World Soil Resources and Food Security (Advances in Soil Science)

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World Soil Resources and Food Security (Advances in Soil Science)

WORLD SOIL RESOURCES AND FOOD SECURITY Advances in Soil Science WORLD SOIL RESOURCES AND FOOD SECURITY Edited by Ra

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WORLD SOIL RESOURCES AND FOOD SECURITY

Advances in Soil Science

WORLD SOIL RESOURCES AND FOOD SECURITY

Edited by

Rattan Lal B. A. Stewart

CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2012 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20110623 International Standard Book Number-13: 978-1-4398-4451-9 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright. com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

Contents Preface......................................................................................................................vii Editors........................................................................................................................ix Contributors...............................................................................................................xi Chapter 1 Sustainable Management of Soil Resources and Food Security...........1 Rattan Lal and B. A. Stewart Chapter 2 Global Food Situation and Unresolved and Emerging Issues............. 11 Shahla Shapouri, Stacey Rosen, and Summer Allen Chapter 3 World Soil Resources: Opportunities and Challenges........................ 29 H. Eswaran, P. F. Reich, and E. Padmanabhan Chapter 4 Soil Resources and Human Adaptation in Forest and Agricultural Ecosystems in Humid Asia............................................. 53 S. Funakawa, T. Watanabe, A. Kadono, A. Nakao, K. Fujii, and T. Kosaki Chapter 5 Pedogenetic Acidification in Upland Soils under Different Bioclimatic Conditions in Humid Asia............................................. 169 S. Funakawa, T. Watanabe, A. Nakao, K. Fujii, and T. Kosaki Chapter 6 Soil Resources Affecting Food Security and Safety in South Asia.................................................................................................... 271 Tapan J. Purakayastha, Bal Ram Singh, R. P. Narwal, and Promod K. Chhonkar Chapter 7 Formation and Management of Cracking Clay Soils (Vertisols) to Enhance Crop Productivity: Indian Experience........................... 317 D. K. Pal, T. Bhattacharyya, and Suhas P. Wani

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Chapter 8 Role of Nuclear and Isotopic Techniques in Sustainable Land Management: Achieving Food Security and Mitigating Impacts of Climate Change............................................................................. 345 Long Nguyen, Felipe Zapata, Rattan Lal, and Gerd Dercon Chapter 9 New Paradigm to Unlock the Potential of Rainfed Agriculture in the Semiarid Tropics..................................................................... 419 Suhas P. Wani, Johan Rockstrom, B. Venkateswarlu, and A. K. Singh Chapter 10 Land Degradation.............................................................................. 471 Freddy O. Nachtergaele, Monica Petri, and Riccardo Biancalani Chapter 11 Where Do We Stand 20 Years after the Assessment of Soil Nutrient Balances in Sub-Saharan Africa?....................................... 499 E. M. A. Smaling, J. P. Lesschen, C. L. van Beek, A. de Jager, J. J. Stoorvogel, N. H. Batjes, and L. O. Fresco Chapter 12 Research Needs for Credible Data on Soil Resources and Degradation....................................................................................... 539 Rattan Lal

Preface World soils, the basis of all terrestrial life, are finite in extent, variable over time and space, and prone to alterations by natural and anthropogenic perturbations. Productive soils of high quality are essential to human well-being, economic and sustainable development, political stability, and ethnic and cultural harmony. Ancient civilizations and cultures (i.e., Mayan, Aztec, Mesopotamian, Indus, Yangtze) arose and thrived on good soils, and survived only as long as soils had the capacity to support them. These and other once thriving civilizations collapsed with declines in the quality of their soils. Even during the twenty-first century, a good soil is the engine of economic development and essential to present and future food security. Yet, the quality of soil resources is threatened by human-induced and natural perturbations. Soil quality is degraded by land misuse and soil mismanagement. Soil resources available for agricultural use are also being diminished for conversion to other uses (e.g., urbanization, industrial, recreational). The Food and Agriculture Organization (FAO) of the United Nations defines soil degradation as a decline in “the current or potential capability of soils to produce goods and services.” Soil use and management produce several goods including food, feed, fiber, fuel, and industrial raw materials. Under both natural and managed ecosystems, soils also generate numerous ecosystem services including moderation of climate, purification and filtration of water, enhancement of biodiversity, an archive of planetary and earth history, a reservoir of germplasm, etc. Whereas soil degradation may lead to reductions in its ability to produce goods and generate services, its capacity to do so also depends on numerous endogenous and exogenous factors. Endogenous factors are those related to soil-forming factors including climate, vegetation, parent material, terrain, and time. The exogenous factors include anthropogenic perturbations including deforestation, biomass burning, drainage, irrigation, and use of inputs (fertilizer, amendments, tillage methods, residues management, vehicular traffic, cropping and farming systems, etc). Exogenous factors may also be natural perturbations such as volcanic eruption, seismic activity, tsunami, etc. The impacts of exogenous factors on the extent and severity are exacerbated by several endogenous factors (i.e., climate, terrain). There exists a vast amount of literature on soil degradation. However, the data reported in the literature are often confusing, contradictory, erratic, and misleading. The problem is confounded by several factors such as: (i) using interchangeably different terminology such as land vs. soil, degradation vs. desertification; (ii) using different methodologies to assess degradation; (iii) using different categories to assess the severity of a degradation process such as light vs. slight, severe vs. strong; (iv) using proxy methods to indirectly assess the extent and severity of degradation; (v) not validating the estimates by ground truthing; (vi) not relating goods produced (e.g., crop yields) to the severity of degradation under different land uses and management; and (vii) not evaluating the masking effects of improved management practices through partial or complete elimination of some soil-related constraints to vii

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agronomic production. Thus, there is a strong need for creating credible data based on the use of standardized methodology and terminology, ground truthing and validation, assessing impacts on agronomic production under a range of management scenarios, and evaluating social and economic impacts on the community. This volume is specifically dedicated to soil resources of the world in terms of their availability and status in the context of the growing demands of increasing world population and rising expectations of living standards. It comprises invited chapters contributed by renowned scientists in their specific fields of expertise including global food situations by Shahla Shapouri, world soil resources by Hari Eswaran and colleagues, soil resources for humid Asia and their acidification by Funakawa and colleagues, soil resources of South Asia by Tapan Purakayastha and others, properties and management of vertisols by D. Pal and colleagues, use of radio isotopic techniques in soil management by Long Nguyen and his colleagues at IAEA, the potential of rainfed agriculture in the semiarid tropics by S. Wani and group, the status of land degradation by F. Nachtergaele and colleagues, and the nutrient balance in sub-Saharan Africa by E. Smaling and others. The volume is a useful reference source for those interested in the state of the soils of the world in relation to food security and environmental quality. Rattan Lal B. A. Stewart

Editors Rattan Lal is a distinguished professor of soil physics in the School of Environment and Natural Resources and director of the Carbon Management and Sequestration Center, Food, Agricultural, and Environmental Sciences/Ohio Agriculture Research and Development Center, at The Ohio State University. Before joining Ohio State in 1987, he was a soil physicist for 18 years at the International Institute of Tropical Agriculture, Ibadan, Nigeria. In Africa, Professor Lal conducted long-term experiments on land use, watershed management, soil erosion processes as influenced by rainfall characteristics, soil properties, methods of deforestation, soil-tillage and crop-residue management, cropping systems including cover crops and agroforestry, and mixed/relay cropping methods. He also assessed the impact of soil erosion on crop yields and related erosion-induced changes in soil properties to crop growth and yields. Since joining Ohio State University in 1987, he has continued research on erosion-induced changes in soil quality and developed a new project on soils and climate change. He has demonstrated that accelerated soil erosion is a major factor affecting emission of carbon from the soil to the atmosphere. Soil-erosion control and adoption of conservation-effective measures can lead to carbon sequestration and mitigation of the greenhouse effect. Other research interests include soil compaction, conservation tillage, mine soil reclamation, water table management, and sustainable use of soil and water resources of the tropics for enhancing food security. Professor Lal is a fellow of the Soil Science Society of America, American Society of Agronomy, Third World Academy of Sciences, American Association for the Advancement of Sciences, Soil and Waste Conservation Society, and Indian Academy of Agricultural Sciences. He is a recipient of the International Soil Science Award of the Soil Science Society of America, the Hugh Hammond Bennett Award of the Soil and Water Conservation Society, the 2005 Borlaug Award, and the 2009 Swaminathan Award. He also received an honorary degree of Doctor of Science from Punjab Agricultural University, India, from the Norwegian University of Life Sciences, Aas, Norway, and the Alecu Russo Balti State University in Moldova. He is a past president of the World Association of the Soil and Water Conservation, the International Soil Tillage Research Organization, and the Soil Science Society of America. He is a member of the United States National Committee on Soil Science of the National Academy of Sciences (1998 to 2002, 2007 to present). He has served on the Panel of Sustainable Agriculture and the Environment in the Humid Tropics of the National Academy of Sciences. He has authored and coauthored around 1500 research papers. He has also written 15 books and edited or coedited another 48. B. A. Stewart is a distinguished professor of soil science at the West Texas A&M University, Canyon, Texas. He is also the director of the Dryland Agriculture Institute, and a former director of the United States Department of Agriculture Conservation and Production Laboratory at Bushland, Texas; past president of the Soil Science Society of America; and member of the 1990–1993 Committee on Long-Range Soil ix

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and Water Policy, National Research Council, National Academy of Sciences. He is a fellow of the Soil Science Society of America, the American Society of Agronomy, the Soil and Water Conservation Society, a recipient of the United States Department of Agriculture Superior Service Award, a recipient of the Hugh Hammond Bennett Award of the Soil and Water Conservation Society, and an honorary member of the International Union of Soil Sciences in 2008. Dr. Stewart is very supportive of education and research on dryland agriculture. The B.A. and Jane Anne Stewart Dryland Agriculture Scholarship Fund was established at West Texas A&M University in 1994 to provide scholarships for undergraduate and graduate students with a demonstrated interest in dryland agriculture.

Contributors Summer Allen Economic Research Service United States Department of Agriculture Washington, D.C. N. H. Batjes ISRIC—World Soil Information Wageningen, the Netherlands T. Bhattacharyya Division of Soil Resource Studies National Bureau of Soil Survey and Land Use Planning Nagpur, India Riccardo Biancalani Land and Water Division Food and Agriculture Organization of the United Nations Rome, Italy Promod K. Chhonkar Division of Soil Science and Agricultural Chemistry Indian Agricultural Research Institute New Delhi, India A. de Jager North and West Africa Division International Fertilizer Development Center Accra, Ghana Gerd Dercon International Atomic Energy Agency Vienna, Austria

H. Eswaran Natural Resources Conservation Service United States Department of Agriculture Washington, D.C. L. O. Fresco University of Amsterdam Amsterdam, the Netherlands K. Fujii Graduate School of Agriculture Kyoto University Kyoto, Japan S. Funakawa Graduate School of Agriculture Kyoto University Kyoto, Japan A. Kadono Graduate School of Urban Environmental Sciences Tokyo Metropolitan University Tokyo, Japan T. Kosaki Graduate School of Urban Environmental Sciences Tokyo Metropolitan University Tokyo, Japan Rattan Lal Carbon Management and Sequestration Center The Ohio State University Columbus, Ohio

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J. P. Lesschen Alterra Wageningen University and Research Centre Wageningen, the Netherlands

Tapan J. Purakayastha Division of Soil Science and Agricultural Chemistry Indian Agricultural Research Institute New Delhi, India

Freddy O. Nachtergaele Land and Water Division Food and Agriculture Organization of the United Nations Rome, Italy

P. F. Reich Natural Resources Conservation Service United States Department of Agriculture Washington, D.C.

A. Nakao Department of Radioecology Institute for Environmental Sciences Aomori-Ken, Japan R. P. Narwal Chaudhary Charan Singh Haryana Agricultural University Haryana, India Long Nguyen International Atomic Energy Agency Vienna, Austria E. Padmanabhan Petroleum and Geoscience Department University of Technology Petronas Perak, Malaysia D. K. Pal Division of Soil Resource Studies National Bureau of Soil Survey and Land Use Planning Nagpur, India Monica Petri Land and Water Division Food and Agriculture Organization of the United Nations Rome, Italy

Johan Rockstrom Stockholm Environment Institute Stockholm, Sweden Stacey Rosen Economic Research Service United States Department of Agriculture Washington, D.C. Shahla Shapouri Economic Research Service United States Department of Agriculture Washington, D.C. A. K. Singh Indian Council of Agricultural Research New Delhi, India Bal Ram Singh Department of Plant and Environmental Sciences Norwegian University of Life Sciences Ås, Norway

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E. M. A. Smaling Royal Tropical Institute (KIT) Amsterdam, the Netherlands and University of Twente Faculty of Geo-Information Science and Earth Observation (ITC) Enschede, the Netherlands B. A. Stewart Dryland Agriculture Institute West Texas A&M University Canyon, Texas J. J. Stoorvogel Land Dynamics Group Wageningen University and Research Centre Wageningen, the Netherlands C. L. van Beek Alterra Wageningen University and Research Centre Wageningen, the Netherlands

B. Venkateswarlu Central Research Institute for Dryland Agriculture Andhra Pradesh, India Suhas P. Wani International Crops Research Institute for the Semi-Arid Tropics Andhra Pradesh, India T. Watanabe Graduate School of Agriculture Kyoto University Kyoto, Japan Felipe Zapata International Atomic Energy Agency Vienna, Austria

Management 1 Sustainable of Soil Resources and Food Security Rattan Lal and B. A. Stewart CONTENTS 1.1 Introduction....................................................................................................... 1 1.2 Soil and Climatic Effects on Agricultural Production......................................2 1.3 Enhancing Soil Resilience through Sustainable Management.......................... 3 1.4 Per Capita Arable Land Area and Energy Use..................................................5 1.5 Diet Preferences.................................................................................................6 1.6 Carbon Sequestration in Terrestrial Ecosystems............................................... 7 1.7 Soil Degradation................................................................................................8 1.8 Conclusions........................................................................................................8 Acronyms.................................................................................................................... 9 References................................................................................................................... 9

1.1  INTRODUCTION The world population is projected to increase from about 7 billion in 2011 to 9.2 billion in 2050. The current rate of increase is about 6 million/month, with almost all growth occurring in developing countries where natural resources are already under great stress. The Green Revolution technology led to the doubling of food production between 1950 and 2010, with only a 10% increase in the area under production [FAO 2010]. However, meeting the food demand of the growing population, rising standards of living, and changes in diet preferences will necessitate an additional 70% increase in production between 2010 and 2050 [Burney et al. 2010]. Yet yields in the Indo-Gangetic plains and other regions have stagnated or declined over the past two decades and may be jeopardized further by climate change and increases in the frequency and intensity of extreme events [Semonov 2009], especially in sub-Saharan Africa [IIED 2010]. Grain yields of wheat [Semenov 2009] and rice [Wassmann et al. 2009] are sensitive to high temperatures. The problem of food insecurity is also exacerbated by increases in the severity and extent of soil degradation. This is especially true because of declines in the soil structure and hydrological properties in conjunction with reductions in the quantity and quality of soil organic carbon (SOC) content caused by a widespread use of extractive farming practices (i.e., indiscriminate residue removal, excessive grazing, the use of animal dung as household fuel 1

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World Soil Resources and Food Security

rather than as manure, and a negative nutrient budget). Over and above the biophysical constraints being exacerbated by a changing climate and an increase in the frequency of extreme events, there are also issues related to the human dimensions. To the resource-poor and small-size land holders of the tropics and subtropics, neither the essential inputs are available (e.g., improved seeds and fertilizers and new equipment such as no-till seed drills and soil testing facilities), nor affordable. One, these inputs are prohibitively expensive. Two, farmers are not sure about their effectiveness, especially under conditions of uncertain rains, frequent droughts, and a high incidence of weeds and other pests. This volume is especially focused on the soil resources of the world and the challenge of their management on a sustainable basis. This introductory chapter describes some of the soil-related constraints on enhancing agricultural production on smallholder farms of the tropics and subtropics, and outlines mitigative and adaptive strategies for changing climates and declining qualities of soil and water resources.

1.2  S OIL AND CLIMATIC EFFECTS ON AGRICULTURAL PRODUCTION There is a strong interaction between soil degradation and extreme events (e.g., drought, inundation), with positive feedback from reducing the agronomic or net primary productivity (NPP). Declines in productivity are accentuated by reductions in the use-efficiency of nutrients and water, both inherent and applied. These interactive effects lead to declines in both plant- and animal-based outputs (Table 1.1). Specific reductions in plant-based outputs are those related to declines in yields of crops (cereals, grain legumes, roots and tubers, oil seeds, fruits and vegetables), fuel [crop residues, wood, charcoal, biomass from switchgrass (Pennistum vergatum), etc.], feed (forages, crop residues, fodder trees), and fiber [cotton (Gossipium hirsutum)]. Because of close interactions between plants and animals, a decline in soil quality also affects animal health and productivity. Healthy soils produce healthy plants, raise healthy animals, improve human health, and vice versa. Consequently, TABLE 1.1 Agricultural Products Affected by Soil Resource Degradation, Climate Change, and Related Extreme Events Production 1.

Food

2.

Fuel

3.

Feed

4. 5.

Fiber Raw materials

Plant-Based Cereals, grain legumes, roots and tubers, oil seeds, vegetables, fruits Wood, charcoal, biomass, residues, sugarcane, oils Forages, fodder trees and shrubs, rangelands, seed cakes Cotton, hemp, banana, agave Bioeconomy, timber, resins, medicines, pharmaceuticals, oils

Animal-Based Milk, poultry, fish, meat Dung, animal fat, residues Fishmeal, meat and poultry by-products Soil, wool, animal hairs Fish oil, animal products

Sustainable Management of Soil Resources and Food Security

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a decline in soil quality coupled with an increase in the frequency of extreme events reduce the output of animal-based products such as milk, meat, poultry, wool, etc. (Table 1.1).

1.3  E NHANCING SOIL RESILIENCE THROUGH SUSTAINABLE MANAGEMENT No technological panacea exists for the global challenges of sustainable use of soil and water resources [The Royal Society 2009]. This is especially true because the constraints on agronomic production differ among climates, soils, and geographical regions. Nonetheless, the strategy is to reverse the degradation trend, restore degraded soils, and enhance resilience to any adverse effects of climate change. Conversion to restorative land use and adoption of recommended management practices (RMPs) would improve soil quality and adaptation to climate change (e.g., onset of rains, changes in soil temperature, moisture regime, and growing season duration). Adoption of RMPs can mitigate climate change [UNFCCC 2008; Lobell and Burney 2010], and is also essential to advancing global food security [Gregory et al. 2005; Lobell et al. 2008]. Furthermore, agricultural RMPs, in addition to enhancing natural processes, are more cost-effective than engineering techniques for carbon capture and storage [McKinsey & Company 2009]. In this context, agricultural intensification in developing countries is an important solution to climate change [World Bank 2009]. Land uses and RMPs that enhance adaptation to climate change also have mitigative effects through (i) sequestering carbon in soils and biota, thereby enhancing the ecosystem carbon pools, and (ii) reducing emissions through conversion of plow-till systems to no-till (NT) or conservation agriculture (CA) systems and enhanced use-efficiency of energy-based inputs (e.g., fertilizers, irrigation). Some RMPs for improving soil quality and enhancing soil resilience are outlined in Figure 1.1. Important among these are (i) integrated nutrient management (INM) for balancing budgets for macronutrients (N, P, K, Ca, Mg) and micronutrients (Zn, Cu, Fe, Se), (ii) improving the chemical properties of soil, such as soil reaction through liming, while also enhancing cation exchange capacity (CEC), (iii) enhancing aggregation and improving soil tilth, (iv) increasing the soil organic carbon (SOC) pool and its quality, along with improvements in the activity and species diversity of soil fauna (microbial biomass carbon, earthworm activity), and (v) moderating soil temperature so that it is within the optimal range (25°C–30°C) in the root zone. Soil-specific RMPs to achieve these goals include CA, cover cropping, manuring, biochar application, and those RMPs that enhance earthworm and other biota activity (Figure 1.1). Similar to soil management, there are also RMPs for improving the use-­efficiency of water resources (Figure 1.1). Important among these are (i) conserving water in the root zone by increasing infiltration capacity, decreasing surface runoff, and reducing evaporation, (ii) harvesting and recycling water through supplemental irrigation by using microirrigation, drip subsurface irrigation (DSI), fertigation, and even condensation irrigation (CI), (iii) using genetically modified crops with deep root systems and built-in mechanisms for drought avoidance and tolerance, and (iv) using an aerobic rice culture that saves water and improves water use-efficiency

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Soil management Nutrients: macro, micro (INM, slow release, nano-enhanced fertilizers) Reaction: liming, leaching Structure: conservation agriculture Temperature: optimize Biota: macro, micro Precision farming: soil-specific management Amendments: manure, biochar

Water management

Soil water retention Infiltration capacity and storage Runoff control Evaporation reduction Supplemental irrigation (micro, DSI, CI) Water harvesting and recycling Plastic mulch Aerobic rice

Farming systems management

1. Crops: rotations, complex systems, perennial culture, GM crops 2. Trees: species management, GM varieties, super-CO2 adsorbing trees, albedo management 3. Animals: species management, methane management

Ecosystem management Silvo-pastoral systems Agro-silvo-pastoral systems Eco-efficiency approach

FIGURE 1.1  Agricultural intensification to enhance soil and ecosystem resilience for mitigation of and adaptation to climate change and related extreme events. CDSI, drip subirrigation; CI, condensation irrigation; INM, integrated nutrient management.

[Bouman et al. 2007]. There exists a strong interaction between soil temperature and the use-efficiency of soil water. Practiced in conjunction with the retention of crop residue mulch and INM needed for complex rotations, CA can moderate surface soil temperature while reducing losses (by runoff and evaporation) and improving use-efficiency of stored and applied water (Figure 1.1). These strategies and the relevant policy interventions have been described elsewhere [The Royal Society 2009; FAO 2010]. Adoption of improved farming systems is integral to the sustainable management of soil and water resources, and to the mitigation of, and adaptation to, climate change. The goal is to integrate the cultivation of crops with the growth of suitable trees and the raising of livestock. Important options include the perennial culture of wheat (Triticum aestivum) and barley (Hordeum vulgare) [Glover et al. 2010], complex rotations (cereals–legumes–forages/hay), agroforestry, silvopastoral systems, and agrosilvopastoral systems (Figure 1.1). Agroforestry systems enhance, stabilize, and sustain production under small-holder agriculture [Zomer et al. 2009; Sileshi et al. 2008]. These farming systems increase land use intensity and have high NPP per unit area, unit time, and unit consumption of inherent and applied resources (e.g., nutrients, water, energy). While stabilizing productivity and restoring soil quality, complex systems also minimize the incidence of pests. Amudavi et al. [2007] show the effectiveness of a push–pull system of integrated pest management (IPM) for

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maize (Zea mays L) cultivation in East Africa by intercropping maize with desmodium (Desmodium uncinatum), which suppresses striga (Striga hermonthica) and growing Napier grass (Pennisetum purpureum) that is more attractive to moths of the maize stalk borer (Busseola fusca) [Cook et al. 2007; Hassanali et al. 2008]. Complex systems involving intercropping with perennial shrubs can also improve drought tolerance through hydraulic lift [Caldwell and Richards 1989]. Complex farming systems may also enhance the use-efficiency of fertilizers and reduce the rate of application. Future demand for nitrogenous fertilizers may require 2% of the total global energy use by 2050 as the Haber–Bosch process is highly energy-intensive [Glendining et al. 2009]. Complex systems based on reduced cultivation may also promote arbiscular mychorrhizal (AM) associations that enhance plant nutrient uptake and agronomic sustainability [Leigh et al. 2009].

1.4  PER CAPITA ARABLE LAND AREA AND ENERGY USE The increase in population, from 7 billion in 2011 to 9.2 billion in 2050, will occur almost entirely in developing countries. The per capita arable land area—which in most countries of South and East Asia is 2.7 L/ha in Germany to 50%] soils) or Acrisols and Alisols (low base saturation [15 y)

2 1 0

0

2

4

6

8

10

–1 –2

Scores of P and NC ratio factor

(c)

Fallow period (y) Factor 3

3

Long fallow (>15 y)

2 1 0

–1

0

2

4

6

8

10

–2 –3

Fallow period (y)

FIGURE 4.20  Distribution of factor scores in relation to land-use stages of shifting cultivation in northern Thailand.

in surface soil layers. In the long fallow, the values of “SOM” factor scores were usually high due to cumulative litter input. The P and NC ratio factor was occasionally high, but only at the cropping phase and the initial stage of fallow, presumably after higher ash input at the burning; it was then low throughout the fallow stages, including long fallow. The fallow practice does not seem to improve the soil properties relating to this factor. The slashand-burn practice was, therefore, considered to be indispensable for improving the soil properties relating to this factor for the next cropping. This is in contrast to the

Soil Resources and Human Adaptation in Ecosystems in Humid Asia

C0 (mg kg–1)

(a)

N0 (mg kg–1)

Long fallow (>15 y)

10000

5000

0

(b)

C0

15000

101

0

2

4

6

8

10

Fallow period (y)

N0

800

Long fallow (>15 y)

600 400 200 0

0

2

4

6

8

10

Fallow period (y)

FIGURE 4.21  Distribution of C0 and N0 in relation to land-use stages of shifting cultivation in northern Thailand.

acidity and/or SOM-related properties of the soils, which already showed improvements in the late stage of fallow. Figure 4.22 shows the fluctuations of C0 and N0 in the shifting cultivation system in JP. Unlike EK or NT, neither C0 nor N0 fluctuate appreciably during the cropping and fallow phases, presumably because the northern temperate climate produces lower amounts of forest litter during the fallow period. Such a mild climate also prevents SOM from actively decomposing, allowing continuous cultivation for several years, as introduced earlier.

4.6.6  General Consideration on Land Uses in Upland Soils in Respective Regions in Relation to Soil Fertility Status As shown in Table 4.10 for EK, the prolonged fallow does not improve the low fertility status of the soils relating to the P and K factor, indicating that a high ash input, probably after long period of fallow, is indispensable for achieving sufficient yields

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World Soil Resources and Food Security (a)

Long fallow (>15 y)

C0

4000

C0 (mg kg–1)

3000 2000 1000 0

0

2

4

6

8

10

12

Fallow period (y) (b)

Long fallow (>15 y)

N0

250

N0 (mg kg–1)

200 150 100 50 0

0

2

4

6

8

Fallow period (y)

10

FIGURE 4.22  Distribution of C0 and N0 in relation to land-use stages of shifting cultivation in Japan.

in the next cropping phase in the traditional shifting cultivation system. As far as P supply, the situation of NT was not very much different, i.e., the factor scores in the P and NC ratio factor were not improved even in the late stage of the fallow phase in NT (Figure 4.20c). In EK, the high ash input after a long period of fallow may also contribute to amelioration of soil acidity problems, resulting in an increased N utilization by crops. This is not the case for the relatively fertile soils in terms of N supply under shifting cultivation in NT. This may be a key factor in explaining why it has been difficult to establish cropping systems without long fallow periods in the traditional shifting cultivation in highly acidic upland soils in EK. Smithson and Giller [2002] also concluded that additions of P and N fertilizer should be seen as necessary in low-nutrient soils of the tropics. From these results, we can conclude that it will be difficult to establish intensive cropping systems in most of the upland areas unless very high amounts of fertilizer as well as liming are applied in EK. Topographic factors should be taken into consideration for developing alternative, more intensive agriculture; that is, the riverside soils should be the first priority for more intensive

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103

uses. As discussed earlier, the riverside soils showed higher net nitrification relative to NH4 formation, and higher N0/C0 ratios than the other EK soils from upper slopes. Soil fertility status relating to acidity as well as P and K factors was also apparently favorable in the riverside soils. Thus the riverside soils were exceptionally fertile in terms of soil acidity, P and K status, and N supply potential, promising a better agricultural response at this location. On the other hand, in the shifting cultivation system by Karen people in NT, some soil properties relating to soil acidity improve at the same time as the SOM-related properties increase the late stage of fallow. The litter input may be supplying bases (obtained by tree roots from further down the soil profile) to the surface soil. This simultaneous increase in SOM and bases in the surface soil, through forest-litter deposition in the late stage of fallow, has an increasing effect on nutritional elements. These functions of the fallow phase can be considered essential to the maintenance of this forest-fallow system. Agricultural production can therefore be maintained with a fallow period of around 10 years in NT, which is somewhat shorter than widely believed. Traditional shifting cultivation in EK, NT, and JP can be seen to be well adapted to their respective soil-ecological conditions. Socioeconomic conditions are, however, drastically changing, making it difficult to sustain these systems. Even so, these systems provide an insight into basic soil processes—an understanding of which is essential to the better management of organic matter in all agroecosystems.

4.7  C  OMPARATIVE STUDY ON PROTON BUDGETS IN SOILS OF CROPLAND AND ADJACENT FOREST IN THAILAND AND INDONESIA Soil acidification is a natural process, accelerated by agriculture, in humid regions under a climate where precipitation exceeds evapotranspiration [Helyar and Porter 1989; Juo et al. 1996]. In the croplands, proton generation associated with nitrification was reported to accelerate soil acidification owing to the enhanced mineralization of soil organic nitrogen [Tanaka et al. 1997; see Section 4.8 in this volume], limited vegetation uptake at the beginning of the cropping season [Poss et al. 1995], and nitrogen fertilization [Bouman et al. 1995]. However, soil acidification is also contributed to by proton sources other than nitrification, i.e., acidic deposition, dissociation of organic anions and carbonic acid and excess uptake of cations over anions by vegetation. Since most of proton-generating processes are associated with the organic matter cycles, they are influenced by the cultivation-induced changes in organic matter cycles, typically the loss of SOM owing to the increased organic matter decomposition and product removal. However, the effects of cultivation on individual processes are various and thus, their influence on soil acidification is still unclear. As discussed in the previous section (Section 4.6), soil acidity fluctuated considerably in different stages of land uses in shifting cultivation systems that incorporated both cropping and fallow phases in their land rotations. The objective of the present section was to evaluate the influence of cultivation on soil acidification by

104

World Soil Resources and Food Security

quantifying proton budget in a soil-vegetation system including solute leaching, vegetation uptake, and organic matter decomposition in croplands and adjacent forests in northern Thailand and East Kalimantan, Indonesia.

4.7.1  Study Plots Experimental plots consisted of one forest and one cropland plot in both Thailand and Indonesia. The forest and cropland plots in Thailand (RP and RPc, respectively) were located in Ban Rakpaendin, Chiang Rai Province (Figure 4.16). The corn (Zea mays L.) had been cultivated during the wet season without fertilization in RPc for 3 years since the conversion of forest to cropland. Soils were derived from sedimentary rocks (RP) and sedimentary rocks associated with granite intrusion (RPc) and classified as Typic Haplustults [Soil Survey Staff 2006]. On the other hand, the forest and cropland plots in Indonesia (BS and BSc, respectively) were located in the experimental forest of the Tropical Rainforest Research Center, Mulawarman University, Bukit Soeharto, East Kalimantan Province (Figure 4.16). The chili (Capsicum sp.) had been cultivated for 2 years after deforestation in BSc. Soils were derived from sedimentary rocks and classified as Typic Paleudults [Soil Survey Staff 2006]. 0.71 Mg DW ha−1 yr−1 of poultry manure was applied at the beginning of cropping season (October 2004–October 2005). Soil solution and precipitation (for the cropland plots) or throughfall (for the forested plots) were collected and the proton budget was calculated (see Appendix for detailed methodology).

4.7.2  Physicochemical Properties of Soils The physicochemical properties of soils are presented in Table 4.11. Soil pH was low throughout the soil profiles in BS (3.8–4.3) and BSc (4.2–4.3) than in RP (4.6–5.0) and RPc (5.4–5.5). Clay contents in soils were higher in RP (70%–75%) and RPc (40%–51%) than in BS (23%–31%) and BSc (19%–33%), although the differences in clay contents between RP and RPc might be due to the influence of granite in RPc. Total carbon contents in the A horizons in the cropland plots were lower (27 and 14 g kg−1 in RPc and BSc, respectively) than in the adjacent forest plots (63 and 23 g kg−1 in RP and BS, respectively).

4.7.3  Carbon Stock and Flow Carbon stock is presented in Table 4.12. In the forest plots, organic carbon was stored as the aboveground biomass (169.1 and 292.6 Mg C ha−1 in RP and BS, respectively) as well as SOM (65.2 and 26.6 Mg C ha−1 in RP and BS, respectively) in the upper 30-cm layers of soil. The higher aboveground biomass and the lower stock of organic matter in the mineral soil in the present study, as compared to temperate forests, were consistent with a previous report [Nakane 1980]. In the cropland plots, C stock in the mineral soil was lower than in the adjacent forest plots. C stock in the mineral soil in RPc and BSc (55.8 and 24.6 Mg C ha−1, respectively) was 9.4 and 2.0 Mg C ha−1 less than in RP and BS, respectively (Table 4.12).

Depth

pH

Exchangeable Total C

Horizon

(cm)

(H2O)

(KCl)

Total N

Bases

(g kg−1)

Al

Particle Size Distribution CEC

Sand

(cmolc kg−1)

Silt

Clay

(%)

Thailand RP

RPc

A

0–7

5.0

4.1

62.6

3.8

5.8

1.9

27.6

5

25

70

BA

7–20

4.9

3.9

19.8

1.5

2.0

3.5

19.9

4

23

73

Bt

20–45+

4.6

4.0

8.9

1.0

1.4

3.2

20.1

6

19

75

Ap

0–7

5.4

4.7

26.8

2.0

8.0

0.2

11.5

42

18

40

BA

7−20

5.5

4.8

15.0

1.3

5.9

0.2

14.4

32

13

55

Bt

20−45+

5.5

4.8

10.8

1.0

4.3

0.4

14.4

39

10

51

Indonesia BS

BSc

0−5

4.0

3.9

22.9

1.6

2.2

3.0

8.5

52

25

23

BA

A

5−25

3.8

3.8

4.2

0.5

0.8

3.9

6.2

49

27

24

B1

25−40

4.0

3.8

3.5

0.5

0.8

4.8

5.0

43

30

27

Bt

40+

4.3

3.8

2.5

0.4

1.0

7.0

5.0

34

35

31

Ap1

0−5

4.3

3.9

14.3

1.6

2.1

1.9

6.8

64

16

19

Ap2

5−20

4.2

3.8

4.8

0.5

1.8

3.3

8.7

54

19

28

B1

20−40

4.2

3.8

3.7

0.5

1.2

4.6

10.3

46

21

33

Bt

40−60+

4.3

3.8

3.2

0.4

0.9

5.9

11.3

54

19

26

Soil Resources and Human Adaptation in Ecosystems in Humid Asia

TABLE 4.11 Physicochemical Properties of Soils in Thai and Indonesian Plots

105

106

TABLE 4.12 Stock and Annual Flow of Carbon in the Thai and Indonesian Plots RP 169.1

RPc

BS

(−)

5.8

(0.8)

292.6

2.4 2.2 1.7

(0.3) (0.2) (0.1)

0.1 0.1 –

(0.0) (0.0)

2.6 65.2

(0.1) (5.0)

– 55.8

(a)

5.5

(0.3)

(b) (c) (d)

5.2 4.0 1.2 – 5.8 −0.2

(0.2) (0.2) (0.1)

(e)

(−) (0.4)

BSc (−)

1.2

(0.2)

1.0 1.2 2.1

(0.4) (0.3) (0.4)

0.2 0.1

(0.1) (0.0)

(2.3)

3.5 26.6

(0.3) (0.8)

24.6

(0.3)

8.2

(0.3)

5.4

(0.5)

3.6

(0.3)

4.3 4.1 0.2 1.7 – −3.9

(0.7) (0.7) (0.1) (0.3)

5.0 4.1 0.9 – 10.6 −0.4

(0.5) (0.5) (0.1)

1.4 1.1 0.3 0.2 – −2.2

(0.1) (0.1) (0.1) (0.0)

(0.8)

(–) (0.8)

Note: The figures in parentheses represent standard errors. a Organic carbon in soil at 0–30-cm depths was counted. b The C input was calculated as the sum of litterfall and root litter (b = c + d). c The annual rates of root litter production were assumed to be 20% of the fine root biomass in the forest [Nakane 1980]. d The C budget in soil was calculated as the difference between organic matter decomposition and C input (e = b − a).

(0.3)

World Soil Resources and Food Security

C stock (Mg C ha−1) Aboveground biomass Fine root biomass O, A horizon BA horizon B1 horizon Soil organic matter O horizon Mineral soil horizonsa C flow (Mg C ha−1 yr−1) Organic matter decomposition Organic matter production C input to soilb Litterfall Root litterc Product removal Wood increment C budget in soild

Soil Resources and Human Adaptation in Ecosystems in Humid Asia

107

Seasonal fluctuations of soil temperature and volumetric water content in the soil and the rates of organic matter decomposition are shown in Figures 4.23 and 4.24, respectively. In RP and RPc, the rates of organic matter decomposition were positively correlated with volumetric water content in the soils (RP: r = 0.91, n = 9, p < 0.01; RPc: r = 0.63, n = 9, p < 0.10), while, in BS and BSc, they were independent of volumetric water content in the soils. Therefore, the annual rates of organic matter decomposition in RP and RPc were calculated as totals of the simulated emission rates using the regression equations and monitored volumetric water content in the soil, whereas, in BS and BSc, the rates were calculated using the average rates of CO2 emission measured. Volumetric water content in soil (L L–1)

Soil temperature (ºC) 35

Thailand RP

30 25

0.5 0.4

20

0.3

15

0.2

10

0.1

5 0 Apr-04

0.6

Jun-04

Aug-04

Oct-04

Dec-04

Feb-05

0.0

Date Volumetric water content in soil (L L–1)

Soil temperature (ºC) 35

Indonesia BS

30 25

0.3

15

5 0 Sep-04

0.5 0.4

20

10

0.6

0.2 Soil temperature at 5-cm depth (Cropland) Soil temperature at 5-cm depth (Forest) 0.1 Volumetric water content in soil at 5-cm depth (Cropland) Volumetric water content in soil at 5-cm depth (Forest) 0.0 Nov-04 Jan-05 Mar-05 May-05 Jul-05 Sep-05

Date

FIGURE 4.23  The seasonal fluctuations of soil temperature and volumetric soil water content.

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World Soil Resources and Food Security C flux (µ gC m2 s–1) 60

Thailand RP

40

20

0 Feb-04 Apr-04 Jun-04 Aug-04 Oct-04 Dec-04 Feb-05 Apr-05

Date

C flux (µ gC m2 s–1) 60

Indonesia BS Forest Cropland

40

20

0 Nov-04

Jan-05

Mar-05

May-05

Jul-05

Sep-05

Nov-05

Date

FIGURE 4.24  Seasonal fluctuations of the rates of organic matter decomposition in the forest and cropland plots. Bars indicate standard errors (n = 5).

The annual flow of carbon is presented in Table 4.12. In the forest plots, the annual rates of organic matter decomposition thus determined were 5.5 and 5.4 Mg C ha−1 yr−1 in RP and BS, respectively. These values were consistent with those reported in tropical seasonal forests (4.1 Mg C ha−1 yr−1) (Section 4.8) and tropical rain forests (4.8–8.9 Mg C ha−1 yr−1) [Bond-Lamberty et al. 2004], respectively. Assuming that annual root litter rates were 20% of the fine root biomass [Nakane 1980], the annual rates of organic matter decomposition were almost balanced with C inputs as the sum of litterfall and root litter (5.2 and 5.0 Mg C ha−1 yr−1 in RP and BS, respectively) in the forest plots. Organic matter was accumulated in the ecosystems as wood increment in the growth stage of forests in RP and BS.

Soil Resources and Human Adaptation in Ecosystems in Humid Asia

109

In the cropland plots, the annual rates of organic matter decomposition were higher in RPc and BSc (8.2 and 3.6 Mg C ha−1 yr−1, respectively) than C inputs (4.3 and 1.4 Mg ha−1 yr−1, respectively). These values suggest a loss of SOM (3.9 and 2.2 Mg C ha−1 yr−1 in RPc and BSc, respectively) in the cropland plots. The SOM loss caused by cultivation can account for the lower SOM stock in the cropland plots, although the lower clay content in the soil of RPc might also contribute to the lower SOM stock than in RP.

4.7.4  Soil Solution Composition The annual volume-weighted mean concentrations of ions in precipitation, throughfall, and soil solution are presented in Table 4.13. The soil solution pH was relatively high in RP and RPc (6.1–6.2 and 5.7–5.8, respectively). The soil solution pH was moderately low (5.2–5.6) in BSc, while the soil solution pH was very low (4.2–4.4) in BS. The concentrations of bicarbonate in soil solution were significant (0.03–0.05 mmolc L−1) in all plots except for BS, where they were negligible owing to the low pH of soil solution. The concentrations of organic anions in soil solution were higher in the O and A horizons in BS (0.20–0.26 mmolc L−1) and in the Ap1 and Ap2 horizons in BSc (0.09–0.13 mmolc L−1), as compared to RP and RPc (0.01–0.02 mmolc L−1) (Table 4.13). The concentrations of organic anions in soil solution were correlated with those of DOC in BS and BSc, and these relationships were expressed by the following regression:

(Orgn–) = 0.087 × (DOC) + 0.042  (r = 0.77***, n = 111, p < 0.01)

(4.19)



(Orgn–) = 0.100 × (DOC) + 0.008  (r = 0.66***, n = 77, p < 0.01)

(4.20)

where (Orgn–) represents the concentration of organic anions in soil solution (mmolc L−1) and DOC represents the concentration of DOC in soil solution (mmol L−1). The higher concentrations of DOC in soil solution in the surface soil horizons in BS and BSc (17.2–34.7 and 9.3–9.7 mg C L−1, respectively), as compared to RP and RPc (3.1–3.9 and 2.2–4.5 mg C L−1, respectively), contributed to proton generation associated with dissociation of one acidic functional group for 11.5 and 10.0 C atoms of DOC, respectively. In the cropland plots, the concentrations of nitrate in soil solution were higher both in RPc (0.34–0.38 mmolc L−1) and BSc (0.06–0.14 mmolc L−1), as compared to the respective adjacent forest plots. The concentrations of nitrate in the Ap horizons were highest especially at the beginning of the cropping season in RPc (April 2004– June 2004) and BSc (October 2004–January 2005) owing to the enhanced decomposition of SOM and the small amount of biomass (Figure 4.25). Although nitrification is generally suppressed in the acidic soils [Kemmitt et al. 2006], the concentrations of nitrate in soil solution were still higher in BSc than in BS. This was consistent with the report by Killham [1990], in which nitrate could be produced by autotrophic bacteria even in the highly acidic soils in croplands. The concentrations of nitrate in soil solution were correlated with the concentrations of Ca (r = 0.99, n = 83, p < 0.01)

110

TABLE 4.13 Water Flux and Annual Volume-Weighed Mean Concentrations of Ions in Throughfall and Soil Solution

Thailand RP

RPc

BSc

a b

Horizon

pH

DOC (mg C L−1)

TF a A BA Bt

2083 1602 1162 825

6.09 6.21 6.13 6.05

2.7 3.9 3.1 3.1

0.029 0.034 0.029 0.029

0.015 0.020 0.012 0.012

0.017 0.021 0.025 0.018

0.058 0.072 0.060 0.069

0.001 0.001 0.001 0.001

0.012 0.007 0.007 0.007

0.104 0.123 0.103 0.108

0.000 0.007 0.006 0.008

0.001 0.009 0.008 0.004

0.003 0.061 0.054 0.060

P a Ap BA B1

2223 1414 1064 1064

6.22 5.80 5.66 5.83

2.9 4.5 2.2 1.9

0.030 0.034 0.048 0.047

0.008 0.017 0.009 0.012

0.014 0.371 0.380 0.338

0.052 0.117 0.076 0.095

0.001 0.002 0.002 0.001

0.009 0.007 0.008 0.008

0.088 0.508 0.486 0.439

0.000 0.004 0.001 0.002

0.001 0.004 0.005 0.006

0.003 0.149 0.120 0.101

TF a O A B1

2031 1619 1196 545

5.23 4.44 4.22 4.39

9.0 34.7 17.2 9.9

0.009 0.001 0.000 0.003

0.101 0.257 0.196 0.119

0.036 0.038 0.044 0.032

0.104 0.136 0.100 0.116

0.006 0.037 0.061 0.041

0.038 0.088 0.035 0.018

0.197 0.256 0.197 0.176

0.003 0.019 0.009 0.005

0.007 0.040 0.038 0.027

0.017 0.070 0.068 0.051

P a Ap1 Ap2 B1

2187 1144 824 824

6.09 5.62 5.57 5.21

4.2 9.7 9.3 5.9

0.024 0.042 0.051 0.038

0.016 0.126 0.087 0.038

0.009 0.143 0.137 0.061

0.022 0.084 0.097 0.083

0.001 0.002 0.003 0.006

0.014 0.020 0.018 0.023

0.045 0.311 0.298 0.187

0.000 0.010 0.008 0.006

0.001 0.046 0.034 0.019

0.001 0.061 0.083 0.067

HCO3–

Orgn− b

NO3–

CI– + SO24–

H+

NH+4

(mmolc L–1)

P and TF represent precipitation and throughfall, respectively. Orgn− represents anion deficit, the negative charge of organic acids.

