 frequency-domain reflectometry (FDR), 257 258 γ-rays, 251 253 geographic positioning and elevation, 267 268 gravimetric sensors, 262 ground-penetrating radar (GPR), 259 260, 260f infrared reflectance spectroscopy, 254 256 laser-induced breakdown spectroscopy (LIBS), 256 magnetic sensors, 261 262 mechanical sensors, 264 266 microwaves, 256 257 multisensor systems, 268 neutron scattering, 252 253 nuclear magnetic resonance (NMR), 259 radio waves, 257 261 seismic sensors, 262 telemetry, 266 267 time-domain reflectometry (TDR), 257 258 ultraviolet radiation, 254 256 visible radiation, 254 256 X-rays, 253 PSS. See Proximal soil sensing Py-FIMS. See Pyrolysis-field ionization mass spectrum Pyrolysis-field ionization mass spectrum (PyFIMS) compound classes identified, 155t, 154 of fulvic acid, 152, 153f of humic acids, 152, 153f of humin, 152 154, 153f of whole soil, 153f, 154

Q QMRA. See Quantitative microbial risk assessment Quantitative microbial risk assessment (QMRA), 98 100 advantage of, 100 exposure model, 98 99 infectivity model, 98 99

R Radio waves, 257 261 Rainfall events, role of, 82 83 Rain splash, 30 31 Reflection seismology, 262 Regolith, 3, 18 Rendzinas, 13 Research and development, need for, 121 123 Restricted irrigation, 91 94 Rock phosphate dissolution, 229 “Rondeel” chicken housing system, case study, 315 318 flow of knowledge, 317 318 knowledge management, 318 players involvement, 315 316 problems and objectives, 315 track record of, 316 317, 316f

S Salmonella adherence to plants, 88 in aquatic biota, 114 in bottom sediments, 107 108 fate and transport, 102 food poisoning outbreaks and, 85, 87 inactivation phase, 103, 104f, 106 107 in irrigation water, 76 77, 79 pathways into plants, 87 88 sunlight and, 106 Saprolite, 17, 21 22 SCAPS. See Site characterization and analysis penetrometer system “Scorpan” model, 8 Seismic sensors, 262 Shigella, 106 111 Site characterization and analysis penetrometer system (SCAPS), 268 Slope of the regression, 103, 104f Slow-wilting, 327, 344 345 and limited TR, 331 335 line N01-11136, 334 335, 335f line PI 416937, 334 335 line PI 471938, 341 Small-pore zeolites, 223 SMZ. See Surfactant-modified zeolite Soil bulk density, measurement of, 273 274 Soil creep, 24 25, 30 Soil formation conceptual models, 5 12, 40t energy, 10 12 factors, 5 8

358 Soil formation (Continued) mechanistic models of, 3 5 pathways, 9 10 processes, 8 9 mass balance model, 33 39 landscape evolution models, 34 35 modeling in landscape, 35 39 Soil-forming factors, 6 Soil mineralogy, 273 Soil mixing, 22 33 bioturbation, 24 30 faunaturbation, 27 29 floraturbation, 29 30 modeling pedoturbation, 31 rain splash, 30 31 rates of, 32f, 33f, 63t soil creep, 30 Soil organic matter (SOM) analysis of, 150 151 by 13C NMR, 150 151 in situ analysis in whole soils, 150 by Py-FIMS, 152 154 chemical and microbial synthesis of, 180 181 chemical structure of, 155 156 compound classes identified, 154 extraction of, 148 150 extractants, 149 150 with dilute base, 148 149 future research, 205 207 and humic acids, 174 175 structure, effect of time on, 179 3D model structure, for SOM plus water, 177 178 Soil production, 12 22 exponential soil production model, 15 16 humped soil production model, 16 18 from parent materials, 13 18 Soil production rates (SPRs), 16 Soil profile model, 36, 37 Soil properties, measurement of, 270 274 carbon, 272 cation exchange capacity, 272 clay, silt, and sand, 273 mineralogy, 273 nutrients and elements, 271 272 pH, 272 273 soil strength, bulk density, and related properties, 273 274 soil water and related properties, 270 271 Soil science, 45 Soil spectrum, in visible rage, 254, 255f