Fe2+

Aln+ b

(mmolc L–1)

Si (mmol L−1)

World Soil Resources and Food Security

Indonesia BS

Na+ + K+ + Mg2+ + Ca2+

Water Flux (mm)

Soil Resources and Human Adaptation in Ecosystems in Humid Asia Nitrate concentration (mmolc L–1) RP

Nitrate concentration (mmolc L–1) BS

0.5

0.5

A horizon

0.4

B1 horizon

0.2 0.1

0.1 0 Apr-04

B horizon

0.3

Bt horizon

0.2

O horizon

0.4

BA horizon

0.3

Jun-04

Aug-04

Date

Oct-04

Dec-04

Nitrate concentration (mmolc L–1)

0.0 Nov-04

Feb-05

May-05 Aug-05 Nov-05

Date

Nitrate concentration (mmolc L–1)

RPc

3.0

BSc

3.0

2.5

Ap horizon

2.5

Ap1 horizon

2.0

BA horizon

2.0

Ap2 horizon

1.5

B horizon

1.5

B1 horizon

1.0

1.0

0.5

0.5

0.0 Apr-04

111

Jun-04

Aug-04

Date

Oct-04

Dec-04

0.0 Nov-04

Feb-05

May-05 Aug-05 Nov-05

Date

FIGURE 4.25  Seasonal fluctuations of concentrations of nitrate in soil solution. Bars indicate standard errors (n = 5).

and Mg (r = 0.99, n = 83, p < 0.01) in soil solution in RPc. The basic cations, mainly Ca and Mg, were leached out with nitrate (Figure 4.26).

4.7.5  Net Proton Generation and Consumption NPGNtr, NPGCar, and NPGOrg (where NPG stands for net proton generation) were calculated from the fluxes of solutes (Figure 4.26). Since cation contents exceed anion contents in plant materials in all plots (Table 4.14), excess cation charge is compensated for by net proton release from root to soil as NPGBio. Within NPGBio, since litterfall was the circulating fraction, NPGBio attributable to litter can be neutralized by cations released from fallen litter. NPGBio (wood increment or product removal) in each of the soil horizons was calculated by distributing it based on the distribution of the fine root biomass in the soil profiles (Table 4.12), according to Shibata et al. [1998]. Using NPGNtr, NPGCar, NPGOrg, and NPGBio, proton budget was calculated in each of the soil horizons (Figure 4.27). In RP, the concentrations of organic anions and nitrate in soil solution were low (0.01–0.03 mmolc L−1) owing to the rapid mineralization of organic anions and nitrate uptake by vegetation, respectively, and thus, NPGOrg and NPGNtr in each of the soil horizons were negligible (Figure 4.27). Although bicarbonate was a dominant anion in soil solution in RP, contribution of NPGCar to soil acidification was minor. In RP,

112

World Soil Resources and Food Security

Horizon RP

TF

Horizon SO42– NO

HCO3–

3

+

NH4 Na+ NH2+ Ca2+ Fe

A

Orgn–

Cl–

TF

3+

HCO3– NO3–

Ca2+

Na+

Fe3+

Aln+

O

Aln+

K+

BS

Orgn–

SO42–

Cl– H+

–2

0

NH4+

K+

Mg2–

A

BA Bt

B1

H+

–8

–6

–4

–2

0

2 4 6 8 (kmolc ha–1 yr–1)

–8

Fluxes of cations (+) and anions (–) Horizon

Orgn–

2 4 6 8 (kmolc ha–1 yr–1)

Fluxes of cations (+) and anions (–)

BSc

HCO3– Na+ NH + 4

SO42–

Ap

–4

Horizon

RPc P

–6

P

Fe3+ NO3–

Cl–

Mg2+

BA

Cl–

SO42–

Ap1

Ca2+

K+

HCO3–

Orgn–

NH4+ Na+ Fe3+ K+ Mg2+Ca2+

NO3–

Aln+

n+

Al

Ap2

Bt

B1

H+

–8

–6

–4

–2

0

2 4 6 8 (kmolc ha–1 yr–1)

Fluxes of cations (+) and anions (–)

–8

–6

–4

–2

0

H+

2 4 6 8 (kmolc ha–1 yr–1)

Fluxes of cations (+) and anions (–)

FIGURE 4.26  Fluxes of solutes at each horizon. P and TF represent precipitation and throughfall, respectively. O, A, Ap1, Ap2, BA, B, B1, and Bt represent soil horizons.

NPGBio (wood increment) distributed in the mineral soil horizons (0.6–0.8 kmolc ha−1 yr−1) and it contributed to soil acidification in each soil horizon (Figure 4.27). Since protons were consumed in the same horizon, proton leaching was negligible in RP (Figure 4.27). In BS, although protons were produced by the dissociation of organic anions in the O horizon (NPGOrg: 2.10 kmolc ha−1 yr−1), they were consumed owing to the mineralization and adsorption of organic anions in the A and B horizons (NPGOrg: −1.8 and −1.7 kmolc ha−1 yr−1, respectively) (Figure 4.27). An increase in the flux of NH +4 leaching from the O horizon (0.6 kmolc ha−1 yr−1) (Figure 4.26) suggested that protons were mainly consumed in the mineralization of organic nitrogen to NH +4 in the O horizon (NPGNtr: −0.78 kmolc ha−1 yr−1) (R–NH2 + H2O + H+ = NH +4 + R–OH) (Figure 4.27). A decrease in the fluxes of NH +4 leaching from the A horizon (1.0 kmolc ha−1 yr−1) (Figure 4.26) suggested that protons were released associated with the excess uptake of NH +4 over NO3− by biomass or adsorption of NH +4 on clays in the A horizon (NPGNtr: 0.9 kmolc ha−1 yr−1) (Figure 4.27). NPGBio (wood increment) distributed in the A and B horizons (0.7 and 2.1 kmolc ha−1 yr−1, respectively), while it also distributed in the O horizon (1.0 kmolc ha−1 yr−1) (Figure 4.27). NPGOrg

OMa production

Na

K

Ca

(Mg ha−1 yr−1) Thailand RP Wood increment Litterfall RPc

Product removal Litterfall Indonesia BS Wood increment Litterfall BSc

a b

Product removal Litterfall Fertilizerb

Mg

Fe

Al

Cl

S

P

(Cation)bio

(kg ha−1 yr−1)

(Anion)bio

NPGBio

(kmolc ha−1 yr−1)

NPGBio/OM Production (molc mol−1 C)

5.8

9.2

20.9

8.7

2.7

4.4

4.0

0.8

0.4

0.7

2.13

0.07

2.06

0.004

3.4

2.8

23.2

40.7

18.8

0.9

1.3

0.6

2.3

2.4

3.90

0.41

3.49

0.012

1.7

2.5

12.6

0.4

2.2

0.2

0.2

0.3

0.3

1.7

0.66

0.08

0.58

0.004

4.1

4.1

42.6

5.9

8.8

0.9

2.0

0.1

3.5

2.4

2.47

0.30

2.17

0.006

10.6

14.7

33.0

29.3

15.8

10.0

0.5

1.0

8.6

2.2

4.39

0.64

3.75

0.004

4.1

3.6

45.2

35.7

18.5

1.7

3.3

0.7

5.0

2.5

4.57

0.41

4.15

0.012

0.2

0.5

11.4

0.0

0.0

0.0

0.0

0.7

0.6

0.0

0.31

0.05

0.26

0.020

1.0 –

1.7 0.4

19.6 2.2

12.9 2.7

3.5 0.8

0.6 0.1

1.1 0.1

4.4 0.1

3.3 0.2

1.2 1.0

1.48 0.25

0.37 0.05

1.11 0.20

0.013 –

Soil Resources and Human Adaptation in Ecosystems in Humid Asia

TABLE 4.14 Uptake of Cations and Anions by Vegetation

OM represents organic matter. Poultry manure was applied at the rate of 0.71 Mg DW ha−1 at the beginning of rainy season.

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Horizon

RP

A BA

A

Bt

B1

Total

Total

–10

–5

0

5

10

(kmolc ha–1 yr–1)

Net proton generation (+) or consumption (–)

–10

–5

0

5

10

(kmolc ha–1 yr– 1)

Net proton generation (+) or consumption (–)

Horizon

Horizon

Ap BA

BS

O

Ap1

RPc

Ap2

Bt

B1

Total

Total

(H+)in – (H+)out

BSc

ΔANC NPGNtr

NPGOrg NPGBio

–10

–5

0

5

10

(kmolc ha–1 yr–1)

Net proton generation (+) or consumption (–)

–10

NPGCar

–5

0

5

10

(kmolc ha–1 yr–1)

Net proton generation (+) or consumption (–)

FIGURE 4.27  Net proton generation and consumption in the soil profiles. TF represents throughfall. O, A, Ap1, Ap2, BA, B, B1, and Bt represent soil horizons.

and NPGBio (wood increment) contributed to acidification of the O horizon (1.8 kmolc ha−1 yr−1) (Figure 4.27). Although most of the protons produced in the O horizon were consumed in the same horizon, 0.6 and 0.7 kmolc ha−1 yr−1 of protons leached from the O and A horizons, respectively (Figure 4.26). In RPc and BSc, protons were produced by nitrification in the Ap horizons (Ap1 horizon in BSc) (NPGNtr: 5.0 and 1.5 kmolc ha−1 yr−1 in RPc and BSc, respectively), most of which were neutralized by basic cations in the Ap horizons and this resulted in leaching of basic cations (Figure 4.26). Owing to the fine root biomass concentrated in the Ap horizons (Table 4.12), NPGBio (product removal) was concentrated in the Ap horizons (0.4 and 0.2 kmolc ha−1 yr−1 in RPc and BSc, respectively) in the cropland plots (Figure 4.27). Although NPGOrg in RPc and BSc was lower than the respective forest plots, dissociation of organic anions also contributed to proton generation in the Ap horizon of BSc (1.1 kmolc ha−1 yr−1). On the other hand, loss of SOM contributes to acid neutralization owing to cation release from the decomposed SOM. The cation release from the decomposed SOM can be estimated using the rates of SOM loss (3.93 and 2.24 Mg C ha−1 yr−1 in RPc

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and BSc, respectively) (Table 4.12), the organic matter to carbon ratio of 1.8, and the cation exchange capacity of SOM (CECSOM) [Poss et al. 1995]. According to the soil pH-CECSOM equation proposed by Helyar and Porter [1989], the average CECSOM can be calculated as 124 and 90 cmolc kg−1 SOM in RPc and BSc, respectively, which corresponds to 0.027 and 0.019 molc for 1 mol of soil organic C, respectively. The basic cation release associated with loss of SOM was estimated as 8.8 and 3.6 kmolc ha−1 yr−1 in RPc and BSc, respectively, which can account for most of the depletion of the acid neutralizing capacity in the Ap horizons (ΔANC: −6.6 and −4.1 kmolc ha−1 yr−1 in RPc and BSc, respectively) (Figure 4.27). Therefore, it was considered that protons were mainly consumed by cations released from the decomposed organic matter in the present study. The contribution of cation release from the applied poultry manure to acid neutralization is low (0.2 kmolc ha−1 yr−1) in BSc (Table 4.14).

4.7.6  Comparison of Soil Acidification Processes in Forests and Croplands Contribution of NPGOrg to soil acidification was different between the topsoil horizons of RP and BS (Figure 4.26). NPGOrg in BS is as high as those in Spodosols under temperate forests (1.2–3.7 kmolc ha−1 yr−1) [Guggenberger and Kaiser 1998]. In BS, the higher fluxes of DOC leaching from the O horizon (360 kg C ha−1 yr−1), as compared to RP (62 kg C ha−1 yr−1), owing to the limited mineralization of DOC in the highly acidic soils are considered to result in higher NPGOrg. In the forest plots, NPGNtr in each soil horizon was different depending on the differences in behaviors (mineralization, uptake, and leaching) of NH +4 and NO3−. Ammonium leaching from the O horizon and its uptake in the A horizon contributes to the spatial heterogeneity of NPGNtr in the soil profile of BS, while mineralization and uptake by vegetation in the same horizon results in negligible NPGNtr throughout the soil profile in RP. Since NPGOrg and NPGNtr could be consumed owing to the mineralization and adsorption of organic anions on clays (e.g., ligand exchange and cation bridge) and vegetation uptake, respectively, their contributions to soil acidification in the complete soil profiles are minor in the forest plots (Figure 4.27). NPGBio is a dominant proton source in the forest plots. Soil acidification in the entire soil profiles is mainly caused by excess cation accumulation in wood in the growth stage of forests (Figure 4.27; Table 4.10). In contrast to the forest plots, the increased decomposition of organic matter is considered to contribute to nitrification predominating over plant uptake and result in higher NPGNtr in the Ap horizons in the cropland plots (5.0 and 1.5 kmolc ha−1 yr−1 in RPc and BSc, respectively) (Figure 4.27). Despite no fertilization, NPGNtr in RPc is comparable to the higher reported values in the fertilized croplands (1.4–11.5 kmolc ha−1 yr−1) [Ridley et al. 1990; Bouman et al. 1995; Poss et al. 1995; Lesturgez et al. 2006; Noble et al. 2008]. Since, in the present study, NPGNtr is caused by the net mineralization and nitrification of soil organic nitrogen, the higher rate of SOM loss in RPc is considered to be responsible for the higher NPGNtr in the Ap horizon in RPc than in BSc. The lower NPGNtr in BSc is considered to be partly attributed to the lower soil pH [Kemmitt et al. 2006]. Although NPGNtr is enhanced by cultivation, its extent is various depending on soil pH, as well as the rates of SOM loss.

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Although cultivation results in a decrease of DOC fluxes in the topsoil owing to a loss of the O horizon and adsorption of organic anions in the Ap horizon of BSc, the DOC fluxes in the Ap horizon of BSc (111 kg C ha−1 yr−1) were still higher than in RPc (62 kg C ha−1 yr−1) (Figure 4.26). The higher fluxes of DOC leaching results in the higher NPGOrg in BSc (1.1 kmolc ha−1 yr−1), as compared to RPc (20 y) 8 6 4 2 0

0

2

4

6

8

10

Fallow period (y)

FIGURE 4.31  Annual soil respiration estimates based on the regression equation obtained.

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4.8.3  In Situ Soil Solution Composition under Shifting Cultivation As decomposed products of SOM, such as NO3−, were expected to be present in the soil solution, we collected the soil solution by a porous-cup method [Funakawa et al. 1992] to analyze the process of N translocation in soil profiles. Figure 4.32 shows that the NO3− concentration in the soil solution was high in May and sharply decreased in early June at all the plots, except for CR01, in which the NO3− concentration remained as high as 0.5 mg N L−1 at a depth of 45 cm, even on 22 June. These data were consistent with the results given in Section 4.7 for RPc in northern Thailand (Figure 4.25) and suggested that a higher amount of NO3− could have been leached out from the top 45 cm of the soil layer in CR01 along with rainfall events. In contrast, a low concentration of NO3− was detected in the soil solution at a depth of 45 cm in NF throughout the rainy season, indicating that almost no N leaching from the forested ecosystem occurred. Figures for fallow forest were between that of CR01 and NF, in that the high concentrations of NO3− were observed only at the initial stage of the rainy season (May), and they soon dropped to a low level.

4.8.4  Fluctuation of Microbial Biomass and Metabolic Quotient, qCO2 To estimate the dynamics of a microbial biomass and its activity in relation to the dynamics of C and N, the amounts of microbial biomass C and N were determined several times during the experimental period by the chloroform fumigation­extraction method [Brookes et al. 1985; Vance et al. 1987]. The metabolic quotient, qCO2, was calculated from the soil respiration rate divided by the microbial biomass C. Figure 4.33a shows that microbial biomass C in NF was the highest, ranging from 530 to 1849 mg kg−1, whereas that in CR01 was usually the lowest throughout the year (85–614 mg kg−1). Salt extractable C in CR01 increased remarkably, up to 600 mg kg−1 in the dry season (Figure 4.33b), in which microbial biomass C dropped. Such

2 1 0

FIGURE 4.32  Concentration of NO3− in a soil solution.

NF F6 F4 F2 CR01

NF F6 F4 F2 CR01

4-Sep

9-Jul

22-Jun

2-Jun

0

18-May

1

F6 NF

3

4-Sep

2

4

9-Jul

3

CR01 F2 F4

45 cm

2-Jun

F6 NF

5

22-Jun

4

(b)

18-May

CR01 F2 F4

15 cm

NO3– concentration (mg N L–1)

5

NO3– concentration (mg N L–1)

(a)

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400

Date (d)

Date

0

4/1/02

2

2/1/02

4/1/02

2/1/02

12/1/01

10/1/01

8/1/01

4/1/01

6/1/01

50

12/1/01

100

4

10/1/01

150

6

8/1/01

200

6/1/01

250

CR01 F1 F2 F4 F6 NF

8

4/1/01

Metabolic quotient, qCO2 (mg C g Bc–1 h–1)

300

0

4/1/02

Date

(c) Microbial biomass N (mg N kg–1)

2/1/02

0

12/1/01

200

4/1/02

2/1/02

12/1/01

10/1/01

8/1/01

0

6/1/01

500

600

10/1/01

1000

800

8/1/01

1500

1000

6/1/01

2000

4/1/01

Salt-extractable C (mg C kg–1)

(b)

4/1/01

Microbial biomass C (mg C kg–1)

(a)

Date

FIGURE 4.33  Seasonal fluctuation of microbial biomass C (a), salt-extractable C (b), microbial biomass N (c), and metabolic quotient (d) during the experiments.

an inverse fluctuation of biomass C and salt extractable C indicates an increase of microbial debris during the dry season due to exposure to a strongly fluctuating environment (such as the soil temperature), with a limited vegetation cover. According to Figure 4.33c, microbial biomass N was the lowest in CR01, whereas the highest was seen in NF. Microbial biomass N exhibited an increasing trend throughout the rainy season, probably due to continuous assimilation of N. Similarly, the value of qCO2 was the highest in the late rainy season (Figure 4.33d) and then sharply dropped at the start of the dry season. Soil respiration also decreased, whereas the microbial biomass C level remained high, except for CR01 (Figure 4.33a). It is considered that a large proportion of the soil microbes could have survived in the forested soils without a drastic decrease of biomass during the dry season, presumably by suppressing their activity.

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4.8.5  S ubstrate-Induced Microbial Activities (Respiration and N Assimilation/Nitrification) by Short-Term Laboratory Incubation To analyze the behavior of the microbial community as a controlling factor of N dynamics in shifting cultivation in more detail, short-term responses of soil microbes after the addition of C and N substrates were comparatively investigated for forest and cropland soils from the study village using fresh soil samples collected in March 2003 (late dry season), early June and July 2003 (rainy season), from a field that was used for rice cropping in 2003 (CR03), a field in its second year of fallow (CR0103), a field in its sixth year of fallow (F4 03), and a seminatural forest stand (NF03). The latter three are the same plots as those used for the field experiments in 2001. Then substrate-induced microbial activities were traced using a fresh soil after addition of glucose (equivalent to 4000 mg C kg−1 soil) with or without 0.0168 g of NH4NO3 (equivalent to approximately 400 mg N kg−1 soil). In treatment with NH +4 , enough N was added to eliminate the microbial activity limitations that occur as a result of N shortage. Then the mineralized C was measured continuously up to 94 h at 25°C. Similarly short-term N transformation after addition of NH +4 –N as an N source was traced up to 168 h at 25°C under aerobic conditions. Figure 4.34a compares cumulative CO2 emissions after the adjustment of glucose levels with or without N in the cropland (CR03) and forest (NF03) soils. Irrespective of N addition, glucose-induced respiration in NF03 was significantly delayed compared with that in CR03. In the case of CR03, N addition resulted in further acceleration of respiration, suggesting that the soils of CR03 originally did not contain enough N for the microbial community to demonstrate its maximum potential or the microbial community could utilize additional N efficiently for multiplication of the community. However, such a trend was scarcely observed for the forest soil, NF03. A higher and more efficient utilization of additional C and N resources is obvious in CR03 compared with NF03. Such a response of the microbial community was observed only in CR03 during the rainy season (after burning and moistening) (Figure 4.34b). The soils from the second and sixth years of fallow forests (CR0103 and F4 03) showed a similar trend as NF03. Even in CR03, the soil collected before the burning event (but after forest clearcutting) did not exhibit active respiration after glucose addition with N. Therefore, the unique property of the microbial community in CR03 was probably introduced after slash-and-burn of fallow forest. At the end of the incubation period (42 h), 2170 mg kg−1 of soil C was mineralized in CR03 (in the +C+N treatment), which amounted to 54% of added C. Coody et al. [1986] reported that, using a 14C-labelling technique, 27%–44% of added glucose was mineralized and most of the remaining glucose was already assimilated by soil microbes within 96 h at 25°C. It is highly probable in this experiment that the added glucose was almost completely consumed, either through respiration or via assimilation by soil microbes—even considering that our results may involve some degree of priming effect and, therefore, an overestimation of substrate decomposition. On the contrary, the soils from fallowed (CR0103 and F4 03) or matured (NF03) forests exhibited slow decomposition rates compared with CR03, and the amounts of mineralized C at 42 h were only 8%, 21%, and 13% of added glucose, respectively. Since

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Cumulative CO2 emission (mg C kg–1)

(a) 2500 2000 1500 1000

100

CR03, June, +C CR03, June, +C+N NF03, June, +C NF03, June, +C+N

50

CR03, June, +C+N CR0103, June, +C+N F403, June, +C+N NF03, June, +C+N Cr03, March, +C+N NF03, March, +C+N

500 0

0

20

40

60

Incubation period (h)

80

Cumulative CO2 emission (mg C kg–1)

(b) 2500 2000 1500 1000 500 0

0

10

20

30

Incubation period (h)

40

FIGURE 4.34  Short-term CO2 emission (cumulative) after glucose addition, with or without NH +4 to the fresh soils; (a) effect of NH +4 addition to cropped (CR03) and forest (NF03) soils, and (b) comparison of the soils in different land-use stages.

the possibility of a deficiency in N was practically eliminated in this experiment, such a slow utilization of the added C could be attributed to a specific property of the microbial community in these forest plots. Figure 4.35 demonstrates the fluctuation of levels of inorganic nitrogen (NH +4 and NO3−) after the addition of NH +4 in the incubation experiment. The nitrifying activity was detected only in the CR03 samples collected in the rainy season (June and July). Minimal nitrifying activity, if any, was traced in the soils from fallowed (CR0103 and F4 03) or matured forest (NF03). As the concentration of NH +4 increased within the period of incubation (168 h), gross mineralization of organic N was superior to gross immobilization of NH +4 . So, in the forested plots including in the initial stage of fallow (CR0103 in the 2nd year), both the nitrifying activity and the NH +4 assimilation are noticeably low. In contrast, judging from the fact that the sum of the remaining NH +4 –N and NO3−–N was decreasing during the experiment, N immobilization also occurred at the same time as active nitrification in cropland (CR03). These results

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200

CNTL 0h 72 h 168 h

0

CNTL 0h 72 h 168 h

100

March

200 100 0

June

July

300 200

CNTL 0h 72 h 168 h

0

CNTL 0h 72 h 168 h

100

Time after addition of NH4

NO3–N NH4–N

NF03

(d) 400

March

June

July

300 200 100 0

CNTL 0h 72 h 168 h

March

Time after addition of NH4

NH4–H and NO3–N (mg kg–1)

400

CNTL 0h 72 h 168 h

NH4–H and NO3–N (mg kg–1)

NO3–N NH4–N

F403

July

300

Time after addition of NH4

(c)

June

CNTL 0h 72 h 168 h

300

400

CNTL 0h 72 h 168 h

July

CNTL 0h 72 h 168 h

June

CNTL 0h 72 h 168 h

March

NO3–N NH4–N

CR0103

CNTL 0h 72 h 168 h

400

(b) NH4–H and NO3–N (mg kg–1)

NO3–N NH4–N

CR03

CNTL 0h 72 h 168 h

NH4–H and NO3–N (mg kg–1)

(a)

Time after addition of NH4

FIGURE 4.35  Short-term transformation of NH4 –N added to the fresh soils.

were generally consistent with the fact that, in most cropped ecosystems, NH +4 is mineralized and then readily nitrified to form NO3− if it is not immobilized again rapidly. In contrast, in forest soils, such an active nitrification is sometimes retarded due to low activity of nitrifying bacteria [Robertson 1982].

4.8.6  Dynamics of Microbial Activities during Different Stages of Shifting Cultivation and the Function of the Fallow Phase Figure 4.33d shows that the values of qCO2 in CR01 were much higher than those in the other plots, indicating a higher activity of soil microbes per unit volume of biomass despite apparent lower biomass there (Figure 4.33a). This is partly because the method used in this study (the chloroform fumigation extraction method) could evaluate the whole biomass including any inactive biomass that may be dominant in the forest soils. Two explanations are possible for this apparent high microbial

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activity in CR01. First, this may be an effect of disturbance, as well as slash-andburn, in CR01. Mamilov and Dilly [2002] reported that stress, such as drying and rewetting, causes high microbial activity, which is expected to be more pronounced in the field just after forest reclamation (as in CR01). According to Wardle and Ghani [1995], who described how qCO2 could be used as a bioindicator of disturbance, the cropland ecosystem in the present study (CR01) is considered to be a more disturbed one than the forested ecosystems. Second, the soil microbial community in CR01 drastically changes after slash-and-burn of the forest due to a sharp increase in soil pH along with ash addition. As was discussed previously, a burning event generally produces higher pH values in soils a few years later than is found in fallow or matured forest in the same area (Figure 4.20a). In any case, the distinct difference in the values of qCO2 between the cropland (CR01) and the others suggests some essential difference in composition and/or function of soil microbial communities. On the other hand, soil solution composition revealed that, in the cropland (CR01) soil, a fairly high concentration of NO3− was released into the soil solution, unlike in the forest soil (NF) (Figure 4.32). The values for fallow forests were between these. A small stand of upland rice with possibly poor nutrient-uptake ability in CR01 during the early rainy season may further increase the chance of NO3− leaching. Ellingson et al. [2000] and Neill et al. [1999] also reported that deforestation and burning increased the concentration of NO3− in the soils in Mexican tropical dry forests and Amazonian forests, respectively. There are two explanations in which a higher amount of NO3− could be released into the solution in CR01 in the present study: there was higher nitrification activity of soil microbes in CR01, and/or higher N immobilization (assimilation) activity of soil microbes in NF. As shown in Figure 4.34, the laboratory incubation experiment for analyzing the substrate-induced respiration clearly showed that the soil microbes in the cropland (CR03) soil responded very quickly to addition of glucose, unlike fallowed or matured forest soils (CR0103, F4 03, and NF03). In the same way, soil microbes rapidly nitrified NH +4 added to NO3− in CR03, while partially assimilating the NH +4 at the same time (Figure 4.35). In the remaining plots, neither accelerated glucose consumption nor active NH +4 utilization were observed. Therefore, all the microbial activities that were tested in CR03 were induced quite rapidly after the addition of the substrates. These properties of the microbial community in CR03 were probably introduced after slash-and-burn of fallow forest. This, together with the fact that a higher qCO2 was observed despite an apparent lower microbial biomass in CR01 (Figure 4.33), indicates that the microbial community in the cropped soils consists of a smaller, but more active, number of microbes compared with that in the fallowed or matured forests, in which a larger number of soil microbes coexisted with a low activity. The main reason for suppressing NO3− release into the soil solution is therefore a lower nitrifying activity of the microbial community in the forest soils than in the cropped soils. Such a high nitrifying activity in cropped soils under shifting cultivation is also reported by Tulaphitak et al. [1985a] and Tanaka et al. [2001]. On the other hand, the contribution of reimmobilization of NH +4 once mineralized is secondary to suppressing NO3− release in the forest soil, as in NF03 and the fallowed

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plots (CR0103 and F4 03), where the microbial response for immobilization of added NH +4 was slow in the incubation experiment. Conversely, the seasonal fluctuation of microbial biomass C and salt extractable C suggests that the microbial community in CR01 was easily destroyed after sharp drought conditions during the dry season. With a succession of secondary vegetation, such a community seemed to be replaced by a more stable one, which did not show a clear decrease of microbial biomass C, even in the dry season. At the same time, it does not increase, or multiply, rapidly even under favorable conditions such as the addition of substrates, as demonstrated in the short-term incubation experiment. The increasing trend of microbial biomass N that is observed during the rainy season (Figure 4.33c) is considered to be a result of slow utilization of N by the microbial community and may contribute to accumulation of N into the SOM pool.

4.8.7  Main Functions of the Fallow Phase in Shifting Cultivation by K aren People in Northern Thailand Based on the results obtained in this study, the functions of the fallow phase in shifting cultivation that have ensured the long-term sustainability of the system can be summarized as follows: First, as discussed in Section 4.6, some soil properties relating to soil acidity improve simultaneiously as the SOM-related properties increase in the late stage of fallow. The litter input may be supplying bases (obtained by tree roots from further down the soil profile) to the surface soil. This simulta­neous increase in SOM and bases in the surface soil, through forest-litter deposition in the late stage of fallow, has an increasing effect on nutritional elements. Second, the decline in soil organic C during the cropping phase could be compensated by litter input during 6–7 years of fallow. With regards to the overall budget, the organic matter input through incorporation of initial herbaceous biomass into the soil system after establishment of tree vegetation (approximately in the fourth year) was indispensable for maintaining the SOM level. Third, the succession of the soil microbial community from rapid consumers of resources to stable and slow utilizers, along with establishment of secondary forest, retards further N loss through leaching and enhances N accumulation into the forest-like ecosystems. It is noteworthy that, during the fallow period, nitrifying activity of soil microbes, which was once activated in the cropping phase, is apparently suppressed. As a result, NO3− effluent from soil layers was remarkably low, even in the initial stage of fallow. The functions of the fallow phase listed above can be considered essential to the maintenance of this forest-fallow system. Agricultural production can therefore be maintained with a relatively short fallow period of around 10 years. Traditional shifting cultivation in the study village can be seen to be well adapted to the respective soil-ecological conditions. Socioeconomic conditions are, however, drastically changing, making it difficult to sustain this system. Under such conditions, we should search for alternative technical tools that could maintain the SOM level, suppress the nitrifying activity of soil microbes and avoid depletion of bases while mitigating soil acidification. This is imperative if the subsistence agriculture seen in this village is intended to continue in the near future.

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4.9  F ACTORS CONTROLLING SOIL ORGANIC MATTER DECOMPOSITION IN SMALL HOME GARDENS IN DIFFERENT REGIONS OF INDONESIA In the Republic of Indonesia, an intensive land management system of small home gardens, or pekarangan, has been developed mainly on Java Island. This land management system is a kind of agroforestry system that allocates tree vegetations and annual crops in a small space and it has both economic and environmental significance for small-scale farmers [Wiersum 1982; Jensen 1993; Hayashi and Ochiai 2004]. On Java Island, land resources are very intensively managed under high population pressure, whereas on surrounding islands such as Kalimantan, degradation of forest and land resources has been accelerated and has become a serious environmental problem. Such degradation is the result of extensive timber cutting and/or government policies for transmigration that aim to distribute the population throughout the entire nation [Sunderlin and Resosudarmo 1996]. Decreases in SOM content are often observed in the degradation processes that occur with conversion of land use from forest to agricultural land (Section 4.5). We consider that SOM dynam­­ ics established in pekarangan, which has a long history on the native islands of many transmigrants, would provide important insights into SOM management strategy in different environments. Even though the practice of pekarangan has been naturally limited to a small farm scale, such information could be useful for tackling the problem of widespread land degradation. To obtain basic information for land management in terms of SOM dynamics under the pekarangan system, the main objectives of the present study were to 1) make a quantitative analysis of field soil respiration, which is derived from SOM decomposition and plant-root respiration; 2) analyze factors that control soil respiration under different climatic conditions in the tropics, i.e., rainforest and savanna climates; and 3) compare different land-use patterns that include different densities of tree species under the pekarangan system.

4.9.1  Description of Study Sites For the intensive study of soil respiration, we installed nine experimental plots at farmers’ home gardens in four regions of Indonesia that differed in terms of climate and geology. The regions were named as follows: BGF and BGC in Bogor, West Java; LBF and LBC in Lembang, in the highlands of West Java; PCF and PCU in Pacet, East Java; and SMF, SMFC, and SMC in Samarinda, East Kalimantan (Figure 4.16). General climatic and geological conditions of the study plots are given in Table 4.18. In each region, two farmers’ home gardens that contained predominately tree species or annual crops were selected for the present study, and are hereafter termed as –F and –C, respectively, except for the plots at Samarinda. For Samarinda, we selected three plots (SMF, SMFC, and SMC), with a decreasing domination of tree species in this order. The home gardens were generally as small as around several hundred square meters. In the F plots, predominated trees reached >10 m in height and the soil surface was usually covered by the tree canopy and occasionally received substantial amounts of litter-fall, whereas, in the C plots, repeated disturbance and minimal residue input with weeding may accelerate SOM depletion. Soil properties

Region Bogor, West Java

Köeppen’s Classification Af

Lembang, West Java

Am/Csb

Pacet, East Java

Aw

Samarinda, East Kalimantan

Af

a b c

Location

Elevation (m)

Mean Annual Temperature (°C)a

S06°36′, E106°52′ S06°48′, E107°36′

250

25.3

3671

Andesite

1250

19.3

1394

350

24.8

2139

Volcanic ejecta, intermediate to mafic Andesite

1000 m

BGF

BGC

Scores of factor 3 (Precipitation factor)

2 1 0

–3

–2

–1

LBF

SMF

1 SMFC

0

SMC

–1

LBC

PCC

–2

2

PCF

Scores of factor 1 (Temperature and decreasing SOM factor) Sedimentary rocks and alluvial soils Limestone Volcanic rocks and sediments Tephra

Scores of factor 4 (Desiccation and high pH factor)

(b)

2

–3

PCC

1

LBC

–2

0

–1 LBF

–1

PCF

0 BGF

1SMC

BGC

SMF

2

SMFC

–2

Scores of factor 1 (Temperature and decreasing SOM factor)

(c)

Scores of factor 3 (Desiccation and high pH factor)

3

West Java Central Java East Java East Kalimantan Java, > 1000 m

3 2 PCC LBC PCF

–2

1 0

BGC SMC

0

LBF

–1

BGF

SMFC

2

4

SMF

–2

Scores of factor 2 (Vegetation and mineralizable factor)

FIGURE 4.38  Scattergrams of the factor scores determined for each study plot with different regions or parent materials.

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The second component showed positive coefficients for land-use patterns and C0, suggesting that under forest-like land use, the amount of mineralizable C increases due to the high addition of root and/or leaf litter to soils, as discussed in Section 4.5. Hence, this component can be referred to as the vegetation and mineralizable C factor. Since our extensive survey samples include a variety of plots in terms of land use, no regional trend is found for the distribution of this factor (Figure 4.38c). The third component showed a high coefficient for AP and was considered to be a precipitation factor. As shown in Figure 4.38a, the plots in West Java exhibit higher scores for this factor, followed by those in East Kalimantan and East/Central Java. The fourth component had a close relationship with the CV value for monthly precipitation, clay content, and pH of soils. A higher value of CV for precipitation indicates the presence of a distinct dry season, which often causes higher pH values in soils under monsoon climates compared to those under rainforest climates (Sections 4.4 and 4.6). Thus, the fourth component can be referred to as the desiccation and high pH factor. The scores are generally high among the plots derived from volcanic rock and sediment (mostly andesite) and from limestone, indicating that this factor involves the influence of parent materials of soils. Most of the variables were closely related to only one component with high coefficients above 0.6, with the exception of wetness, which was affected by both factors relating to temperature and precipitation. In the next step, stepwise multiple regression analysis (p < 0.25) was conducted to examine the contribution of each factor of annual soil respiration or Cem under the fixed condition (T = 298 K and θ = 0.3 L L−1), which was determined by field measurement or laboratory incubation data. The parameters obtained are given in Table 4.24 and are summarized as follows. Annual soil respiration based on field measurement is primarily determined by the temperature and decreasing SOM factor, indicating that—along with temperature increases—C turnover through both plant and soil microbial activities seems to be accelerated. Although the negative contribution of the precipitation factor is small, its ecological reason is not clear. In contrast, annual soil respiration determined by the laboratory incubation data positively influenced the vegetation and mineralizable C and desiccation and high pH factors, indicating that soil respiration with a microbial origin is mainly determined by the pool size of mineralizable C with positive influences from favorable pH soil conditions. On the other hand, Cem (at T = 298 K and θ = 0.3 L L−1) determined by the field conditions is poorly explained by the factors obtained (r 2 = 0.24, p < 0.25), while that determined from laboratory incubation data is mainly related to the vegetation and mineralizable C and temperature and decreasing SOM (negative) factors. Thus, as the SOM pool size increases, Cem at the fixed condition also increases.

4.9.5  S OM Dynamics in Soils Situated in Different Climatic and/or Geological Conditions Using the equations given in Table 4.24 and factor scores of each soil sample from the extensive survey, annual soil respiration determined by field measurements and laboratory incubation data are plotted against the total SOM stock (Mg C ha−1) in the

148

TABLE 4.24 Factors Affecting Soil Respiration Parameters Coefficients and Probability for Each Factor

Parameters

Constant

Factor 1

Factor 2

Factor 3

Factor 4

Temperature and Decreasing SOM Factor

Vegetation and Mineralizable C Factor

Precipitation Factor

Desiccation and High pH Factor

r2

For field measurement 21.482

***

6.265

***





−0.728

*





0.86

***

59.248

***





9.087

*

−5.560

*





0.24

*

Annual soil respiration from surface 5-cm layers of soil (Mg C ha−1) Cem at T = 298 K and θ = 0.3 L L−1 (kg C ha−1 d−1)

5.778

***





5.872

***





1.263

*

0.81

***

10.943

***

−1.872

*

7.163

***









0.62

**

Note: Significant at *25%, **5%, and ***1% levels, respectively.

World Soil Resources and Food Security

Annual soil respiration (Mg C ha−1) Cem at T = 298 K and θ = 0.3 L L−1 (kg C ha−1 d−1) For incubation experiment

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surface 5-cm soil layers (Figure 4.39). The amount of SOM stock is calculated based on the total C content (g kg−1) and bulk density (g cm−3) estimated using the following equation, which was obtained for the surface soils in the monitoring plots:

Bulk density = 1.82 – 0.23 ln (total C content)



(r 2 = 0.59, p < 0.05).

(4.24)

Within each of the regions, despite differences in vegetation and SOM stocks in surface soil layers, annual soil respiration determined by field measurement is essentially identical (Figure 4.39a). Whole soil respiration is strongly controlled by climatic factors. As a result, annual soil respiration estimated for the plots shows a clear regional trend; that is, it is highest in East Kalimantan, followed by the low-elevation areas of Java, and then the high elevation areas of Java.

Annual soil respiration rate determined by field experiment (Mg C ha–1)

(a)

30

West Java PCC

25

SMF SMC

20

Central Java

SMF

PCF

East Java East Kalimantan Java, > 1000 m

BGF

BGC

15

LBF

10

LBC

5 0

0

50

100

150

Total C stock in surface 5-cm layers of soil (Mg C ha–1)

Annual soil respiration estimated by incubation experiment (Mg C ha–1)

(b)

Upland crops Upland crops with trees Trees with upland crops Forest 'T' indicates tephra origin

30 25

T

20

y = 0.72x – 3.73 r2 = 0.43 (Excluding 'T'plots)

15 10

SMFC

5

PC SM

0 –5

0

BGF BGC LBF, T PCP

10

T

20

T

SM T T LBF, T

T T

30

40

50

Total C stock in surface 5-cm layers of soil (Mg C ha–1)

FIGURE 4.39  Scattergrams between total C stock in surface 5-cm layers of soils and the amount of annual soil respiration estimated based on field (a) or laboratory incubation (b) experiments.

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On the other hand, annual soil respiration with microbial origin is higher under more forest-like land use (Figure 4.39b). However, plots under such forest-like use are also expected to receive more litter input and actually tend to accumulate higher amounts of SOM in the upper soil layers. It is notable that the soils derived from tephra accumulate exceptionally high amounts of SOM while suppressing SOM decomposition, indicating that these soils are very advantageous for SOM management by farmers.

4.9.6  Possible Land Management Systems in Different Regions of Java and East K alimantan As described above, annual soil respiration, including both plant root and microbial respiration, is highest in the East Kalimantan plots (Figure 4.39a), indicating that primary production is rapidly consumed through high biological activity and would not be readily accumulated as SOM in tropical rainforest climates. In Figure 4.39b, a relationship can be seen between SOM stock (SOM0–5) and annual soil respiration with microbial origin (ASR MB0–5) in surface 5-cm layers of soils, i.e., ASR MB0–5 = 0.72 SOM0–5 − 3.73, when excluding soils derived from volcanic tephra. This finding indicates that a high proportion of SOM stock, equivalent to 70%, can be decomposed within one year. Even though an additional litter supply may compensate for the loss of SOM, SOM stock can be easily exhausted after the conversion of land use from a more forest-like system to upland-cropping systems. A continuous input of organic materials into soils is therefore indispensable for maintaining the SOM level over a long period of time. Hayashi [2002] observed a gradual increase of soil fertility, including SOM levels, in home gardens of transmigrants, in which tree vegetation predominated, since the Imperata field had been cleared in South Kalimantan. High input of organic or inorganic resources may, however, have been easier through the use of external resources, such as domestic waste or crop residues from surrounding cropping fields, under traditional management of pekarangan in Java or Bali Island with high population density. Additionally, it is also notable that tephraderived soils, which are considered to be more resistant to rapid decreases in SOM because of their high potential for retaining SOM, are commonly found on these islands. Jensen [1993] counted the existence of a “medium fertile soil with large nutrient reserve” as one of the important requirements for sustainability of the home gardens in Java. Considering the lack of these advantages found in Java and Bali, additional efforts may be required to maintain the SOM level in home gardens in Kalimantan. Thus, land management—including a more forest-like system, which usually supplies higher amounts of litter-fall—is a more viable option in Kalimantan under present circumstances. It may also be applicable to land-use strategy on an entire regional scale when considering the high decomposition rate relative to the stock of SOM under nonvolcanic soils in East Kalimantan.

4.10  GENERAL DISCUSSION AND CONCLUSION As discussed so far, intensive land management, historically, has been available only in limited areas of upland soils in humid Asia, especially in tropical regions.

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There were several constraints that limited the development of intensive agriculture. From the aspects of soil management, rapid decomposition of SOM, high acidity, and probably high erodability should be counted as main difficulties. In the present work, the former two points are discussed in relation to ecosystem- and pedogeneticprocesses. Soils developed from volcanic tephra, mafic volcanic materials, or their secondary sediments are apparently advantageous to both SOM and soil acidity management. Although the volcanic materials mixed with 2:1 minerals often exhibited strong acidic natures, as typically observed in “nonallophonic” Andisols in Japan [Shoji et al. 1985], the distribution of such soils were virtually limited in the regions studied (i.e., Java and Sumatra Islands). As clearly described in Sections 4.5 and 4.9, SOM-related soil fertility is higher in the soils from volcanic materials than in others. Intensive agricultural management is, therefore, typically observed in this volcanic area. Besides Andisols and Cambisols, soils distributed in the tropics could generally be categorized into four groups with regard to land management and pedogenesis; Alisols with extremely high acidity (i.e., high exchangeable Al with high CEC), Acrisols with relatively lower contents of exchangeable Al, Arenosols (sandy soils), and Ferralsol-related soils (Ferralsols, Plinthosols, and probably Nitisols). Since the distribution of the Ferralsols group is limited in humid Asia, the remaining three compose the majority of soils in this region. The sandy soils may exhibit unique properties in terms of water and nutrient retention in soils, as well as drastic fluctuations of soil environments such as temperature and moisture, and hence, must be investigated separately in more detail. The main discussion in the present work is focused on the differences between Acrisols and Alisols. As discussed in Sections 4.2 through 4.4, from a pedological viewpoint, distribution patterns of Acrisols and Alisols in humid Asia can be related to the difference in the vermiculitization process of dioctahedral mica, which is explained mainly by climatic factors. That is, dioctahedral mica inherent to sedimentary rocks or felsic igneous rocks is thought to weather to form expandable 2:1 minerals (vermiculitization) under the lower pH conditions associated with the udic or perudic soil moisture regime, whereas, in contrast, mica is relatively stable under the higher pH conditions associated with the ustic soil moisture regime and other primary minerals such as feldspars dissolve to form kaolin minerals and gibbsite. The distribution pattern of clay mineralogy and pedogenetic-processes summarized above are considered to have restricted possible options in agricultural activities by human beings under low-input conditions. There are several differences between shifting cultivation systems on Alisols under rainforest climates and those on Acrisols under monsoon climates. In East Kalimantan of Indonesia, farmers traditionally planted upland rice for one year only after clearing and burning the forest cover, and then left the land for more than 20 years as fallow. Only on relatively fertile soils near rivers was cultivation of crops with a shorter fallow of around eight years practiced. Extremely high acidity and resulting N mineralization characteristics, as well as low P supplying potential and high decomposition rates of SOM (Sections 4.6 and 4.9), had forced farmers to keep relatively long periods of fallow with heavy dependence on forest ecosystems for fertility recovery of soils. Under this condition, even transformation of land use to a pekarangan system did not seem

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to be easy. Plantation enterprises for tree crops are progressively widespread today in this region, at the expense of forest reclamation. In contrast, in the shifting cultivation system on Acrisols under the monsoon climate in northern Thailand, the positive function of the fallow phase was more clearly observed, even after relatively short periods of fallow. Karen people in our study village usually planted upland rice only for 1 year, followed by approximately 7 years of fallow. In the late stage of fallow, some soil properties relating to soil acidity improved at the same time as the SOM-related properties increased. This simulta­ neous increase in SOM and bases in the surface soil, through forest-litter deposition in the late stage of fallow, had an increasing effect on nutritional elements. The decline in soil organic C during the cropping phase could be compensated by litter input during the 6–7 years of fallow. Moreover, the succession of the soil microbial community from rapid consumers of resources to stable and slow utilizers, along with establishment of secondary forest, retards further N loss through leaching and enhances N accumulation into the forest-like ecosystems. These functions of the fallow phase can be considered essential to the maintenance of the forest-fallow system under monsoon climates. Agricultural production can therefore be maintained with a relatively short fallow period of around 10 years. The shifting cultivation system under monsoon climates was demonstrated to be dependent upon relatively fertile soils with consciousness of possible risk for soil erosion on the generally steep slopes in the region. The development of infrastructure presently stimulates the agriculture activities in this region to move in more commercially oriented directions. Although it is difficult to find shifting cultivation activities in temperate zones, a set of data obtained in Japan indicated that neither C0 nor N0 fluctuate appreciably during the cropping and fallow phases, presumably because the northern temperate climate produces lower amounts of forest litter during the fallow period. Such a mild climate also prevented SOM from actively decomposing, allowing continuous cultivation for several years, which were recorded to be rather usual in Japan. Commercial cropping with intensive land managements is dominant today in upland areas of Japan, which is largely affected by volcanic ejecta. Traditional shifting cultivation systems in each of the regions can be seen to be well adapted to the respective soil-ecological conditions. Socioeconomic conditions are, however, drastically changing, making it difficult to sustain this system. Under such conditions, we should search for alternative technical tools that could maintain the SOM level, suppress the nitrifying activity of soil microbes and avoid depletion of bases whilst mitigating soil acidification. This is imperative if the subsistence agriculture seen in these regions is intended to continue in the near future.

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APPENDIX—ANALYTICAL METHODS 4.A1  C  OMPOSITION OF SOIL WATER EXTRACT FOR THERMODYNAMIC ANALYSIS Soil water extracts were collected by continuously shaking the soils for 1 week at 25°C and at 1 atm with a soil (40 g) to water ratio of 1:2, followed by filtering through a 0.025-μm pore membrane filter (Millipore). For these samples, pH and F concentration were determined with glass electrodes. Na+, K+, Cl−, NO3− , and SO 2− 4 concentrations were determined via high-performance liquid chromatography (Shimadzu, Ion chromatograph HIC-6A equipped with a conductivity detector CDD-6A and shim-pack IC-C3 for cations and shim-pack IC-A1 for anions). The concentrations of Si, Al, Ca, Mg, Fe, and Mn were determined using ICP–AES. To eliminate Al complexed with organic matter, the extracts were passed through a column filled with a partially neutralized (pH 4.2) cation exchange resin [Amberlite IR-120B(H); Hodges 1987]. The amount of Al not adsorbed by the resin was determined by ICP–AES and assigned to Al complexed with organic matter, as opposed to the fraction retained that was assigned to inorganic monomeric Al. Ionic activities were calculated with the extended Debye–Hükel equation, using a successive approximation procedure [Adams 1971]. Inorganic monomeric Al was distributed among Al3+, Al(OH)2+, Al(OH)+2 , Al(OH)30 , Al(OH)−4 , AlF2+ and Al(SO4)+ according to the equilibrium constants of Lindsay [1979]. Clay minerals were assumed to be at or very near to equilibrium with the soil water extracts. The stability of minerals was evaluated using stability diagrams and solubility diagrams; these diagrams represent the relative stability and solubility of minerals, as outlined by van Breemen and Brinkman [1976] and Karathanasis [2002], respectively. Thermodynamic mineral data used in these diagrams are from

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Karathanasis [2002] for gibbsite, kaolinite, quartz, and amorphous silica, and from Lindsay [1979] for amorphous Al hydroxide, muscovite, and microcline.

4.A2  Q  UANTIFICATION OF THE FRAYED EDGE SITE USING RADIOCESIUM INTERCEPTION POTENTIAL (RIP) METHODOLOGY Quantification of the frayed edge site using a simple chemical adsorption-desorption experiment, such as determining the CEC, has proven difficult because the frayed edge site is not accessible to hydrated cations, but can irreversibly adsorb monovalent cations with low hydration energy (e.g., Cs+, Rb+, K+, and NH +4 ) with the formation of an inner-sphere complex. Although the irreversible adsorption sites can be quantified using a K fixation method [Alexiades and Jackson 1965; Coffman and Fanning 1974] or a Rb fixation method [Ross et al. 1989], these methods clearly overestimate the amount of the frayed edge site because K and Rb saturation and drying in these methods induces the irreversible collapse of the expanded layers and, therefore, fixation mostly occurs in expanded layers with a vermiculitic nature [Komarneni and Roy 1980]. However, Cremers et al. [1988] obtained a quantitative index of the frayed edge site from the solid/liquid distribution coefficient of Cs and the concentration of K in solution, which was described as:

FES −1 K Cs D ·mK = K c(Cs− K) ·[FES] = RIP (mol kg )

(4.A1)

−1 where K Cs D is the solid/liquid distribution coefficient of Cs (L kg ), mK is the K conFES −1 centration in solution (mol L ), K c(Cs−K) is the selectivity coefficient of Cs against K in the frayed edge site, and [FES] is the amount of the frayed edge site in soil (mol kg−1). Cs As K FES c(Cs− K) is a constant, then K D ·mK is regarded as proportional to the amount of the frayed edge site. Two experimental conditions are necessary to make Equation 4.A1 valid. First, exchangeable sites must be masked from Cs and K with silver thiourea so that Cs and K adsorption occurs only on the frayed edge site. Based on this assumption, the Cs–K exchange reaction on the frayed edge site can be expressed as:



[K FES] + mCs = [CsFES] + mK

(4.A2)

where [K FES] (or [CsFES]) is the amount of K (or Cs) adsorbed on the frayed edge site (mol kg−1), and mK (or mCs) is the concentration of K (or Cs) in solution (mol L –1). As this reaction is homovalent exchange, the selectivity coefficient (i.e., K FES c(Cs− K)) can be expressed as:



K FES c(Cs− K) = {[Cs FES ]·mK}/{[K FES ]·mCs}.

(4.A3)

Second, the amount of Cs adsorbed on the frayed edge site (i.e., [CsFES]) must be minimized using carrier-free 137Cs so that it can be assumed that the amount of K adsorbed on the frayed edge site (i.e., [K FES]) is identical to the amount of the frayed

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edge site (i.e., [FES] in Equation 4.A1). Based on this assumption, Equation 4.A3 becomes Cs K FES c(Cs− K) (Cs → 0) = K D ·mK/[FES].



(4.A4)

Thus, Equation 4.A1 is accomplished by transposing the denominator on the righthand side to the left-hand side in Equation 4.A4. The method of Cremers et al. [1988] was followed by Wauters et al. [1996], who found that silver thiourea, which was used to mask the exchangeable sites, could be successfully replaced with a specific Ca/K ratio in solution (i.e., 0.1 mol L−1 CaCl2 + 0.5 mmol L−1 KCl) to make the method more available. The K Cs D ·mK value from the method of Wauters et al. [1996], namely the radiocesium interception potential (RIP), is now widely accepted by many researchers in Europe as a quantitative index of the frayed edge site to fix 137Cs in soils [e.g., Delvaux et al. 2000; Popov 2006; Schimmack and Auerswald 2004; Smolders et al. 1997; Wageneers et al. 1999]. As the RIP does not result in an overestimation of the frayed edge site because of the collapse of the expanded layers [de Koning et al. 2007], it can be used in the prediction of 137Cs dynamics in soils, such as 137Cs transfer from soil to plant at a realistic contamination level [Delvaux et al. 2000]. For the RIP determination, 0.1 g of soil clay was weighed into triplicate dialysis bags (cellulose dialysis membrane, Wako Chemicals USA, Inc.) containing 5 mL of 0.1 mol L−1 CaCl2 and 0.5 mmol L−1 KCl solution. The bags were transferred to 250 mL plastic bottles holding 200 mL of 0.1 mol L−1 CaCl2 and 0.5 m mol L –1 KCl solution, and then shaken for 2 hours twice a day for 5 days. We replenished the outer solution each time before shaking to maintain equilibration. Afterward, each dialysis bag was transferred to a 50 mL plastic bottle with 25 mL of Ca–K–137Cs solution (i.e., 0.1 mol L−1 CaCl2 and 0.5 m mol L−1 KCl solution spiked with 10 kBq of carrier-­free 137Cs). After continuously shaking the bottles for 5 days, 137Cs activity (Bq mL−1) in the outer solution was measured with a liquid scintillation counter (Aloka, LSC6100, Radioisotope Research Center of Kyoto University), and the distribution coef137Cs in the solution. The ficient of Cs (K Cs D ) calculated from the depletion in the concentration of K in the solution (mK) was assumed to be 0.5 mmol L−1.

4.A3  T  HEORETICAL CALCULATION OF WATER FLUXES, SOIL ACIDIFICATION RATES, AND NET PROTON GENERATION 4.A3.1  Water Fluxes Water fluxes of soil solution percolated from the bottom of the O and B horizons were estimated by applying Darcy’s law to the unsaturated hydraulic conductivity and the gradient of the hydraulic heads in surface soil (0–5-cm depth) and subsoil (40–45-cm depth). The one dimensional, vertical flow equation (Richard’s equation) in the unsaturated soil zone was written as follows: C ( h)

δh δ δh = K (h) +1 δt δ z δz

− S ( h)

(4.A5)

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where C (m−1) is the differential water capacity, t (day) is the time, Z (m) is the height, h (m) is the soil water pressure head, K (m day−1) is the unsaturated hydraulic conductivity and S (day−1) is the sink term accounting for water uptake by vegetation and lateral water flow. The unsaturated hydraulic conductivity characteristic described by Mualem-van Genuchten functions [Mualem and Dagan 1978; van Genuchten 1980] was written as follows:

K = K S × Se0.5 × 1 − 1 − S

1 m e

2

m



(4.A6)



Se =

(θ − θ ) = (θ − θ ) r

s

1 + (−α h)n

−m



(4.A7)

r

where Se is the effective saturation. Ks (m day−1) is the saturated hydraulic conductivity. θs (L L−1) and θr (L L−1) are the saturated and residual water contents, respectively. n is a fitting parameter and m = 1 − 1/n. Ks and water retention curves were experimentally obtained for undisturbed soils sampled by 0.1 L cores according to the methods of Klute [1986] and Klute and Dirksen [1986], respectively. Based on the water retention curve, pressure heads were calculated from volumetric soil water contents monitored every 30 minutes with TDR sensors (CS615, Campbell Scientific) and data loggers (CR 10X, Campbell Scientific). Parameters were optimized based on the water retention curve (θ − h) by Sigmaplot 8.0 [SPSS Inc. 2002]. Parameters were adjusted and validated to balance the water budget during the winter season. Daily water fluxes were calculated using the parameters of Mualemvan Genuchten functions to describe the physical properties of the soils and volumetric soil water contents monitored during the experimental periods. Fluxes of water percolated from the bottom of the A horizon were estimated by the following equation:



(

)

qA = qO − qO − qB ×

rA rA + rB

(4.A8)

where qO, qA, and qB (mm d−1) are the daily fluxes of water percolated from the bottom of the O, A, and B horizons, respectively. rA and r B (Mg ha−1) are fine root biomass in the A and B horizons, respectively.

4.A3.2  Soil Acidification Rate and Net Proton Generation The soil profile was divided into three compartments corresponding to the natural soil stratification. Soil acidification rate and net proton generation (NPG) resulting

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from cation excess uptake by vegetation, nitrification, dissociation of organic acids, dissociation of carbonic acid, and net proton influx from the overlying horizon were evaluated in each soil horizon compartment. Soil acidification was defined as the decrease of acid neutralizing capacity (ANC) of the solid phase of soil [van Breemen et al. 1984]. ANC(ref pH = 3) (kmolc ha−1) was defined as the sum of basic cation equivalence minus the sum of strongly acidic anion equivalence at a reference soil pH of 3, written as follows:

ANC(ref pH=3) = 2(Na2O) + 2(K2O) + 2(CaO) + 2(MgO) + 2(FeO) + 6(Al2O3) – 2(SO3) – 2(P2O5) – (HCl)

(4.A9)

where parentheses denote molar concentration. The soil acidification rate was calculated as the change in ANC over a given period, i.e., ΔANC (kmolc ha−1 yr−1), which was induced by net proton generation. NPG can be divided into several sources. NPGBio was calculated from cation excess uptake by vegetation, assuming that vegetation uptake was equal to the sum of wood increment and litterfall. NPG resulting from cation excess uptake by vegetation in each soil horizon compartment was allocated to the three soil horizon compartments based on the distribution of fine root biomass in each soil horizon [Shibata et al. 1998]. Soil acidification rate and NPG resulting from nitrification, dissociation of organic acids, dissociation of carbonic acid, and net proton influx from the overlying horizon were calculated from the input–output budget of ions, i.e., the difference between ion fluxes percolating from the overlying horizon and ion fluxes leaching out of the soil horizon compartment [Bredemeier et al. 1990]. Ion fluxes were calculated from ionic concentrations multiplied by the water fluxes during each period. Using ion fluxes, soil acidification rate (ΔANC) was calculated to be:

ΔANC = {(Anion)out – (Anion)in} – {(Cation)out – (Cation)in} – {(Cation) bio – (Anion) bio}

(4.A10)

where (Cation) and (Anion) represent the equivalent sum of cations and anions, respectively, which are responsible for ANC. The ionic species counted in the present + + 2+ 2+ 3+ n+ study were Cl–, H 2PO −4 , and SO 2− 4 for anions and Na , K , Mg , Ca , Fe , and Al for cations. The notations in parentheses represent the following: the suffix “in” represents ion fluxes percolating into the soil compartment by throughfall and soil solution from the overlying horizon (or throughfall for the O horizon); the suffix “out” represents ion fluxes leaching out of the soil compartment by soil solution; and the suffix “bio” represents ion fluxes caused by vegetation uptake (kmolc ha−1 yr−1). Net proton generation by cation excess uptake by vegetation (NPGBio) is

NPGBio = (Cation)bio – (Anion)bio.

(4.A11)

Net proton generation by transformation of nitrogen (NPGNtr) is

{

} {

}

NPG Ntr = ( NH +4 )in − ( NH +4 )out + (NO3− )out − ( NO3− )in .

(4.A12)

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Net proton generation by the dissociation of organic acids (NPGOrg) is

NPGOrg = (Orgn–)out – (Orgn–)in

(4.A13)

where Orgn– represents the negative charge of organic acids. Net proton generation by carbonic acid dissociation (NPGCar) is

NPG Car = (HCO3− )out − (HCO3− )in .

(4.A14)

Net proton influx from the overlying horizon is

{(H+)in − (H+)out}.

(4.A15)

The sum of the acid load is neutralized by the decrease of ANC stoichiometrically. Theoretically, ΔANC, NPGBio, NPGNtr, NPGOrg, NPGCar, and net proton influx from the overlying horizon have the following relationship:

ΔANC = NPGBio + NPGNtr + NPGOrg + NPGCar + {(H+)in − (H+)out}. (4.A16)

4.A4  D  ETERMINATION OF POTENTIALLY MINERALIZABLE CARBON (PMC) AND NITROGEN (PMN) Fresh soil samples were passed through a 2-mm mesh sieve and their moisture contents adjusted to 60% of field moisture capacity. Mineralized C and N were determined in duplicate more than six times over a period of 133 days, using aerobic incubation at a constant temperature of 30°C. The C and N mineralization patterns were fitted to equations by the least squares method using SigmaPlot 8.0 [SPSS Inc. 2002]. As model functions, first-order kinetic models with single (Fi model) or double (Fi + Fi model) sets of parameters were first prepared for mineralized C:

Ct = C0{1 – exp(–kCt)}

(4.A17)



Ct = αC0{1 – exp(–kC1t)}+ (1 – α)C0{1 – exp(–kC2t)}

(4.A18)

where Ct (mg kg−1) is the amount of mineralized C at time t (d), C0 (mg kg–1) is the pool of readily mineralizable C (mg kg−1) (i.e., PMC), and kC, kC1, and kC2 are rate constants (d−1; kC1 > kC2). In some cases, the time course of C mineralization was simulated well by the Gompertz equation as follows:

Ct = C0exp[−exp{−kC(t − t0)}]

(4.A19)

where t0 (d) is the time when C equals 1/e of C0. If kC for the Gompertz or first-order model or kC2 for the double first-order model was less than 0.003 d−1, the calculated PMC was not used for the following analysis because k < 0.003 means that less than one-third of the PMC is mineralized during the 133-day incubation experiment and

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the value of PMC is calculated with an extreme extrapolation, indicating less reliability of the fitting calculation. For simulating N mineralization patterns, in addition to Fi and Gompertz models (Equations 4.A20 and 4.21), a logistic model (Lo model; Equation 4.A22) was also prepared:

Nt = N0(1 − exp(−k Nt))

(4.A20)



Nt = N0exp[–exp{–k N(t – t0)}]

(4.A21)



Nt = Nmax/{1 + (Nmax/Nint – 1)exp(–k Nt)}

(4.A22)

where Nt (mg N kg−1) is the cumulative N released in time t (d), Nt (mg N kg−1) (i.e., PMN) is potentially mineralizable N, Nmax is the calculated maximum amount of mineral N, Nint is the calculated initial amount of mineral N, k N (d–1) is the rate constant, and t0 (d) is the calculated time when N equals 1/e of N0. The third equation and PMN in this equation is calculated as the difference between Nmax and Nint.

Acidification 5 Pedogenetic in Upland Soils under Different Bioclimatic Conditions in Humid Asia S. Funakawa, T. Watanabe, A. Nakao, K. Fujii, and T. Kosaki CONTENTS 5.1 Introduction................................................................................................... 171 5.2 Pedogenetic Soil Acidification by Different Proton Sources in Forested Ecosystems in Japan...................................................................................... 172 5.2.1 Experimental Plots............................................................................ 172 5.2.2 Soil Solution Composition................................................................. 176 5.2.3 NPGOrg, NPGNtr, NPGBio, and ΔANC................................................ 178 5.2.4 Pedogenetic Implication of the Proton Budget.................................. 183 5.3 Fates of Soil Acidity and Dissolved Organic Matter during Pedogenetic Soil Acidification of Forest Soils in Japan..................................................... 185 5.3.1 Study Soils......................................................................................... 186 5.3.2 Physicochemical Properties and Titratable Alkalinity and Acidity of the Soils............................................................................ 186 5.3.3 Soil Solution Composition................................................................. 196 5.3.4 Eluvi-Illuviation of Inorganic Al and Subsequent Adsorption of DOC................................................................................................... 196 5.3.5 Comigration of Al and DOC in the Soil Profiles..............................200 5.3.6 Dynamics of Titratable Alkalinity and Acidity in the Pedogenetic Processes....................................................................... 201 5.4 Changes in the Blockage Effect of Hydroxy-Al Polymers on the Frayed Edge Site of Illitic Minerals during the Process of Pedogenetic Acidification in Japan.................................................................................... 203 5.4.1 Soils Studied...................................................................................... 203 5.4.2 Soil Types and Clay Mineralogy....................................................... 203 5.4.3 RIP Variation and the Sequential Transformation of HIV to Vermiculite........................................................................................205 5.4.4 Blockage Effect of Hydroxy-Al Polymers on the Frayed Edge Site.....................................................................................................207 169

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5.5 Charge Characteristics of Forest Soils Derived from Sedimentary Rocks in Japan in Relation to Pedogenetic Acidification Processes.............209 5.5.1 Soil Samples......................................................................................209 5.5.2 Analytical Methods........................................................................... 212 5.5.3 General Charge Characteristics of the Soils...................................... 213 5.5.4 Contribution of Each Component to the Charge Characteristics of the Soils—Statistical Analysis...................................................... 215 5.5.5 Contribution of Each Component to the Charge Characteristics of the Soils—Analysis by Successive Removal of Soil Components.......219 5.5.6 Changes in the Charge Characteristics of the Soils through Pedogenetic Acidification.................................................................. 220 5.5.7 Conclusions of Sections 5.2 to 5.5..................................................... 221 5.6 Fluxes of DOC under Tropical Forests under Different Geological Conditions in East Kalimantan, Indonesia.................................................... 223 5.6.1 Experimental Plots and Experimental Design.................................. 223 5.6.2 Physicochemical Properties of Soils and C Stock in Soils and Ecosystems........................................................................................224 5.6.3 Organic Matter Decomposition......................................................... 232 5.6.4 Concentrations and Fluxes of DOC in Throughfall and Soil Solution.............................................................................................. 232 5.6.5 Influence of Parent Rocks on DOC Dynamics.................................. 236 5.7 Contribution of Different Proton Sources to Soil Acidification under Tropical Forests under Different Geological Conditions in East Kalimantan, Indonesia................................................................................... 236 5.7.1 Chemical Properties of the Soils Studied.......................................... 237 5.7.2 Soil Solution Composition................................................................. 237 5.7.3 Fluxes of Ions in Solute Leaching and Vegetation Uptake and Proton Budgets in Soils..................................................................... 238 5.7.4 Dominant Soil Acidification Processes in Tropical Forests.............. 242 5.7.5 Proton Generation and Consumption in Soil Profiles....................... 242 5.7.6 Acid Neutralization in Soils.............................................................. 243 5.7.7 Implication of Proton Budgets for Pedogenetic Soil Acidification...................................................................................... 243 5.8 Relationship between Chemical and Mineralogical Properties and the Rapid Response to Acid Loads of Soils in Humid Asia................................ 245 5.8.1 Soils Studied......................................................................................246 5.8.2 Acid Titration Experiment and Source of Soil Alkalinity................248 5.8.3 Column Experiment for Eight Selected Soils.................................... 251 5.8.4 Interpretation of Acid-Neutralizing Reactions under Laboratory and Field Conditions.......................................................................... 255 5.9 General Discussion on the Pedogenetic Acidification Process...................... 256 5.9.1 Organic Matter Dynamics................................................................. 257 5.9.2 Soil Acidification Rate in the Entire Soil Profile.............................. 257 5.9.3 Factors Controlling Proton Generation and Consumption in Relation to Organic Matter Cycles....................................................260 5.9.4 Pedogenetic Implication for Proton Budget in Soil........................... 261

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5.9.5 Fates of Soil Acidity and Dissolved Organic Matter during Pedogenetic Soil Acidification of Forest Soils.................................. 262 5.9.6 Response of Soil Minerals against Further Acid Load..................... 262 References............................................................................................................... 263

5.1  INTRODUCTION Soils in humid Asia exhibit relatively incipient mineralogical characteristics because of the dominant steep slopes, crust movement, and volcanic activity on young alpine fold belts [FAO 2001] compared with soils developed on stable plains associated with the Precambrian shield in eastern South America or equatorial Africa. Among these continents, geological components are also different; distribution of mafic (or basic) parent materials is limited and sedimentary rocks as well as igneous felsic rocks are major parent rocks in humid Asia [Geological Survey of Japan 2004], whereas some metamorphic or basic rocks are dominated in tropics of other continents [FAO/Unesco 1971, 1977]. Because of these essential differences in topographical and geological conditions, dominant upland soils in humid Asia are Ultisols, in contrast to the humid tropics in other continents where Oxisols are predominant [Soil Survey Staff 1999]. In Chapter 4, the distribution pattern of upland soils in humid Asia (i.e., Indonesia, Thailand, and Japan) was presented and the ecological processes under conventional upland farming and shifting cultivation in these regions were comparatively analyzed. In the present work, pedogenetic soil acidification processes under relatively natural undisturbed conditions in these regions are analyzed to clarify the ecosystem processes that would bring the difference in soil properties and human activities presented in Chapter 4. Soil acidification is one of the major pedogenetic processes in soils under climates where precipitation exceeds evapotranspiration. Both external and internal acid loads such as proton extrusion concomitant with excess uptake of cation over anion by vegetation, nitrification, dissociation of organic acids, and dissociation of carbonic acid are responsible for soil acidification under leaching conditions [van Breemen et al. 1983; Binkley and Richter 1987; Ulrich 1989]. Although many studies on soil acidification have tended to focus on the effects of acidic deposition or external acid loads, the importance of understanding pedogenetic soil acidification induced by internal acid loads is recognized [Krug and Frink 1983; Hallbäcken and Tamm 1986]. It is one of the major driving forces of mineral weathering and could also be regarded as an ecosystem process that favors acquiring mineral resources from soil and/or the lithosphere. The contribution of internal acid loads to pedogenetic soil acidification was reported to vary greatly from soil to soil. Acidity originating from the dissociation of organic acids and nitrification in surface soils was reported to be responsible for podzolization [Funakawa et al. 1992; Brahy et al. 2000a]. Organic acids, particularly low molecular weight organic acids, were found to contribute to congruent mineral dissolution and Al eluviation caused by complexation in podzolic soils [Lundström 1993; van Hees et al. 2000]. In contrast, acidity originating from the dissociation of carbonic acid was reported to be responsible for andosolization and brunification.

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Carbonic acid in ando soils contributed to incongruent dissolution of minerals derived from volcanic materials and formation of amorphous Al silicates [Ugolini et al. 1988; Ugolini and Sletten 1991], while in brown forest soil it contributed to incongruent mineral dissolution and resulted in accumulation of oxides [Ugolini et al. 1990]. In the following sections, pedogenetic acidification processes under different geological and/or climatic conditions are comparatively analyzed using the proton budget method. The fate of organic matter, which is one of the key processes to regulate soil acidification and mineral weathering, is also traced in detail in relation to the properties of soils as cumulative results of these processes. Finally these processes are interpreted from the viewpoint of an ecosystem process that enhances to acquire mineral resources from soil and/or the lithosphere into biosphere.

5.2  P  EDOGENETIC SOIL ACIDIFICATION BY DIFFERENT PROTON SOURCES IN FORESTED ECOSYSTEMS IN JAPAN Since pedogenetic soil acidification is controlled by the balance of proton generation and consumption (e.g., dissociation and decomposition of organic acids, nitrification, and nitrate uptake by vegetation) in the biogeochemical cycle of forested ecosystems, a quantitative analysis of both the proton-generating and -consuming processes is required to understand the respective pedogenetic processes in relation to soil acidification. Calculation of proton budget allows us to evaluate the contribution of different proton sources to soil acidification [van Breemen et al. 1984]. In particular, the contribution of external acid loads to soil acidification was successfully quantified in the whole soil compartment by application of the theory of proton budget [van Breemen et al. 1983; Bredemeier et al. 1990]. However, predominant protongenerating and -consuming processes that might be responsible for pedogenetic soil acidification must surely be different in each soil horizon; for example, nitric acid and organic acids produced in surface soil horizons may work as the temporary acids and be consumed in deeper soil horizons by nitrate uptake of vegetation and decomposition of organic acids. Therefore, it is necessary to evaluate the protongenerating and -­consuming processes in each soil horizon compartment in order to analyze the pedogenetic processes in a whole soil profile based on the proton budget. In this section, we attempt to apply the theories of proton budget and acidneutralizing capacity to the analysis of the acidifying processes in each soil horizon compartment, and then accordingly to analyze the dominant soil forming processes in three representative forest soils in Japan, i.e., ando soil, podzolic soil, and brown forest soil.

5.2.1  Experimental Plots Three plots were selected to include ando soil under a cold temperate forest, podzolic soil under a cold temperate forest, and brown forest soil under a warm temperate forest (Figure 5.1); these sites are representative of 91.7% of total forest area in Japan (i.e., 11.5% for ando soils, 3.6% for podzolic soils, and 76.5% for brown forest soils),

173

Pedogenetic Acidification in Humid Asia Kyoto soils, JP1–4

TG

130º E

140º E

NG

40º N

KT

JP5–10

30º N

Mie soils

FIGURE 5.1  Location of the experimental plots used in the analysis in Sections 5.2 to 5.5.

according to Classification of Forest Soil in Japan [Forest Soil Division 1976]. Site descriptions are given in Table 5.1. Detailed information was given in Shinjo et al. [2006]. At the Nagano plot (NG), the soil formed by the process of andosolization was derived from secondary sediments of volcanic products and was classified as Acrudoxic Melanudands [Soil Survey Staff 2006]. Compared to the other two plots,

TABLE 5.1 Site Description of Japanese Plots Coordinates Mean annual air temperature (°C) Mean annual precipitation (mm) Elevation (m) Soil typea Parent materials Vegetation Slope Position a b

NG

TG

KT

N35°57′, E138°28′ 6.9b

N35°37′, E135°10′ 10.7

N35°01′, E135°47′ 15.9b

1422b

1782b

1490b

1440 Acrudoxic Melanudands Volcanic ash

675 Andic Haplohumods Sedimentary rocks, granite Fagus crenata

90 Typic Dystrudepts Sedimentary rocks

Quercus mongolica var. crispula S70°E, sloping (5%) Lower slope

S76°W, sloping (19%) Upper slope

Quercus serata Shiia cuspidata N85°W, steep (47%) Upper slope

Soils were classified according to Soil Taxonomy [Soil Survey Staff 2006]. Average of 1984−2004 taken from nearest meteorological stations, i.e., Nobeyama, Miyazu, and Kyoto.

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soil pH was relatively high, ranging from 4.6 (A horizon) to 5.0 (Bw horizon) (Table 5.2). At the Tango plot (TG), located in Tango Peninsula, Kyoto Prefecture, the soil was affected by podzolization and derived from sedimentary rocks and granite. It was classified as Andic Haplohumods. Soil pH was low, ranging from 3.8 (AE horizon) to 4.5 (Bs horizon). At the Kyoto plot (KT), located on Mt. Yoshida, Kyoto Prefecture, the soil was affected by brunification and derived from sedimentary rocks. It was classified as Typic Dystrudepts. Soil pH was 4.2 throughout the profile (Table 5.3). At these three plots, throughfall and soil solution was collected by precipitation collectors and tension-free lysimeters, draining a surface area of 200 cm2, beneath the O, A, and B horizons (or the A2 horizon at NG) in each 5 replications, respectively, during the period from June 2003 to February 2005. After filtration through 0.45-μm cellulose acetate membrane filters, the chemical composition of the solution samples was determined, followed by analysis using proton budget method (see the Appendix of Chapter 4 (4.A3) for detailed methodology). Water fluxes of soil solution percolated from the bottom of the O and B horizons were estimated by applying Darcy’s law to the unsaturated hydraulic conductivity and the gradient of the hydraulic heads in surface soil (0- to 5-cm depth) and subsoil (40- to 45-cm depth) (also described in Appendix of Chapter 4). The annual water fluxes, as the

TABLE 5.2 Stock and Annual Flow of Carbon in the Japanese Plots

C Stock (Mg C ha−1) Aboveground biomass Fine root biomass O horizon A horizona B horizona Soil organic matter O horizon Mineral soil horizonsb C Flow (Mg C ha−1 yr −1) Soil respiration Root respiration Decomposition of soil organic matter Decomposition of O horizon Decomposition of mineral soil Litterfall Wood increment a b

NG

TG

KT

78 2.0 0.4 0.6 1.0

83 2.9 2.1 0.6 0.2

115 4.2 1.4 1.5 1.3

3.6 187

30.6 121

3.4 66

5.0 1.5 3.4 1.6 1.9 1.7 1.5

8.2 2.8 5.5 3.4 2.0 2.1 2.5

9.3 4.2 5.1 4.1 1.0 2.9 10.1

The A and B horizons corresponded to the A1 and A2 horizons, respectively, at NG. Organic carbon in soil at 0−30 cm depths was counted.

pH Depth Site

Particle Size Distribution

Exchangeable Total C

Bases

Al

CEC

Sand

(cmolc kg−1)

Silt

Clay

Sio

(%)

Feo

Alo

(g kg−1)

Fed

Ald

Horizon

(cm)

(H2O)

(KCl)

(g kg−1)

(g kg−1)

NG (Andisols)

A1 A2 A3 BA Bw

0–5 5–15 15–35 35–50 50–76+

4.6 4.6 4.8 4.9 5.0

4.3 4.2 4.4 4.6 5.2

212 167 134 98 23

6.6 1.7 0.5 0.3 0.7

2.4 3.2 3.0 1.7 0.2

57.3 53.6 42.1 37.2 17.3

25 26 24 27 37

31 31 25 24 20

44 43 51 50 43

3.5 6.1 6.8 13.2 16.5

16.3 14.4 20.2 20.1 18.7

24.7 25.1 32.8 36.7 36.3

26.7 25.3 30.1 34.7 35.4

24.3 20.3 26.3 20.8 13.3

TG (Spodosols)

AE Bhs Bs BC C

0–9 9–35 35–72 72–86 86–95+

3.8 4.4 4.5 4.6 4.7

3.5 4.4 4.5 4.2 4.3

125 66 18 5 3

0.8 0.5 0.6 0.8 0.9

10.1 5.6 4.5 6.1 5.5

49.5 36.3 19.9 15.9 12.4

12 14 27 56 71

40 42 32 18 14

49 45 41 27 15

0.1 2.2 2.2 0.3 0.3

11.6 14.9 7.0 2.8 1.3

4.9 17.5 11.7 3.1 2.3

27.8 29.6 23.3 18.4 12.9

6.5 18.3 11.9 5.3 3.2

KT (Inceptisols)

A Bw1 Bw2 Bw3

0–7 7–15 15–45 45–65+

4.2 4.2 4.2 4.2

3.7 3.9 4.1 4.1

45 13 8 5

0.4 0.2 0.1 0.2

6.8 4.5 3.7 3.5

19.5 13.9 10.2 10.0

47 43 48 50

6 14 4 1

47 43 48 50

0.1 0.1 0.2 0.2

2.6 1.9 1.6 1.7

4.4 2.1 2.2 1.8

12.4 17.4 17.6 14.6

6.3 4.4 3.7 3.2

Pedogenetic Acidification in Humid Asia

TABLE 5.3 Physicochemical Properties of Soils in Japanese Plots

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sum of daily water fluxes of throughfall and soil solution, are summarized in Table 5.4. Water fluxes of throughfall were 1155, 1676, and 1657 mm yr−1 at NG, TG, and KT, respectively. Water fluxes leached from the bottom of the B horizon were 616, 873, and 660 mm yr−1 at NG, TG, and KT, respectively. The water losses, i.e., the differences between water fluxes of throughfall and fluxes of water percolated from the bottom of the B horizon, were 539, 803, and 997 mm yr−1 at NG, TG, and KT, respectively (Table 5.4). These figures were comparable to potential evapotranspiration estimated by Thornthwaite [1948], i.e., 561, 598, and 822 mm yr−1 at NG, TG, and KT, respectively.

5.2.2  Soil Solution Composition Solution pH at NG (Andisol site) was relatively high (6.0–6.1) throughout the soil profile, but at KT (Inceptisol site) it was moderately low (4.3–4.5) (Table 5.4). At TG (Spodosol site), it was low in the O horizon (3.9), followed by an increase in pH with depth (4.1 for the A horizon and 4.6 for the B horizon, respectively). The concentrations of dissolved organic carbon (DOC) in soil solution in the O horizon were highest at TG (28.9 mg C L−1), followed by KT (15.4 mg C L−1), and NG (6.8 mg C L−1) (Table 5.4). The DOC concentrations in soil solution were highest in the O horizon, followed by a decrease with depth due to adsorption, and microbial decomposition at TG and KT (Table 5.4). The DOC concentrations of soil solution in the O horizon (11.4–70.6 mg C L−1) were correlated with soil temperature (1.7°C –20.7°C) at TG (r = 0.50**, n = 18, p < 0.05), indicating that DOC was produced primarily by the microbial decomposition of litter in the O horizon. At NG, DOC concentrations in soil solution were low throughout the soil profile (1.4–6.8 mg C L−1) due to DOC decomposition in the O horizon and the high adsorption capacity of amorphous materials in mineral soil. Organic acids were dominant among the biologically produced anions (organic acids, nitrate, and bicarbonate) at all plots (Table 5.4). The concentrations of organic acids were positively correlated with DOC concentrations at NG (r = 0.71, n = 171, p < 0.01), TG (r = 0.73, n = 210, p < 0.01), and KT (r = 0.77, n = 277, p < 0.01). According to linear regression analysis, the negative charge per 1 mol DOC was calculated to be 0.30 at NG, which corresponded to one dissociated acidic functional group for 3.3 C atoms. A low carbon to charge ratio in soil solution of Andisol was also reported by Ugolini et al. [1988], suggesting the presence of low molecular weight organic acids. The negative charge per 1 mol DOC at TG and KT was 0.08 molc and 0.13 molc, respectively. These figures correspond to one dissociated acidic functional group for 12.2 C and 7.4 C atoms at TG and KT, respectively. The high carbon to charge ratios in soil solution at TG and KT suggest the presence of high molecular weight fulvic acids, which contain 7 C atoms for each acidic functional group [Thurman 1985]. The NO3− concentrations in soil solution were highest at TG (0.07–0.30 mmolc −1 L ), followed by NG (0.08–0.12 mmolc L−1), and KT (0.00–0.08 mmolc L−1) (Table 5.4). In all plots, the NO3− concentrations in soil solution were highest in the O horizon (O and A horizons at KT) due to mineralization and nitrification, followed by a decrease with depth due to vegetation uptake (Table 5.4). It was considered that NO3−

Water Flux

DOC

HCO

− 3

Org

n− a

NO

− 3



CL + SO

2− 4

H

+

+ 4

NH

Fe2+

Aln+

(mm)

pH

(mg C L )

NG

b

TF O A1 A2

1155 779 718 616

6.0 6.0 6.1 6.0

5.3 6.8 1.5 1.4

0.039 0.022 0.023 0.018

0.059 0.222 0.070 0.074

0.021 0.116 0.107 0.075

0.067 0.091 0.109 0.079

0.001 0.001 0.001 0.001

0.014 0.019 0.001 0.009

0.159 0.406 0.302 0.238

0.000 0.001 0.000 0.000

0.004 0.012 0.003 0.002

0.049 0.526 0.426 0.183

TG

TFb O A B

1676 1189 945 873

5.4 3.9 4.1 4.6

4.7 28.9 13.0 2.3

0.025 0.001 0.000 0.001

0.105 0.333 0.255 0.138

0.024 0.298 0.215 0.074

0.248 0.455 0.393 0.376

0.004 0.123 0.080 0.026

0.009 0.186 0.087 0.006

0.386 0.703 0.548 0.487

0.000 0.009 0.006 0.000

0.064 0.140 0.066 0.003

0.012 0.137 0.105 0.037

KT

TFb O A B

1657 973 805 660

5.2 4.3 4.4 4.5

5.6 15.4 14.0 4.3

0.048 0.022 0.016 0.013

0.031 0.121 0.134 0.076

0.043 0.071 0.081 0.004

0.125 0.224 0.216 0.258

0.003 0.062 0.034 0.027

0.025 0.039 0.045 0.006

0.216 0.287 0.288 0.271

0.000 0.005 0.003 0.000

0.003 0.056 0.078 0.041

0.003 0.062 0.107 0.211

b

−1

(mmolc L )

Si

Horizon

a

(mmolc L )

Na+ + K+ + Mg2+ + Ca2+

−1

(mmol L−1)

−1

Pedogenetic Acidification in Humid Asia

TABLE 5.4 Water Flux and Annual Volume-Weighed Mean Concentrations of Ions in Throughfall and Soil Solution

Orgn− represents anion deficit, the negative charge of organic acids. TF represents throughfall.

177

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was produced from organic nitrogen by heterotrophic fungi in the acidic forest soils, since autotrophic nitrification is limited in acidic soils [Killham 1990]. The concentrations of bicarbonate while low were significant throughout the soil profile at NG (0.02 mmolc L−1) and KT (0.01–0.02 mmolc L−1), but were negligible in soil solution at TG because solution pH was low (Table 5.4). Judging from the fact that the concentrations of bicarbonate in soil solution were much lower than those of organic acids and NO3− , the contribution of dissociation of carbonic acid to soil acidification was negligible at all plots in the present study. The concentrations of the essential nutrient elements including K, Mg, and Ca showed maximum levels in the O horizon, followed by a decrease with depth as K, Mg, and Ca were taken up by vegetation at all plots (Table 5.4). The sum of K, Mg, and Ca concentrations were 0.19–0.39 mmolc L−1, 0.17–0.40 mmolc L−1, and 0.17–0.22 mmolc L−1 at NG, TG, and KT, respectively. One of the reasons for the highest concentrations of DOC and basic cations at TG was considered to be due to the presence of ectomycorrhiza in the Fagus crenata. In this case, the ectomycorrhiza could enhance cation availability by enhancing DOC production by fungi [Griffiths et al. 1994]. The Al concentrations of soil solution at NG were negligible throughout the soil profile presumably due to the formation of insoluble complexes with humic acids (Table 5.4) [Ugolini and Sletten 1991]. At TG, Al concentrations of soil solution in the A horizon (0.14 mmolc L−1) were higher than those in the O horizon (0.06 mmolc L−1), followed by a decrease in the B horizon (0.07 mmolc L−1) due to removal of counter anions, especially organic acids and NO3− , in soil solution (Table 5.4). The same pattern was observed at KT (0.06, 0.08, and 0.04 mmolc L−1 in the O, A, and B horizons, respectively). Given that Al content in litterfall was negligible at TG, as discussed later in detail, high Al concentrations of soil solution in the O horizon were probably due to Al release by weathering of aluminosilicate minerals admixed to the O horizon and organic matter decomposition of dead roots at TG [Rustad and Cronan 1995]. In contrast, at KT, the Al released by litter decomposition also contributed to the high Al concentrations of soil solutions in the O horizon, as Al input by litterfall was high at about 9.0 kg ha−1 yr−1 (data not shown). Aluminum mobilization could be enhanced in the O and A horizons due to complexation with organic acids. On the other hand, mobility of organic acids was decreased by saturation with Al. The ratio of charge of Al to the negative charge of organic acids increased at TG as soil solution percolated in the A (0.55) and B horizons (0.48) compared to the O horizon (0.19), while it was similarly high at KT throughout the O (0.46), A (0.58), and B horizons (0.54) (Table 5.4).