Index

Soil strength, measurement of, 273 274 Soil texture, measurement of, 273 Soil urease adsorption, 228 Soil water content, measurement of, 270 271 Soil weathering, 12 22 SOM. See Soil organic matter Sorghum, 331 332, 339 Sorghum bicolor L., 331 332, 339 Soybean aquaporins (AQPs), 334 335 dose response curves, of drop in transpiration in, 335f drought-tolerant traits, 327, 328t genetic variability, 328t of TR response to VPD, 333f genotype R01-416F, 343 344 genotype R01-581F, 343 344 quantitative trait loci (QTL), 334 slow-wilting, 327, 344 345 and limited TR, 331 335 line N01-11136, 334 335, 335f line PI 416937, 334 335 line PI 471938, 341 SPRs. See Soil production rates Standards for microbial quality of irrigation water, 91 98 Staphylococcus aureus, 76 77 Stilbite, 227 Streptomyces, 196 Streptomyces aureofaciens, 195 Streptomyces globisporus, 193, 203 Streptomyces rimosus, 195 Sunlight, 106 Surface NMR, 259 Surfactant-modified zeolite (SMZ), 231 232 Sustainable development, 295, 299 300 and innovation in agriculture, 293 cases studies, 301 318 knowledge flow role, 300 301 problems of, 296 297 TransForum program, 297 298, 299 300 Synthrophomonas wolfei, 198 199

T Tacit knowledge, 300 301, 320 TC. See Total coliform TCN. See Terrestrial cosmogenic nuclide TDR. See Time-domain reflectometry Telemetry, 266 267 Terminal restriction fragment length polymorphism (TRFLP), 197

359

Index

Terrestrial cosmogenic nuclide (TCN), 16, 20, 43 Tetracenomycin, 199 Time-domain reflectometry (TDR), 257 258 Total coliform (TC), 91 Total denudation rate, 20 TR. See Transpiration rate “TransForum” innovation program, 297 298 Connected value development, 298 Transpiration coefficient k, for soybean, 329 330 Transpiration rate (TR) soybean genetic variability response to VPD, 333f Travel time, factors affecting, 81 TRFLP. See Terminal restriction fragment length polymorphism Triketide pyrone, 199 Triticum aestivum L., 334 T-shaped skills, 319, 321

U UK Food Safety Agency, 120 Ultraviolet radiation, 254 256 Unrestricted irrigation, 91 94

V Value capture, 314 315, 317 318 connected value development, 298 objective, 298 299 Value creation, 314 315, 318 connected value development, 298 objective, 298 299 Value proposition, 314 315, 318 connected value development, 298 objective, 298 299 Vapor pressure deficit (VPD), 331 Vibrio cholerae, 114 Visible radiation, 254 256 Vis NIR spectrometers, 255 256 VPD. See Vapor pressure deficit

W Well water, 81 Wireless sensor networks, 266 267

X X-rays, 253 X-ray diffraction (XRD), 253 X-ray fluorescence (XRF), 253 Xylem embolism, 338

Y Yersinia enterocolitica, 76 77, 106 107

Z Zea mays L., 331 332 Zeolites agricultural applications, 229 234 crop yields, improving, 233 heavy metal contaminated soils, remediation of, 233 234 herbicide use efficiency, improving, 233 nitrogen use efficiency, enhancing, 230 231 organic manure efficiency, enhancing, 232 233 phosphorus use efficiency, improving, 232 soil physico-chemical and microbial properties, improving, 230 wastewater treatment, 234 water use efficiency, improving, 233 agricultural importance of, 227 ammonium trapping, 228 229 rock phosphate dissolution, 229 chemical composition of, 225t classification of, 222 223 nutrient interactions nitrate leaching, 228 soil urease adsorption, 228 origin and history, 221 222 physical and chemical properties of, 224 227 researchable issues, 235