5.2.3  NPGOrg, NPGNtr, NPGBio, and ΔANC Based on fluxes of ions at each horizon (Figure 5.2), and excess cation uptake by vegetation, NPGOrg, NPGNtr, and NPGBio (where NPG stands for net proton generation), and ΔANC were calculated according to the method presented in Appendix of Chapter 4 (4.A3) (Figure 5.3). NPGOrg in the O horizon was 1.0, 2.2, and 0.7 kmolc ha−1 yr−1 at NG, TG, and KT, respectively (Figure 5.3). These values are generally consistent with the report by Guggenberger and Kaiser [1998] in which the fluxes of DOCassociated protons leached from the O horizon ranged from 0.4 to 3.7 kmolc ha−1 yr−1

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Pedogenetic Acidification in Humid Asia (b)

(a) HCO–3 H+ + Na + Fe3+ SO42– NH4

TF O

TF

K+ Mg2+ Ca2+

Orgn– NO–3

–2

Orgn–

NO–3

Cl–

H+ Na+ NH4+K+Mg2+

Aln+

B

NG

–4

Fe3+

A

A2 –6

HCO–3

Ca2+

O

Aln+

Cl–

A1

SO42–

0

2

(c)

4

–15

–10

–5

0

(kmolc ha–1 yr–1)

5

10

15

(kmolc ha–1 yr–1)

HCO–3

TF O

TG

6

Mg2+

NH+4

Orgn– SO42– NO3– Cl– H+ Na+

K+

Ca2+

Fe3+

Aln+

A B KT

–15

–10

–5

0

5

10

15

(kmolc ha–1 yr–1)

FIGURE 5.2  Fluxes of solutes at each horizon. TF represents throughfall. O, A, A1, A2, and B represent soil horizons.

in the acidic forest soils. On the other hand, protons were consumed by decomposition and adsorption of organic acids in the NG, TG, and KT subsoils (Figure 5.3). At TG, the DOC fluxes of throughfall (TF) and the O, A, and B horizons were 78, 344, 123, and 20 kg C ha−1 yr−1, respectively, and hence the O horizon was the main source of DOC. At KT, the DOC fluxes of throughfall and the O, A, and B horizons were 93, 149, 113, and 28 kg C ha−1 yr−1, respectively; here the DOC fluxes of throughfall were appreciably high and, therefore, both the canopy and the O horizon were the main sources of DOC at KT. Leaching of secondary metabolites as tannin and microbial metabolites on leaves could be responsible for DOC in throughfall. In contrast, at NG, the DOC fluxes of throughfall and the O, A, and B horizons were 61, 53, 11, and 9 kg C ha−1 yr−1, respectively, and there was no clear increase in DOC flux in the O horizon. Based on the aforementioned decomposition rates of organic matter in the O horizon (Table 5.3) and the DOC fluxes leached from the O horizon, the latter were high compared to mineralized carbon (the sum of the effluxes of CO2 released by organic matter decomposition and DOC fluxes leached from the O horizon) in the O horizon in the highly acidic (podzolic) soil of TG (10.1%), in comparison with NG (3.3%) and KT (3.6%). DOC leached from the O horizon was reported to be the

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World Soil Resources and Food Security

(a)

(b) NG

O A1 A2 Total –6

O

(H+)in–(H+)out ΔANC NPGNtr

B

NPGOrg NPGBio

Total

NPGCar

–4

TG

A

–2

0

2

4

6

–6

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–)

–4

–2

0

2

4

6

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–)

(c) KT

O A B Total –6

–4

–2

0

2

4

6

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–)

FIGURE 5.3  Vertical variation of NPGOrg, NPGNtr, NPGBio, and ΔANC. TF represents throughfall. O, A, A1, A2, and B represent soil horizons.

water soluble lignocellulose degradation product in the course of lignin decomposition by litter-decomposing fungi and white-rot fungi [Guggenberger and Zech 1994]. Predominant fungal activity and inhibited bacterial activity might therefore result in high DOC production in highly acidic soils. According to Figure 5.3, in the O horizon, protons were produced by nitrification that was predominant over nitrate uptake by vegetation and microorganisms, since NPGNtr was positive at all plots except for KT, where NPGNtr was small. On the other hand, protons were consumed by predominant nitrate uptake by vegetation and microorganisms, since NPGNtr was negative in the A and B horizons at all plots. Protons were produced by mineralization and nitrification in the O horizon, 0.7 and 1.1 kmolc ha−1 yr−1 at NG and TG, respectively, since NO3− leaching from the O horizon, 0.9 and 3.5 kmolc ha−1 yr−1 at NG and TG, respectively, was higher than NO3− flux of throughfall (Figure 5.2). High NO3− flux leached from the O horizon at TG was presumably due to low NO3− uptake by ectomycorrhizal roots [Harley and Smith 1983]. Judging from the fact that NO3− leaching from the O horizon, 0.7 kmolc ha−1 yr−1, was almost equivalent to NO3− flux of throughfall at KT, the canopy of evergreen forests was the main source of NO3−. Protons were produced in the canopy by nitrification on leaves and NO3− exudation from leaves at KT. At all

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181

plots, NPGOrg and NPGNtr in the O horizon were almost completely consumed in the A and B horizons, and hence the contribution of NPGOrg and NPGNtr to soil acidification in the whole soil compartment was low. Cation contents exceeded anion contents in litter and wood materials at all plots (Table 5.5). Excess cation charge was compensated for by the net proton load to soils as NPGBio. NPGBio in the whole soil compartment was 3.4, 3.3, and 7.2 kmolc ha−1 yr−1 at NG, TG, and KT, respectively. NPGBio was highest among proton sources in the whole soil compartment at all plots, although NPGBio for litter production would be almost neutralized by litterfall returned to soil. NPGBio was then distributed to each soil horizon based on the pattern of fine root biomass in the soil profile. At TG, NPGBio was concentrated in the O horizon, 2.4 kmolc ha−1 yr−1, while it was also distributed in the A and B horizons at NG, 0.7–1.7 kmolc ha−1 yr−1 and at KT, 2.2–2.6 kmolc ha−1 yr−1 (Figure 5.3). Concentrated fine root biomass in the O horizon at TG was responsible for the high NPG due to cation excess uptake by vegetation (Figure 5.3; Table 5.3). Regarding one of the controlling factors of fine root distribution, which was responsible for NPGBio, Aber et al. [1985] reported that a fine root and ectomycorrhiza system developed in the O horizon enhanced NH +4 mobility in acidic soils. This finding is consistent with the higher NH +4 concentrations of soil solution found in the O horizon at TG, 0.19 mmolc L−1, compared to NG, 0.02 mmolc L−1, and KT, 0.04 mmolc L−1, in the present study (Table 5.4). The major proton consuming processes were cation release by organic matter decomposition, exchange reactions, and mineral weathering. Protons were neutralized by basic cations at NG, while protons were neutralized partly by Al mobilization in acidic soils at TG and KT. The fluxes of Al leached from the O and A horizons were 0.77 and 1.32 kmolc ha−1 yr−1 at TG, and 0.55 and 0.63 kmolc ha−1 yr−1 at KT, respectively (Figure 5.2). According to Figure 5.3, at NG, the soil acidification rate (−ΔANC) was low throughout the soil profile, −2.0, 0.4, and −1.0 kmolc ha−1 yr−1 in the O, A, and B horizons, respectively. Protons produced in each horizon were completely consumed in the same horizon at NG, since {(H+)in − (H+)out} was negligible in the A and B horizons. Also seen from Figure 5.3, at TG, the soil acidification rate was high in the O horizon, with ΔANC at −3.9 kmolc ha−1 yr−1. Since the protons produced (the sum of NPGOrg, NPGNtr, NPGCar, and NPGBio) were not completely compensated by cation release in the O horizon, 1.4 kmolc ha–1 yr–1 of protons {(H+) in – (H+)out} were leached from the O horizon and consumed in the A and B horizons (Figures 5.2 and 5.3). At KT, the soil acidification rate was moderately high throughout the soil profile, with ΔANC ranging from –2.6 to –1.3 kmolc ha–1 yr–1. Although most of the protons produced in the O horizon were consumed in the same horizon, 0.5 kmolc ha–1 yr–1 of protons {(H+)in – (H+)out} were leached from the O horizon and consumed mainly in the A horizon (Figures 5.2 and 5.3). According to van Breemen et al. [1984], soil acidification rates in the whole soil compartment range from 7.5 to 16 kmolc ha−1 yr−1 in soils at neutral pH due to carbonic acid dissociation, from 2.5 to 7.5 kmolc ha−1 yr−1 in acidic soils with weatherable minerals caused by vegetation uptake and nitrification, and less than 2.5 kmolc ha–1 yr–1 in podzolic soils caused by vegetation uptake and dissociation of organic acids. The soil acidification rate in the whole soil compartment of TG (2.5 kmolc ha−1 yr−1) was a value intermediate between the soil acidification rates of podzolic soils

182

TABLE 5.5 Uptake of Cations and Anions by Vegetation OM Production

Na

K

Ca

Mg

Fe

Al

Cl

S

P

(Cation)bio

(kg ha−1 yr−1)

(Anion)bio

NPGBio

(Mg C ha−1 yr−1)

(kmolc ha−1 yr−1)

NG

Wood increment Litterfall

1.5 1.7

2.0 1.5

1.3 11.7

10.6 35.1

1.2 6.6

0.6 4.2

0.1 1.0

0.1 0.2

0.2 1.4

2.0 3.5

0.78 2.92

0.08 0.20

0.71 2.71

TG

Wood increment Litterfall

2.5 2.1

4.4 2.9

5.5 9.2

15.8 21.2

5.2 4.7

0.6 2.2

0.1 0.4

0.1 0.2

0.4 2.0

0.1 0.8

1.57 1.93

0.03 0.15

1.55 1.78

KT

Wood increment Litterfall

10.1 2.9

18.1 2.2

29.1 18.9

29.1 26.7

6.2 9.2

4.1 1.8

1.8 9.0

0.7 0.4

1.9 2.7

1.1 2.4

3.84 3.73

0.17 0.24

3.67 3.49

World Soil Resources and Food Security

Horizon

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183

and acidic soils with weatherable minerals. Soil acidification rates in the whole soil compartment of NG (3.1 kmolc ha−1 yr−1) and KT (5.9 kmolc ha−1 yr−1) were similar to those of acidic soils with weatherable minerals. In the present study, taking together the data at NG and TG and the findings of Binkley [1992], the low soil acidification rates mainly result from low cation excess uptake by vegetation, caused by low wood increment (0.7 and 1.5 kmolc ha−1 yr−1 at NG and TG, respectively, and 0.5 kmolc ha−1 yr−1 on average) (Table 5.5). In the case of Binkley’s [1992] study, the low cation excess accumulation in wood might also be attributed to low cation contents in plant materials due to a low cation pool of soils derived from parent materials such as glacial till, as well as lower cation contents in plant materials of coniferous vegetation and low primary production due to low air temperatures and the old age of forests. Shibata et al. [1998] suggested that the higher contribution of internal proton generation by basic cation accumulation in vegetation to soil acidification in Japanese volcanogenous regosols was related to a larger basic cation pool of soils. The latter was considered to be responsible for higher cation content in plant materials. The higher cation excess accumulation in wood at NG and TG compared to those reported by Binkley [1992] was considered to be related to a higher cation pool of soils, as well as to the younger age of forests. In contrast, cation excess accumulation in wood was higher at KT (3.7 kmolc ha−1 yr−1) than at NG and TG; in this case, it was considered to be related to higher primary production under the warmer climate. Pedogenetic soil acidification was considered to include cation leaching by proton generation through the dissociation of organic acids and nitrification, and subsequent cation excess accumulation in wood in the growth stage of forests. Although the contribution of the temporary acids such as organic acids and nitric acid, which were produced in the O horizon and then consumed in the A and B horizons, to soil acidification in the whole soil compartment was low. Translocation of the temporary acids, as well as distribution of root biomass, contributed to the temporal and spatial heterogeneity of proton generation and consumption in the soil profile, resulting in different pedogenetic soil acidification.

5.2.4  Pedogenetic Implication of the Proton Budget The soil solution studies by Ugolini et al. [1988, 1990] and Ugolini and Sletten [1991] demonstrated the essential role of proton donors including organic acids and carbonic acid on pedogenesis, i.e., podzolization, andosolization, and brunification. The pedogenetic soil acidification processes of NG, TG, and KT can be described in relation to andosolization, podzolization, and brunification, respectively. At NG, the distribution pattern of Alo and Alp in soil with depth suggests that an organo-mineral complex was dominant in surface soil horizons (Table 5.3). On the other hand, the occurrence of allophane was suggested in subsoil by the ratio of (Alo – Alp)/(Sio – Sip), which was similar to the ideal Al/Si ratio of Al-rich allophane, i.e., 2 (Table 5.3). The low concentrations of DOC and Al in soil solution indicated that in situ weathering was a major weathering process at NG (Table 5.4). The low soil acidification rate and low NPGOrg contributed to incongruent dissolution, resulting in the formation of amorphous Al silicates (Figure 5.3). These processes were in accordance with the concept of andosolization suggested by Ugolini and Sletten

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World Soil Resources and Food Security

[1991]. Acidity originating from organic acids was the determining factor of pedogenesis of ando and podzolic soils derived from volcanic parent materials. Lower acidity was responsible for andosolization, while higher acidity due to the dissociation of organic acids was responsible for podzolization [Shoji et al. 1982]. Lower acidity, together with volcanic parent material, was considered to be responsible for andosolization at NG. At TG, the distribution pattern of Alo and Alp in soil with depth suggested eluviation of Al from the A horizon and illuviation in the BA and B horizons (Table 5.3). High fluxes of DOC and Al leached from the O and A horizons and their illuviation in the B horizon at TG coincides with the concept of podzolization (Figure 5.2). In podzolic soils derived from volcanic ash, organic acids were found to be responsible for the eluviation of Al by congruent dissolution in surface soil and carbonic acid was responsible for the accumulation of Fe and Al oxides by incongruent dissolution in subsoil [Ugolini and Dahlgren 1987]. In the present study, it was considered that organic acids were the dominant anions, and that bicarbonate was negligible throughout the soil profile at TG due to lower pH of soil derived from acidic parent materials (Table 5.4). In addition, it was shown that cation excess uptake by vegetation due to concentrated fine root biomass in the O horizon resulted in intensive soil acidification in surface soil and subsequent high proton efflux to subsoil (Figure 5.3). This is consistent with the report by Nielsen et al. [1999] for the change in vegetation from heath to spruce, the root biomass of which concentrated in the surface soil and accelerated podzolization. At KT, higher Alo and Feo contents in surface soil horizons suggested that Al and Fe oxides were immobilized in metal-humus complexes (Table 5.3), which is in accordance with the concept of brunification. The immobilization of organic matter by oxides was supported by the lower Fe concentrations in soil solution and higher saturation of the negative charge of organic acids by Al in soil solution throughout the soil profile at KT, compared to TG (Table 5.4). Ugolini et al. [1990] suggested that incongruent dissolution by carbonic acid results in accumulation of Fe oxides. However, judging from the fact that the dominant anions in soil solution were organic acids and those of carbonic acid were low at KT (Table 5.4), pedogenesis of brown forest soils appears to be variable in terms of anion contribution [Ugolini et al. 1990]. On the other hand, at KT, high fluxes of Al leached from the O, 0.55 kmolc ha−1 yr−1, and A horizons, 0.63 kmolc ha−1 yr−1, and subsequent decrease of Al flux in the B horizon, 0.27 kmolc ha−1 yr−1, suggested that pedogenesis of brown forest soil includes incipient podzolization, as also suggested by Hirai et al. [1988] (Figure 5.2). However, judging from the higher Al input by litterfall (1.00 kmolc ha−1 yr−1) (Table 5.4), biological cycling of Al as well as weak podzolization resulted in Al mobilization in the O and A horizons. The difference in the intensity of podzolization between KT and TG was caused by 1) distribution patterns of the vegetation root system that determined the soil horizons subjected to intensive acidification by NPGBio, 2) the different contributions of strong acids to soil acidification, and 3) the capacity of acid neutralization. Net proton generation by cation excess uptake by vegetation was concentrated in the O horizon at TG; it was also distributed in the A and B horizons at KT in proportion to the distribution of the fine root biomass. This is consistent with

Pedogenetic Acidification in Humid Asia

185

the report by Nielsen et al. [1987] for the change in vegetation from heath to oak, the latter root biomass distributed throughout the soil profile converted podzolic soil to brown forest soil, i.e., depodzolization. In relation to the second point above, in spite of KT showing the highest soil acidification rate, the contribution of strong acids, e.g., NPGOrg and NPGNtr, to soil acidification was low, since both decomposition and adsorption of organic acids by oxides were high and nitrate uptake by vegetation was also sufficiently high to consume the net proton generation by nitrification in the O horizon (Figure 5.3). As for the third point, the high net proton efflux to the A horizon, 0.71 kmolc ha−1 yr−1, accelerated net Al leaching from the A horizon, 0.55 kmolc ha−1 yr−1, by mineral weathering at TG, while at KT net Al leaching from the A horizon was low, 0.08 kmolc ha−1 yr−1, due to low net proton efflux to the A horizon, 0.33 kmolc ha−1 yr−1 (Table 5.4; Figure 5.2). High acid neutralization through the release of basic cations by a high rate of organic matter decomposition in the O horizon of KT might be responsible for the low proton efflux to subsoil (Figure 5.2).

5.3  F ATES OF SOIL ACIDITY AND DISSOLVED ORGANIC MATTER DURING PEDOGENETIC SOIL ACIDIFICATION OF FOREST SOILS IN JAPAN In the previous section, DOC leaching toward subsoils under highly acidic conditions was observed, resulting in Al translocation through the soil profiles. These metal ions and migrated DOC often form organo-mineral complexes in subsoils, which is substantially stable against biodegradation. The carbon sequestration in soil is today receiving global concern, in relation to the countermeasure against global warming. The process and fate of metal ions and DOC should also be clarified from this context. It is reported that, in the pedogenesis of acidic forest soils, Al is considered to play an important role through, for example, dissolution from primary minerals, hydrolysis and formation of Al hydroxides, formation of organo-mineral complexes, and translocation through the soil profile [e.g., Driscoll 1989]. Among them, the Al hydroxides can work as one of the major components of the active ANC of soils through direct dissolution as well as indirectly through anion adsorption [Davis and Hem 1989; Funakawa et al. 1993; Parfitt 1978]. Funakawa et al. [1993] described the soil profiles from nonvolcanic origin in the cool temperate zone forests of northern Kyoto as being a sequence of different stages of soil acidification with lower horizons representing earlier stages. The first stage is characterized by the accumulation of amorphous Al compounds as a result of Al illuviation (weak podzolization), and is typically observed in the B horizons; successive monomerization of these amorphous Al hydroxides due to an increasing acid load forms the second stage; and a clear decrease in amorphous Al content, weakened structure, and consequent eluviation of Fe under reducing conditions composes the third stage. On the other hand, it is known that the sesquioxidic properties of the B-horizon soils are variable according to the soil temperature regimes [Hirai et al. 1991]. It is worthwhile to compare corresponding pedogenetic processes among soils under different bioclimatic conditions. In this section, we compared the pedogenetic

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World Soil Resources and Food Security

acidification processes in forest soils under cool and warm temperate climatic conditions in southwestern Japan (focusing on soil solution composition dynamics and soil acidity/alkalinity of soils) to further understand the long-term processes of SOM accumulation and soil acidification.

5.3.1  Study Soils A total of 20 sites were selected from the eastern slope of Mt. Odaigahara (alti tude 1695 m) in southern Mie Prefecture and Ashiu Experimental Forest of Kyoto University in northern Kyoto Prefecture, Kinki District, Japan (Figure 5.1; Table 5.6). Elevation ranged from 110 to 1600 m and both thermic and mesic soil temperature regimes (TSTR and MSTR, respectively) were included. All the soils were located on upper convex slopes and the influence of volcanic ejecta could be small. Using these soils, acid- and alkali-titration data were obtained by adding 0.10 mol L−1 HCl or NaOH to the soil suspension using a potentiometric automatic titrator (soil to solution ratio of 1:10; 0.10 mol L−1 NaCl as the supporting electrolyte). Titratable acidity was determined as OH− consumption at pH 8.3, and titratable alkalinity as H+ consumption at pH 3.0 [Funakawa et al. 1993; Kinniburgh 1986]. In addition to general physicochemical properties and the titration data of the soils, in situ soil solution composition—including Al speciation into inorganic monomeric, organic monomeric, and strongly complexed species—was determined after collecting soil solution using a porous cup method. Detailed data were presented in Funakawa et al. [2008].

5.3.2  Physicochemical Properties and Titratable Alkalinity and Acidity of the Soils The general physicochemical and mineralogical characteristics of the soils studied are summarized as follows (Table 5.7):





1. The pH(H2O) of the soils, especially in the surface horizons, was very low, normally less than 5. The decrease in soil pH from the C horizon toward the surface reflects pedogenetic soil acidification processes. Among exchangeable cations, Al usually dominated and the base saturation of the soils was below 5% in most cases, suggesting that acid neutralization through cation exchange between H+ supplied and exchangeable bases of the soils was limited under the present situation. 2. Most of the soils showed a rather fine texture, the clay contents usually exceeding 40%. The dominant clay mineral species were mostly hydroxy-Al interlayered vermiculite (HIV) and vermiculite occasionally appeared in the highly acidified—pH(H2O) below 4.2—upper horizons. The Sio content of nearly zero suggests that amorphous Al–Si compounds were virtually absent, unlike Al hydroxides and/or organo-mineral complexes. 3. B horizon soils in MSTR contained higher amounts of amorphous compounds (Alo + 1/2Feo) and organic matter (Cp) than those in TSTR (Figure 5.4), consistent with earlier results reported by Hirai et al. [1991].

187

Pedogenetic Acidification in Humid Asia

TABLE 5.6 Brief Description of Selected Soils Studied in Kinki District, Japan USDAa

MD1

Typic Dystrudepts

MD2

Typic Dystrudepts

KD1

Lithic Dystrudepts

KD2

Humic Dystrudepts

TT

Andic Dystrudepts

OT

Alic Hapludands

H2

Andic Dystrudepts

S3

Alic Hapludands

760 m

mesic

Cryptomeria japonica, Fagus crenata, Quercus mongolica var. crispula, Benzoin umbellatum, Sasa palmata

N4b

Andic Haplorthods

790 m

mesic

Fagus crenata, Quercus mongolica var. crispula, Sasa palmata

H1b

Andic Haplohumods

800 m

mesic

Magnolia salicifolia, Hydrangea paniculata, Quercus mongolica var. crispula, Fagus crenata, Cryptomeria japonica, Daphniphyllum humile

a

Altitude

Soil Temperature Regime

Site

Major Vegetation

Mie Soils (Mt. Ohdaigahara) 110 m thermic Cleyera japonica, Neolitsea aciculata, Quercus glauca, Camellia japonica, Meliosma rigida 180 m thermic Quercus glauca, Eurya japonica, Meliosma rigida, Castanopsis cuspidata, Myrsine seguinii, Camellia japonica 840 m thermic/mesic Camellia japonica, Illicium anisatum, Hydrangea paniculata, Leucosceptrum stellipilum, Quercus mongolica var. crispula, Acer spp., Sasamorpha borealis var. borealis 830 m thermic/mesic Camellia japonica, Illicium anisatum, Hydrangea paniculata, Quercus mongolica var. crispula, Acer spp., Sasamorpha borealis var. borealis 1210 m mesic Schizophragma hydrangeoides, Pourthiaea villosa, Fagus japonica, Stuartia monadelpha, Sasamorpha borealis var. borealis 1220 m mesic Enkianthus cernuus, Quercus mongolica var. crispula, Betula grossa, Clethra barvinervis, Stuartia monadelpha, Acer spp. Kyoto Soils (Ashiu Experimental Forest) 880 m mesic Cryptomeria japonica, Fagus crenata, Acer sieboidianum, Sasa palmate

According to Soil Taxonomy [Soil Survey Staff 2006].

188

TABLE 5.7 Physicochemical Properties of Soils in Japanese Plots pH

Particle Size Distribution

Exchangeable

Depth

Total C

Bases

Al

CEC

Sand

(cmolc kg )

Silt

Clay

Feo

Sio

Fep

Cp

Horizon

(cm)

(H2O)

(KCl)

(g kg )

O A BA Bw BC

2–0 0–8 8–23 23–40 40–50+

3.9 4.5 4.6 4.6 4.7

3.2 3.9 4.0 3.9 4.0

312.7 67.7 21.8 13.4 7.4

3.4 0.7 0.3 0.3 0.2

14.6 9.5 7.2 8.3 7.6

77.8 25.4 23.7 19.0 16.0

6 11 11 13 40

13 24 23 24 18

82 65 66 63 42

4.8 8.3 6.5 4.2 3.3

6.3 7.8 5.2 4.1 3.1

0.1 0.1 0.1 0.1 0.1

6.3 13.4 8.5 3.7 1.3

6.3 7.8 5.2 4.1 3.1

43.4 17.4 6.3 4.2 2.1

MD2

A BA Bw1 Bw2 BC

0–8 8–20 20–45 45–62 62–90+

4.7 4.6 4.7 4.7 4.8

4.1 3.9 3.9 3.9 3.9

121.3 36.8 15.2 8.7 6.5

1.0 0.5 0.3 0.3 0.3

7.5 10.9 9.2 10.9 11.1

34.5 25.3 22.5 22.2 32.9

9 8 8 9 11

27 27 24 28 25

64 65 69 63 64

6.9 9.4 6.9 5.2 4.0

11.3 6.7 5.1 4.5 3.9

0.1 0.0 0.0 0.0 0.0

9.7 12.5 7.4 6.3 2.3

11.3 6.7 5.1 4.5 3.9

33.6 10.7 2.2 3.3 2.3

KD1

A BA Bw BC

0–3 3–12 12–32 32–44

4.6 4.7 4.8 4.9

3.4 3.7 3.7 4.0

124.4 44.6 37.0 25.8

2.4 0.6 0.4 0.3

16.6 22.5 17.6 9.6

34.9 34.6 31.6 17.9

56 25 20 47

18 27 24 19

26 48 56 35

7.0 11.6 16.9 8.7

5.7 9.2 9.4 6.3

0.1 0.0 0.0 0.1

3.8 5.0 7.2 3.6

5.7 9.2 9.4 6.3

28.2 17.0 15.6 6.0

KD2

A(o) AB Bw1

0–4 4–16 16–29

4.0 4.2 4.6

3.2 3.4 3.7

208.3 94.5 26.6

1.4 0.5 0.2

17.4 18.5 9.3

55.9 38.9 23.6

23 24 29

26 28 28

51 47 42

8.9 12.1 20.2

7.2 8.4 7.9

0.0 0.0 0.0

6.9 6.0 4.3

7.2 8.4 7.9

42.1 29.2 11.5

−1

(g kg )

Alp

MD1

−1

(%)

Alo

Site

a

(g kg )

−1

−1

World Soil Resources and Food Security

29–45 45–60+

4.7 4.7

3.8 3.9

13.0 9.6

0.2 0.2

7.4 6.1

22.5 12.7

33 46

25 22

41 33

8.8 5.8

5.6 4.0

0.0 0.0

3.3 3.4

5.6 4.0

6.3 3.8

TT

A(o) BA Bw1 Bw2 BC

0–5 5–20 20–40 40–60 60–70

3.6 4.1 4.6 4.7 4.7

3.1 3.5 3.9 4.0 4.0

265.0 52.2 26.5 19.3 8.1

2.4 0.6 0.3 0.2 0.2

17.7 18.0 7.6 4.7 5.4

54.2 29.1 18.5 12.9 10.9

24 28 26 22 35

27 24 26 26 22

49 48 48 52 43

5.5 12.7 12.6 10.2 5.5

4.8 6.6 5.8 6.0 3.9

0.0 0.0 0.0 0.1 0.1

4.3 8.5 7.4 4.3 1.6

4.8 6.6 5.8 6.0 3.9

42.6 19.9 11.3 7.5 3.2

OT

A(o) BA Bw1 Bw2 BC C

0–8 8–24 24–38 38–52 52–62 62–70+

3.8 4.1 4.5 4.7 4.8 4.8

3.2 3.6 3.9 4.1 4.2 4.3

223.7 76.9 61.3 55.9 32.4 16.3

1.7 0.6 0.3 0.2 0.1 0.1

15.7 23.9 10.6 6.8 4.6 2.8

29.7 40.4 26.9 21.9 17.6 12.7

10 14 20 23 34 43

17 26 32 37 19 16

73 60 48 40 47 41

6.4 21.1 19.6 20.8 16.4 7.9

4.6 9.4 9.0 10.8 11.3 9.6

0.3 0.0 0.0 0.1 0.2 0.6

5.0 16.2 13.5 10.4 8.7 2.5

4.6 9.4 9.0 10.8 11.3 9.6

43.9 32.3 25.2 20.3 16.0 8.2

H2

A AB BA Bw BC

0–6 6–14 14–28 28–48 48–60

4.0 4.2 4.4 4.6 4.8

3.3 3.5 3.7 3.9 3.9

115 75 49 18 13

3.0 1.2 0.7 0.5 0.5

12.2 12.1 9.2 6.4 5.3

37.3 31.5 23.5 15.8 13.4

16 12 11 15 20

31 33 35 33 33

53 55 54 52 46

16.6 18.2 19.5 11.5 6.7

7.9 8.3 9.0 7.7 6.6

0.2 0.4 0.3 0.6 0.7

11.2 13.7 18.3 10.0 6.4

5.7 6.8 7.9 7.1 5.7

33.7 31.1 19.9 10.3 6.7

S3

A1(o) A2 AB BA Bw BC

0–8 8–21 21–34 34–48 48–70 70–95

3.5 4.3 4.6 4.7 4.7 4.7

2.9 3.6 3.9 4.0 4.0 4.0

226 88 61 46 17 11

4.0 0.8 0.5 0.4 0.9 0.5

13.4 10.8 6.4 4.5 4.5 4.3

63.7 38.6 30.0 26.9 20.1 16.0

6 9 11 8 9 12

40 39 46 43 42 38

54 52 43 48 49 51

11.8 20.6 20.8 16.8 10.1 8.8

6.9 11.4 15.1 16.8 11.4 9.8

0.2 0.2 0.8 1.4 1.1 1.5

10.7 22.0 15.6 12.5 4.9 5.4

189

6.1 64.5 11.6 40.4 13.8 31.4 13.8 23.9 7.2 10.6 5.9 6.0 (continued)

Pedogenetic Acidification in Humid Asia

Bw2 BC

190

TABLE 5.7 (Continued) Physicochemical Properties of Soils in Indonesian Plots pH

Particle Size Distribution

Exchangeable

Depth

Total C

Bases

Al

CEC

Sand

Clay

Feo

Sio

Fep

Cp

Horizon

(cm)

(H2O)

(KCl)

(g kg )

0–6 6–12 12–15 15–30 30–40 40–52 52–83 83–100+

3.6 3.8 4.1 4.2 4.5 4.6 4.6 4.7

2.9 2.9 3.2 3.5 3.8 4.0 4.0 4.1

213 102 58 53 21 15 15 13

3.3 1.3 0.7 0.6 0.4 0.4 0.4 0.3

8.7 14.6 14.6 12.4 6.9 6.7 5.8 4.7

62.2 40.2 34.4 30.8 20.6 19.8 19.5 19.4

11 11 12 9 15 8 7 16

47 46 44 36 37 46 41 37

43 43 44 55 48 46 52 47

5.7 14.4 21.6 34.8 20.5 10.8 9.9 11.0

3.9 5.1 6.1 10.6 10.1 9.8 10.1 11.2

0.2 0.6 0.3 0.4 0.6 1.0 0.6 1.5

6.0 11.8 14.4 14.2 8.4 5.8 5.1 4.2

2.4 3.7 4.0 7.2 5.9 5.5 5.9 5.7

43.5 37.7 26.5 30.6 11.0 10.4 10.1 7.5

H1b

A(o) E Bh Bw BC

0–9 9–19 19–22 22–36 36–49

3.7 3.8 4.0 4.3 4.4

2.9 3.0 3.3 3.6 3.7

281 29 54 42 33

3.8 0.7 0.5 0.6 0.5

6.5 7.9 16.7 8.9 7.5

71.2 17.8 33.7 27.5 19.0

20 27 31 33 37

40 40 29 27 27

40 33 40 40 36

4.4 1.4 22.0 16.0 7.9

3.7 1.9 6.7 8.8 7.2

0.2 0.3 0.2 0.5 0.3

3.9 1.2 12.6 8.9 7.0

3.6 1.6 5.4 6.6 6.6

63.6 8.7 35.1 27.1 21.2

−1

(g kg )

Alp

A(o) AB BA Bs Bw1 Bw2 BC C

−1

(%)

Alo

N4b

(g kg )

−1

−1

A(o) horizon described here contains >200 g C kg−1 soil and is often regarded as a deposited organic layer in some soil classification systems.

World Soil Resources and Food Security

a

(cmolc kg )

Silt

Site

a

191

Pedogenetic Acidification in Humid Asia Thermic Intermediate Mesic Mesic, podzolic

30

Cp (g kg–1)

25 20 15 10 5 0

0

5

10

15

20

25

Alo + 1/2Feo (g kg–1)

30

FIGURE 5.4  Contents of (Alo + 1/2Feo) and Cp in the B-horizon soils (at around 30 cmlayers of soils).

MD1

8

BC Bw2

BA

pH 8.3

7

pH

pH

Examples of acid and alkali titration curves of the selected soils are presented in Figure 5.5. In general, the titratable alkalinity required to acidify the soils to pH 3.0 was higher in the B or C horizons than in the surface horizons for soils in the MSTR (OT), whereas it was at a maximum at or near the surface for soils in the TSTR (MD1). The titratable acidity required to neutralize soils to pH 8.3 increased from the C horizon toward the surface, presumably because of the increasing influence of organic matter dissociation. As this process may mask other important relationships between titration data and physicochemical properties, we refer only to soils with a total carbon content of less than 100 g kg−1 in the following discussion on the titration data. Table 5.8 shows correlation coefficients between the titration data and the selected chemical properties of the soil (n = 78). The titratable alkalinity required to acidify soils to pH 3.0 is highly correlated with some extractable Al or Fe compounds (Alo [r = 0.88**], Ald [r = 0.89**], Alp [r = 0.76**], and Fep [r = 0.76**]) and also with pH(NaF) (r = 0.78**). These correlations indicate the importance of amorphous

OT

C

8

Bw2

BA pH 8.3

7

6

pH 5.5

5

pH 4.5

4 3 pH 3.0 10 5 0 Addition of 0.1 M HCl (mL)

5

10 15 20 Addition of 0.1 M NaOH (mL)

6

pH 5.5

5

pH 4.5

4 3 pH 3.0 10 5 0 Addition of 0.1 M HCl (mL)

5

FIGURE 5.5  Examples of acid and alkali titration curves of the soils.

10 15 20 Addition of 0.1 M NaOH (mL)

192

TABLE 5.8 Correlation Coefficients among the Physicochemical Properties and Titration Data of the Soils (n = 78) pH(H2O) Initial pH Titratable alkalinity by pH 3.0 Titratable acidity by pH 8.3 OH−consumption in the range of pH 4.5 to 5.5 OH− consumption in the range of pH 5.5 to 8.3

Exch. Bases

Exch. Al

ECEC

CEC

Clay

0.94**

0.88**

–0.68**

–0.72**

–0.78**

–0.66**

0.09

0.37 –0.63** –0.67**

0.54** –0.52** –0.70**

0.78** –0.35 –0.68**

–0.18 0.54** 0.50**

–0.43* 0.63** 0.91**

–0.45* 0.67** 0.94**

–0.07 0.82** 0.81**

0.23 0.14 0.20

0.12 0.82** 0.51**

–0.45**

−0.28

−0.07

0.44*

0.34

0.38

0.68**

0.11

0.84**

Cp

Alo

Feo

Ald

Fed

Alp

Fep

Alo + 1/2Feo

Alp + 1/2Fep

−0.27 0.28 0.83** 0.40

0.45** 0.88** 0.26 −0.20

0.02 0.43* 0.65** 0.23

0.56** 0.89** 0.13 −0.29

−0.11 0.33 0.55** 0.33

0.26 0.76** 0.46** –0.03

−0.17 0.31 0.72** 0.36

0.26 0.73** 0.50** 0.01

0.05 0.58** 0.63** 0.18

0.93**

0.50**

0.80**

0.39

0.62**

0.69**

0.82**

0.72**

0.81**

Note: Significant at *5% and **1% level.

pH(KCl)

Total C –0.40

World Soil Resources and Food Security

Initial pH Titratable alkalinity by pH 3.0 Titratable acidity by pH 8.3 OH– consumption in the range of pH 4.5 to 5.5 OH– consumption in the range of pH 5.5 to 8.3

pH(NaF)

0.84**

Pedogenetic Acidification in Humid Asia

193

Al, including organo-mineral complexes and, probably, Fe compounds as an active acid buffering component in soils with low base saturation through protonation of oxides and/or partial monomerization of Al as also reported for acidic forest soils by Kamoshita et al. [1979] and Funakawa et al. [1993]. Between Alo (cmol kg−1) and titratable alkalinity to pH 3.0 (Alk3 in cmol kg−1), the following relationship was obtained:

Alk3 = 0.29Alo + 0.57 (r 2 = 0.76).

(5.1)

The value of the slope, 0.29, indicates that only a small portion of the Alo fraction can contribute to the acid neutralization reaction. Thus, the titratable alkalinity derived from amorphous Al oxide was higher in soils under MSTR than in soils under TSTR. Some titration curves display a conspicuous pH buffer zone in the pH range 4.5 to 5.5 (Figure 5.5). Based on the fact that OH− consumption in this zone (OH45 in cmol kg−1) is highly correlated with exchangeable Al content (exAl in cmolc kg−1) (Table 5.8; r = 0.91**), the buffering effect can be attributed to the hydrolytic reactions of exchangeable Al:

OH45 = 0.49exAl + 0.61 (r 2 = 0.83).

(5.2)

This regression has a slope of 0.49, indicating that the amount of OH− consumed in the pH range 4.5 to 5.5 is equivalent to half the exchangeable Al content. One reason for this discrepancy may derive from the incomplete hydrolysis of the Al3+ ion under relatively high rates of OH− addition. No clear relationship was observed between the extent of this buffering and the soil temperature regime of the samples. The OH− consumption in the pH range 5.5 to 8.3 is correlated with total carbon content, Cp and Alp + 1/2Fep (Table 5.8; r = 0.84**, 0.93** and 0.81**, respectively), indicating that the acidity in this pH range originates from organo-mineral complexes via the dissociation of the acidic functional groups of soil humus and/or the deprotonation of oxide surfaces, as follows:

R–COOH + OH− = R–COO − + H2O,

(5.3)



M+– −OOC–R + OH– = M–OH + −OOC–R,

(5.4)



M–OH + OH− = M–O − + H2O,

(5.5)

where M− represents metal ions (such as Fe or Al) at the surface of soil particles and R–COOH represents the acidic functional groups of organic matter. Following regression equation was obtained between Cp content and OH− consumption in the pH range 5.5 to 8.3 (OH58):

OH58 = 0.86Cp + 6.95 (r 2 = 0.87).

(5.6)

194

TABLE 5.9 Concentrations of Ions in Soil Solution from Forest Soils in Kinki District, Japan Alb DOC

Orgn− a NO3– Cl–+SO42−

Horizon pH (mg C L−1)

MD2

KD1

KD2

TT

(mmolc L−1)

Na++K++Mg2++Ca2+

Fe3+

Aln+

(mmolc L−1)

Si

Inorganic Monometric

(mmol L−1)

Organic Monomeric

StronglyComplexed

(×10−3 mmol L−1)

15

5.2

13.5

0.102

0.000

0.176

0.007

Mie Soils 0.215

0.001 0.054

0.117

4.6

(26)

10.3

(57)

3.0

(17)

40

5.2

6.9

0.061

0.000

0.150

0.006

0.165

0.001 0.038

0.097

6.3

(49)

5.9

(46)

0.6

(5)

15

5.5

3.2

0.029

0.000

0.098

0.003

0.112

0.000 0.012

0.043

1.9

(48)

1.9

(48)

0.2

(4)

40

5.5

2.2

0.028

0.000

0.100

0.003

0.115

0.000 0.009

0.049

2.4

(79)

1.2

(39)

0.0

(0)

15

5.1

3.1

0.023

0.000

0.177

0.008

0.158

0.000 0.032

0.074

8.5

(80)

1.6

(15)

0.6

(5)

40

5.0

1.3

0.016

0.000

0.190

0.009

0.164

0.000 0.033

0.054

8.5

(78)

0.9

(8)

1.5

(13)

15

5.0

4.4

0.048

0.003

0.105

0.010

0.109

0.000 0.036

0.060

5.6

(46)

5.3

(44)

1.2

(10)

40

5.2

1.2

0.021

0.004

0.101

0.006

0.105

0.000 0.013

0.035

3.3

(74)

1.0

(22)

0.1

(3)

15

4.7

4.0

0.042

0.124

0.096

0.018

0.173

0.000 0.069

0.037

16.1

(70)

4.8

(21)

2.0

(9)

40

4.9

2.3

0.023

0.144

0.061

0.013

0.167

0.000 0.046

0.033

11.9

(77)

2.0

(13)

1.5

(10)

World Soil Resources and Food Security

MD1

H+

H2

H1b

S3

N4b

a b

15

4.9

3.7

0.033

0.012

0.081

0.012

0.073

0.001 0.040

0.085

7.0

(53)

4.2

(32)

2.0

(15)

40

5.2

1.1

0.018

0.000

0.059

0.006

0.060

0.001 0.011

0.031

2.5

(67)

1.1

(29)

0.1

(4)

10

4.5

3.0

0.231

0.192

0.435

0.035

Kyoto Soils 0.488

0.001 0.135

0.041

41.0

(91)

2.5

(6)

1.5

(3)

35

4.7

1.3

0.228

0.174

0.265

0.018

0.426

0.000 0.088

0.042

26.4

(90)

0.6

(2)

2.4

(8)

55

5.4

2.5

0.172

0.049

0.190

0.004

0.300

0.000 0.021

0.039

5.1

(73)

0.2

(3)

1.7

(24)

10

3.9

23.7

0.177

0.315

0.306

0.125

0.302

0.009 0.189

0.085

42.7

(68)

12.9

(21)

7.3

(12)

20

4.6

20.6

0.223

0.060

0.275

0.028

0.342

0.012 0.074

0.068

13.7

(55)

7.7

(31)

3.4

(14)

40

4.7

10.8

0.145

0.102

0.252

0.022

0.299

0.002 0.083

0.050

20.5

(74)

4.6

(17)

2.7

(10)

15

4.5

8.7

0.229

0.317

0.366

0.030

0.509

0.001 0.183

0.065

51.5

(84)

4.5

(7)

5.1

(8)

40

4.8

4.1

0.244

0.343

0.307

0.015

0.619

0.000 0.092

0.047

25.2

(82)

1.0

(3)

4.7

(15)

60

5.1

6.1

0.270

0.229

0.371

0.007

0.644

0.000 0.037

0.058

7.8

(63)

1.3

(10)

3.4

(27)

10

4.5

6.8

0.150

0.070

0.219

0.033

0.220

0.003 0.090

0.023

22.8

(76)

5.0

(17)

2.2

(7)

20

4.7

1.8

0.150

0.084

0.192

0.019

0.239

0.003 0.067

0.032

18.5

(82)

1.8

(8)

2.3

(10)

45

5.1

2.7

0.139

0.018

0.192

0.008

0.247

0.000 0.024

0.031

7.2

(89)

0.5

(6)

0.4

(5)

Pedogenetic Acidification in Humid Asia

OT

Orgn– represents anion deficit, the negative charge of organic acids. Parentheses denotes the percentage of total Al.

195

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World Soil Resources and Food Security

As the carboxylic functional group content of fulvic acid in cool temperate zone soils is reported to be 0.61–0.85 cmol g−1 organic matter [Stevenson 1982], the slope obtained in the regression, 0.86 cmol g−1 C (equivalent to 0.51 cmol g−1 organic mat ter), seemed to be reasonable if most of the carboxylic groups of the Cp fraction react with added OH− as in the first and second reactions shown above. In early research relating to titration, the buffer region between pH 5.5 and 8.3 was often attributed to hydroxy-Al interlayer components [Schwertmann and Jackson 1963, 1964]. The third reaction above may also involve edges of layer silicates. Titratable acidity in this pH range was generally higher in MSTR rather than TSTR B horizon soils.

5.3.3  Soil Solution Composition Table 5.9 summarizes the soil solution composition for each soil profile. The concentration of DOC was generally lower than 1 mmol L−1, except for MD1 and H1b, in which it sometimes exceeded 2 mmol L−1. In most of the soils, the concentration of Al decreased in the subsoils with a concomitant increase in pH and decrease in DOC (Table 5.9), suggesting precipitation of Al through hydrolysis and/or coprecipitation of Al–organic matter complexes in the subsoils. The organic monomeric and strongly complexed Al fractions are relatively small (less than 50% of total dissolved Al except for MD1), and the inorganic monomeric Al is the major fraction. The concentration of inorganic monomeric Al drastically decreases as the pH approaches 5. In contrast, the concentration of organic monomeric Al generally has a positive correlation with DOC concentration in each of soil profiles, suggesting that DOC supplied from the upper soil layers and/or litter layers is primarily responsible for mobilizing Al in the form of organo-mineral complexes. Although there were significant concentrations of Al in the soil solution, Fe was rarely present except for Spodosols (N4b and H1b), indicating that translocation of Fe did not occur in these soil profiles.

5.3.4  E luvi-Illuviation of Inorganic Al and Subsequent Adsorption of DOC Figure 5.6 summarizes the soil solution composition and characteristics of the soil solid phase in each soil profile with the reaction of inorganic Al in the left figure and that of organic Al and DOC in the right figure. Three patterns of acid neutralization, that is, surface neutralization, subsurface neutralization, and limited neutralization, were observed judging from titratable alkalinity and the concentration of inorganic monomeric Al and the pH of the soil solution. In the surface layers of the MD1 and MD2 profiles, the solution pH was higher than 5, inorganic monomeric Al below 5 × 10 −3 mmol L−1, and titratable alkalinity higher than 10 cmol kg−1. Most of the acidity supplied by the soil surface, if any, is probably neutralized in these layers (surface neutralization). In the surface horizons of the other profiles, the titratable alkalinity of the surface soils is lower than 10 cmol kg−1, solution pH is 5 or below, and larger concentrations of inorganic monomeric Al (>5 × 10 −3 mmol L−1) are dissolved. In the KD2, OT, H2, S3, and N4b profiles the concentrations of inorganic monomeric Al in the soil solution decreased at or below the AB or Bw horizon,

197

Pedogenetic Acidification in Humid Asia

(a)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

A

20 40 Depth (cm)

5

20

MD1 A

60

6

0

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

Alo ( 10

b A

60

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

1 2 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Solution pH ( )

A

40

Typic dystrudepts Depth (cm)

a

(b)

) (g kg–1) 20

a

a

4

Alo ( 10

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a a

20

A

KD2

40

5

6

Solution pH ( )

(c)

Depth (cm)

4

60

0

40 b

Humic dystrudepts

B

Depth (cm)

b

60

1 2 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

Alo ( 10

A

20

B

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a a

40

A

MD2 Typic dystrudepts

60 Depth (cm)

4 5 6 Solution pH ( )

0

1 2 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

40 Depth (cm)

a

20

20 A

A a A

60

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

FIGURE 5.6  Chemical properties and titration data of the soil profiles superimposed with the soil solution composition. Error bars are standard error. Values with the same letter are not significantly different (p < 0.05). Alo, Feo, extraction in the dark with acid (pH 3) 0.2 mol L−1 ammonium oxalate; Alp, Fep, extraction with 0.1 mol L−1 pyrophosphate at pH 10; DOC, dissolved organic carbon.

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World Soil Resources and Food Security

(d)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

Alo ( 10

a a

20

TT

40

A

Andic dystrudepts

60

5

6

Solution pH ( )

(e)

Depth (cm)

4

0

Alo ( 10

B

60

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

1 2 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

A a

40 Depth (cm)

a

20

A

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a a

20 A 40

5

6

0

Alo ( 10

A

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

1 2 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

A

40 b 60

60

Solution pH ( )

(f )

KD1 Lithic dystrudepts

Depth (cm)

4

A

Depth (cm)

a

20

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a a

20

A

40

5

60

6

Solution pH ( )

0

60

1 2 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

FIGURE 5.6 (Continued)

A

40 b Depth (cm)

4

Alic hapludands

B

Depth (cm)

b

OT

20

B

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

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Pedogenetic Acidification in Humid Asia

(g)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

Alo ( 10

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a a

20 a

5

6

Solution pH ( )

60

4

5

6

0

2 4 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Solution pH ( )

(i)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0

Alo ( 10

a B

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a

a

20

A

40

5

60

6

Solution pH ( )

S3

Alic hapludands

40 60

B 0

20

Depth (cm)

c

AB

Depth (cm)

b

4

60

A

B

40 Depth (cm)

Andic haplorthods

B

a

a

20

N4b

40

B

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

AB

a Ba

0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

A

Depth (cm)

c

60

Alo ( 10

20

b

40

2 4 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Titratable alkalinity ( ) (cmol kg–1) 20 10 0 a

Andic dystrudepts

B

0

A

20

H2

Depth (cm)

60 Depth (cm)

4

AB

40 b

(h)

A

2 4 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

FIGURE 5.6 (Continued)

A a B

a

B 0 0.5 1 DOC ( ) (mmol L–1) or organic monomeric Al ( ) (*0.01 mmoL L–1) in soil solution

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World Soil Resources and Food Security

(j) Titratable alkalinity ( ) (cmol kg–1) 20 10 0

Alo ( 10

Titratable acidity initial pH–pH 5.5 ( ) Alo + 1/2Feo ( ) pH 5.5–8.3 ( ) or Alp + 1/2Fep ( ) (g kg–1) (cmol kg–1) 40 20 0 10 20

) (g kg–1) 20

a a

A

20

b

40

20

Andic haplohumods

40

60

4

5

6

Solution pH ( )

0

2 4 Inorganic monomeric Al in soil solution ( ) (*0.01 mmoL L–1)

Depth (cm)

B

Depth (cm)

b

H1b

B

A AB

a

a

B

60

0 0.5 1 1.5 2 2.5 DOC ( ) (mmol L–1) or organic –1 monomeric Al ( ) (*0.01 mmoL L ) in soil solution

FIGURE 5.6 (Continued)

where the titratable alkalinity increases to over 10 cmol kg−1, along with a pH rise to approximately 5.2 (subsurface neutralization). In contrast, the titratable alkalinity of the KD1, TT, and H1b soils never exceeded 10 cmol kg−1 at any horizons, solution pH remains at or below 5, and inorganic monomeric Al concentrations are high even in the B horizons (limited neutralization). These results show that a large part of the soil solution acidity can be removed, and inorganic monomeric Al in solution precipitated through hydrolysis by soil layers with high titratable alkalinity, that is >10 cmol kg−1 in the condition of our study. Although such an acidity-neutralizing layer was found at or near the soil surface under TSTR (MD1 and MD2), it was occasionally formed in the subsoil under MSTR, at which soil solution acidity is often neutralized, resulting in accumulation of solution phase Al through hydrolysis and precipitation. Dissolved organic matter in the soil solution, still possessing a certain concentration of dissociated acidic functional groups, can be adsorbed on these amorphous compounds of Al. Thus, as organo-mineral complexes accumulated in B horizons are considered to also comprise the active fraction of titratable acidity, the process described here also contributes to the increase in titratable acidity in the B horizon soils.

5.3.5  Comigration of Al and DOC in the Soil Profiles The concentration of organic monomeric Al in the soil solution substantially decreases with depth in KD2, TT, OT, and all of Kyoto soils (Table 5.9; Figure 5.6). As DOC also decreases to some extent with depth, precipitation of complexes of Al and dissolved organic matter complexes may have occurred in the subhorizon, as discussed by DeConinck [1980] for the formation of spodic horizons. As fairly large amounts of amorphous compounds (Alo + 1/2Feo) already exist in the subhorizons, adsorption of Al-dissolved organic matter complexes onto amorphous compounds may also be possible. This process may be involved in soil formation under MSTR. It seems, to some extent, similar to podzol formation, at least in terms of Al translocation, and enhances the accumulation of titratable acidity in subsoils.

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Pedogenetic Acidification in Humid Asia

5.3.6  Dynamics of Titratable Alkalinity and Acidity in the Pedogenetic Processes Figure 5.7 summarizes the amounts of titratable acidity, titratable alkalinity, and total C in the soil profiles on a kmol ha−1 basis using all the data from the present study (20 profiles) and data from previous studies (4 profiles from northern Kyoto:

Titratable alkalinity in the 15–45 cm layers of soil (kmol ha–1)

(b)

1000 Thermic Intermediate Mesic Mesic, podzolic

800 600 400 200 0

50 40 30 20 Cp in the 15 10 of soil –45 cm lay s (Mg – ers ha 1)

0

0

Ex ch lay Al in ers th (km e 1 ol 5–4 c h a –1 5 cm )

Titratable acidity in the 15–45 cm layers of soil (kmol ha–1)

(a)

500 400 300 200 100

Thermic Intermediate Mesic Mesic, podzolic

500 400 300 200 100 0

0

50

100

150

Titratable alkalinity in the 0–15 cm layers of soil (kmol ha–1) (c) Total C in the 15–45 cm layers of soil (Mg ha–1)

150

100

Thermic Intermediate Mesic Mesic, podzolic

50

0

0

50

100

150

Total C in the 0–15 cm layers of soil (Mg ha–1)

FIGURE 5.7  Amounts of (a) titratable acidity, (b) total C, and (c) titratable alkalinity on a kmol ha−1 basis. Cp, pyrophosphate extractable C; Exch Al, exchangeable Al. (Data presented here include data cited from Funakawa, S. et al., Soil Sci. Plant Nutr. 49, 387–396, 2003; Funakawa, S., et al., Soil Sci. Plant Nutr., 39, 677–690, 1993.)

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World Soil Resources and Food Security

ON1 and ON2 in TSTR, and KT1 (podzolic soil) and KT2 in MSTR) [Funakawa et al. 2003]. As discussed previously, pedogenetic acidification in forest soils with MSTR is characterized by an accumulation of acidity in the form of amorphous compounds and/or organo-mineral complexes in the B horizons, originally supplied from the overlying horizon. Figure 5.7a shows that the titratable acidity in subsoil layers (15–45 cm) amounts to 292–816 kmol ha−1 and is higher among soils in MSTR than in TSTR because of a higher contribution of organo-mineral complexes (represented by Cp). As the in situ annual acid load on acid soils (i.e., low and intermediate rates of soil acidification) are reported to mostly range from 1 to 7 kmol ha−1 yr−1 [van Breemen et al. 1984], the amounts of titratable acidity observed in the present study are equivalent to hundreds of times the annual acid load. The cumulative acid load that accompanies DOC adsorption and/or precipitation enhances the accumulation of total C in the subsoil layers (Figure 5.7b). As the DOC flux that can accumulate between the 15-cm and 40- to 45-cm layers of soils is calculated to be 0.10 (MD2) to 1.29 (H1b) Mg ha−1 yr−1 (average: 0.40) based on the soil solution composition in the present study assuming the annual amount of percolating water to be 1,000 mm, the amounts of organic C accumulated in the subsoil layers (i.e., 25–112 Mg ha−1; Figure 5.7c) also corresponding to hundreds of times the annual DOC flux. Thus, the pedogenetic processes, including acid transfer and C migration to the subsoil layers are considered to be prolonged at least for hundreds of years. According to Figure 5.7b, the amounts of titratable alkalinity in the 15- to 45-cm layers of soils tend to be higher compared with those in the surface 15-cm layers of soils in MSTR. As amorphous Al hydroxides (Alo) compose major parts of the titratable alkalinity, they play an important role in retarding further acidification through protonation and/or partial monomerization. In this context, amorphous Al hydroxides, which would otherwise be leached out directly from the soil profile, can be regarded as making a temporary contribution to the acid-neutralizing capacity of the soil. This process delays the outflow of acids (H+ or Al3+) from soils. Both stepwise processes of precipitation of inorganic Al and a subsequent adsorption of DOC, and comigration and precipitation of Al–soluble organic matter complexes are involved in the accumulation processes of organo-mineral complexes in the B horizons. In contrast, for soils under TSTR, the acid-buffering reactions, if any, occur mostly at or near the soil surface in the present study. One possible explanation for such an apparent deep penetration of soil acidification in MSTR is that, under MSTR, the amount of percolating water is higher than under TSTR because the precipitation is higher and evapotranspiration is lower at high elevations in the region, and that such a difference in the degree of leaching possibly brings more intensive soil acidification under MSTR. The fact that the titratable alkalinity required to acidify soils to pH 3.0 and the titratable acidity required to neutralize soils to pH 8.3 in B horizon soils is apparently lower in TSTR soils than MSTR soils reflects the lower accumulation of amorphous and/or organo-mineral complexes in the B horizons of the former.

Pedogenetic Acidification in Humid Asia

203

5.4  C  HANGES IN THE BLOCKAGE EFFECT OF HYDROXY-AL POLYMERS ON THE FRAYED EDGE SITE OF ILLITIC MINERALS DURING THE PROCESS OF PEDOGENETIC ACIDIFICATION IN JAPAN As discussed in the preceding sections, the pedogenetic acidification process largely modifies soil properties relating to sesquioxidic properties and organic materials. Pedological research and mineralogical analyses of many soil profiles also revealed that HIV-dominated B horizons lie below eluvial, Al-, or Fe-leached horizons rich in vermiculite or smectite in Spodosols and the environmentally associated Inceptisols [Maes et al. 1999; Kitagawa 2005], strongly suggesting that the pedogenetic acidification process brings the sequential transformation from HIV to vermiculite or smectite as soils are podzolized. In the present section, in order to detect gradual transition of 2:1 minerals during the pedogenetic acidification, the RIP methodology introduced previously [see Chapter 4] is applied to discuss the fate of illitic minerals under podzolization. We first described the vertical distribution of HIV, vermiculite, and the frayed edge site in profiles of forest soils with different degrees of podzolization, and then explored the interactions between the frayed edge site and hydroxy-Al polymers by directly comparing the amount of the frayed edge site before and after extracting the hydroxy-Al polymers from soil clays using Tamura’s method [1958].

5.4.1  Soils Studied For this experiment, we collected samples from ten soil profiles in mountainous forest areas with an elevation of 240–1000 m in southwestern Japan (Figure 5.1). The soils were mostly derived from sedimentary rocks and overall chemical and mineralogical properties are similar to those analyzed in the previous sections. The soil moisture and temperature regimes are, respectively, similarly udic and mesic. Common soil types in this region are Udepts (in the USDA soil taxonomy). Spodosols and Andisols are also sparsely distributed in this region. Analytical items relating to chemical and mineralogical properties in this study were the RIP experiment as well as several soil characteristics including the acid-oxalate-extractable Fe (Feo) [Mckeague and Day 1966], dithionite-citrate-bicarbonate (DCB) extracted-Fe (Fed) [Mehra and Jackson 1960], and total Fe (Fet) of the soils (for > Kao HIV >> Kao HIV >> Kao, Mica HIV >> Kao HIV >> Kao HIV, Kao >> Mica HIV, Kao > Mica

57 14 23 43 26

21 26 37 16 26

23 60 40 41 48

9.3 9.4 10.8 9.6 5.8

14.7 21.1 20.8 7.9 12.6

0.2 0.0 0.1 0.6 0.0

HIV, Kao >> Mica HIV >> Kao HIV >> Kao HIV > Mica, Kao HIV > Kao > Mica

World Soil Resources and Food Security

Site

Soil Classificationa

Particle Size Distribution

Exchangeable

Dystrudepts Dystrudepts Hapludands Dystrudepts Dystrudepts

S3 N1b KT2

Fulvudands Hapludands Dystrudepts

N4b SW1

Haplorthods Haplorthods

H1b KT1

Haplorthods Haplorthods

a b

c

BC Bw2 Bw2 Bw BA Bw BC Bw Bw Bw

32–44 29–45 35–55 25–50 14–28 28–48 48–57 48–70 19–55 15–36

B-mesic B-mesic B-mesic B-mesic E/uB B-mesic B-mesic B-mesic B-mesic B-mesic

4.92 4.65 4.85 4.62 4.19 4.63 4.79 4.65 4.77 4.88

25.8 13.0 35.0 41.0 75.0 18.0 13.0 17.0 33.0 44.3

0.3 0.2 0.2 0.6 1.2 0.5 0.5 0.9 0.3 0.7

9.6 7.4 3.0 6.1 12.1 6.4 5.3 4.5 3.1 6.7

Bw1 E Bs C Bh E2

30–40 2–10 23–45 60–73+ 19–22 6–11

B-mesic E/uB Bs-pod B-mesic Bs-pod E/uB

4.51 3.74 4.20 4.88 3.99 4.24

Haplorthods, mesic 21.0 0.4 6.9 57.0 0.8 13.4 37.0 0.5 15.6 12.0 0.4 2.5 54.0 0.5 16.7 42.7 0.5 10.6

17.9 22.5 15.6 28.2 31.5 15.8 13.4 20.1 19.9 32.6

47 33 22 10 12 15 21 9 16 21

19 25 19 49 33 33 33 42 32 34

35 41 59 41 55 52 46 49 53 46

6.3 5.6 13.4 9.2 8.3 7.7 6.6 11.4 20.6 16.0

8.7 8.8 17.1 13.4 18.2 11.5 6.7 10.1 15.9 25.6

0.1 0.0 0.4 0.6 0.4 0.6 0.7 1.1 2.9 0.0

HIV >> Kao HIV >> Kao HIV >> Kao HIV > Kao > Mica HIV > Kao HIV > Kao HIV > Kao HIV > Kao HIV > Kao HIV > Kao

20.6 32.0 33.9 12.8 33.7 21.7

16 31 19 67 31 16

37 36 31 16 29 57

48 33 50 17 40 28

10.1 3.0 8.2 8.3 6.7 2.4

20.5 4.4 22.8 4.0 22.0 9.2

0.6 0.3 0.3 1.2 0.2 0.0

HIV > Kao Mica-Sm >> Kao Mica-Vt > Kao HIV > Mica > Kao Vt, Sm, Mica, Kao Vt > Kao

Pedogenetic Acidification in Humid Asia

KD1 KD2 MT SW2 H2

According to Soil Taxonomy [Soil Survey Staff 2006]. E/uB: E or upper B horizon soils; Bs-pod: Bs horizon of podzolic soils; B-mesic: Bw and lower B horizons of forest soils with MSTR; B-thermic: Bw and lower B horizons of forest soils with TSTR. Kao, kaolin minerals; Mica, clay mica; Sm, emectite; Vt, vermiculite; HIV, hydroxy-Al interlayered vermiculite.

211

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World Soil Resources and Food Security

4 profiles (ON1, ON2, KT2, and KT1) in the Kuta and Ohno Experimental Forests of Kyoto Prefectural University in Kyoto Prefecture (see also Tables 5.6 and 5.7). Soil samples were collected from either the E or upper B horizon (mostly above 20-cm depths) or lower B to C horizons (mostly below 30-cm depths). Since the main objective of this study was to analyze the charge characteristics of soil mineral components along with pedogenetic processes, we excluded soil samples from the surface few centimeters, which were expected to be considerably affected by organic matter accumulation. The general physicochemical properties of the soils studied are presented in Table 5.10, along with their taxonomic classification. The soils from the high elevation area (above 600 m) of Mt. Odaigahara and all the soils from the Ashiu and Kuta Experimental Forests were subjected to cool temperate forest conditions with a mesic soil temperature regime (MSTR), while the soils from the low elevation area (below 600 m) of Mt. Odaigahara and those from the Ohno Experimental Forest were subjected to warm temperate forest conditions with a thermic soil temperature regime (TSTR). The soils tested included Dystrudepts, Hapludands, and Haplorthods and all the soils were derived from sedimentary or metamorphic rocks, which were not appreciably affected by volcanic ejecta. In most cases, crystalline HIV predominated with small amounts of mica and kaolin minerals in the clay fraction, whereas expansible 2:1 minerals such as vermiculite and smectite sometimes predominated in the upper layers of podzolized soils. Soil samples collected were air-dried, passed through a 0.2-mm mesh sieve, and analyzed for charge characteristics by the ion adsorption method.

5.5.2  Analytical Methods Determination of cation and anion retention, or cation and anion exchange capacities (CEC and AEC), of the soils at different ionic concentrations and pH values was performed according to the method of Schofield [1950]: In a 50-mL centrifugation tube, 2 g of soils were washed with a 1 mol L−1 NaCl solution 3 times and then with a 0.2, 0.1, 0.02, or 0.005 mol L−1 NaCl solution at pH values ranging between 4.5 and 8. The samples were equilibrated in the NaCl solutions at each concentration or pH value for 2 days. The solution pH was maintained at the objective pH using dilute HCl or NaOH solution, if necessary. After centrifugation, Na and Cl ions adsorbed on the soil were extracted with 0.5 M KNO3. The content of Na was determined by atomic absorption spectrophotometry (AAS) [Shimadzu, AA-840-01] and the Cl content by mercury (II) thiocyanate colorimetry at 460 nm [Frankenberger et al. 1978]. The amounts of Na and Cl in the occluded solution were corrected by subtraction based on their concentration in the supernatant and the weight of the occluded solution. The negative and positive charges of the soil were calculated from the amount of adsorbed Na and Cl, respectively. In addition, to analyze the effect of organic matter on the charge characteristics of the soils, the cation retention of the soils was measured after the destruction of organic matter with H2O2, as follows: 25 mL of deionized water and 5 mL of a 30% H2O2 solution were added to 2 g of soil and heated at 80°C until the soil organic matter and H2O2 were completely decomposed. After centrifugation, the samples were washed twice with 80% ethanol and then air-dried

Pedogenetic Acidification in Humid Asia

213

and weighed. The ion adsorption method described above was then applied, using a 0.1 mol L−1 NaCl solution as the electrolyte. The pH of the equilibrated solution was adjusted within the range of 5.5 to 6.9. In addition, in order to evaluate the effect of the sesquioxidic components on the charge characteristics of the soils, two representative B horizon soils (OT-Bw2 and MD1-Bw2) under MSTR or TSTR, which contained certain amounts of free oxides and interlayered materials, were used to determine the CEC, with a 0.1 mol L−1 sodium acetate buffer solution adjusted to pH 5 or 6 by acetic acid as electrolyte, after successive removal of organic matter by H 2O2 treatment with heating at 80°C, Al and/or Fe oxides by dithionite-citrate-bicarbonate (DCB) treatment with heating at 80°C [Mehra and Jackson 1960], and interlayered materials of 2:1 clay minerals by 0.33 M sodium citrate treatment with heating at 100°C for 8 hours [Tamura 1958]. Data obtained were analyzed using SYSTAT 8.0 software [SPSS Inc. 1998].

5.5.3  General Charge Characteristics of the Soils The CEC at different pH values and ionic concentrations is shown in Figure 5.12 for selected soils. Since anion retention was always less than 0.5 cmolc kg−1 even for a pH value below 5, the contribution of the sesquioxidic components to anion retention through a positively charged surface was limited for the soils studied, in contrast to volcanic or highly weathered soils, which showed fairly large amounts of positive charges within low pH ranges below 5 [Espinoza et al. 1975; Van Raij and Peech 1972]. It is assumed that the variable positive charge of the soils in the present study was neutralized by the permanent negative charge of the soils, which were derived from 2:1 clay minerals, unlike in the above-cited volcanic or highly weathered soils. Hence, the following discussion will focus on cation retention. In order to analyze the charge characteristics on a quantitative basis, the cation retention curves were fitted to the following equation and the coefficients a, b, and c were calculated by multiple regression analysis:

log CEC = a pH + b log C + c,

(5.7)

where C is the ionic concentration expressed in mol L−1. Higher values for a and b indicate a larger contribution of the pH and/or concentration-dependent negative charges to the total charge of the soils. In contrast, the value of c is a parameter that represents log CEC at pH 0 in a 1 mol L−1 solution. For example, based on Figure 5.13, the contribution of the variable negative charge to the total charge was especially low (a = 0.09 and b = 0.12) in the SW1-E sample (Haplorthods) compared to most of the B horizon soils. There was a positive correlation between a and b with a slope of almost unity (Figure 5.13a), suggesting that the negative effects of H+ concentration and the positive effects of the ionic strength of the supporting electrolyte on the appearance of the variable negative charge were similar. The negative correlation between (a + b)/2 and c indicates that the development of variable charge characteristics contributed to the decrease of the CEC in a low pH range (Figure

0

4

5

7

8

CEC (cmolc kg–1)

log CEC = 0.14pH + 0.17 log C + 0.78 50 R2 = 0.93 40 30 20 10 0

4

5

6

pH

7

8

30

30 20

20

10

10 0

4

OT Bw2

30 20 10

0

4

5

6

pH

7

8

7

8

0

4

5 OT C

6 pH

7

8

40 30

30 20

20

10

10 0

4

5

6

pH

7

8

0

4

SW1 Bs

5

6

pH

7

8

SW1 C

30

log CEC = 0.14pH + 0.20 log C + 0.65 log CEC = 0.27pH + 0.29 log C – 0.53 40 R2 = 0.87 R2 = 0.88 30

20

20

40

CEC (cmolc kg–1)

CEC (cmolc kg–1)

CEC (cmolc kg–1)

log CEC = 0.09pH + 0.12 log C + 0.93 R2 = 0.90

6 pH

log CEC = 0.26pH + 0.40 log C + 0.00 log CEC = 0.34pH + 0.37 log C – 0.89 40 R2 = 0.93 R2 = 0.91

SW1 E 40

5

10

0

10

4

5

6

pH

7

8

0

4

5

6

pH

7

8

FIGURE 5.12  CEC of the soils determined at different pH values and electrolyte concentrations. ○ 0.2 M, △ 0.1 M, □ 0.02 M, × 0.005 M.

World Soil Resources and Food Security

CEC (cmolc kg–1)

OT BA

6 pH

40

CEC (cmolc kg–1)

10

MD1 BC

log CEC = 0.14pH + 0.24 log C + 0.47 log CEC = 0.17pH + 0.15 log C + 0.10 40 R2 = 0.87 R2 = 0.79

CEC (cmolc kg–1)

CEC (cmolc kg–1)

CEC (cmolc kg–1)

20

MD1 Bw2

214

MD1 BA

log CEC = 0.24pH + 0.21 log C – 0.13 40 R2 = 0.91 30

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Pedogenetic Acidification in Humid Asia

0.5

b

0.4

(b)

y = 1.02x + 0.04 R2 = 0.67

0.4

0.3 0.2

0.2 0.1

0.1 0 0

y = –0.13x + 0.24 R2 = 0.72

0.3

(a + b)/2

(a)

0.1

0.2

a

0.3

0.4

0.5

–1

–0.5

0

0

c

0.5

1

FIGURE 5.13  Relationships between coefficients a and b (a) and c and (a + b)/2 (b) values in the regression equation, log CEC = a pH + b log C + c, which represents the charge characteristics of the soils. △ E or upper B horizon soils, ○ Bs horizon of Haplorthods, ▲ Bw and lower B horizons of Dystrudepts and Hapludands under MSTR, ● Bw and lower B horizons of Dystrudepts under TSTR.

5.13b). These results are consistent with earlier reports [Okamura and Wada 1983]. The values of (a + b)/2 and c of the E and B horizons of the Haplorthods and the upper B horizons of the Dystrudepts ranged from 0.11 to 0.23 (mean: 0.17) and from −0.13 to 0.93 (mean: 0.57), respectively. Some of these soils showed especially high c values. The values of (a + b)/2 and c of the B horizons of Dystrudepts with TSTR ranged from 0.14 to 0.25 (mean: 0.19) and from 0.01 to 0.47 (mean: 0.24), respectively. These values were similar to those of Ultisols or Oxisols in Thailand, which were reported by Wada and Wada [1985]. Corresponding values for the B horizons of Dystrudepts or Hapludands with MSTR ranged from 0.23 to 0.39 (mean: 0.29) and from −0.89 to 0.17 (mean: –0.28), respectively, which were comparable to those of the B horizons of Andisols [Okamura and Wada 1983; Wada and Wada 1985]. These soils were dominated by a variable negative charge with a rather small apparent permanent negative charge.

5.5.4  C  ontribution of Each Component to the Charge Characteristics of the Soils—Statistical Analysis Table 5.11 lists the correlation coefficients between the physicochemical properties and several parameters relating to the charge characteristics of the soils. Acid ammonium oxalate-extractable Al (Alo) showed a positive correlation and exchange acidity a negative correlation with coefficients a and b, while properties relating to soil acidity (such as pH(H2O), pH(KCl), and the content of exchangeable Al) showed significant correlations with c. These relationships indicate the importance of amorphous compounds and/or soil acidity for the charge characteristics of these soils. To analyze the effect of physicochemical properties on charge characteristics, principal component analysis was conducted for the soils, followed by stepwise multiple linear regression. Variables employed included pH(H2O), pH(KCl), sum of exchangeable bases, levels of exchangeable Al, CEC, total C, pyrophosphate­extractable C, Al, and Fe (Cp, Alp, and Fep, respectively), acid ammonium oxalate

216

TABLE 5.11 Correlation Coefficients between Physicochemical Properties and Charge Characteristics of the Soils

c

pH (H2O)

pH (KCl)

Exch. Bases

Exch. Al

CEC

Total C

Alo

Feo

Sio

Alo + 1/2Feo

0.58** 0.41* 0.51**

0.75** 0.66** 0.74**

−0.29 −0.37 −0.35

−0.63** −0.56** −0.62**

−0.39* −0.34 −0.38*

−0.10 −0.08 −0.10

0.67** 0.68** 0.71**

0.14 0.27 0.22

0.54** 0.51** 0.55**

0.47* 0.54** 0.53**

−0.73**

−0.83**

0.31

−0.52**

0.09

−0.48**

0.38*

0.78**

Note: *Significant at 5% level, **significant at 1% level, n = 28.

0.59**

−0.26

Clay Content −0.08 0.08 0.01 0.18

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a b (a + b)/2

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Pedogenetic Acidification in Humid Asia

(pH 3)-extractable Al, Fe, and Si (Alo, Feo, and Sio, respectively), DCB-extractable Fe (Fed), Alo + 1/2Feo, Alp + 1/2Fep, and clay content. Table 5.12 shows the factor pattern for the first three principal components after varimax rotation, which accounted for 81% of the total variance. High positive coefficients were given to Cp, Alo, Feo, Sio, Alp, Alo + 1/2Feo, and Alp + 1/2Fep for the first component. These variables corresponded to the properties derived from amorphous and/or organo-mineral complexes and, hence, the first component was referred to as the amorphous materials factor. Based on Figure 5.14, B horizon soils under TSTR showed relatively low scores for the first component, while those under MSTR showed high scores. The second component exhibited high coefficients, positive or negative, with pH(H2O), pH(KCl), exchangeable Al, CEC, total C, Cp, and Fep, indicating the existence of a close relation with the soil acidity and organic matter content. The scores of this component were especially high among upper layer soils (Figure 5.14), suggesting that the component reflected soil acidification accompanied by the accumulation of organic matter from surface horizons. This factor was referred to as the acidity factor. The third component showed high coefficients with Fed and the clay content and was considered to be a weathering factor. Most of the variables were closely related

TABLE 5.12 Factor Pattern for the First Three Principal Components Relating to Soil and Environmental Variables Variable pH(H2O) pH(KCl) Exch. Bases Exch. Al CEC Total C Cp Alo Feo Sio Alp Fep Fed Alo + 1 / 2Feo Alp + 1 / 2Fep Clay content Eigenvalue Proportion

PC1

PC2

PC3

0.05 0.48 0.11 −0.17 0.18 0.42 0.62 0.95 0.74 0.61 0.94 0.51 0.34 0.94 0.80 0.06

−0.91 −0.80 0.58 0.85 0.85 0.81 0.72 −0.17 0.42 −0.31 0.08 0.64 0.24 0.11 0.41 –0.01

0.08 0.03 0.08 0.32 0.33 −0.01 0.04 0.05 0.29 −0.33 0.20 0.40 0.78 0.18 0.34 0.83

7.38 0.46

4.11 0.26

1.42 0.09

Amorphous materials

Acidity

Weathering

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World Soil Resources and Food Security 3 2

Factor 2

1

–2

–1

0

0

1

2

–1 –2

Factor 1

FIGURE 5.14  Scattergram between the first and second principal component scores determined for each sampled soil. △ E or upper B horizon soils, ○ Bs horizon of Haplorthods, ▲ Bw and lower B horizons of Dystrudepts and Hapludands under MSTR, ● Bw and lower B horizons of Dystrudepts under TSTR.

to only one component with high coefficients above 0.6, except for Cp, which was affected by both the accumulation of organo-mineral complexes in the B horizons and percolation of organic matter from the soil surface. In the next step, stepwise multiple regression analysis was conducted to examine the contribution of each factor to (a + b)/2 or c, which represents the charge characteristics of the soils. The following equations were obtained:

(a + b)/2 = 0.238 + 0.049 × (amorphous materials factor) − 0.041 × (acidity factor),

(5.8)



c = 0.043 − 0.210 × (amorphous materials factor) + 0.346 × (acidity factor) + 0.115 × (weathering factor).

(5.9)

These equations clearly indicated that:

1. The value of (a + b)/2, which represents the variable charge characteristics of the soils, increased in parallel with the increase in the amorphous material content, while it decreased during the process of soil acidification. 2. The value of c was contributed negatively by the amorphous materials factor and positively by the acidity and weathering factors. The negative contribution of the amorphous materials factor to the value of c indicates that such components prevented the appearance of the permanent negative charge of 2:1 minerals by blocking it through the variable positive charges of the oxide surface. Soil acidification is considered to release such blocked negative charge sites through dissolution of amorphous materials. The weathering factor may be related to the direct increase in the colloidal surface area through the increase in the clay fraction.

219

Pedogenetic Acidification in Humid Asia

5.5.5  Contribution of Each Component to the Charge Characteristics of the Soils —Analysis by Successive Removal of Soil Components

CEC after H2O2 treatment (cmolc kg–1)

(a)

(b) 40

1:1

30 20 10 0

10

20

30

CEC of original sample (cmolc

40 kg–1)

20 15 10 5 0 2

1

0 –1 –2

1 r) or to c t fac Fa ity d ci (A

0

CEC decrease after H2O2 treatment (cmolc kg–1)

In the last part of this experiment, several chemical treatments were applied to remove each component of the soil in order to detect changes in charge characteristics. Since such treatments could, however, have affected the nature and/or composition of the remaining components of the soil, the following considerations may not be conclusive. Figure 5.15 shows the relationship between the CEC of the H2O2-treated samples (determined in a 0.1 mol L−1 NaCl solution with pH adjusted in the range of 5.5 to 6.9) and the CEC calculated for untreated samples (at the same pH and ionic concentration). The decomposition of organic matter scarcely affected the CEC of forest soil B horizons, suggesting that the direct contribution of the acidic functional groups of soil organic matter to the variable negative charge was not appreciable among these B horizon soils. This can be attributed to the fact that most of the acidic functional groups were already bound to oxide surfaces and the few R-COOH ligands that remained were dissociated along with the pH increase. However, there were several exceptions among the E or upper B horizon soils, which showed a significant decrease of the CEC after H2O2 treatment (Figure 5.15a). Since the contribution of variable charge characteristics, or (a + b)/2, was low for these soils (Figure 5.13a), such a decrease of CEC after H2O2 treatment might be attributed to a decrease in the permanent negative charge through the destruction of 1.4 nm minerals by the treatment. Figure 5.15b shows that these soils were already affected by extensive soil acidification, i.e., higher scores for the acidity factor. These soils may have been somewhat unstable due to partial destruction of their structure. Figure 5.16 depicts the changes in the CEC (at pH 5.0 and 6.0 in 0.1 M sodium acetate buffer solutions) of the OT-Bw2 (MSTR) and MD1-Bw2 (TSTR) soils by

0.1

0.2

0.3

0

(a + b)/2

FIGURE 5.15  CEC of the soils before and after H2O2 treatment for decomposing organic matter (a) and the relationship between the charge decrease, variable charge characteristics ((a + b)/2), and soil acidity (scores of acidity factor) (b). △ E or upper B horizon soils, ○ Bs horizon of Haplorthods, ▲ Bw and lower B horizons of Dystrudepts and Hapludands under MSTR, ● Bw and lower B horizons of Dystrudepts under TSTR.

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60

Interlayered materials removed

CEC/clay (cmolc kg–1)

50 40 30

Free oxides removed

20 10

Organic matter removed

0 OT OT Bw2, Bw2, MD1 pH5 pH6 Bw2, pH5

Original soil MD1 Bw2, pH6

FIGURE 5.16  Changes of CEC after successive removal of organic matter, free oxides, and interlayered materials in the 2:1–2:1:1 intergrades.

successive treatments with H2O2, DCB, and sodium citrate solution with heating (to remove organic matter, free oxides of Al and/or Fe, and interlayered materials, respectively). The CEC significantly increased after the DCB treatment, suggesting that the DCB-extractable fraction of free oxides had prevented the appearance of the permanent negative charge of 2:1 minerals by blocking it through the variable positive charge of the oxide surface. Extraction of interlayered materials by a 0.3 mol L−1 sodium citrate solution with heating resulted in a further increase in the permanent negative charge of the soils, presumably due to the removal of Al hydroxides and K+ ions in the interlayer space of 2:1 minerals. Thus the free oxides and/or interlayered materials were considered to have neutralized the layer charge of 2:1 minerals.

5.5.6  C  hanges in the Charge Characteristics of the Soils through Pedogenetic Acidification Figure 5.17 plots the relationship between (a + b)/2 and the CEC calculated for low pH (5.0) and low electrolyte (0.01 mol L−1) levels, at which the contribution of the acidic functional groups of soil organic matter to the CEC is limited, if any. The B horizons of the forest soils were characterized by the predominance of a variable negative charge with a CEC/clay less than 10 cmolc kg−1 (at pH 5, 0.01 mol L−1) and (a + b)/2 values above 0.1. Generally the B horizon soils with MSTR gave higher (a + b)/2 values (normally in the range of 0.2 to 0.4) than the B horizon soils with TSTR [(a + b)/2 of 0.1–0.25]. This difference was attributed to the contribution of amorphous compounds, mainly of Al, with ZPC values around 7.0 to 7.7 [Parks 1965]. These compounds neutralized the permanent negative charge in the pH range below the ZPC and developed a variable negative charge above the ZPC. In contrast, the soils from the upper B horizons of the Dystrudepts and the E to B horizons of the Haplohumods were characterized by relatively high CEC/clay values, i.e., mostly above 15 cmolc kg−1 (at pH 5, 0.01 mol−1), and low (a + b)/2 values in the

221

Pedogenetic Acidification in Humid Asia E and Bs horizons of haplorthods and upper B horizons of dystrudepts under MSTR

CEC/clay at 0.01 M, pH 5 (cmolc kg–1)

40

Bw horizons of dystrudepts under TSTR

30 20

Bw and lower B horizons of dystrudepts and hapludands under MSTR

10 0

0

0.1

0.2

0.3

(a + b)/2

0.4

0.5

FIGURE 5.17  Relationship between the (a + b)/2 values and CEC/clay determined in 0.01 M electrolyte at pH 5. △ E or upper B horizon soils, ○ Bs horizon of Haplorthods, ▲ Bw and lower B horizons of Dystrudepts and Hapludands under MSTR, ● Bw and lower B horizons of Dystrudepts under TSTR.

range of 0.1 to 0.2. These findings indicate the predominance of a permanent negative charge, with only a small decrease in the CEC, even for a low pH value around 5. As reported in Sections 5.3 and 5.4, most of the amorphous and/or interlayered materials were lost in these soils during the process of pedogenetic acidification. A large part of the negative charge thus exposed could be occupied by exchangeable Al. In conclusion, the processes of pedogenetic acidification directly affected the charge characteristics of the forest soils as follows. During the formation of the B horizons, accumulation of amorphous compounds and/or interlayered Al hydroxides resulted in the development of a variable charge. Amorphous compounds, which were preferentially formed in the B horizon of soils with MSTR, contributed significantly to the development of variable charge characteristics. During extensive soil acidification, the E and upper B horizons of the forest soils occasionally lost amorphous and/or interlayered compounds. As a result, the permanent negative charge of the expansible 2:1 minerals, which can retain high amounts of monomeric Al, became predominant in these soils.

5.5.7  Conclusions of Sections 5.2 to 5.5 The soil solution study, which was carried out at 3 plots in Japan, revealed that protons were produced by the dissociation of organic acids and nitrification mainly in the O horizon, while being consumed by adsorption and decomposition of organic acids or nitrate uptake by vegetation in the deeper soil horizons. Cation excess uptake by vegetation was highest among the proton sources in the whole soil compartment and hence was responsible for pedogenetic soil acidification in the growth stage of forests. Pedogenetic soil acidification was thus closely related to biological processes; they commonly included cation leaching by proton generation through the dissociation of organic acids and nitrification and subsequent cation excess accumulation in wood in the growth stage of forests. Next, the fates of DOC and acidity (i.e., Al and

222

World Soil Resources and Food Security

protons) supplied from overlying horizons were traced based on the soil solution composition and titratable alkalinity and acidity in the soil profiles. Two processes were postulated for pedogenetic acidification, that is, eluvi-illuviation of inorganic Al followed by subsequent adsorption of DOC and comigration of Al and DOC in the form of organo-mineral complexes. Both processes were conspicuous in MSTR soils and significantly contributed to SOM storage in the subsoil layers. Pedogenetic acidification in forest soils with MSTR was characterized by an accumulation of acidity in the form of amorphous compounds and/or organo-mineral complexes in the B horizon. It seems, to some extent, similar to podzol formation, at least in terms of Al translocation. Amorphous Al hydroxides protect against further acidification through protonation and/or partial monomerization and can thus be regarded as a temporary storage of acid-neutralizing capacity of the soil, which would otherwise be leached out directly from the soil profile. In contrast, the acid-buffering reactions of TSTR soils seemed to occur, if at all, mostly at or near the soil surface and the contribution of the B-horizon soils was limited. Thus, the pedogenetic processes in MSTR and TSTR were different in terms of the dynamics of acidity and Al. The pedogenetic acidification-modified soil properties relate not only to the organo­mineral complexes, but also to crystalline illitic minerals. The analysis using XRD and RIP methodologies revealed that soils located in the upper horizons were subjected to more intensive weathering and the content of HIV decreased in contrast to the increase in vermiculite. An increase in the vermiculitic nature upward in a profile was consistent with both an increase in the Cs-fixed capacity and a decrease in the amount of hot-citrate-extracted Al. However, the behavior of RIP was unique in that it first rose in parallel with the Cs-fixed capacity toward the surface, but then Under thermic soil temperature regime Organo-mineral complexes are present in small amounts and usually decrease with depth.

After intensive acidification, expandable 2:1 minerals are dominated. Under mesic soil temperature regime High concentrations of organo-mineral complexes are present, composing an active ANC. The layer gradually moves to downward along with acidification.

FIGURE 5.18  Alteration of illitic minerals along with pedogenetic acidification in forest soils in Japan.

Pedogenetic Acidification in Humid Asia

223

declined in the upper layers of podzolic soils. This progressive decrease in RIP at the late stage of podzolization suggested the frayed edge site was more susceptible to  the  accumulative acid loads compared to the bulk of the vermiculitic charges. Last, the change in the charge characteristics of the soils well demonstrated these pedogenetic acidification processes, including both transition of crystalline clay minerals and amorphous and/or organo-mineral complexes. The main conclusions in these four sections are the unique alteration process of illitic minerals and associated organo-mineral complexes along with pedogenetic acidification in Japan, as illustrated in Figure 5.18.

5.6  F LUXES OF DOC UNDER TROPICAL FORESTS UNDER DIFFERENT GEOLOGICAL CONDITIONS IN EAST KALIMANTAN, INDONESIA In forest ecosystems, most of the organic matter supplied to the organic (O) horizon mineralizes to CO2, but a portion is leached as DOC, as soil water percolates [McDowell and Likens 1988; Zech and Guggenberger 1996]. DOC transported into the mineral soil horizons may be mineralized, leached, or adsorbed onto mineral surfaces. The DOC fluxes from the O horizon, as well as root litter, are an important C source for mineral soils, and therefore may contribute to SOM formation over a long timescale. In boreal and temperate forests, the importance of this DOC flux to the soil C cycle and SOM formation has been quantified [Michalzik et al. 2001; Kleja et al. 2008; Sanderman and Amundson 2008; Section 5.2 in this chapter]. On the other hand, in tropical forests, the role of DOC in the soil C cycle and its contribution to SOM formation have not been fully understood because of limited data [McDowell 1998; Tobón et al. 2004a; 2004b; Schwendenmann and Veldkamp 2005]. The aims of this study were to: (1) quantify the DOC fluxes under tropical forest serpentines and mudstones; (2) evaluate the role of these DOC fluxes in the soil C cycle and SOM formation; and (3) evaluate the influence of parent materials (serpentine, mudstone, and sandstone) on DOC fluxes.

5.6.1  Experimental Plots and Experimental Design Experiments were carried out in the natural secondary forest, slightly damaged by the fires in 1982–1983 and 1997–1998, in Bukit Soeharto (BS plot) from September 2004 to October 2005, the pristine forest in Bukit Bankirai (BB plot) from October 2005 to October 2006, and tropical secondary forests in Kuaro (KR1, KR2, and KR3 plots) from August 2006 to August 2007. All the plots were located in East Kalimantan Province, Indonesia (Figure 5.19). The parent materials of this area are largely sedimentary rocks, but there are patches of serpentine (ultramafic) intrusions. Soils at both BS and BB are derived from sedimentary rocks, whereas KR1, KR2, and KR3 were located along a traverse across serpentine-sedimentary rock (mudstone) (Figure 5.19). The site description is given in Table 5.13. At these plots, soil respiration rates were quantified once or twice per month in five replicates using a closed-chamber method together with the monitoring of soil

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World Soil Resources and Food Security

E116º

E116º

E117º BB

Serpentine

Kuaro

KR3 KR2

S1º

Balikpapan

belt



BS

KR1

20 km

S2º

FIGURE 5.19  Location of the experimental plots in East Kalimantan, Indonesia.

temperature at a depth of 5 cm and volumetric water contents of soils at depths of 5, 15, and 30 cm using data-loggers. Soil solutions were collected in five replicates using tension-free lysimeters beneath the O, A, and B1 horizons (0-, 5-, and 30-cm depths, respectively). Throughfall was collected using a precipitation collector in five replicates. These samples were collected once or twice per month for 1 year in each plot. Then, fluxes of DOC from each horizon were calculated by multiplying the water fluxes by the DOC concentrations in throughfall and soil solutions. The water fluxes of throughfall were measured using precipitation collectors, whereas those of soil water percolating at depths of 5, 15, and 30 cm were estimated by applying Darcy’s law to the unsaturated hydraulic conductivity and the gradient of the hydraulic heads at each depth, as described in the Appendix of Chapter 4 (4.A3). To collect litterfall, circular litter traps of 60-cm diameter were used. The fine root biomass, both in the O horizon and mineral soils, was estimated. The aboveground biomass was estimated by applying the diameters of stems at breast height (DBH), to the regression equations obtained by Yamakura et al. [1986]. The C and N contents, as well as phosphorus and Klason-lignin concentrations of the plant materials or foliar litters, were also determined [Allen et al. 1974].

5.6.2  Physicochemical Properties of Soils and C Stock in Soils and Ecosystems The data are presented in Tables 5.14 and 5.15. The soil pH values were highest in the KR1 soil from serpentine (6.2–6.4), followed by the KR2, and KR3 soils from mudstone (4.6–5.6 and 4.5–4.6, respectively). Soil pH at the BS and BB plots was consistently low. The contents of DCB-extractable Fe oxides were highest in KR1 (176–216 g kg−1), followed by KR2 (67–78 g kg−1), KR3 (30–38 g kg−1), BB (9–18 g kg−1), and BS (7–15 g kg−1). The total C contents were higher in the KR1 and KR2 soils (73 g kg−1)

East Kalimantan, Indonesia Coordinates Mean annual air temperature (°C) Mean annual precipitation (mm) Elevation (m) Soil typea Parent materials Vegetation

a

BS

BB

KR1

KR2

KR3

S00°51′, E117°06′ 27

S01°01′, E116°52′ 27

S01°51′, E116°02′ 27

S01°49′, E115°59′ 27

S01°49′, E115°56′ 27

2187

2427

2256

2256

2256

99 Typic Paleudults Sedimentary rocks

80 Typic Paleudults Sedimentary rocks Shorea leavis Dipterocarpus cornutus

204 Typic Paleudults Sedimentary rocks Serpentine Harpullia arborea Durio spp.

167 Typic Paleudults Sedimentary rocks

Shorea leavis Dipterocarpus cornutus

92 Rhodic Eutrudox Sedimentary rocks Serpentine Harpullia arborea Bauhinia purpurea

Pedogenetic Acidification in Humid Asia

TABLE 5.13 Site Description of Indonesian Plots

Harpullia arborea Artocarpus lanceolata Durio spp.

Soils were classified according to Soil Taxonomy [Soil Survey Staff 2006].

225

226

TABLE 5.14 Physicochemical Properties of Soils in Indonesian Plots

Depth Site

(H2O)

Particle Size Distribution

Exchangeable (KCl)

Total C

Total N

Bases

(g kg )

Al

CEC

Sand

(cmolc kg )

Silt

Clay

(%)

Feo

Alo

Ald

BS (Indonesia)

A BA B1 Bt

0–5 5–25 25–40 40+

4.0 3.8 4.0 4.3

3.9 3.8 3.8 3.8

22.9 4.2 3.5 2.5

1.6 0.5 0.5 0.4

2.2 0.8 0.8 1.0

3.0 3.9 4.8 7.0

8.5 6.2 5.0 5.0

52 49 43 34

25 27 30 35

23 24 27 31

3.6 1.4 0.9 0.6

0.7 0.7 0.8 1.0

6.6 9.1 11.3 14.6

1.0 1.4 1.7 2.2

309 413 502 643

BB (Indonesia)

A BA B1 Bt BC

0–5 5–20 20–37 37–70 70+

4.2 4.1 4.1 4.1 4.2

3.4 3.7 3.8 3.8 3.8

36.1 10.7 7.1 3.7 3.3

2.3 0.9 0.6 0.4 0.4

1.2 0.8 1.1 0.8 0.9

6.7 5.5 5.7 4.6 6.7

13.0 8.3 8.5 9.0 13.6

42 45 43 39 34

31 31 33 34 25

27 24 24 27 40

2.5 2.8 1.9 1.6 1.8

1.2 1.3 1.3 1.2 1.2

9.0 10.7 11.4 15.0 18.2

1.7 1.9 2.1 2.6 3.1

400 369 440 531 630

−1

(g kg )

ANC(s)

m

(cm)

−1

(g kg )

Fed

Horizon

−1

−1

(cmolc kg−1)

World Soil Resources and Food Security

pH

A1 A2 B1 B2 B3

0–5 5–20 20–35 35–50 50–67+

6.3 6.2 6.4 6.4 6.4

5.8 5.7 6.1 5.8 6.2

72.7 23.8 7.5 5.7 4.8

6.4 2.6 0.8 0.7 0.6

19.1 5.0 2.0 1.8 2.7

0.0 0.0 0.0 0.0 0.0

24.8 11.4 7.2 7.0 7.7

6 14 19 16 38

39 45 37 37 33

55 41 44 47 28

3.5 4.4 6.1 6.2 6.7

1.4 2.2 1.8 1.7 1.7

175.7 196.2 208.4 217.1 215.9

8.5 9.9 10.7 11.2 10.9

1358 1608 1817 1853 1725

KR2 (Indonesia)

A BA Bt1 Bt2

0–5 5–20 20–45 45–70

5.6 4.6 4.8 5.1

5.2 3.9 4.1 4.2

73.1 24.9 14.2 10.0

5.5 2.9 1.8 1.4

14.5 1.4 1.0 0.8

0.2 2.9 2.3 1.4

19.8 13.7 17.0 24.8

6 5 4 7

15 18 10 11

79 77 86 82

4.0 4.5 2.5 1.9

5.0 2.8 2.6 2.5

66.6 70.9 71.3 78.1

12.0 10.9 10.7 11.8

1837 2012 2011 1996

KR3 (Indonesia)

A BA Bt1 Bt2 Bt3

0–4 4–15 15–30 30–45 45–50+

4.5 4.5 4.5 4.6 4.6

3.7 3.7 3.7 3.8 3.8

38.7 17.9 11.1 7.4 7.5

3.5 1.7 1.2 0.9 1.0

1.8 1.0 1.2 1.4 0.9

4.9 4.9 6.3 7.0 7.7

20.8 17.2 19.7 23.3 27.1

28 29 28 25 25

24 15 15 15 9

48 56 57 60 66

3.3 2.4 1.9 1.3 1.4

2.2 1.9 1.7 1.7 1.7

29.7 31.6 34.0 36.2 37.5

4.9 5.1 5.1 5.3 5.7

1163 1245 1293 1430 1459

Pedogenetic Acidification in Humid Asia

KR1 (Indonesia)

227

228

TABLE 5.15 Stock and Annual Flow of Carbon in the Indonesian Plots BS

C flow (Mg C ha–1 yr –1) Soil respiration Root respiration Decomposition of soil organic matter Litterfall Wood increment

292.6

(−)

346.0

KR1

(−)

134.2

KR2

(−)

259.8

KR3

(−)

282.1

(−)

1.0 1.2 2.1

(0.4) (0.3) (0.4)

2.3 1.3 2.0

(0.3) (0.2) (0.5)

– 1.5 3.1

(0.2) (0.5)

– 0.6 0.4

(0.2) (0.2)

0.3 0.4 1.1

(0.1) (0.1) (0.2)

3.5 26.6

(0.3) (0.8)

4.5 51.4

(1.1) (4.6)

4.1 65.8

(1.1) (2.2)

2.9 74.7

(0.4) (3.2)

3.6 55.0

(0.2) (2.1)

7.5 2.0 5.4

(0.5) (0.2) (0.5)

6.7 2.4 4.2

(0.4) (0.2) (0.5)

6.4 2.2 4.2

(0.3) (0.4) (0.3)

6.9 2.2 4.7

(0.3) (0.4) (0.2)

8.0 3.8 4.2

(0.4) (0.4) (0.1)

4.1 10.6

(0.5) (–)

3.6 11.1

(0.1) (–)

4.8 7.2

(0.3) (–)

4.0 9.8

(0.7) (–)

4.5 5.1

(0.3) (–)

Note: The figures in parentheses represent standard errors. a Organic carbon in soil at 0- to 30-cm depths was counted.

World Soil Resources and Food Security

C stock (Mg C ha−1) Aboveground biomass Fine root biomass O horizon A horizona B horizona Soil organic matter O horizon Mineral soil horizonsa

BB

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Pedogenetic Acidification in Humid Asia

than the other soils (23–39 g kg−1). The O horizons in KR1 and KR2 consisted of the Oi layer only, while in the other acidic soils (KR3, BS, and BB), there were Oea and Oi. The aboveground biomass was lower in KR1 (134 Mg C ha−1) than in the others (260–346 Mg C ha−1). The C stock in the O horizon was consistently low at all plots (2.6–4.5 Mg C ha−1). The C stock in the mineral soil was highest in KR2 (74.7 Mg C ha−1). The higher aboveground biomass and the lower C stock in the O horizon and C flux (kg C ha–1 month–1) 1000

C flux (kg C ha–1 month–1) 1000

BS

800

800

600

600

400

400

200

200

0 Sep-04 Dec-04 Feb-05 Apr-05 Jul-05 Sep-05 Nov-05

Date

C flux (kg C ha–1 month–1) 1000

0 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07

Date

C flux (kg C ha–1 month–1) 1000

BB

800

800

600

600

400

400

200

200

0 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06

Date

Whole soil respiration OM decomposition Root respiration

KR1

KR2

0 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07

Date

C flux (kg C ha–1 month–1) 1000

KR3

800 600 400 200 0 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Date

FIGURE 5.20  The seasonal fluctuations of soil respiration, OM decomposition, and root respiration. Bars indicate standard errors (n = 5).

230

Water Flux

DOC

HCO3−

Orgn− a

NO3−

Cl− + SO24−

H+

NH+4

Fe2+

Aln+ b

(mmolc L−1)

Si

Horizon

(mm)

pH

(mg C L−1)

BS

TF O A2 BA-B1

2031 1619 1196 545

5.23 4.44 4.22 4.39

9.0 34.7 17.2 9.9

0.009 0.001 0.000 0.003

0.101 0.257 0.196 0.119

0.036 0.038 0.044 0.032

0.104 0.136 0.100 0.116

0.006 0.037 0.061 0.041

0.038 0.088 0.035 0.018

0.197 0.256 0.197 0.176

0.003 0.019 0.009 0.005

0.007 0.040 0.038 0.027

0.017 0.070 0.068 0.051

BB

TFc O A BA-B1

2068 1914 1639 893

5.54 4.08 3.97 4.07

4.7 24.6 19.1 6.0

0.003 0.000 0.001 0.000

0.086 0.261 0.211 0.132

0.024 0.137 0.161 0.103

0.098 0.174 0.161 0.170

0.005 0.118 0.131 0.087

0.030 0.106 0.057 0.030

0.171 0.283 0.269 0.219

0.001 0.016 0.013 0.009

0.004 0.053 0.066 0.061

0.008 0.069 0.075 0.072

c

(mmolc L–1)

Na+ + K+ + Mg2+ + Ca2+

(mmol L−1)

World Soil Resources and Food Security

TABLE 5.16 Water Flux and Annual Volume-Weighed Mean Concentrations of Ions in Throughfall and Soil Solution

TFc O A1 A2

2240 1907 1071 566

6.34 6.12 5.74 6.20

7.3 31.6 13.4 5.4

0.047 0.082 0.030 0.160

0.143 0.272 0.165 0.098

0.020 0.098 0.466 0.308

0.067 0.230 0.169 0.192

0.000 0.001 0.002 0.001

0.017 0.028 0.042 0.031

0.257 0.643 0.770 0.691

0.000 0.004 0.001 0.000

0.002 0.003 0.004 0.000

0.015 0.140 0.275 0.627

KR2

TFc O A BA-Bt

2211 1922 967 553

6.18 6.52 5.89 5.14

6.2 8.6 5.7 1.7

0.048 0.183 0.026 0.002

0.070 0.120 0.053 0.028

0.027 0.091 0.071 0.062

0.069 0.113 0.040 0.041

0.001 0.000 0.001 0.007

0.012 0.017 0.008 0.010

0.198 0.470 0.167 0.098

0.000 0.000 0.003 0.002

0.003 0.006 0.009 0.008

0.018 0.021 0.046 0.065

KR3

TFc O A BA-Bt1

2205 1594 645 416

6.07 5.57 4.97 5.11

5.7 16.4 10.1 2.8

0.036 0.039 0.009 0.000

0.075 0.216 0.180 0.036

0.013 0.035 0.060 0.019

0.060 0.135 0.101 0.040

0.001 0.003 0.011 0.008

0.013 0.017 0.033 0.008

0.168 0.385 0.284 0.067

0.000 0.004 0.003 0.001

0.002 0.016 0.016 0.011

0.015 0.075 0.055 0.027

a b c

Pedogenetic Acidification in Humid Asia

KR1

Orgn– represents anion deficit, the negative charge of organic acids. The total charge equivalent of Al ions was calculated as the equivalent sum of Al3+, AlOH2+, and Al(OH)+2 . TF represents throughfall.

231

232

World Soil Resources and Food Security

mineral soil, compared to the temperate forests (Table 5.2), are consistent with previous reports [Nakane 1980].

5.6.3  Organic Matter Decomposition The seasonal fluctuations of soil respiration rates at all plots are presented in Figure 5.20. Soil temperature varied little over the year at all plots, while the volumetric water content in the soils increased gradually after the dry period (September– November). The rates of soil respiration, organic matter decomposition, and root respiration varied over the year. However, these fluctuations were independent of soil temperature and moisture. The annual rates of soil respiration, organic matter decomposition, and root respiration, which were calculated using the average rates of CO2 emission measured (Figure 5.20), are presented in Table 5.15. The annual rates of organic matter decomposition were similar (4.2–5.4 Mg C ha−1 yr−1) among all plots (Table 5.15). These values are similar to the lowest values reported for other tropical rain forests (4.8–8.9 Mg C ha−1 yr−1), but higher than in temperate forests [Bond-Lamberty et al. 2004]. Organic matter decomposition was almost balanced by C inputs via litterfall (4.0–4.8 Mg C ha−1 yr−1) (Table 5.15).

5.6.4  C  oncentrations and Fluxes of DOC in Throughfall and Soil Solution Soil solutions were moderately acidic to neutral (5.0–6.5) in all plots (Table 5.16). Along the gradient of soil pH, soil solution pH was lowest in KR3 (5.0–5.6), followed by KR2 (5.2–6.5), and KR1 (5.7–6.2). The DOC concentrations in soil solutions were highest in the O horizon, and there was a decrease with increasing depth in each plot (Figure 5.21). The DOC concentrations in the O horizon solutions varied seasonally in KR1 (19.9–70.5 mg C L−1) and KR3 (11.4–45.9 mg C L−1) (Figure 5.21). The DOC concentrations were highest during the dry periods, followed by a gradual decrease during the rainy season and in the O horizon solution. They were consistently low in KR2 (4.6–16.8 mg C L−1) (Figure 5.21). The DOC fluxes in throughfall and B1 horizon (30-cm depth) vary within a narrow range of 97–182 and 10–54 kg C ha−1 yr−1, respectively, at all plots (Figure 5.22). The DOC (i.e., Orgn−) fluxes in the soil profiles increased markedly in the O horizon, and there was a decrease with increasing depth in each plot. The DOC fluxes from the O horizon were highest at all the plots. However, these fluxes varied between 166–603 kg C ha−1 yr−1. The DOC fluxes from the O horizon corresponded to 13.8%, 13.0%, 12.6%, 4.1%, and 5.8% of C input in BS, BB, KR1, KR2, and KR3, respectively, and to 10.3%, 11.1%, 14.3%, 3.5%, and 6.2% of organic matter decomposition, respectively. The concentrations and fluxes of DOC were the largest in the O horizon (Figure 5.22), and are consistent with previous reports [Michalzik et al. 2001]. The annual volume-weighted mean concentrations and fluxes of DOC in the O horizon solutions vary widely between the five plots studied. The DOC fluxes from the O horizon in BS and KR1 are comparable with the highest values reported for tropical forests, 166–603 kg C ha−1 yr−1 in the present study, and those in temperate forests, 100–482

233

Pedogenetic Acidification in Humid Asia DOC concentration (mg C L–1) 80

BS

DOC concentration (mg C L–1) 80

60

60

40

40

20

20

0

Sep-04 Nov-04 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05

Date

DOC concentration (mg C L–1) 80

BB

0

Aug-06 Oct-06 Dec-06 Feb-07 Apr-07 Jun-07 Aug-07

Date

DOC concentration (mg C L–1) 80 KR2

60

60

40

40

20

20

0

Oct-06 Dec-06 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06

Date

KR1

0

Aug-06 Oct-06 Dec-06 Feb-07 Apr-07 Jun-07 Aug-07

Date

DOC concentration (mg C L–1) Throughfall O horizon A horizon B1 horizon

80

KR3

60 40 20 0

Aug-06 Oct-06 Dec-06 Feb-07 Apr-07 Jun-07 Aug-07

Date

FIGURE 5.21  The seasonal fluctuation of concentrations of DOC in throughfall and soil solution. Bars indicate standard errors (n = 5).

kg C ha−1 yr−1 from Michalzik et al. [2001] and Yano et al. [2004] and 690–836 kg C  ha−1 yr−1 from Moore [1989] and Moore and Jackson [1989]. In contrast, DOC fluxes in KR2 are close to the lowest reported values. Among the Ultisol soils from sedimentary rocks in our study, DOC fluxes from the O horizons vary between 166–261 kg C ha−1 yr−1 for the Ultisol soils from mudstone (KR2 and KR3) to 470–562 kg C ha−1 yr−1 for those from sandstone (BS and BB). Comparing the DOC fluxes from the O horizon on a basis of C input, the DOC fluxes correspond to 4.1%–13.8% of C input (Figure 5.22). The magnitude of DOC

234

World Soil Resources and Food Security The annual fluxes of C (kg C ha–1 yr–1) The stock of C (kg C ha–1) KR1 KR2 Aboveground biomass 134 × 103 260 × 103

KR3

BS

BB

282 × 103

293 × 103

346 × 103

Throughfall litterfall 4783

164

136

OM decomposition 4231 O horizon Mineral soila Soil solution

4 090 603

65.4 × 103 31

Serpentine

4008

4475

126

4701

4236

2 881

3 605

74.7 × 103

55.0 × 103

166

4084

182

261

10

12 Mudstone

3618

97

4249

5446

3 454

4 493

562

470

26.6 × 103

Sedimentary rocks

51.4 × 103

54

54 Sandstone Acidic

FIGURE 5.22  The C stock and the annual C fluxes via litterfall, organic matter (OM decomposition, throughfall, and soil solution) in East Kalimantan, Indonesia. aThe C stock in soils at the depths of 0–30 cm was counted.

production in the O horizon is variable among the soils studied. According to published data and those from our study, the proportion of DOC flux relative to C input increases with decreasing soil pH (Figure 5.23), with the exception of KR1. The substantial DOC translocation from the O horizon may be common to the highly acidic soils (soil pH < 4.3; Spodosols and acidic Ultisols) under a humid climate. This is consistent with the significant contribution of DOC, the sources of organic acids, to podzolization and soil acidification [Ugolini and Dahlgren 1987; Do Nascimento et al. 2008]. Lower soil pH may be favorable for substantial DOC production due to: (1) enhanced litter solubilization by fungal activities [Kalbitz et al. 2000] and inhibited mineralization [Saggar et al. 1999; Kemmitt et al. 2006]; and (2) recalcitrance of DOC produced from litter rich in polyphenol and lignin [Kalbitz et al. 2003, 2006]. This is partly supported by our study, in which DOC production from lignin-rich organic materials in KR3 (42%) was higher than in KR2 (28%). However, the variation of DOC concentration in the O horizons between KR1, KR2, and KR3 could not be entirely accounted for by lignin concentration in foliar litter. In our study in East Kalimantan, Indonesia, the local-scale variation of DOC concentration in the O horizons among five soils could be accounted for by litter P availability (Figure 5.24). The DOC concentrations in the O horizon solution increased with decreasing P concentration in the foliar litter. This relationship also is confirmed for data in Amazonian tropical rain forest soils from different parent materials (Figure 5.24) [Tobón et al. 2004a, 2004b]. The P concentration, as well as the lignin concentration, in foliar litter is an important factor regulating DOC

235

Pedogenetic Acidification in Humid Asia Proportion of DOC flux relative to C input (%)

Published data Sandstone Mudstone Serpentine

30 25

r = –0.63 p < 0.01, n = 27

20 15

BB

BS

10

KR1

5 0

KR3 3

KR2

4

5

6

Soil pH

FIGURE 5.23  Relationship between soil pH and proportion of DOC flux relative to C input in the O horizon. (Data sources include Moore, T.R., Water Resource Res., 25, 1321–1330, 1989; Moore, T.R., and R.J. Jackson, Water Resource Res., 25, 1331–1339, 1989; Yavitt, J.B. and T.J. Fahey, J. Ecol., 74, 525–545, 1986.)

DOC concentration (mg C L–1) 40

Published data Sandstone Mudstone Serpentine

BS

30

KR1

BB

20 KR3 10 KR2

Columbia 0 0.00

0.02

0.04

0.06

0.08

0.10

Foliar P (%)

FIGURE 5.24  Relationship between P concentration in foliar litter and the concentration of DOC in the O horizon solution. (Data sources include Qualls, R.G. et al., Ecology 72, 254–266, 1991; Johnson, C.E. et al., Ecosystems, 3, 159–184, 2000; Tobón, C. et al., Biogeochem., 69, 315–339, 2004; Tobón, C. et al., Biogeochem., 70, 1–25, 2004; Kleber, M. et al., Geoderma, 138, 1–11, 2007; Raich, J.W. et al., For. Ecol. Manag., 39, 128–135, 2007.)

236

World Soil Resources and Food Security

biodegradability [Wieder et al. 2008]. The DOC mineralization (consumption) rates could be constrained by P limitation in tropical forest soils [Cleveland et al. 2002]. Consequently, litter P availability may determine whether DOC released from litter is rapidly mineralized or leached from the O horizon. Litter P availability is strongly constrained by the P content in parent materials in tropical soils [Kitayama et al. 2000]. In our study, the lower litter P availability derived from P-poor parent materials (serpentine) or acidic conditions (sandstone) is considered to constrain DOC consumption and result in substantial DOC fluxes from the O horizon of KR1 and the highly acidic Ultisol soils from sandstone.

5.6.5  Influence of Parent Rocks on DOC Dynamics The relative importance of parent materials on DOC dynamics is poorly understood, compared to the importance of climate and vegetation [Nambu et al. 2008]. Judging from the fact that C fluxes of organic matter production and decomposition in the five tropical forests studied are similarly high (Figure 5.22), organic matter production and decomposition are considered to largely depend on climate and vegetation, rather than parent material. Similarly, the DOC fluxes from the O horizon are considered to depend primarily on climate [Zech et al. 1997; Kalbitz et al. 2000], and they are reported to increase with increasing amounts of precipitation, substrate supply, and its decomposition at regional to global scales [Kleja et al. 2008]. However, in our study, the DOC fluxes from the O horizon varied among the soils from different parent materials despite similar amounts of precipitation and C input (Figure 5.22). These local-scale variations of DOC fluxes also are reported between the clayey and sandy soils under similar climate and vegetation conditions [Don and Schulze 2008]. Our data indicate the importance of parent materials in regulating the DOC fluxes through their effects on soil pH and litter P availability (Figures 5.23 and 5.24), although the effects of parent materials on the DOC fluxes are not unclear under different climate and vegetation conditions [Dosskey and Bertsch 1997]. To date, parent materials generally have been considered to influence stabilization of organic matter (e.g., DOC adsorption) through effects on soil texture and clay mineralogy [Sollins et al. 1996]. In addition, our data suggest that parent materials play an important role in the translocation, as well as stabilization, of organic matter at a local scale among tropical forest soils.

5.7  C  ONTRIBUTION OF DIFFERENT PROTON SOURCES TO SOIL ACIDIFICATION UNDER TROPICAL FORESTS UNDER DIFFERENT GEOLOGICAL CONDITIONS IN EAST KALIMANTAN, INDONESIA In the humid tropics, the vast majority of soils are highly weathered and acidified because of intensive leaching over long periods of time [Eyre 1963]. Soil-acidifying processes vary with parent materials and vegetation, as well as climate (leaching intensity) [Ugolini and Sletten 1991]. Parent materials influence soil-acidifying processes through effects on soil texture, acidity, and the acid-neutralizing capacity

Pedogenetic Acidification in Humid Asia

237

(ANC) of soils [Ugolini and Sletten 1991]. For example, the soil-acidifying processes (e.g., mobility of organic acids) vary depending on parent material texture and they appear to determine the dominant pedogenetic processes (ferralitization vs. podzolization), which occurred in a sequence of Oxisol–Ultisol–Spodosol under tropical forests [Do Nascimento et al. 2008]. Parent materials determine the distribution of soil types (Ultisols and Oxisols) in East Kalimantan [Petersen 1991]. To understand the dominant pedogenetic processes in this region, the effects of parent materials on soil-acidifying processes and their impacts on weathering reactions need to be clarified for different parent materials. The dominant soil-acidifying processes can be identified by quantifying the relative importance of the individual proton sources to soil acidification using proton budgets in soil vegetation systems and including solute leaching and vegetation uptake [van Breemen et al. 1983, 1984]. To date, only a few studies have dealt with the dominant acidifying processes in tropical soils [Johnson et al. 1983; van Breemen et al. 1984]. The objectives of our study were to: (1) analyze the dominant acidifying processes by quantifying proton budgets in five Indonesian soils derived from different parent materials; and (2) evaluate the effects of parent materials on pedogenetic soil acidification. The analysis was carried out using the same dataset as given in the previous section (Section 5.6).

5.7.1  Chemical Properties of the Soils Studied General physicochemical properties of the soils were introduced in the previous section. The ANC of the soils increased with soil pH and clay content, varying from 309–643 cmolc kg−1 in the BS and BB soils to 1163–2012 cmolc kg−1 in the KR soils (Table 5.14). The higher ANC values are mainly contributed by Mg and Fe in the KR1 soil, but by Al in the KR2 and KR3 soils. The O horizons had only an Oi layer in the KR1 and KR2 soils, while the acidic BS, BB, and KR3 soils (pH < 4.5) had an Oea layer as well as an Oi layer. In the BB and BS soils, the O horizons are acidic (pH 4.5 to 5.0), consistent with lower contents of basic cations (17–63 cmolc kg−1). The higher contents of basic cations in the O horizons of the KR soils (87–129 cmolc kg−1) are probably a consequence of parent materials rich in basic cations.

5.7.2  Soil Solution Composition Soil solutions were strongly acidic in the BS and BB soils (pH 4.0–4.4), while they were moderately acidic at the KR sites (pH 5.0–6.2) (Table 5.16). DOC concentrations in the O horizon solution were higher in the BS, BB, and KR1 soils (24.6–​ 34.7 mg C L−1) compared to the KR2 and KR3 soils (8.6–16.4 mg C L−1). Organic acids were the dominant anions in the O horizon solution in all plots (0.22–0.27 mmolc L−1) except for the KR2 soil. Concentrations of organic acids and DOC in the soil solution decreased with depth. From linear regression analysis between the concentrations of DOC and organic acids in soil solutions, the negative charge per 1 mole of DOC (0.09–0.17 molc) corresponds to one dissociated acidic functional group for 5.9–11.5 C atoms. The high DOC to charge ratios in the soil solution suggests the

238

World Soil Resources and Food Security

presence of high molecular weight fulvic acids, which contain 7 C atoms for each acidic functional group [Thurman 1985]. Bicarbonate is present in moderately acidic soil solutions of the KR sites, while it is negligible in acidic soil solutions of the BS and BB sites. In the O horizon of the KR2 soil, bicarbonate was the dominant anion (0.18 mmolc L−1) owing to the relatively high solution pH (6.5), while concentrations decreased with depth (Table 5.16). Nitrate concentrations were low (0.02–0.16 mmolc L−1) at all plots except for the A1 and A2 horizons of the KR1 soil (0.31–0.47 mmolc L−1), where the understory vegetation was the nitrogen-fixing Bauhinia purpurea. The major accompanying cations were K+, Mg2+, and Ca2+ in the KR1, KR2, and KR3 soils, while they were H+,  NH +4  , and Aln+, as well as basic cations, in the BS and BB soils. The highest concentrations of Si in the soil solutions were measured in the KR1 soil (0.14–0.63 mmol L−1) compared to the other soils from sedimentary rocks (0.02–0.08 mmol L−1).

5.7.3  Fluxes of Ions in Solute Leaching and Vegetation Uptake and Proton Budgets in Soils Cation contents exceeded anion contents in litter and wood materials at all plots (Table 5.17). The excess cation charge was compensated for by the net proton load to the soil as NPGBio. NPGBio in each of the soil horizons was calculated by distributing it based on the distribution of the fine root biomass in the soil profiles (Table 5.15), according to Shibata et al. [1998]. Based on the fluxes of solutes entering and leaving the soil horizon compartment (Figure 5.25) and vegetation uptake (Table 5.17; NPGBio) in each of the soil horizons, net proton generation and soil acidification rates were calculated based on the proton budget theory (Figure 5.26). In the entire soil profiles, NPGBio was highest among the proton sources at all plots (Figure 5.26). NPGBio was present mainly in the A and B horizons of soils at all plots (1.7–10.8 kmolc ha−1 yr−1), while it was also present in the O horizons of the KR3, BS, and BB soils (1.5–3.1 kmolc ha−1 yr−1) (Figure 5.26). In the O horizons, proton sources include NPGOrg, NPGCar, and NPGNtr, as well as NPGBio (Figure 5.26). NPGOrg is the largest proton source in the O horizons (1.8–3.2 kmolc ha−1 yr−1) except for the KR2 soil, where NPGCar is higher than NPGOrg (0.8 kmolc ha−1 yr−1). In the moderately acidic O horizons of the KR1 and KR2 soils, protons were produced by the dissociation of carbonic acid (NPGCar: 0.5–2.5 kmolc ha−1 yr−1) (Figure 5.26). In the A and B horizons, protons were consumed by the mineralization and adsorption of organic acids and by the protonation of carbonic acid (Figure 5.26). Protons were produced by nitrification in the O horizons of the KR1, KR2, KR3, and BB soils (NPGNtr: 0.3–1.3 kmolc ha−1 yr−1), while they were consumed by nitrate uptake by vegetation or microorganisms in the A and B horizons. In the acidic O horizons of the BS and BB soils, an increase in the NH +4 flux (Figure 5.25; 0.6 kmolc ha−1 yr−1) indicated that protons were mainly consumed by mineralization of organic + N to NH 4 (NPGNtr: –0.7 kmolc ha–1 yr–1) (Figure 5.26). In the A horizons of the BS and BB soils, protons were released owing to the excess uptake of NH +4 over NO3− by biomass or adsorption of NH +4 on clays (NPGNtr: 0.9–1.1 kmolc ha−1 yr−1) (Figure 5.26). Exceptionally, in the A1 horizon of the KR1 soil, where the understory

OM Production

Na

K

Ca

(Mg C ha−1 yr−1)

Mg

Fe

Al

Cl

S

P

(Cation)bio

(kg ha−1 yr−1)

(Anion)bio

NPGBio

(kmolc ha−1 yr−1)

BS

Wood increment Litterfall

10.6 4.1

14.7 3.6

33.0 45.2

29.3 35.7

15.8 18.5

10.0 1.7

0.5 3.3

1.0 0.7

8.6 5.0

2.2 2.5

4.66 5.05

0.64 0.41

4.02 4.63

BB

Wood increment Litterfall

11.1 3.6

15.5 5.4

34.8 22.9

30.9 15.0

16.7 17.1

1.9 1.9

5.3 4.3

1.1 0.7

9.1 5.4

2.4 3.2

5.13 3.52

0.67 0.46

4.46 3.06

KR1

Wood increment Litterfall

7.2 5.7

3.3 5.3

52.6 63.7

59.4 82.3

8.7 26.4

1.6 0.2

0.8 2.1

4.4 1.1

3.6 7.5

5.6 4.1

5.32 8.37

0.53 0.63

4.79 7.74

KR2

Wood increment Litterfall

9.8 3.6

3.4 2.5

55.4 31.1

132.5 81.1

9.8 33.7

0.5 0.9

1.2 18.9

1.9 0.5

5.4 9.1

5.4 5.0

9.13 9.85

0.57 0.75

8.57 9.10

KR3

Wood increment Litterfall

5.1 4.5

3.0 4.7

23.3 27.2

42.2 57.3

2.9 33.3

0.9 2.3

2.1 1.9

1.0 1.0

3.4 8.3

2.3 4.4

3.34 6.80

0.31 0.69

3.02 6.11

Pedogenetic Acidification in Humid Asia

TABLE 5.17 Uptake of Cations and Anions by Vegetation

239

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World Soil Resources and Food Security Horizon

SO42–

TF O

Orgn–

Horizon HCO3– Na+ Na+ 4

Cl–

HCO3– Na+ Mg2+Ca2+ Na+ SO42– NO3– 4

TF

K+

Mg2+

O

Ca2+

Orgn–

K+

Cl–

Aln+ Fe3+

NO3–

A1

A

A2 –15

BA, B1

KR1 –10

–5

0

5

10

–15

15

(kmolc ha–1 yr–1) Fluxes of cations (+) and anions (–)

Horizon

–10

–5

0

5

10

15

(kmolc ha–1 yr–1) Fluxes of cations (+) and anions (–)

Horizon + – SO42– NO3 Cl– Na Na4+

TF

Orgn–

O

BB

H+

SO42–

TF

2+ Ca2+ + HCO– 3 K Mg

Na+ Mg2+ Ca2+

Orgn–

O

NO3– Cl– H+ Na+ K+ 4

Aln+ Fe3+

A

A

KR2

BA, Bt –15

–10

–5

0

5

10

15

(kmolc ha–1 yr–1) Fluxes of cations (+) and anions (–)

Horizon

BS

BA, B1 –15

–10

–5

0

5

10

15

(kmolc ha–1 yr–1) Fluxes of cations (+) and anions (–)

HCO– 3 – Na+ Na4+ SO42– NO3

TF

Aln+ Orgn–

O

Cl– K+ Mg2+ Ca2+

A BA, Bt

KR3

–15

–10

–5

0

5

10

15

(kmolc ha–1 yr–1) Fluxes of cations (+) and anions (–)

FIGURE 5.25  Fluxes of solutes at each horizon. TF represents throughfall. O, A, A1, A2, BA, B1, Bt1, and Bt represent soil horizons.

vegetation was the nitrogen-fixing Bauhinia purpurea, protons were produced by nitrification (NPGNtr: 3.2 kmolc ha−1 yr−1) (Table 5.16). In the O horizons of the KR soils, acid loads contributed mainly by NPGNtr, NPGOrg, and NPGCar (3.5–4.3 kmolc ha−1 yr−1) was completely neutralized by basic cations. On the other hand, in the O horizons of the BS and BB soils, the intensive

241

Pedogenetic Acidification in Humid Asia Horizon

Horizon

O

O

KR1

A1

A

A2

BA, B1

Total

Total

–10

–5

0

5

10

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–) O

KR2

–10

O A

BA, Bt

BA, B1

Total

Total –5

0

5

10

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–) O

0

5

10

–10

–5

0

5

10

BB

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–)

KR3 (H+)in – (H+)out

A

NPGNtr

NPGOrg NPGBio

BA, Bt1

NPGCar ∆ANC

Total –10

–5

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–)

A1

–10

BS

–5

0

5

10

(kmolc ha–1 yr–1) Net proton generation (+) or consumption (–)

FIGURE 5.26  Net proton generation and consumption in the soil profiles. TF represents throughfall. O, A, A1, A2, BA, B1, Bt1, and Bt represent soil horizons.

acid loads contributed mainly by NPGOrg and NPGBio (3.2–7.0 kmolc ha−1 yr−1) were largely neutralized by basic cations but a portion of protons were transported downward. The protons transported from the O horizon ((H+)in − (H+)out: 0.5–1.4 kmolc ha−1 yr−1) are neutralized in the B horizons of the BS and BB soils (Figure 5.26) or are leached further downward.

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World Soil Resources and Food Security

5.7.4  Dominant Soil Acidification Processes in Tropical Forests NPGBio is a dominant proton source in all the soil profiles (Figure 5.26). The soil acidification rates in tropical forests are higher than those for temperate forests (0.1–4.6 kmolc ha−1 yr−1 from Bredemeier et al. [1990], Binkley [1992], Shibata et al. [2001] and Section 5.2) (Figure 5.26). The higher acid loads in tropical regions is considered to be caused by higher biomass production and resulting higher NPGBio in tropical regions (Tables 5.15 and 5.17). Since NPGBio attributable to litter production would be neutralized by cations released from the fallen litter, soil acidification is mainly caused by excess cation accumulation in wood (3.0–8.6 kmolc ha−1 yr−1) during forest growth. Considering the complete biomass turnover of wood (cation release from coarse woody debris) in a steady state forest, NPGBio attributable to wood production might also be a neutral process for proton budgets on pedogenetic timescales. In our study, net-leaching losses of cations are typically small on an annual basis (Figure 5.25), but could be significant over pedogenetic timescales. Although the impacts of these processes on pedogenetic soil acidification are difficult to quantify and remain to be studied, our data quantitatively support the idea that higher rates of cation cycling through biomass and soils result in consistently high acid loads to soils under tropical forests (Figure 5.26) compared to those under temperate forests (Figure 5.3).

5.7.5  Proton Generation and Consumption in Soil Profiles In all the soil profiles, the contributions of NPGNtr, NPGCar, and NPGOrg to soil acidification are minor (Figure 5.26). This is consistent with the fact that complete cycles of C and N are balanced with no net proton fluxes in forest ecosystems [Binkley and Richter 1987]. However, translocation of the temporary acids (carbonic, organic, and nitric acids), as well as distribution of root biomass, contributed to heterogeneity of proton generation and consumption throughout the soil profiles, which varied from soil to soil (Figure 5.26). Organic acid dissociation is a common proton-generating process in the O horizons of the soils studied. DOC-associated proton generation accounts for 18%–77% of total proton generation in the O horizons (Figure 5.26). The large contribution of organic acids to soil acidification in the KR1, BS, and BB soils arises from the substantial fluxes of DOC production in the O horizon, which in turn is primarily caused by the greater fluxes of precipitation and C input and the quality of the foliar litter. On the other hand, carbonic acid dissociation is also a proton-generating process in the less acidic O horizons of the KR1 and KR2 soils (Table 5.16; pH > 5) because of their weakly acidic nature. This is consistent with substantial proton generation by carbonic acid dissociation in soils at neutral pH reported by Johnson et al. [1983], van Breemen et al. [1984], and Gower et al. [1995]. Although NPGCar associated with active root and microbial respiration has generally been recognized as a dominant acidifying process in tropical regions, the process is dominant only in moderately acidic and neutral soils.

Pedogenetic Acidification in Humid Asia

243

Proton generation by nitrification is generally the dominant process involved in NPGNtr in the O horizons, while proton consumption by mineralization of organic N + to NH 4 is also involved in NPGNtr in the highly acidic O horizons of the BS and BB soils (Figure 5.26). These differences are dependent on the balance between mineralization, nitrification, and NH +4 and NO3− uptake by vegetation and microorganisms (Figure 5.26). NPGBio is the dominant proton-generating process in the mineral soil horizons of the KR1 and KR2 soils, while NPGBio is present in both the organic and mineral horizons of the KR3, BS, and BB soils (Figure 5.26). Soil acidity and vegetation type could be the factors controlling the distribution of fine roots, and thus NPGBio. In acidic soils, fine root and ectomycorrhizal systems are developed in the O horizons [Fujimaki et al. 2004]. Dipterocarps, which are the dominant vegetation on the BS and BB soils, have fine roots and ectomycorrhizal systems developed in the O horizons of acidic Ultisols [Ashton 1988]. The high NPGBio in the O horizons of the KR3, BS, and BB soils arises from the presence of a fine root mat (Table 5.15; 0.3–2.3 Mg C ha−1 yr−1), which is related to soil acidity (pH < 4.5) and ectomycorrhizal associations of Dipterocarps in the BS and BB soils. Soil acidity and vegetation have a strong influence on the intensity and distribution of acids.

5.7.6  Acid Neutralization in Soils The release of cationic components is the principal mechanism of acid neutralization in organic and mineral soil horizons [van Breemen et al. 1984]. In the O horizons, the extent of acid neutralization varies with basic cation contents. The higher basic cation contents in the O horizons of the KR soils (87–129 cmolc kg−1) are considered to result in complete acid neutralization. In the O horizons of the BS and BB soils, lower basic cation contents (17–63 cmolc kg−1), as well as the intensive acid loads, probably result in incomplete acid neutralization and net eluviation of protons, Al, and Fe. In mineral soil horizons, the extents of acid neutralization depend on the ANC of soils and their parent materials. Based on both published data and those from our study, soil ANC is variable, depending on parent materials and the extent of soil acidification or weathering and clay migration. Our data show that parent materials have a strong influence on soil ANC with the BS and BB soils from sandstone having less ANC and a lower pH than the KR soils from serpentine and mudstone (Figure 5.27). In the KR soils from serpentine or mudstone, their high ANC suggests that their acidity is completely neutralized by basic cation release (Figures 5.26 and 5.27). In the BS and BB soils from sandstone, acidity is not completely neutralized due to their low ANC (Figures 5.26 and 5.27). Thus, parent materials have a strong influence on acid neutralization processes through their effects on basic cation contents in the O horizons and on soil ANC.

5.7.7  Implication of Proton Budgets for Pedogenetic Soil Acidification Acid loads are consistently higher in tropical regions than in temperate regions (Figures 5.3 and 5.26). This supports the presence of strongly weathered soils in the

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World Soil Resources and Food Security

Soil pH 6.5

KR1

6

KR2

5.5

KR3

5

BS

4.5

BB

4

Oxisols on andesitic PM (Costa Rica)

3.5 3

Spodosols on sandstone (Malaysia) 0

500

1000

1500

2000

2500

3000

Acid neutralizing capacity in soil (mANCs) (cmolc kg–1)

FIGURE 5.27  Relationship between soil pH and the acid neutralizing capacity of soil (mANCs). PM represents parent materials. (Data sources include Andriesse, J.P., Geoderma, 2, 201–227, 1969; Kleber, M. et al., Geoderma, 138, 1–11, 2007.)

humid tropics from the viewpoint of acidification [Eyre 1963]. However, since the kind, intensity, and distribution of acid loads vary with parent materials and vegetation, weathering reactions and pedogenetic soil acidification could also differ among tropical soils. The effects of parent materials on the dominant acidifying processes and pedogenesis can be characterized using the fluxes of Si, Al and Fe, and proton budgets throughout the soil profiles. Judging from the low concentrations of Al and Fe in the moderately acidic and neutral soil solutions of the KR1 and KR2 soils (Table 5.16), the accumulated Al and Fe oxides appear to arise from in situ weathering rather than eluviation or illuviation processes. In the KR1 soil, the substantial fluxes of Si (2.67–3.55 kmol ha−1 yr−1, calculated from the data in Table 5.16) and the high contents of Fe oxides throughout the soil profiles (Table 5.14) support the concept of ferralitization, which implies an absolute loss of Si (desilication) and a relative accumulation of Al and Fe oxides [Cornu et al. 1998]. Dissolution of olivine by acids (Mg1.6Fe0.4SiO4 (olivine) + 4H+ = 1.6Mg2+ + 0.4Fe2+ + H4SiO4) and Si leaching are considered to result in desilication, shown by a decrease of Si content from 45% for serpentine to 9% in the KR1 soil [Effendi et al. 2000]. The fluxes of Si leaching from the KR1 soil (3.55 kmol Si ha−1 yr−1) are higher than those of Oxisols from sedimentary rocks under Amazonian forests (1.1 kmol Si ha−1 yr−1) because of the higher dissolution rates of serpentine (olivine) than of quartz and kaolinite [Cornu et al. 1998]. This is consistent with the rapid formation of Oxisols (ferralitization) from easily weatherable serpentine, as compared to sedimentary rocks [Pfisterer et al. 1996]. In the KR2 and KR3 soils from sedimentary rocks, no net loss of Si occurs. In the KR2 soil, the high rates of NPGCar and the minor contribution of NPGOrg to soil acidification have contributed to incongruent dissolution of Fe-rich parent materials, which results in the accumulation of Al and Fe oxides throughout the profile.

Pedogenetic Acidification in Humid Asia

245

This process in the KR2 soil is similar to brunification, which implies accumulation of Al and Fe oxides owing to incongruent dissolution by weak acids (e.g., carbonic acid) in brown forest soils formed under temperate forests [Ugolini et al. 1990; Section 5.2]. In the highly acidic BS and BB soils, the intensive acid loads contributed by NPGOrg and NPGBio in the O horizons results in net eluviation of protons, Al, and Fe (Figure 5.26). These acidification processes are similar to podzolization [Cronan and Aiken 1985; Guggenberger and Kaiser 1998; Sections 5.2 and 5.3], which involves the complexing of Al and Fe with organic acids and their translocation downward. However, translocation of Al and Fe in the BS and BB soils is different from podzolization because of the absence of spodic B horizons. The degree of podzolization is considered to be controlled by the ANC and Fe contents in the parent materials [Duchaufour and Souchier 1978]. The higher ANC and Fe contents in the BS and BB soils (309–643 cmolc kg−1 and 3.6%–3.9% Fe2O3, respectively), as compared to the typical values for the tropical Spodosols (av. 291 cmolc kg−1 and < 2% Fe2O3, respectively) (Figure 5.27) are considered to reduce the mobility of organic acids and, thus, the degree of podzolization.

5.8  R  ELATIONSHIP BETWEEN CHEMICAL AND MINERALOGICAL PROPERTIES AND THE RAPID RESPONSE TO ACID LOADS OF SOILS IN HUMID ASIA As discussed so far, pedogenetic acidification is one of major ecosystem processes under humid climate. Soil response to an external or internal acid load is important: soil acidification causes declines in nutrient levels and Al toxicity, and geochemical processes or soil mineral weathering due to acid load and accompanying nutrient cation release compose the essential part of a biogeochemical process in ecosystems [Likens and Bormann 1995]. There are various acid-neutralizing reactions in soil including 1) exchange reactions or cation desorptions; 2) acid and anion adsorptions; 3) secondary mineral dissolutions; and 4) primary mineral dissolutions. These reactions are quantitatively expressed in terms of the proton budget or changes in acid-neutralizing capacity (ΔANC) [van Breemen et al. 1983, 1984]. In Japan, acid neutralizations due to cation exchange [Sato and Ohkishi 1993; Shibata et al. 2001; Takahashi et al. 2001], secondary mineral dissolution [Funakawa et al. 1993; Baba and Okazaki 2000; Section 5.3], and primary mineral dissolution [Sato and Takahashi 1996] were reported. However, in Southeast Asia, fundamental information on soil acid-neutralizing reactions is still scarce, though high sensitivity to acid loads in the region is assumed [Kuylenstierna et al. 2001]. The rate of acid neutralization may differ among the neutralizing reactions, and the buffering capacity of each reaction may differ among soils. Kinetic analysis, explained in detail by Sparks [1989], must be useful in characterizing the complex and simultaneous reactions in soil. The objective of the present study is to clarify the dominant process of acid neutralization in soils from humid Asia regions such as Japan, Thailand, and Indonesia. To achieve the objective, we investigated the soil chemical and mineralogical properties and rapid soil response to acid load by acid

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World Soil Resources and Food Security

titration and column experiments. In the column experiment, we kinetically analyzed cation releases to gain information on the source of soil alkalinity. Then, we investigated the relationship between soil chemical and mineralogical properties and the soil response to acid load.

5.8.1  Soils Studied We selected 46 subsoil samples in Japan (16 samples), Thailand (14 samples), and Indonesia (16 samples), which were formed under a natural or secondary forest and well-drained residual soils. Three Andisol soils were included in the Japanese soils, whereas the remaining 13 were Inceptisols or Spodosols. Most of Thai and Indonesian soils were Ultisols, except for 1 Alfisol and 2 Inceptisols from Thailand, and 2 Alfisols and 4 Inceptisols from Indonesia. They have representative clay mineral composition and are derived from various parent materials in each region being used in our previous study on clay mineral distributions [Sections 4.2 and 4.4]. We used soils from a subsurface horizon with a low total carbon content to mitigate the effect of organic matter. In addition to the analyses on general physicochemical properties, acid titration was conducted to investigate rapid soil response to acid load under the same experimental condition as Section 5.3. After titration, the concentrations of Na, K, Mg, Ca, Al, Mn, Fe, and Ti were determined to identify the acidneutralizing reactions in each of soils. The eight soils were selected from the 46 soil samples for the column experiment (Table 5.18), which are different in the degree of acidification, have representative chemical and mineralogical properties (e.g., pH, CEC, clay mineral species, and oxalate extractable Al), and are widely distributed soil types in each region. The four soils from Japan included a volcanic soil (JP-V) and three forest soils derived from sedimentary rocks in cool and warm temperate and subtropical zones, which were denoted JP-S1, JP-S2, and JP-S3, respectively. TH-S was from a sedimentary rock area in Thailand. ID-L, ID-I, and ID-S from Indonesia were formed on representative geological rocks, i.e., limestone and intermediate–basic igneous, and noncalcareous sedimentary rocks, respectively. Detailed information on the profiles was reported by Mori et al. [2005] and Watanabe et al. [2007]. The experimental procedure was roughly described as follows. Because the clay fraction dominates cation and anion exchange reactions and secondary mineral dissolution, soils were diluted with quartz sand to obtain a clay content of 30.0 % for the comparison of soils with different clay contents. Then 5.0 g of each sample (soil plus quartz sand) was packed in a column. Extraction with 0.01 mol L−1 HCl was conducted at a flow rate of 1 mL min−1 for 60 or 120 min, until the effluent pH reached a value near that of the extractant. HCl (0.01 mol L−1) was used to enhance mineral dissolution and exchange reactions and to detect the potential capacity of the reactions. Effluent was collected every 10 min and the concentrations of Na, K, Mg, Ca, Al, Fe, Mn, and Si were determined. To investigate the release manner of each element, the time course of element release was simulated using first-order kinetics, Y = A(1 − exp(−kt)), where Y is the cumulative amount of ions released, A is the convergence value, t is the extraction time, and k is the rate constant of the reaction.

Exchangeable

Sample JP-V JP-S1 JP-S2 JP-S3 ID-L TH-S ID-I

ID-S

a

Soil Classification Parent Rock Acrudoxic Melanudands Typic Fulvudands Andic Dystrudepts Typic Dystrudepts Typic Dystrustepts Ustic Haplohumults Typic Dystrudepts

Typic Paleudults

Volcanic ejecta Sandstonemudstone Sandstonemudstone Sedimentary rock Limestone Sandstonemudstone Andesiticbasaltic volcanic breccia Sandstonemudstone

Temp.

pH

a

Total C

(H2O) (g kg )

CEC

Bases

Al

Clay

Alo

(%)

Feo

Sio

Fed

Ca

Exch./Total

Mg

Mg

Horizon Bw1

5.0

23.2

17.3

0.7

n.d.

43

48.6 25.4 22.6 42.0 69.5 223.9

0.4

0.1

9.3

Bw2

4.7

55.9

21.9

0.3

6.8

40

13.7 26.1

15.4

Bw2

4.6

13.4

19.0

0.3

8.3

63

5.1

22.3

Bt

4.7

5.6

14.0

0.8

7.7

37

25.5

Bw2

5.8

15.7

29.3

17.2

n.d.

24.3

BA

5.6

11.0

10.5

2.7

24.9

Bw2

4.7

10.2

30.8

26.6

Bt

4.2

6.2

20.5

−1

(cmolc kg )

Ca

6.2

−1

(g kg )

Total

(°C)

−1

(cmolc kg )

Selective Dissolution

−1

(%)

0.3 30.4

4.5

40.4

1.1

0.2

5.7

0.3 32.1

2.5

62.5

1.1

0.2

1.7

0.7

0.1 56.3

0.5

27.0

3.9

2.0

76

5.0

5.7

1.3 63.6 22.3

16.3

65.3 14.4

0.7

51

1.8

4.1

0.2 35.3

4.6

26.4

20.8

5.8

5.7

9.4

66

2.8

5.3

0.3 31.8

7.1

32.8

46.0

6.7

0.5

9.9

49

2.1

1.3

0.1 37.3

2.4

27.8

2.7

Pedogenetic Acidification in Humid Asia

TABLE 5.18 Physicochemical Properties of Soils Used for the Column Experiment

0.7

Temperature was calculated from the mean annual temperature at the nearest meteorological stations and elevations at the sites and stations with a lapse rate of 5.5°C km−1.

247

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World Soil Resources and Food Security

5.8.2  Acid Titration Experiment and Source of Soil Alkalinity The amount of consumed H+ during the acid titration experiment, i.e., titratable alkalinity by a solution at pH 3.0, differed in each region and ranged from 2 to 11 cmolc kg−1. In Japan, the amount of consumed H+ was large for the Andisol samples (Table 5.19; 6–9 cmolc kg−1) and small for the soils with low clay content derived from felsic rocks (≤3 cmolc kg−1). Thai soils had low titratable alkalinity, mostly less than 3.5 cmolc kg−1, in spite of their higher pH(H2O) values, except for the soils at high altitude locations, where Alo content was high. Indonesian soils differed in their alkalinity reflecting parent materials; alkalinity was low in the soils from noncalcareous sedimentary rocks, and high in the soils from limestone or volcanic rocks. The proportion of sources of soil alkalinity in the acid titration experiment is shown in a ternary diagram for released base (Ca, Mg, K, Na) and metal (Al, Mn, Fe, Ti) ions and acid and anion adsorption (Figure 5.28). The alkalinity of Japanese soils was mainly caused by the release of metal ions, especially Al (Table 5.19), which correlated with Alo content (Figure 5.29a). In the two Andisol samples, not much Al was released in spite of a high Alo content. The reason for the low Al release rate was considered to be the dominance of the other H+ consuming reaction, i.e., acid and anion adsorption. The source of alkalinity in tropical soils was base ions (Figure 5.28), especially Ca and Mg (Table 5.19). The amount of released bases was strongly correlated with that of exchangeable bases (Figure 5.29b). The proportion of released metal ions in soil alkalinity were statistically larger in Japanese soil samples than in Thai and Indonesian soil samples (p < 0.001), while the proportion of released base ions was smaller (p < 0.001). In all the regions, acid and anion adsorption was more important for soils with lower alkalinity (Rs = −0.72, p < 0.001; Figure 5.29c). In the Andisol samples having high alkalinity, the proportion of acid adsorption was large relative to the amount of consumed H+ because of the large amount of variable charge sites in the soils. The amount of H+ consumed by acid adsorption weakly correlated with clay content (Rs = 0.39, p < 0.01) and pH (Rs = 0.35, p < 0.05). The ratio of exchangeable amount to total amount of Ca and Mg in some soils was high (Table 5.18), whereas the ratio of exchangeable amount to total amount of K and Na was usually low (less than 1%). Ca- and/or Mg-containing minerals, such as calsite, Ca-feldspar (anorthite), and pyroxene, typically have low resistance to weathering. They must weather rapidly and release Ca and Mg into soils, which would be retained in clay minerals. In contrast, K- and/or Na-containing minerals, such as K/Na-feldspars (e.g., microcline and albite) and muscovite have high resistance to weathering, and the monovalent ions are weakly retained in soils, which may result in the low ratio. The exchangeable/total ratios of Ca and Mg in soils were correlated with mean annual temperature (Rs = 0.52, p < 0.001; Rs = 0.43, p < 0.01) and pH (Rs = 0.43, p < 0.01 for Mg). Temperature was thought to enhance the weathering of primary minerals, which releases the base cations; the exchangeable bases were leached out by acidification. The ratios indicate that a high proportion of total amounts of the bases were labile in some Thai and Indonesian soils (Table 5.18), which means that the bases are easily utilized by plants but exposed to leaching by an acid load caused by acid precipitation, continuous cropping, etc.

Consumed H+

Samples

Number of Samples

AVE

SE

Released Cations Ca2+ AVE

Mg2+ SE

Average values in samples from different countries Indonesia 16 3.99 1.52 a 1.06 1.34 Thailand 14 3.45 1.00 a 0.31 0.57 Japan 13 3.84 1.05 a 0.10 0.34 Japan, 3 7.99 1.33 b 0.17 0.33 Andisols Samples used for the column experiment JP-V 8.95 0.28 JP-S1 9.09 0.06 JP-S2 4.34 0.03 JP-S3 2.47 0.03 ID-L 10.85 7.09 TH-S 3.27 0.44 ID-I 4.05 1.18 ID-S 2.71 0.05

AVE

K+

SE

AVE

Na+ SE

AVE

Al3+ SE

Adsorbed H+

Mn4+

AVE

SE

0.51 0.70 2.17 4.59

0.68 0.93 1.13 1.43

AVE

SE

0.15 0.26 0.05 0.04

0.55 0.59 0.39 0.20

AVE

SE

1.36 1.52 1.23 2.83

0.50 0.60 0.53 0.72

(cmolc kg–1)

a a a a

0.78 0.51 0.16 0.10

0.15 0.10 0.11 0.45 1.34 0.85 0.95 0.17

1.13 0.62 0.40 0.25

a a a a

0.07 0.14 0.05 0.06

0.06 0.05 0.03 0.04 0.04 0.06 0.06 0.14

0.18 0.38 0.16 0.13

ab b a ab

0.05 0.01 0.05 0.04

0.04 0.06 0.03 0.13 0.10 0.01 0.04 0.01

0.28 0.07 0.19 0.16

a a a a

5.53 5.99 2.62 0.37 0.15 0.05 0.25 1.09

a a b c

0.08 0.00 0.00 0.00 0.64 1.04 0.01 0.01

a a a a

a a a b

Pedogenetic Acidification in Humid Asia

TABLE 5.19 Consumed H+, Released Cations, and Adsorbed H+ during the Acid Titration Experiment

2.33 2.80 1.50 1.43 1.49 0.81 1.52 1.20

Note: AVE, average; SE, standard error. The values with the same letters are not significantly different by Tukey test (p < 0.05).

249

250

World Soil Resources and Food Security Japan Thailand Indonesia

0 100

Anion and acid adsorption 50

Base ions (Ca, Mg, K, Na)

50

100 0

0 100

50 Metal ions (Al, Mn, Fe, Ti)

FIGURE 5.28  Ternary diagram outlining the source of alkalinity (base and metal ions and acid adsorption) in the acid titration experiment.

(b)

10

Japan Thailand

8

Indonesia

6 4 2 0

0

10

20

Alo

Acid adsorption (%)

(c)

Released bases (cmolc kg–1)

Released Al (cmolc kg–1)

(a)

30

40

(g kg–1)

50

10

Japan Thailand

8

Indonesia

6 4 2 0

0

5

10

15

20

25

Sum of exchangeable bases (cmolc

80

30

kg–1)

Japan Thailand

60

Indonesia

Andisols

40 20 0

0

2

4

6

8

10

Consumed H+ (cmolc kg–1)

12

FIGURE 5.29  Contribution of the different sources of alkalinity of soils in the acid titration experiment: (a) Oxalate-extractable Al (Alo); (b) exchangeable bases; and (c) anion and acid adsorption.

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Pedogenetic Acidification in Humid Asia

5.8.3  Column Experiment for Eight Selected Soils The eight soils selected for the column experiment varied in soil pH value, the amount and source of alkalinity in the acid titration experiment, and the mean annual temperature under which they formed (Table 5.18). All the soils were already acidified under humid climate conditions. ID-L and TH-S had relatively high pH values because they formed under an ustic moisture regime, and ID-L was derived from limestone. ID-L, TH-S, and ID-I were rich in exchangeable Ca and Mg. A large amount of exchangeable Al was retained in strongly acidified soils with pH values below 5. According to Watanabe et al. [2007], x-ray diffractograms showed that JP-V has a small amount of crystalline clay minerals, whereas the other Japanese soils were determined to have an appreciable amount of 2:1-type clay minerals (1.4 nm), which were HIV. Gibbsite was clearly detected in all the Japanese soils, whereas no or only small amounts of gibbsite was present in the four tropical soils. For the tropical soils, clay mineral compositions were characterized by a large amount of kaolin minerals, and the 2:1-type clay minerals of the tropical soils were mica in TH-S, smectite in ID-I, and vermiculite in ID-S. The amounts of Alo and Feo were large in JP-V and JP-S1 (Table 5.18). In JP-V, the large amount of Alo and Sio were attributed to the presence of allophane/imogolite. Figure 5.30 shows that releases of base cations, especially Ca and Mg, contributed to acid neutralization in ID-L, TH-S, and ID-I, whereas Al release contributed to acid neutralization in the other soils. The relative contribution of cation releases in each soil sample were similar to that in the titration experiments (Figure 5.30; Table 5.19), though Al release was enhanced due to lower endpoint pH value. All the regression curves for Ca, Mg, K, Na, and Al releases in Figure 5.31 had low p-values ( 0.05. JP-V and ID-L have no exchangeable Al. The pH value at 0 min indicates pH(H2O).

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World Soil Resources and Food Security

ID-S

ID-I

TH-S

ID-L

JP-S3

JP-S2

JP-S1

JP-V

of Al released was small. They had either small amounts of exchangeable A1 or none at all (Figure 5.31); thus, Al release depended on slow mineral dissolution. For the soils except for JP-V and ID-L, the low release rate of Si indicates a weak silicate mineral dissolution (Figure 5.30). In JP-V and ID-L, Si was assumed to be released by Sio dissolution (Table 5.18). In JP-V, Sio was attributed to allophane/­ imogolite, but in ID-L the mineral contributing to Sio content was not apparent. The effluent pH values of JP-V, JP-S1, and JP-S2 decreased gradually, owing to Al release (Figure 5.31). For ID-L, the exchange reaction was extended and pH did not decrease in the first 10 min because of the large amount of exchangeable bases. For the other soils, pH decreased rapidly because of the small amount of exchangeable bases and low Alo content. The rate constants (k) of element releases are shown in Figure 5.32, where some releases with high p-values (>0.05) (marked with daggers) seem to be zero-order reactions. Generally, the variation of k-values was consistent with the acid-­neutralizing reactions assumed before. The k-value of Ca and Mg releases, which was assumed to be caused by exchange reactions, were as large as 10 −1 min−1. Meanwhile, the k-values of Al, K, Si, and Na releases were small. The k-values of Al releases from ID-I, ID-S, and JP-S3 were smaller than those of Ca and Mg, though both releases were mainly caused by exchange reactions, implying that exchangeable Al is more strongly retained in the soils. The k-values of Al releases from JP-V, JP-S1, ID-L, and TH-S, and those of K and Si, which were mainly released by mineral dissolution, were smaller than those of the exchange reactions. Na release was mainly caused by the exchange reaction occurring beneath the residual layer of feldspars, and the k-value of Na release was intermediate between those of Ca and Mg releases and mineral dissolution.

0 –1 –2

log k (min–1)



–3



–4 –5 –6 –7



† †

Ca Mg K Na Al Si

FIGURE 5.32  Rate constants (k) of the element released in the column experiment expressed in logarithms. *p > 0.05.

Pedogenetic Acidification in Humid Asia

255

5.8.4  Interpretation of Acid-Neutralizing Reactions under Laboratory and Field Conditions The results of the acid titration and column experiments indicated that the following reactions were mainly assumed for rapid soil response to internal and external acid loads in each region: exchange reactions of bases in the tropical soils with large amounts of exchangeable bases (especially Ca and Mg), Al dissolution in the Japanese soils with high Alo content, and anion adsorption in both the Japanese and tropical soils with small amounts of exchangeable bases and low Alo content and, thus with low alkalinity. Because of the low pH and high addition rates of HCl over a short time in the titration and column experiments, all the acid-neutralizing reactions were enhanced and some differences in acid neutralization between the experimental and the field conditions were assumed. Differences in the relative contributions of the acid-­neutralizing reactions between the experimental and the field conditions is not revealed by the experiments, because the degree of enhancement is not apparent for each reaction. However, the regional trend in the relative contribution to acid neutralization is considered to be similar between the experimental and the field conditions, because Al dissolution coincided with Alo distribution. On the other hand, slow dissolution of primary minerals occurs during long times of weathering under the field conditions, and it releases Al, Ca, Mg, etc., which results in acid neutralization, formation of secondary minerals, and replenishment of exchange bases. This long-term effect of primary mineral dissolution must be considered in the field. The exchange reaction of bases and the dissolution of Al are important in the field conditions [van Breemen et al. 1983; Chadwick and Chorover 2001]. The pH range of the exchange reaction of bases is approximately 5.5 to 4.0 [Chadwick and Chorover 2001; Brady and Weil 2002]. Al dissolution is important at low pH values (3.0–5.0) [van Breemen et al. 1983]. Acid and anion adsorption are also assumed to occur in response to an acid load [van Breemen et al. 1983; Brady and Weil 2002]. In this study, soils with low total carbon content were used, which decreased the amounts of acid adsorption to organic matter. Al dissolution in the Japanese soils is considered to be important even under the field conditions compared with the tropical soils, although its relative contribution to acid neutralization is lower than in the experiments under low pH conditions. Japanese soils have low pH values of less than 5 where Al dissolution is important [van Breemen et al. 1983]. At progressive stages of the soil acidification process, monomerization of amorphous Al(OH)3 and eluviation of Al is reported by Funakawa et al. [1993]. Alo dissolution is quantitatively important for acid neutralization because of the large buffering capacity: 10 g kg−1. Alo has an acid-neutralizing capacity comparable to that of 111 cmolc kg−1 exchangeable cations, being assumed to be present as Al(OH)3 and to dissolve as Al3+. The contribution of gibbsite dissolution to acid neutralization [Chadwick and Chorover 2001], which occurs at pH (KCl) values below 4.2 [Ulrich 1989], was also assumed in the Japanese soils, where gibbsite is usually present. In the Thai and Indonesian soils, rapid acid neutralization due to mineral dissolution was limited, because the amount of Alo and gibbsite content were generally small, and kaolinite, which is a dominant clay mineral, dissolves more slowly than gibbsite [Nagy 1995; Lasaga 1998].

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World Soil Resources and Food Security

The Al3+ activity in soil, which is ecologically significant because of its toxicity to vegetation, is mainly governed by Al(OH)3 solubility and pH [Chadwick and Chorover 2001], although in O/A horizons organic matter decreases Al3+ activity [Cronan et al. 1986]. Low-crystalline minerals in soil are assumed to have both positive and negative effects on Al3+ activity. At a given pH, Al3+ activity is higher in soils where poor-crystalline minerals control the activity compared with soils where crystalline minerals do; for example, when poor- and well-crystalline minerals control Al3+ activity at a pH of 4, the activities are 123 and 3 mg L−1, respectively [Lindsay 1979]. In this case, poor-crystalline minerals have a more adverse effect on the biota. In contrast, low-crystalline minerals were more reactive because they dissolved rapidly in the titration and column experiments, and these acid-neutralizing reactions and mineral dissolution are expected to keep the soil pH high, resulting in lower Al3+ activity. The low pH and high Alo content in some Japanese soils in the present study are considered to result in high Al3+ activity, exchange reactions of Al3+ with base ions, and low base saturation, which explains the low contribution of base ion releases to acid neutralization in these soils. K release from mica is predominant in soils with a pH(H2O) value below 5.0– 5.5, resulting in vermiculite formation as discussed in the previous sections. K and Na releases from feldspars as an initial dissolution were expected to occur easily under lower pH conditions, but to subside after a deeper surface residual layer forms. Because the releases are diffusion-controlled reactions [Chou and Wollast 1984; McLean and Watson 1985] and depend on the K/Na activity in soil, the release of these monovalent ions was thought to be enhanced in a specific zone, such as the rhizosphere. In the rhizosphere, low K/Na activity and strong acidic conditions are possible [Campbell and Greaves 1990] and K and Na may be easily released, even from the minerals.

5.9  G  ENERAL DISCUSSION ON THE PEDOGENETIC ACIDIFICATION PROCESS The natural soil acidification processes vary from ecosystem to ecosystem, depending on climate, vegetation, and parent materials [van Breemen et al. 1983, 1984; Ugolini and Sletten 1991]. According to van Breemen et al. [1984], the dominant acidification processes were carbonic acid dissociation in soils at neutral pH, vegetation uptake and nitrification in acidic soils with weatherable minerals, and vegetation uptake and dissociation of organic acids in podzolic soils. Johnson [1977] and Johnson et al. [1983] suggested the importance of carbonic acid to soil acidification in the tropical forests. In the present study, soil solution composition and proton budget in each of the soil horizons were quantitatively evaluated in temperate and tropical forests, and the factors controlling proton generation and consumption were assessed in relation to climate and soil types (Sections 5.2, 5.6, and 5.7). At the same time, the fates of the acids as well as H+ and Al translocated were traced (Section 5.3), and the resulting modification of soil minerals was also analyzed using different methodologies, e.g., analyzing RIP (Section 5.4), charge characteristics (Section 5.5), and assessing the rapid response of minerals against acid addition (Section 5.8). In the present section, these data—obtained in different regions and from the preceding works for the

Pedogenetic Acidification in Humid Asia

257

forest in northern Thailand, i.e., RP [Fujii et al. 2008; Section 4.7]—are comparatively analyzed.

5.9.1  Organic Matter Dynamics Since natural soil acidification induced by the internal acid load is associated with the organic matter cycles in the ecosystems [Binkley and Richter 1987; Devries and Breeuwsma 1987], the amounts of production and decomposition of organic matter and the balance between them have strong influences on proton generation and consumption, and thus the proton budget. The stock and flow of C in ecosystems and soils analyzed in the present study are summarized in Table 5.20. The aboveground biomass increases with air temperature from 78 to 346 Mg C ha−1 yr−1. The wood increment in the warm temperate and tropical forests (5.11–11.12 Mg C ha−1 yr−1) is higher than those in the cool temperate forests (1.50–2.50 Mg C ha−1 yr−1). The litterfall also increases with air temperature, ranging from 1.70–2.90 Mg C ha−1 yr−1 in the temperate forests to 3.62–4.78 Mg C ha−1 yr−1 in the tropical forests. Assuming that the C input to the soil is the sum of litterfall, root litter, and DOC leached as throughfall, the C budget between C input and its decomposition was almost balanced in each of the soils.

5.9.2  Soil Acidification Rate in the Entire Soil Profile In the present study, NPGBio was a dominant proton source in the entire soil profile. NPGBio attributable to the wood increment ranged from 0.7–8.6 kmolc has−1 yr−1, while NPGBio attributable to litter production ranged from 1.8–9.1 kmolc ha−1 yr−1. NPGBio attributable to the wood increment and litter production increased with the respective biomass production (Figure 5.33). Litter production and wood increment contributed to net proton generation at the rates of 0.010–0.030 molc and 0.004– 0.010 molc for production of 1 mol organic C, respectively. Judging from the balance between C input and its decomposition (Table 5.20), NPGBio attributable to litter production could be neutralized by cation release from the fallen litter. Therefore, soil acidification (0.5–6.1 kmolc ha−1 yr−1) is contributed mainly by excess cation accumulation in wood in the growth stage of the forests (Figure 5.33). The complete proton cycles in the processes of carbon and nitrogen cycles (e.g., mineralization, nitrification, and nitrate uptake by vegetation, dissociation and protonation of carbonic acid, and dissociation, mineralization, and adsorption of organic acids) result in the minor contribution of NPGNtr, NPGCar, and NPGOrg to soil acidification in the entire soil profiles [Binkley and Richter 1987]. However, translocation of the temporary acids (carbonic, organic, and nitric acids) and distribution of the root biomass contribute to the heterogeneity of proton generation and consumption throughout the soil profiles, and result in different processes of natural soil acidification (e.g., podzolization). In TG, KT, BS, and BB, NPGBio and NPGOrg concentrated in the O horizon are responsible for intensive acid loads (sum of NPGs) in the O horizon (3.1–7.0 kmolc ha−1 yr−1) (Figure 5.34). While the intensive acid load (3.1–7.0 kmolc ha−1 yr−1) could be largely neutralized by cation release from organic matter and mineral weathering

258

TABLE 5.20 Stock and Flow of Organic Carbon in Ecosystems Studied in Section 5.3

Site

a b c

Mean Annual Precipitation

Aboveground Biomass

DOC Flux

(°C)

(mm)

(Mg C ha−1)

7 11 16 25

1422 1782 1490 2084

78 83 115 169

1.50 2.50 10.10 5.79

1.70 2.10 2.90 3.99

0.40 0.59 0.84 1.25

3.40 5.50 5.10 5.46

−1.64 −3.32 −2.12 −1.44

58 78 83 40

35 344 112 32

2 16 4 1

27

2187

293

10.56

4.08

0.88

5.45

−1.18

182

562

13

27

2427

346

11.12

3.62

1.14

4.25

−0.53

97

470

13

27

2256

134

7.23

4.78

0.93

4.23

0.72

164

603

12

27

2256

260

9.85

4.00

0.18

4.70

−0.57

136

166

4

27

2256

282

5.11

4.50

0.36

4.24

0.39

126

261

6

Wood Increment

Litterfall

Root Littera

Decomposition of OMb

C Budget

(Mg C ha−1 yr−1)

Throughfall

O Horizon

(kg C ha−1 yr−1)

The annual rates of root litter incorporation were assumd to be 20% of the fine root biomass in the forest [Nakane 1980]. OM represents organic matter. C input is the sum of litterfall and DOC in throughfall.

DOC/C Inputc (%)

World Soil Resources and Food Security

NG, Japan TG, Japan KT, Japan RP, Thailand BS, Indonesia BB, Indonesia KR1, Indonesia KR2, Indonesia KR3, Indonesia

Mean Annual Air Temperature

259

Pedogenetic Acidification in Humid Asia

NPGBio (kmolc ha–1 yr–1) 10

KR2

KR2

8

Wood increment

KR3

6

KR1

BS

4

KT RP NG

2 TG 0

Litter production

KR1

0

BS KT

KR3

BB

BB

TG NG

RP 5

10

15

Biomass production (Mg C ha–1 yr–1)

FIGURE 5.33  Relationship between the biomass production of litter and wood and NPGBio.

(2.0–4.8 kmolc ha−1 yr−1), some excess protons (0.5–2.2 kmolc ha−1 yr−1) are transported into the A and B horizons in these soils (Figure 5.34). In the mineral soil horizons, the acid load contributed mainly by NPGBio, as well as the proton load transported from the overlying horizon, could be compensated for by proton consumption associated with the mineralization of organic anions and nitrate uptake by vegetation. In some Japanese plots, acid neutralization by the reactions with amorphous and/or organo-mineral complexes could be emphasized (Sections 5.3 and 5.8). In the other plots (NG, RP, KR1, KR2, and KR3), the acid load (1.7–4.3 kmolc ha−1 ∆ANC (kmolc ha–1 yr–1)

1:1

–8

–6

–4

–2

0

0

Moderately acidic soils (soil pH: 4.5–6.4)

KR1 KR2 KR3 RP

NG

BB

Highly acidic soils (soil pH: 3.8–4.3)

TG BS

KT 2

4

Acid load (kmolc

6

ha–1 yr–1)

8

FIGURE 5.34  Relationship between the acid load and the soil acidification rate in the O horizon.

260

World Soil Resources and Food Security Proportion of NPGBio in the O horizon relative to NPGBio in the entire soil profile (%) 100 80

TG

60 BB

40

KT

20 0

BS 3

4

KR3 NG

RP 5 Soil pH

KR2

KR1 6

7

FIGURE 5.35  Relationship between soil pH (0–5 cm) and the proportion of NPGBio in the O horizon relative to NPGBio in the entire soil profile.

yr−1) could be completely neutralized by basic cations in the same horizons (Figure 5.34). The surface neutralization observed for some Japanese soils in Section 5.3 (MD1 and MD2) might be included into this category. These differences in acid neutralization are considered to be caused by those of acid-neutralizing capacity in the O horizon. The contents of basic cations in the O horizon decreased with decreasing soil pH (Figure 5.35). In the moderately acidic soils, acid load could be completely neutralized by basic cations in the O horizon. In the highly acidic soils of TG, KT, BS, and BB, intensive acid load by NPGBio and NPGOrg in the O horizon, as well as the lower contents of basic cations in the O horizon, results in incomplete acid neutralization and net eluviation of protons and Al (Figure 5.34).

5.9.3  Factors Controlling Proton Generation and Consumption in Relation to Organic Matter Cycles The NPGBio contributes to intensive acidification in the O horizon of the highly acidic soils (pH < 4.3) in TG, KT, BS, and BB, while NPGBio distributed evenly in the A and B horizons of the moderately acidic soils in the other plots. The proportion of NPGBio distributed in the O horizon relative to that in the entire soil profiles increased with decreasing soil pH (Figure 5.35). The increased distribution of NPGBio in the O horizon with decreasing soil pH is caused by the presence of a fine root mat in the highly acidic soils. In the highly acidic soils, a fine root and ectomycorrhiza system was reported to develop in the O horizon to enhance NH +4 mobility [Aber et al. 1985]. Further, Fagaceae and Dipterocarpaceae are known to have a fine root and ectomy­corrhiza system developed in the O horizons of the acidic soils [Ashton

Pedogenetic Acidification in Humid Asia

261

1988]. Judging from this, vegetation species are also considered to contribute to the increased distribution of NPGBio in the O horizon. NPGOrg also substantially contributes to acidification of the O horizon. The NPGOrg in the present study (1.8–3.2 kmolc ha−1 yr−1), except for RP and KR2, is comparable with the higher values reported for the Spodosols in the temperate forests (0.8–3.7 kmolc ha−1 yr−1) [Cronan and Aiken 1985; Guggenberger and Kaiser 1998]. The higher NPGOrg in the O horizon is caused by the higher fluxes of DOC, which has one dissociated acidic functioning group per 5.9–12.2 C atoms in TG, KR1, BS, and BB. The fluxes of DOC leached from the O horizon are dependent on the fluxes of C input and the proportion of DOC leaching relative to C input. The higher DOC fluxes in the highly acidic soils of TG, BS, and BB could be accounted for by the increased proportion of DOC leaching relative to C input with decreasing soil pH. In KR1, the limited mineralization of DOC of the litter with the low phosphorus content, as well as the high fluxes of C input, is considered to result in the high fluxes of DOC leaching from the O horizon (Table 5.20), as discussed in Section 5.6. Owing to the weak acid nature of carbonic acid, NPGCar contributes to acidification of the O horizons only in KR1 and KR2. This is consistent with the high NPGCar in the soils at neutral pH reported by Johnson et al. [1983], van Breemen et al. [1984], and Gower et al. [1995]. NPGCar is dependent on the soil solution pH. As compared to NPGNtr associated with SOM loss in the cropland plots (1.5–5.0 kmolc ha−1 yr−1) [see Section 4.7], NPGNtr was lower (4 dSm−1, the exchangeable Na2+ % (ESP) is >1%) [Ahmad 1996].

7.5  M  ANAGEMENT INTERVENTION IN VERTISOLS VIS- À-VIS ENHANCEMENT OF CROP PRODUCTIVITY The loss and gain of Ca2+ ions during the formation of PC and dissolution of soil modifiers have a relevance both in soil exchange and soil solution for crop productivity by improving the hydraulic property of soils [Pal et al. 2006] besides their (Ca2+ ions) role as environmental sensors [Nayyar 2003]. The cultivation of sugarcane and rice has been successful because of the continuous supply of Ca2+ ions by the soil modifiers, even in HT climates. Sustainability of such agricultural land use is likely to remain as a viable management intervention for years, until the Vertisols become devoid of soil modifiers forever. However, despite their role in improving sHC, the use of irrigation, either with canal or well water, cannot help sustain the good crop yield because of the development of high pH, ECe, CaCO3, and ESP in Vertisols in different climatic environments. The presence of CaCO3 (mainly the PC) in Vertisols has generally been considered of doubtful significance in replacing exchangeable Na+ ions by Ca2+ ions of CaCO3 at a pH around 8.0. However, it is generally affected by other factors such as the application of gypsum, followed by cropping. The beneficial effect of naturally endowed gypsum has been realized in the Vertisols of southern Peninsular India, even in SAD climates. Such gypsum-containing Vertisols have sHC > 30 mm hr−1 and ESP < 15 (Table 7.7), despite rapid formation of PC because of the much higher solubility of gypsum than Ca-zeolites [Pal et al. 2006]. Even after realizing the beneficial role of gypsum in the slightly to highly sodic soils of the Indo-Gangetic Plains (IGP) and Vertisols, in terms of better physical, chemical, and biological properties [Gupta and Abrol 1990; Rao and Ghai 1985], the use of gypsum as management intervention in Vertisols of the dry climates of western, central, and southern Peninsular India is not commonly practiced [Venkateswarlu 1984; Pal et al. 2009c], unlike in similar soils

333

Formation and Management of Cracking Clay Soils (Vertisols)

TABLE 7.7 Physical and Chemical Properties of Vertisols Endowed with Gypsum in Semiarid Dry Parts of Tamil Nadu, India Depth (cm) 0–6 6–20 20–41 41–74 74–104 104–128 128–140 140+

pH (1:2 Water)

ECe (dS m−1)

CaCO3 (0.6%) in the organic production system have been due to the sequestration of carbon (Table 7.8) as compared to conventional systems [Venugopalan et al. 2004]. The limits of SOC content of the typical soil association of smectitic and noncalcareous Mollisols–Alfisols–Vertisols of tropical India under various land uses indicate that the clay mineral type of soils could be one of the important factors influencing the build-up of SOC. Such agricultural management intervention can help the sequestration of SOC, even up to 1% [Bhattacharyya et al. 2005]. Due to improvement in SOC

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TABLE 7.8 Comparison of Chemical Properties of Surface Soils (0–20 cm) under Organic and Conventional Production Systems (Based on 55 Soil Samples) Conventional Farming Properties pH (1:2 water) OC (%) CaCO3 (%)

Organic Farming

Range

Mean

Range

Mean

7.7–8.4 0.20–0.80 2.4–12.2

8.0 0.54 6.2

7.1–8.1 0.30–1.70 1.1–12.5

7.7 0.76 5.3

Source: Venugopalan, M.V., et al., Effect of organic and conventional cotton production systems on soil properties: A case study in Yavatmal district, Maharashtra, International symposium on strategies for sustainable cotton production—A global vision, Vol. II, 118–121. 2004.

and the subsequent dissolution of CaCO3, the pH of soils under organic production systems remained below 8.1 (Table 7.8). A long-term heritage watershed experiment initiated in 1976 at the ICRISAT Centre, Patancheru, Andhra Pradesh, India under rainfed conditions to demonstrate how an improved system of catchment management (IM) in combination with an appropriate cropping system can sustain increased productivity and improve the soil quality of Vertisols [Wani et al. 2003, 2007], in comparison to the existing traditional farming (TM) system. The improved system followed soil and water conservation practices, where excess rainwater was removed in a controlled manner. The soil and water conservation practices consisted of improved, legume-based crop rotation and improved nutrient management. In the TM system, sorghum or chickpeas were grown in the postrainy season with organic fertilizers, and in the rainy-season, the field was maintained as a cultivated fallow. The updated results from this experiment (Figure 7.4) indicate that the average grain yield of the improved cropping system over 30 years was 5.1 t ha−1 yr−1, nearly a fivefold increase in the yield over the TM system with an average yield of about 1.1 t ha−1 yr−1. The annual gain in yield in the IM system was 82 kg ha−1 yr−1 as compared to 23 kg ha−1 yr−1 in the TM system (Figure 7.4). The IM system thus has a higher carrying capacity (21 persons versus 4.6 persons ha−1 of the TM system) [Wani et al. 2009]. The IM system shows increased rainwater use efficiency (65% versus 40%), reduced runoff (from 220 mm to 91 mm) and soil loss (from 6.64 t ha−1 to 1.6 t ha−1), along with increased crop productivity and carrying capacity of land [Wani et al. 2003]. All these benefits have, however, been possible in the improvement of the hydraulic properties of Vertisols under IM systems as compared to TM systems (Table 7.9). Vertisols under IM and TM system have comparable pH, clay, and fine clay content (weighted mean, WM, in the 0–100 cm), however, the sHC value (WM) of IM has increased by almost 2.5 times due to the reduction in ESP through the dissolution of CaCO3 (Table 7.9). The CaCO3 (WM) content of IM decreased from 6.2% under the TM system to 5.7% (WM). In the past 24 years, the rate of dissolution of CaCO3 is 21 mg yr−1 in the first 100 cm of the profile. Under the IM system, the inclusion of pigeonpea,

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which produces piscidic acid in root exudates that solubilize iron-bound phosphorous (Fe-P) [Ae et al. 1990] and the rootlets in soil through which rainwater passes, or other sources of CO2 could have caused the increase in solubility of PC and a slight increase in exchangeable Ca/Mg (Table 7.9). Increased SOC sequestration in soils of the tropics induced dissolution of native CaCO3 and its leaching [Bhattacharyya et al. 2001]. The further importance of inorganic carbon in sequestering carbon in soils of dry regions is highlighted by Sahrawat [2003]. The improvement in soil properties through the IM system is also reflected in the classification of Vertisols. The Vertisols under the IM system qualify as typic Haplusterts, after being originally classified as sodic Haplusterts in the TM system (Table 7.9). The contribution of the dissolution of CaCO3 to the improvement of soil quality of the Vertisols under the IM system validates the soil carbon transfer model [Bhattacharyya et al. 2004]. The IM system with better hydraulic properties has also helped the Vertisols to sequester more SOC since 1976. At present, soils under the IM system contain 0.53% SOC in the 0–100-cm soil depth, in comparison with soils under the TM system, which contain 0.42% (Table 7.9). The rate of addition of soil carbon for the past 24 years since 1977 has been around 5 mg yr−1 in the first 100 cm of the soil profile of the IM system (Table 7.9). A study was conducted by ICRISAT and its partners to determine the carbon status of Vertisols at 21 benchmark sites covering arid, semiarid, and moist HT locations in India to identify carbon sequestrating systems [ICRISAT 2004]. The study indicates that after 20 years, the Vertisols sequestered more organic carbon than ferruginous Alfisols. The legume-based systems (high management) sequestered more carbon than the cereals and the horticultural systems, whereas grasslands sequestered more carbon than the annual crops [Bhattacharyya et al. 2007a,c; Sahrawat et al. 2005; Ramesh et al. 2007]. 8

Observe potential yield

Yield (t/ha)

6

4

2

Rate of growth 82 kg ha–1 yr–1

Carrying capacity 21 persons ha–1

BW 1

Carrying capacity 4.6 persons ha–1 Rate of growth 23 kg ha–1 yr–1

0 1976

1979

BW 4C

1982

1985

1988

1991

1994

1997

2000

2003

2006

Year

FIGURE 7.4  Three year moving average of sorghum and pigeonpea grain yields under improved (IM) and traditional management (TM) in a deep Vertisol catchment at Patancheru, India. (Based on data from Wani, S.P. et al., International Journal of Environmental Studies 64, 719–727, 2007.)

336

Horizon

Fine Clay Clay (%), (%), Weighted Fine Weighted Depth Clay Mean in Clay Mean in cm % 0–100 cm % 0–100 cm

Organic CEC sHC mm Carbon CaCO3 (cmol(p+) Exchangeable hr−1, (%) (%), kg −1), Ca/Mg, ESP sHC Weighted pH Organic Weighted Weighted CEC Weighted Weighted Weighted mm Mean in H2O Carbon Mean in CaCO3 Mean in cmol(p+) Mean in Exchangeable Mean in Mean in hr −1 0–100 cm (1:2) % 0–100 cm % 0–100 cm kg−1 0–100 cm Ca/Mg 0–100 cm ESP 0–100 cm Kasireddipalli Soil (Sodic Haplusterts) under Traditional Management (TM)a

Ap

0–12

48.0

53.0

26.4

33.0

7.0

7.8

0.6

Bw1

12–30

51.4

29.7

6.0

4.0

7.8

0.4

0.42

6.0 6.2

6.2

48.7 52.1

52.2

3.2 2.8

2.2

2.0

Bss1

30–59

52.5

32.5

6.0

8.1

0.4

6.0

52.2

2.1

7.1

Bss2

59–101 55.6

36.4

2.0

8.3

0.4

6.4

53.5

1.8

13.0

4.0

Bss3

101– 130

59.4

30.8

2.0

8.3

0.4

6.5

57.8

3.1

8.0

BCk

130– 160

58.0

38.7

1.0

8.2

0.1

9.1

49.5

1.5

22.2

8.3

World Soil Resources and Food Security

TABLE 7.9 Modification of Physical and Chemical Properties of Vertisols through the Improved Management System at ICRISAT, Patancheru in the 24 Years since 1977

0–12

52.1

54.7

28.8

32.8

17.0

7.5

1.0

Bw1

12–31

51.5

28.1

16.0

11.0

7.8

0.6

0.53

4.5

4.2

5.7

54.3

50.4

56.0

2.4

2.9

2.4

2.0 2.0

Bss1

31–54

54.2

34.0

10.0

7.8

0.4

6.2

55.6

1.7

3.0

Bss2

54–84

57.3

40.0

9.0

8.2

0.4

5.1

56.4

1.9

7.0

Bss3

84–118 56.5

26.0

7.0

8.1

0.5

8.6

61.6

3.8

7.0

Bss4

118– 146

59.3

31.7

3.0

8.2

0.5

8.4

58.2

2.1

7.0

BC

146– 157

60.0

41.5

--

8.2

0.3

7.4

55.2

1.1

9.0

Source:

4.5

Pal, D.K., et al., Developing a model on the formation and resilience of naturally degraded black soils of the Peninsular India as a decision support system for better land use planning, NRDMS, DST Project Report, Nagpur, India, 2003c; b Bhattacharyya, T., et al. Physical and chemical properties of selected benchmark spots for carbon sequestration studies in semi-arid tropics of India. Global Theme on Agro-ecosystems Report No. 35, 2007c, Andhra Pradesh, India, ICRISAT and ICAR. a

Formation and Management of Cracking Clay Soils (Vertisols)

Kasireddipalli Soil (Typic Haplusterts) under Improved Management (IM)b Ap

337

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Despite an overall benefit of IM systems in enhancing the crop productivity and improving the soil quality of Vertisols of semiarid tropics under rainfed conditions, its widespread adoption at the farmers’ level is still fraught with crop failure due to capricious rainfall patterns and socioeconomic constraints [Myers and Pathak 2001].

7.6  CONCLUDING REMARKS Vertisols are a relatively homogeneous soil group. They occur in a wide range of climatic conditions and exhibit remarkable variability in their properties, either in the presence or absence of soil modifiers (mainly Ca-bearing minerals like Ca-zeolites and gypsum). This review has created a window of updated knowledge that should assist the stakeholders of cracking clay soils (Vertisols and their intergrades) in better understanding the efficient use and management of their soils in the varied climatic environments of the world. Sustaining the productivity of rice and sugarcane in Vertisols endowed with soil modifiers is possible for a considerable period in HT climates. However, the pedogenic threshold (signifying the natural degradation process), in both zeolitic and nonzeolitic Vertisols of drier climates (SHM, SHD, SAM, SAD, and AD) is causing the decline in productivity of cereals, cash crops (cotton), and legumes. The rate of formation of CaCO3 and the concomitant development of subsoil sodicity in the Vertisols of India provide a realistic scenario as to how the dry climatic conditions pose a threat to agriculture (Figure 7.3), as it demands extra resources for raising crops (especially the winter crops) from resource-poor farmers [Pal et al. 2009a]. Research initiatives on the significance of PC and soil modifiers in the management of the Vertisols of dry climates at the NBSS and LUP (ICAR), Nagpur, India [Srivastava et al. 2002; Pal et al. 2006, 2009a, 2009b, 2009c] suggest that for sustained performance of crops in soils of dry climates, the replenishment of Ca2+ ions both in the soil solution and in the exchange complex appears to be a viable technological intervention. The solubility of PC can be enhanced by establishing crops through the IM system of ICRISAT, with inclusion of legumes and improved soil water and crop management options. The extra soluble Ca2+ ions would lower the equilibrium pH and ESP and make Vertisols more permeable to both air and water. The favorable soil water status would cause the enhancement of crop productivity. Soil modifiers may facilitate further improvement in water statuses in soils to release adequate amounts of water for crops. Vertisols of dry climates can thus show a natural resilience [Pal et al. 2009a]. Rainfed agriculture is predominant (80%) globally; however, current productivity levels are hovering around 1 to 1.5 t/ha−1 yr−1. Unlike the holistic approach taken for irrigated agriculture in Green Revolution areas in India, subsistence rainfed agriculture has so far been dealt with by compartments, such as soil conservation, water management, improved cultivars, and fertilizer application. The full potential of the technologies has not been realized, nor have these technologies been adapted on a sufficiently large scale to have a substantial impact [Wani et al. 2007]. In view of stagnating food grain production in the IGP areas, the maintenance of national

Formation and Management of Cracking Clay Soils (Vertisols)

339

buffer stock has become more dependent on the contributions by the few states of the northwestern part of the IGP that represent high crop productivity regions [Dhillon et al. 2010]. The total area of the IGP is 43.7 Mha, which produces 50% of the total food grain to feed 40% of the Indian population [Abrol and Gupta 1998; Pal et al. 2009d]. On the other hand, cracking clay soils (Vertisols and their intergrades) are less intensively cultivated as compared to the IGP areas [Bhattacharyya et al. 2007b], even though they occupy a nearly 66-Mha area [Bhattacharyya et al. 2009]. Therefore, areas dominated by cracking clay soils deserve immediate national attention so as to avoid the pitfalls encountered in the high productivity regions of the IGP [Bhattacharyya et al. 2007b; Dhillon et al. 2010]. Adaptation of the IM system may make Vertisols of dry climate more resilient and capable of producing more food grains required for the populous Indian subcontinent.

ACRONYMS AD arid hot ECe electrical conductivity of the saturation extract EMP exchangeable magnesium percentage ESP exchangeable sodium percentage HT humid tropical ICAR Indian Council of Agricultural Research IGP Indo-Gangetic Plains IM improved management MAR mean annual rainfall NBSS and LUP National Bureau of Soil Survey and Land Use Planning NPC nonpedogenic calcium carbonate PC pedogenic calcium carbonate SAD semiarid dry SAM semiarid moist sHC saturated hydraulic conductivity SHD subhumid dry SHM subhumid moist SOC soil organic carbon TM traditional management WDC water dispersible clay WM weighted mean

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Soil Survey Staff. 2003. Keys to soil taxonomy, 9th ed. Washington, DC: U.S. Department of Agriculture, Natural Resources Conservation Series. Soil Survey Staff. 2006. Keys to soil taxonomy. 10th ed. Washington, DC: U.S. Department of Agriculture, Natural Resources Conservation Services. Srivastava, P., Bhattacharyya, T., and D.K. Pal. 2002. Significance of the formation of calcium carbonate minerals in the pedogenesis and management of cracking clay soils (Vertisols) of India. Clays and Clay Minerals 50:111–126. Swindale, L.D. 1989. Approaches to agrotechnology transfer, particularly among Vertisols. In Management of Vertisols for improved agricultural production: Proceedings of an IBSRAM Inaugural workshop, 18–22 Feb. 1985, ICRISAT Centre, India. Patancheru, India: ICRISAT. Syers, J.K., Penningde Vries, F.T., and P. Nyamudeza (ed.). 2001. The sustainable management of Vertisols. Wallingford: CAB International Publishing 304pp. van der Merwe, A.J., DeVilliers, M.C., Buchmann, C., Beukes, D.J., and M.C. Walters. 2001. Vertisols management in South Africa, ed. J.K. Syers, F.T. Penning de Vries, and P. Nyamudeza, 85–100. Wallingford: CAB International Publishing. Venkateswarlu, J. 1984. Soil problems with Vertisols with particular reference to surface soil conditions and water relations. In ACIAR/IBSRAM Proceedings of the International workshop on soils, 12–16 September 1983, Townsville, 105–115. Venugopalan, M.V., Chandran, P., Pal, D.K., Challa, O., and S.L. Surge. 2004. Effect of organic and conventional cotton production systems on soil properties: A case study in Yavatmal district, Maharashtra. In International symposium on strategies for sustainable cotton production—A global vision, Vol II. Crop Production. 22–25 November,  2004. 118–121. Dharwad, Karnataka, India: University of Agricultural Sciences. Wani, S.P., Pathak, P., Jangawad, L.S., Eswaran, H., and P. Singh. 2003. Improved management of Vertisols in the semi-arid tropics for increased productivity and soil carbon sequestration. Soil Use and Management 19:217–222. Wani, S.P., Sahrawat, K.L., Sreedevi, T.K., Bhattacharyya, T., and Srinivas, Rao. 2007. Carbon sequestration in the semi-arid tropics for improving livelihoods. Int. J. Environ. Stud. 64:719–727. Wani, S.P., Rockstroem, J., and T. Oweis (ed.). 2009. Rain-fed agriculture: Unlocking the potential. Comprehensive Assessment of Water Management in Agriculture Series. Oxfordshire: CAB International Publishing. Yaalon, D.H. 1983. Climate, time and soil development. In Pedogenesis and soil taxonomy, I. Concepts and interaction, ed. L.P. Wilding, N.E. Smeck, and G.F. Hall, 233–251. Amsterdam: Elsevier.

of Nuclear and 8 Role Isotopic Techniques in Sustainable Land Management Achieving Food Security and Mitigating Impacts of Climate Change Long Nguyen, Felipe Zapata, Rattan Lal, and Gerd Dercon CONTENTS 8.1 Introduction...................................................................................................346 8.2 Basic Principles of Nuclear and Isotopic Techniques.................................... 347 8.2.1 Isotopes..............................................................................................348 8.2.2 Applications of Isotopic and Nuclear Techniques in SLM Research: Principles.......................................................................... 349 8.2.2.1 Radioactive Isotopes........................................................... 349 8.2.2.2 Stable Isotopes.................................................................... 350 8.2.3 Summary........................................................................................... 353 8.3 Toward SLM in Agroecosystems.................................................................. 357 8.3.1 Linkage between SLM, SOM, and Soil Quality............................... 357 8.3.2 Developing and Implementing the Soil-Water-Nutrient Management Approach...................................................................... 358 8.3.3 Use of NIT in Integrated Nutrient Management Studies.................. 359 8.3.4 Use of NIT in Water Management Studies in Agriculture................ 371 8.3.4.1 Introduction......................................................................... 371 8.3.4.2 Use of the SMNP in Water Use Efficiency Studies............ 372 8.3.4.3 Use of Stable Isotopes for Determining Water Used by Plants in Agroecosystems................................................... 373 8.3.4.4 Use of Carbon Isotope Discrimination in Plants as a Tool for Assessing Plant WUE........................................... 374 345

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8.4 Looking Ahead to Challenges Facing Agriculture........................................ 375 8.4.1 Contributing to Achieving Sustainable Land and Water Management while Mitigating Climate Change Impacts.................. 375 8.4.1.1 Combating Land Degradation and Improving Soil Quality................................................................................ 375 8.4.1.2 Development of an Integrated Approach to SoilWater- Plant Technologies in Agroecosystems.................... 376 8.5 Overview of SOC Cycling Studies in Agroecosystems................................ 382 8.5.1 Introduction....................................................................................... 382 8.5.2 Isotopic Techniques in C-Cycling Studies......................................... 382 8.5.2.1 Black Carbon...................................................................... 385 8.5.2.2 Biochar................................................................................ 386 8.6 Land Use, Management, and Soil C Sequestration in Agroecosystems........ 388 8.6.1 Introduction....................................................................................... 388 8.6.2 Assessment of Recommended Practices for SOC Management in Agroecosystems............................................................................. 389 8.7 Area-Wide (Watershed) Studies on Soil, Sediment, and SOC Redistribution and Identification of Sediment and Carbon Sinks in a Watershed...................................................................................................... 392 8.7.1 Introduction....................................................................................... 392 8.7.2 Use of Environmental Radionuclides in Soil Erosion Research in Agroecosystems............................................................................. 393 8.8 Mitigating GHG Emissions in Agroecosystems............................................ 394 8.8.1 Soils as Sinks and Sources of GHG.................................................. 394 8.8.2 Selected Applications of Isotope Techniques in GHG Emission Studies in Agroecosystems................................................................ 395 8.9 Conclusions.................................................................................................... 398 References............................................................................................................... 399

8.1  INTRODUCTION The present world population of 6.7 billion is expected to reach 8 billion by the year 2020. Most of the population increases will occur in developing countries, where the majority depend upon agriculture for their livelihoods. Against this background of projections of increased population growth and pressure on the worldwide availability of land and water resources, many developing countries will face major challenges to achieving sustainable food security considering their available per capita land area, the severe scarcity of fresh water resources, and particular infrastructure and socioeconomic conditions [Pinstrup-Andersen 1999; Lal 2000]. This scenario is further compounded by increased global land degradation, particularly increased risks of soil erosion and desertification in sub-Saharan Africa and South Asia [Scherr 1999]. Worldwide soil degradation is currently estimated at 1.9 billion hectares and is increasing at a rate of 5 to 7 million hectares each year [Lal 2006]. According to a recent study using data over a 20-year period, land degradation is increasing both in severity and extent in many parts of the world [FAO 2010a]. The consequences

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of land degradation include productivity decline, food insecurity, damage to basic resources and ecosystems, and loss of biodiversity, all of which are intricately linked with long-term social, economic, and environmental impacts ultimately resulting in human migration and sociopolitical unrest [Doos 1994; Bruinsma 2003; UNEP 2010]. Besides these pressing issues, several environmental drivers that also need to be addressed include: (1) increasing risks and impacts of global warming and climatic variability, (2) rising energy demands, in particular renewable energy sources, (3) expanding urbanization and industrialization and related infrastructure development, and (4) deteriorating water and air quality. All of these issues are likely to have negative impacts and induce changes in agroecosystems, which will place increasing pressures on sustainable land and water resources to produce sufficient food, feed, fiber, and fuel for the ever-increasing world population [Lal 2000; Verchot and Cooper 2008]. Sustainable land management (SLM) will require the combined use of the following strategies to preserve land and water resources: (1) agricultural intensification on the best arable lands that are already being farmed to enhance food security with minimum environmental degradation, (2) rational utilization of marginal lands, and (3) combating land degradation and restoring degraded soils [Lal 2000]. A key element across all land types and an integral part of sustainable agriculture would be to enhance soil quality for environmental sustainability of agroecosystems [Karlen et al. 2001; Arshad and Martin 2002; Carter 2002]. In this context, there is a strong need for high quality, innovative research to develop—in a relatively short time— land-specific technologies that will address the most strategically important issues of SLM in the agroecosystems of the developing world. The purpose of this chapter is to highlight the role of isotopic and nuclear-based techniques in the development of integrated soil, water, nutrient, and plant management practices for SLM in agroecosystems. This review is made within the objectives, approaches and strategies, and main project activities of the Soil and Water Management and Crop Nutrition (SWMCN) subprogram of the Joint Food and Agriculture Organization (FAO) and International Atomic Energy Agency (IAEA) Division of Nuclear Techniques in Food and Agriculture [FAO/IAEA 2009]. It aims to provide information on the relevant nuclear and isotopic methods that can be used to address current and emerging issues related to SLM in agricultural research. As land and water in agroecosystems are dynamic components of terrestrial ecosystems, there are multitudes of applications of stable and radioactive isotopes as tracers of global biogeochemical and hydrological processes, their interactions, and main driving factors. However, these aspects are beyond the scope of this review and therefore not included here.

8.2  BASIC PRINCIPLES OF NUCLEAR AND ISOTOPIC TECHNIQUES This section contains a brief account of the basic principles of nuclear and isotopic (NTI) techniques to aid in understanding the use and application of these techniques in agroecosystems. For details, readers are referred to treatises, IAEA Web sites, and training manuals available on the subject [L’Annunziata 2003; IAEA 1990, 2001, 2002a, 2002b, 2003a, 2003b, 2008a, 2088b; US-EPA 2008; US-NNDC 2008].

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8.2.1  Isotopes Isotopes are defined as atoms of the same atomic number (Z) but different atomic weight (A). The atom is the basic structural unit of matter. The atom of a given element such as phosphorus (P) has a set number of positively-charged protons and a neutral neutron in the nucleus. The number of protons (Z) and neutrons (N) present in the nucleus is referred to as the mass number (also as the atomic mass or atomic weight, A = Z + N), while the number of protons (Z) is termed the atomic number. An atom of a given element may have two or more isotopes. For example, phosphorus (P) has three isotopes (1531P, 1532P, and 1533P), which have the same number of protons (Z = 15) but different atomic weights (A of 31, 32, and 33, respectively) and different numbers of neutrons (N, defined as the difference between A and Z, of 16, 17, and 18, respectively). Isotopes may exist in both stable and unstable (radioactive) forms, depending on the stability of the nucleus in an atom. For example, a sulfur atom consists of five isotopes (32S, 33S, 34S, 35S, and 36S); one of them (35S) is a beta emitter radioactive, while the four others (32S, 33S, 34S, and 36S) are stable. A radioactive isotope is an atom with an unstable nucleus that spontaneously emits radiation (alpha or beta particles and/or gamma electromagnetic rays). The nonstability occurs because the ratio of neutrons (N) over protons (Z) in the nucleus is outside the belt of stability (i.e., outside a particular number due to an excess of either protons or neutrons), which varies with each atom. In contrast, a stable isotope is an atom with a stable nucleus (i.e., the N:Z ratio in the nucleus of an atom is within the belt of stability) and hence it does not spontaneously emit any radiation. Stable isotopes exist in light and heavy forms with heavy isotopes (higher atomic weight than light isotopes) accounting for less than 1.5% (Table 8.1). Stables isotopes are measured by an elemental analyzer coupled to an isotope ratio mass spectrometry (IRMS) in which the sample is combusted into a gas, which is fed into the mass spectrometer, where the ratio of the stable isotopes of interest  (e.g., 13C/12C, 2H/1H, 15N/14N, 18O/16O, 33S/32S) is���������������������������� determined. Recent developments in spectroscopic techniques such as wavelength-scanned cavity ring-down

TABLE 8.1 Average Abundances of Stable Isotopes (% Abundance in Brackets) of Major Elements Commonly Occurring in Agroecosystems Element

Heavy Isotope

Light Isotope

Carbon Hydrogen Nitrogen Oxygen

C (1.108%) H (0.0156%) 15N (0.366%) 18O (0.204%) 17O (0.037%) 33S (0.76%) 34S (4.22%) 36S (0.02%)

C (98.892%) H (99.984%) 14N (99.634%) 16O (99.759%)

Sulfur

13 2

12 1

S (95.02%)

32

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spectroscopy (WS-CRDS) combined with a combustion module offer an advantage of a large analytical throughput of stable isotope determination [Crosson 2008]. In the case of radioisotopes, the radiation emitted can be tracked by means of specific radiation detection devices, for instance Geiger–Müller counters or scintillation counters for beta emitters, gamma spectrometers for gamma-emitting radionuclides, and alpha spectrometers for alpha-emitting radioisotopes. The international unit (SI) of activity decay is the Becquerel (Bq), which is equal to one disintegration per second (dps). The old unit commonly used was the Curie, which is equivalent to 3.7 × 1010 dps or 3.7 × 1010 Bq. Environmental radionuclides is a term commonly used to refer to those radionuclides (natural or manmade) that are widely distributed in the environment or landscape and, while occurring at very low levels, are readily measurable. In soil erosion and sedimentation investigations, work has focused on the use of a particular group of environmental radionuclides, namely fallout radionuclides such as cesium-137 (137Cs), excess lead-210 (210Pbex), and beryllium-7 (7Be), which are gamma-emitters. These fallout radionuclides, which are deposited on the soil surface by dry deposition and rainfall, are ������������������������������������������������������������������� adsorbed to fine soil particles and their distribution in soil profiles and across agricultural landscapes resulting from the movement of soil particles can be used to document soil erosion and deposition rates and patterns [Zapata and Nguyen 2009].

8.2.2  A  pplications of Isotopic and Nuclear Techniques in SLM Research: Principles Both stable and radioactive isotopes are used in quantifying nutrient and water pools and fluxes in the soil-plant system. Specific chemical sources and pollutants can be also traced in the system using stable isotopic tracers. Stable isotopes do not pose safety hazards and can be used in a wide range of conditions (laboratory, glasshouse, and field conditions) at natural abundance or enriched levels. In contrast, the use of radioactive isotopes is normally restricted to laboratory and glasshouse conditions because of stringent safety measures and protection procedures relating to storage and transport, source handling, sampling, analyses, and waste disposal required to prevent the harmful effects of the radiation from radioactive isotopes on environments and living organisms. Strict compliance to international and national regulations is required. 8.2.2.1  Radioactive Isotopes Radioactive isotopes are used as tracers to investigate the kinetics of applied nutrients such as phosphorus (P) or sulfur (S) in the soil-plant system and also to assess the fertilizer recovery of labeled fertilizers by the crop as affected by soil type and fertilizer management practices (e.g., timing, method, source, etc.). In these studies, radioactive isotopes (or radioisotopes) of the same element (nutrient) for instance 32P for investigating P or 35S for S are added to the soil and the specific radioactivity (SR) of radioactive isotopes in soils and plants are determined. The SR is used to describe the amount of radioactivity per unit of material added (e.g., 32P or 35S added to soils as labeled fertilizer products or plant materials) as well as the amount of radiotracer

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per unit of the element being traced. For instance, when using 32P in a tracer experiment, the SR of P in the soil or plant material is Bq 32P per 1 g of soil or 1 g of plant dry matter and the SR of P in the soil P or in the plant P is Bq 32P mg−1 P. The usefulness of a radioisotope as a tracer depends on the following properties: i) its halflife (the time required for the radioactivity of a radioisotope to decay to half of its initial activity); ii) its decay mode or type of nuclear transformation and radiation emitted; and iii) its decay energy (amount of energy released in a particular nuclear decay). The basic energy unit is the electron volt. Energies are ranging typically from several kilo electron volts (Kev) to several mega electron volts (MeV). The halflife of a radioisotope must be suitable (sufficiently long) in relation to the duration of the experiment. The mode and energy of decay will determine how the radioisotope will be measured. 8.2.2.2  Stable Isotopes Stable isotopes offer an advantage over the conventional, nonisotopic techniques because they can be used to address a range of important issues such as:

1. The extent of nutrient cycling in the soil-plant systems and the fate of fertilizers in agricultural landscapes 2. The sources of pollutants from agricultural landscapes 3. The sources of water used by plants and crop water use efficiency 4. The extent of atmospheric nitrogen capture (biological nitrogen fixation) by crops for their growth and its contribution to the following crops 5. The decomposition and turnover rates of carbon and nitrogen from crop residues 6. The extent of nitrous oxide and carbon dioxide emissions from soil organic matter (SOM) and recently added organic manure, crop residues, or wastewater.

In the soil-plant system, both heavy and light isotopes of the same element take part  in physical-chemical-biological processes, but because of different atomic weights, they react at different rates. For example, plants preferentially take up carbon dioxide containing the lighter carbon isotope (12C) in photosynthesis, but the degree of carbon isotopic discrimination against the heavier 13C depends on the type of plants and water availability. Thus, the 13C/12C ratios in plant tissues provide a measure of crop water use efficiency. Similarly, physical processes such as soil evaporation can discriminate oxygen (O) in soil water against heavy 18O isotope because the lighter 16O is more readily evaporated. 8.2.2.2.1  Natural Abundance Isotopic Tracer Studies Natural abundance studies rely on the natural differences in the ways that heavy and light isotopes are fractionated by physical, chemical, and microbiological processes that lead to either enrichment or deletion of the heavy isotopes. These enrichment– deletion processes can be useful to track changes of an element in the environment. In these natural abundance studies, isotopic data are expressed as the ratio (R) of the heavy to light isotopes in the sample (R Sample) compared to the same ratio in

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an international standard (R Standard), using the delta (δ) notation. Since the differences in the absolute isotopic ratios (R) between the sample and standard are very small, they are expressed as parts per thousand or per mil (‰) deviation from the standard. For example, for δ values of nitrogen (N) and oxygen (O) in samples (e.g., soils or plants):

δ15N sample = [(15N/14N sample) / (15N/14N standard) − 1] × 1000



δ18O sample = [(18O/16O sample) / (18O/16O standard) – 1] × 1000

For N, the international standard is AIR (Atmospheric air; Table 8.2) with an accepted absolute 15N/14N ratio of 0.003676 (Table 8.2). Samples with ratios of 15N/14N greater than 0.003676 have positive delta values and those with ratios of 15N/14N lower than 0.003676 have negative delta values. 8.2.2.2.2  Isotopically Enriched Studies Heavy isotopes of essential elements in the soil-plant systems such as C, H, N, and O do not exist in abundance in nature (natural abundances of 100000

(b)

Savanna/steppe

Dry tropical/temperate Wet tropical Dry tropical (temperate winter)

Others

Desert Other

FIGURE 9.7  (a) Renewable liquid freshwater (blue water – hatched in dark) stress per capita using LPJ dynamic modeling year 2000. (From Rockström, J., et al., Water Resources Research, 45, W00A12, 2009.) (b) Renewable rainfall (green and blue water – hatched in semi dark and dark) stress per capita using LPJ dynamic modeling year 2000. (From Rockström, J., et al., Water Resources Research, 45, W00A12, 2009.)

because the average amount of water per capita in each pixel could obscure large differences in actual access to a reliable water source. In addition, these water quantities only include blue water. The full resource of rainfall, and notably green water, i.e., soil moisture used in rainfed cropping and natural vegetation, is not included. In a recent assessment that included both green and blue water resources, the level of water scarcity changed significantly for many countries (Figure 9.7b). Among the regions that are conventionally (blue) water scarce, but still have sufficient green and blue water to meet the water demand for food production, are large parts of sub-Saharan Africa, India, and China. If green water (on current agricultural land) for food production is included, per capita water availability in countries such as Uganda, Ethiopia, Eritrea, Morocco, and Algeria more than doubles or triples. Moreover, low ratios of transpiration to evapotranspiration (T/ET) in countries

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World Soil Resources and Food Security

Water productivity (m3/t grain)

such as Bangladesh, Pakistan, India, and China indicate high potential for increasing water productivity through vapor shift [Rockström et al. 2009]. Absolute water stress is found most notably in arid and semiarid regions with high population densities such as parts of India, China, and the MENA region. The MENA region is increasingly unable to produce the food required locally due to increasing water stress from a combination of population increase, economic development, and climate change, and will have to rely more and more on food (and virtual water) imports. For the greater part of the world, the global assessment of green and blue water suggests that water stress is primarily a blue water issue and large opportunities are still possible in the management of rainfed areas, i.e., the green water resources in the landscape [Rockström et al. 2009]. The current global population that has blue water stress is estimated to be 3.17 billion, expected to reach 6.5 billion in 2050. If both green and blue water are considered, the number currently experiencing absolute water stress is a fraction of this (0.27 billion), and will only marginally exceed today’s blue water stressed in 2050. Given the increasing pressures on water resources and the increasing demands for food and fiber, the world must succeed in producing more food with less water. Hence, it is essential to increase water productivity in both humid and arid regions. Some describe the goal as increasing the “crop per drop” or the “dollars per drop” produced in agriculture. Regardless of the metric, it is essential to increase the productivity of water and other inputs in agriculture. Success will generate greater agricultural output, while also enabling greater use of water in other sectors and in efforts to enhance the environment. Water productivity can vary with household income, as farmers’ yields vary as a result of local input and management styles. In a household level study of 300 farmers in eight sub-Saharan countries, the more wealthy farmers had generally higher yield levels [Holmen 2004], and subsequently better water productivity (Figure 9.8). The differences were significant between the wealthier classes and poorest classes. 3000 2500 2000 1500 1000 500 0

Very poor

Below average wealth

Average wealth

Above average

Very wealthy

FIGURE 9.8  Water productivity for maize yields and income levels for smallholder farming systems in sub-Sahara Africa. (Based on Holmen, H., Currents, 34, 12–16, 2004.)

New Paradigm to Unlock the Potential of Rainfed Agriculture

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More than 1000 m3 additional water was required per ton of maize grain produced by the poorest farmers compared to the wealthiest farmers. Data suggest that yield improvements for the purpose of poverty alleviation can also significantly improve water productivity, especially in current low-yielding rainfed (green water) agriculture in sub-Saharan Africa and parts of South Asia. Improved water use efficiency and productivity can improve food security. A sectoral approach to managing water is a cause of low water use efficiency.

9.3.2  Water Alone Cannot Achieve Food Security 9.3.2.1  S oil Health: An Important Driver for Enhancing Water Use Efficiency Soil health is severely affected due to land degradation and is in need of urgent attention. Often, soil fertility is the limiting factor to increased yields in rainfed agriculture [Stoorvogel and Smaling 1990; Rego et al. 2005]. Soil degradation through nutrient depletion and loss of organic matter causes serious yield decline, closely related to water determinants as it affects water availability for crops due to poor rainfall infiltration and plant water uptake due to weak roots. Nutrient mining is a serious problem in smallholder rainfed agriculture. In sub-Saharan Africa, soil nutrient mining is particularly severe. It is estimated that approximately 85% of African farmland in 2002–2004 experienced a loss of more than 30 kg/ha of nutrients per year [IFDC 2006]. The International Crops Research Institute for the Semi-Arid Tropics’ (ICRISAT) on-farm diagnostic work in different community watersheds in different states of India as well as in Southern China, North Vietnam, and Northeast Thailand showed severe mining of soils for essential plant nutrients. Exhaustive analysis in selected states in India showed that 80%–100% of farmers’ fields are deficient not only in total nitrogen but also micronutrients like zinc, boron, and secondary nutrients such as sulfur beyond the critical limits (Table 9.2a and b) [Rego et al. 2007; Sahrawat et al. 2007]. A substantial increase in crop yields was experienced after micronutrient amendments, and a further increase of 70% to 120% occurred when both micronutrients and adequate nitrogen and phosphorus were applied to a number of rainfed crops (maize, sorghum, mung bean, pigeonpea, chickpea, castor, and groundnut) in farmers’ fields [Rego et al. 2005; Sahrawat et al. 2007; Srinivasarao et al. 2010]. Evidence from on-farm participatory trials in different rainfed areas in India clearly indicated that investments in soil fertility improvement directly improved water management, resulting in increased rainwater productivity. Rainwater productivity (i.e., for grain yield per mm of rainfall) was significantly increased in the example above as a result of micronutrient amendment. The rainwater productivity for grain production was increased by 70%–100% for maize, groundnut, mungbean, castor, and sorghum by adding boron, zinc, and sulfur [Rego et al. 2005]. In terms of net economic returns, rainwater productivity was substantially higher by 1.50 to 1.75 times. Similarly, rainwater productivity was increased significantly when integrated land, nutrient, and water management options were adopted as well as use of improved cultivars in semiarid regions of India [Wani et al. 2003; Sreedevi and Wani 2009]. Gains in rainwater use efficiency with improved land, nutrient, and water management options were far higher in low rainfall years (Figure 9.9).

432

TABLE 9.2a Percent of Farmers’ Fields Deficient in Available Nutrients in Various States (Districts within States) of India State Andhra Pradesh

Chattisgarh Gujarat Jharkhand Karnataka

Rajasthan Tamilnadu Total

Adilabad, Ananthapuram, Kadapa, Khammam, Kurnool, Mahabubnagar, Medak, Nalgonda, Prakasam, Rangareddy, Warangal Kanker Junagadh Gumla, Kharsawan Bengaluru, Rural, Bijapur, Chamrajanagar, Chikballapur, Chitradurga, Dharwad, Haveri, Kolar, Raichur, Tumkur Kollam, Pathanamthitta, Thiruvananthapuram Badwani, Dewas, Guna, Indore, Jhabua, Mandla, Raisen, Rajagarah, Sagar, Sehore, Shajapur, Vidisha Alwar, Banswara, Bhilwara, Bundi, Dungarpur, Jhalwar, Sawai Madhopur, Tonk, Udaipur Kanchipuram, Karur, Salem, Tirunelveli, Vellore

OC a %

Av P ppm

Av K ppm

Av S ppm

Av B ppm

Av Zn ppm

3650

76 b

38

12

79

85

69

40 82 115 17,712

– 12 42 70

63 60 65 46

10 10 50 21

90 46 77 84

95 100 97 67

50 85 71 55

28 341

11 22

21 74

7 1

96 74

100 79

18 66

421

38

45

15

71

56

46

119 22,508

57 69

51 45

24 19

71 83

89 70

61 58

Source: Rego, T.J., et al., Journal of Plant Nutrition, 30, 1569–1583, 2007; Sahrawat, K.L., et al., Current Science, 93(10), 1–6, 2007; Wani, S.P., et al., In Conservation Farming: Enhancing Productivity and Profitability of Rain-Fed Areas, Soil Conservation Society of India, New Delhi, 163–178, 2008; and unpublished data sets of ICRISAT. a OC = organic carbon; AvP = available phosphorus; AvK = available potash; AvS = available sulfur; AvB = available boron, AVZn = available zinc. b = Per cent of farmers fields deficient, i.e., below critical limit for a particular nutrient. * = Extensive soil sampling undertaken to interpolate analysis at district level using GIS.

World Soil Resources and Food Security

Kerala Madhya Pradesh

District

No. of Farmers

State Andhra Pradesh Mean Range Chattisgarh Mean Range Gujarat Mean Range Jharkhand Mean Range Karnataka

Mean Range Kerala Mean Range

District Adilabad, Ananthapuram, Kadapa, Khammam, Kurnool, Mahabubnagar, Medak, Nalgonda, Prakasam, Rangareddy, Warangal

Kanker

Junagadh

Gumla, Kharsawan

Bengaluru, Rural, Bijapur, Chamrajanagar, Chikballapur, Chitradurga, Dharwad, Haveri, Kolar, Raichur, Tumkur

Kollam, Pathanamthitta, Thiruvananthapuram

No. of Farmers

OC a %

Av P ppm

Av K ppm

Av S ppm

Av B ppm

Av Zn ppm

0.41 0.08–3.00

9.1 0.0–247.7

129 0–1,263

9.6 0.0–801.0

0.34 0.02–4.58

0.81 0.08–35.60

– –

6.99 0.0–63.6

128.9 4.1–11.66

6.53 1.4–34.6

0.25 0.1–0.78

0.91 0.4–3.07

0.77 0.21–1.90

6.9 0.4–42.0

104 30–635

16.0 1.1–150.4

0.22 0.06–0.49

0.44 0.18–2.45

0.53 0.19–1.13

5.3 0.0–72.4

63 8–247

7.8 1.3–50.0

0.17 0.06–0.80

0.68 0.24–2.90

0.43 0.01–3.60

12.3 0.0–480.0

133 4–3750

13.2 0.1–4647.4

0.57 0.02–26.24

0.97 0.06–235.00

1.04 0.36–2.57

22.0 1.2–137.0

101 33–313

3.4 1.0–11.0

0.31 0.18–0.48

1.88 0.56–7.20 (continued)

3650

40

82

115

17712

New Paradigm to Unlock the Potential of Rainfed Agriculture

TABLE 9.2b Mean and Range Values of Nutrient Content in Soil Samples in Various States (Districts within States) of India

28

433

434

TABLE 9.2b (Continued) Mean and Range Values of Nutrient Content in Soil Samples in Various States (Districts within States) of India State Madhya Pradesh Mean Range Rajasthan

Badwani, Dewas, Guna, Indore, Jhabua, Mandla, Raisen, Rajagarah, Sagar, Sehore, Shajapur, Vidisha

Alwar, Banswara, Bhilwara, Bundi, Dungarpur, Jhalwar, Sawai Madhopur, Tonk, Udaipur

Kanchipuram, Karur, Salem, Tirunelveli, Vellore

OC a %

Av P ppm

Av K ppm

Av S ppm

Av B ppm

Av Zn ppm

0.65 0.28–2.19

5.0 0.1–68.0

190 46–716

9.6 1.8–134.4

0.43 0.06–2.20

0.72 0.10–3.82

0.72 0.09–2.37

8.1 0.2–44.0

116 14–1,358

10.6 1.9–274.0

0.60 0.08–2.46

1.27 0.06–28.60

0.51 0.14–1.37

9.2 0.2–67.2

122 13–690

11.3 1.0–93.6

0.34 0.06–2.18

0.78 0.18–5.12

0.44 0.01–3.60

11.5 0.0–480.0

133 0–3750

12.4 0.0–4647.4

0.53 0.02–26.24

0.94 0.06–235.00

341

421

119

22,508

Source: Rego, T.J., et al., Journal of Plant Nutrition, 30, 1569–1583, 2007; Sahrawat, K.L., et al., Current Science, 93(10), 1–6, 2007; Wani, S.P., et al., In Conservation Farming: Enhancing Productivity and Profitability of Rain-Fed Areas, Soil Conservation Society of India, New Delhi, 163–178, 2008; and unpublished data sets of ICRISAT. a OC = Organic Carbon; AvP = Available Phosphorus, AvK = Available Potash, AvS = Available Sulfur, AvB = Available Boron, AVZn= Available Zinc. * = Extensive soil sampling undertaken to interpolate analysis at district level using GIS.

World Soil Resources and Food Security

Mean Range Tamilnadu Mean Range Total Mean Range

District

No. of Farmers

435

New Paradigm to Unlock the Potential of Rainfed Agriculture

Rainfall Improved system (BW1)

14

Traditional system (BW4C)

11

Rainfall (mm)

Rainfall use efficiency (kg/ha/mm of rainfall)

16

8 5 2000 3 0

1000 1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

0

Year

FIGURE 9.9  Increased rainwater use efficiency in low rainfall years.

In addition, soil organic matter—an important driving force for supporting biological activity in soil—is very much in short supply, particularly in tropical countries. In addition to its importance for sustainable crop production, low soil organic matter in tropical soils is a major factor contributing to their poor productivity [Lee and Wani 1989; Syers et al. 1996; Katyal and Rattan 2003], the accelerated decomposition of soil organic carbon (SOC) due to agriculture and release of carbon (C) in the atmosphere also contributes to global warming [IPCC 1990; Lorenz and Lal 2005]. Management practices that augment soil organic matter and maintain it at a threshold level are needed. Sequestration of C in soil has attracted the attention of researchers and policymakers alike as an important mitigation strategy for minimizing impacts of climate change [Lal 2004; Velayutham et al. 2000; ICRISAT 2005; Bhattacharya et al. 2009; Srinivasarao et al. 2009]. Agricultural soils are among the earth’s largest terrestrial reservoirs of C and hold potential for expanded C sequestration [Lal 2004]. Improved agricultural management practices in the tropics such as intercropping with legumes, application of balanced plant nutrients, suitable land and water management, and use of stress-tolerant high-yielding cultivars improved SOC content and also increased crop productivity [Wani et al. 1995, 2003a, 2005, 2007; Lee and Wani 1989; ICRISAT 2005; Srinivasarao et al. 2009]. Farm bunds and degraded common lands in the villages could be productively used for growing nitrogen-fixing shrubs and trees to generate nitrogen-rich loppings. For example, growing Gliricidia sepium at a close spacing of 75 cm on farm bunds could provide 28–30 kg nitrogen per ha in addition to valuable organic matter. Also, large quantities of farm residues and other organic wastes could be converted into valuable sources of plant nutrients and organic matter through vermicomposting [Nagavallama et al. 2005]. Vermicompost is a good source of plant nutrients along with organic C addition

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TABLE 9.3 Nutrient Composition of Vermicompost and Garden Compost Nutrient Element Organic carbon Nitrogen Phosphorus Potassium Calcium Magnesium Sodium Zinc Copper Iron Manganese

Vermicompost (%) 9.8–13.4 0.51–1.61 0.19–1.02 0.15–0.73 1.18–7.61 0.093–0.568 0.058–0.158 0.0042–0.110 0.0026–0.0048 0.2050–1.3313 0.0105–0.2038

Garden Compost (%) 12.2 0.8 0.35 0.48 2.27 0.57 0.75)

FIGURE 10.3  Land degradation index. (From Nachtergaele, F.O. et al., Global Land Degradation Information System (GLADIS), beta version. An information database for Land Degradation Assessment at Global Level, 2010.)

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0

483

Land Degradation

severely (defined as those that imply a near irreversible loss of biomass, soil, water biodiversity, economic output, or social progress), the overall index is set at severe. These critical values correspond roughly to a limit of 25 (37.5 for soil) in each original axis. When analyzed on a regional and country basis, Figures 10.3 and 10.4 show that actual ongoing degradation processes over the past 20–30 years were the most pressing in steeplands, mainly because of soil erosion, and also in parts of West and Central Europe due to intensive agriculture leading to soil pollution and ongoing urbanization. Around the Mediterranean and in Eastern Brazil, high pressures on biodiversity are noted. In the Near East, water depletion is a major problem, while in Central Asia and Northern India and Pakistan, pressures were due to a combination of factors, in the latter certainly mainly due to water depletion. The African continent does show a moderate rate of degradation, with the Sahel showing up as a particularly improving area due to improved rainfall and better economic performance over the past two decades. South America shows an overall favorable picture except in the easternmost tip of Brazil, where biodiversity is under stress (Table 10.4). Comparing the LDI with the EDI (Figure 10.3 vs. Figure 10.8) clearly shows that the inclusion of the socioeconomic considerations improves the overall land degradation pictures and a number of areas that were moderately degrading environmentally are better rated when the socioeconomic trends are considered too (most notably Brazil and parts of China because of economic development), although some become worse (Central Asia because of slightly declining social provisions). Figure 10.5 shows what particular good or service is particularly under threat and where, indicating soil constraints as the most prevalent, followed by biodiversity loss, and water depletion. (a) Comparison of ecosystem G&S provisions

(b) Comparison of ecosystem G&S provisions

Biomass

Biomass

100

Social

50

100

Soil

Social

0

Economic

50

Soil

0

Water

Biodiversity Forest - virgin (ESSI 0.67) Sparsely vegetated areas - moderate or higher livestock density (ESSI 0.24)

Economic

Water

Biodiversity Forest - with agricultural activities (ESSI 0.66) Crops and mod. intensive livestock density (ESSI 0.37)

FIGURE 10.4  Changes in the environmental capacity to provide goods and services (1990–2005).

484

TABLE 10.4 Sample of Average Country Land Degradation Indices Trend in Soil Health/Axis 2 Process

Cyprus Lebanon Albania Greece Spain Lesotho

48.2 53.5 53.9 54.0 54.7 48.1

38.4 33.9 39.6 35.1 39.4 32.9

Bangladesh Togo Brazil Benin Burkina Faso

50.8 54.8 51.0 58.6 61.8

40.6 42.9 53.0 43.1 39.6

Trend in Productivity Value/Axis 5 Process

Trend in Social and Cultural Provisions/Axis 6 Process

Land Degradation Index

Most Degraded Countries 49.4 21.0 48.3 21.0 89.9 22.0 66.5 22.2 47.1 22.7 96.5 41.3

52.7 55.7 62.7 52.0 56.8 47.5

49.0 59.7 49.0 50.0 50.0 54.0

0.75 0.75 0.75 0.73 0.73 0.70

Least Degraded Countries 84.5 32.6 97.0 35.7 93.2 32.5 99.3 36.3 99.3 36.3

71.6 62.2 94.7 79.5 96.7

67.0 63.0 52.0 68.0 65.0

0.44 0.43 0.41 0.38 0.36

Trend in Water Stress / Axis 3 Process

Biodviersity Risk Resilience/Axis 4 Process

Source: Nachtergaele, F.O., and Petri, M., Mapping Land Use Systems at a Global and Regional Scale for Land Degradation Assessment, LADA Technical Report #8, FAO, Rome, 2008. Note: Sample average values of whole land degradation index and land degradation in biomass, soil, water, biodiversity, productivity and socio-cultural provision by countries. The land degradation indicators are estimated using the GLADIS method explained in Sections 10.4 to 10.7.

World Soil Resources and Food Security

Country

Greenness and Deforestation Trend/Axis 1 Process

Land Degradation

0

1,750 3,500

Wastelands

7,000 Km

Geographic coordinates

Water

High degr. (> 0.75)

Moderate degr. (0.75 to 0.50)

N

Stable

(0.50 to 0.40)

Mod. improvement (< 0.40)

485

FIGURE 10.5  Main threatened ecosystem goods and services. (From Nachtergaele, F.O. et al., Global Land Degradation Information System (GLADIS), beta version. An information database for Land Degradation Assessment at Global Level, 2010.)

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World Soil Resources and Food Security

TABLE 10.5 Environmental Degradation by Major Land-Use System

Land-Use Systems Shrubs with high livestock presence Irrigated agriculture with high livestock presence Irrigated agriculture Agriculture crops and high livestock presence Grasslands with high livestock presence Shrubs with moderate livestock presence Grasslands– protected Grasslands– extensive pastoralism Rainfed agriculture Forestry–unused Shrubs–extensive pastoralism Wetlands–protected Wetlands–mangrove

Greenness and Deforestation Trend/Axis 1 Process

Trend in Soil Health/ Axis 2 Process

Trend in Water Stress/Axis 3 Process

Biodiversity Risk Resilience/ Axis 4 Process

Environmental Degradation Index

56.5

35.2

68.0

33.5

0.67

56.6

37.0

53.1

32.9

0.64

53.4 54.1

38.9 36.2

58.7 75.2

33.1 33.2

0.62 0.59

53.3

38.7

71.2

34.7

0.58

54.1

38.8

68.9

34.8

0.57

52.3

37.5

85.9

35.5

0.56

52.6

38.8

86.3

35.7

0.56

51.8 50.9 52.4

47.3 41.7 44.2

80.7 92.3 81.1

32.4 34.9 40.3

0.53 0.51 0.50

51.8 50.4

45.1 49.1

91.4 94.4

36.0 42.9

0.47 0.46

Source: Nachtergaele, F.O., and Petri, M., Mapping Land Use Systems at a Global and Regional Scale for Land Degradation Assessment, LADA Technical Report #8, FAO, Rome, 2008. Note: Average values of environmental degradation index and environmental degradation in biomass, soil, water, and biodiversity by land use system. The land degradation indicators are estimated using the GLADIS method explained in Sections 10.4 to 10.7.

Greenness and Deforestation Trend/Axis 1 Process

Trend in Soil Health/Axis 2 Process

French Guiana Libyan Arab Jamahiriya Western Sahara Suriname Russian Federation Namibia

53.7 49.9 50.0 52.4 52.6 53.2

49.4 49.6 50.0 49.2 38.1 42.4

Least Impacted Countries 99.4 48.0 50.0 98.7 92.9 87.8

Swaziland Sierra Leone India Haiti Malawi Rwanda Burundi

46.5 51.3 57.8 51.0 47.8 41.2 45.2

35.5 29.8 32.1 16.7 37.7 27.1 29.8

Most Impacted Countries 66.3 99.1 46.6 85.1 86.3 95.9 94.3

Country

Trend in Water Stress/Axis 3 Process

Trend in Productivity Value/Axis 5 Process

Trend in Social and Cultural Provisions/ Axis 6 Process

Land Degradation Impact Index

38.0 44.0 44.8 38.0 35.0 32.9

50.1 64.1 49.8 60.7 52.3

47.0 50.4 50.0 47.0 41.6 59.8

0.001 0.001 0.001 0.002 0.002 0.003

37.3 33.9 32.1 36.0 40.0 34.0 35.9

49.7 53.4 73.7 50.6 73.5 57.3 48.4

55.0 65.0 64.0 61.0 65.0 62.0 60.0

0.115 0.124 0.126 0.134 0.134 0.233 0.256

Biodiversity Risk Resilience/Axis 4 Process

Land Degradation

TABLE 10.6 Sample Country Average Index Illustrating the Impact of Land Degradation

Note: Example average values of land degradation impact index and land degradation index in biomass, soil, water, biodiversity, productivity and socio-cultural provision by countries. The land degradation indicators are estimated using the GLADIS method explained in Sections 10.4 to 10.8.

487

488

World Soil Resources and Food Security

Considered by land-use systems, cropped agriculture in combination with irrigation and livestock-rearing puts the most pressures on the ecosystem, as illustrated in Table 10.5, where the trends in biophysical pressures are combined in an environmental degradation index. Higher livestock numbers and more intensive cropped agriculture results in a higher environmental degradation rate. Forestry, protected areas, and wetlands—if unused—show the lowest environmental degradation. However, these changes and trends labeled degradation mask the fact that they have to be seen against an original state of the ecosystem (Figure 10.2). Given that many developing nations start off from an extremely poor biophysical and socioeconomic resource base, trends are likely positive, or less negative, in these areas. On the other hand, industrial nations are often concentrated in more favorable climates, having overall more fertile soils and a better socioeconomic base to start from. Consequently, problems with overuse of the ecosystem capacities by intensification, particularly affecting water and biodiversity resources, result in a relatively bleak negative picture as far as overall degradation is concerned in these industrial countries.

10.8  THE IMPACT OF LAND DEGRADATION An analysis simply based on the changes in the provisioning capacity of ecosystems is incomplete if it is not matched with an analysis of their impact on the affected people. This has been done in GLADIS by using the degradation trend as given in Figure 10.3 and matching it with the number of people affected and their poverty level. It is notable that the picture changes considerably when using this approach (Table 10.6) as little changes in poor and densely populated areas have a significant effect, while moderate to strong degradation trends in more affluent or desolate areas have a much smaller effect. Consequently, nearly the whole of Africa (with the exception of the wastelands, but in particular the Sahel, Central Africa, and Ethiopia) and southeast Asia (India and parts of China in particular) are most affected (Figure 10.6). Note: At the moment of going to press, the authors are revising the outcome of the GLADIS system on the basis of comments received from users. Although the overall methodology and main global results will remain fundamentally unchanged, the appearance of the maps and the details of the country figures will be revised. Hence, the data showed here are not supposed to be used for operational purposes.

10.9  N  ATIONAL AND LOCAL LAND DEGRADATION STUDIES: INDICATORS AND MONITORING There is a plethora of methods, indicators, and punctual studies concerning specific aspects of land degradation at local and national levels. Barry and colleagues inventoried more than 900 different land degradation indicators in use in a sample of UNCCD countries. Efforts to harmonize these were undertaken by the UNCCD in a scientific conference [UNCCD 2009], but the political will to accept and report

Land Degradation

0

1,750 3,500

7,000 Km

Reduced threat

Geographic coordinates

Water

Biomass

Soil

Water

N

Biodiversity

Economic

Social

489

FIGURE 10.6  Impact of land degradation processes. (From Nachtergaele, F.O. et al., Global Land Degradation Information System (GLADIS), beta version. An information database for Land Degradation Assessment at Global Level, 2010.)

490

World Soil Resources and Food Security

on a number of indicators, using standard methods, proved to be lacking in most countries. Four methods deserve to be highlighted in this respect:







1. The WOCAT/LADA approach [Liniger et al. 2009] inventories in a participatory way at provincial levels the main parameters that describe the state, cause, and impact of degradation, and at the same time inventories the type and extent of sustainable land management interventions. The method has been standardized and tested in six countries (Argentina, China, Cuba, Senegal, South Africa, and Tunisia). This method allows us to obtain a baseline for future monitoring as illustrated in Figure 10.5. 2. The coupled human–environment (H–E) approach [Lebel et al. 2006] promotes the integrated consideration of biophysical and socioeconomic parameters linking institutional and policy considerations with land degradation, considering threshold tipping points beyond which systems can no longer be restored. This integrated approach has been applied, particularly at the local level, in drylands [Mortimer 2009]. 3. Remote sensing approaches: these have the significant advantage that data are continuously collected in an objective way and, as such, are ideally suited for monitoring purposes. Moreover, the resolution and detail of data available has increased during the past decade at a very fast pace. A disadvantage is that the observations are limited to land cover and derivatives that limit their scope somewhat. Examples are land cover change studies such as [Wessels et al. 2004]. Lately, soil properties have also been investigated in combination with ground truthing with some success [Sanchez et al. 2009]. 4. Local sampling techniques and surveys are quite objective and the most detailed of all, but they are generally rather expensive (with the notable exception of those promoted by LADA-local [McDonagh et al. 2008]. Another disadvantage of the sampling approach is the difficulty of extrapolating results, and the analytical variability often exceeds the changes of the parameter in time.

An example of punctual applied academic studies concerns soil nutrient decline: soil erosion and the removal of the harvest and crop residues depletes the soils. Several studies in the 1990s indicated soil nutrient depletion, particularly in sub-Saharan Africa [Elwell and Stocking 1988]. Nutrient budgets were made [Stoorvogel and Smaling 1990] and calculations performed for sub-Saharan Africa with 950,000 km2 affected [Henao and Baanante 2006]. These kinds of studies also have their limitations due to different interpretations of the same data and the assumed economic costs [Hartemink and Van Keulen 2005].

10.10  THE CAUSES OF LAND DEGRADATION It is undeniable that there are two groups of distinct causes for land degradation that have some areas of overlap. These are summarized in Table 10.1 and are (1) natural causes and (2) human-induced causes. Natural causes involve the inherent capacity

Land Degradation

491

of the ecosystem to provide goods and services. These include climatic ones such as drought, inherent climatic factors determining the capacity to generate biomass and provide ground cover and biodiversity, soil- and terrain-related causes such as slope and soil vulnerability to water and wind erosion, and water availability. Humaninduced causes are largely determined by land use and land-use change, economic factors related with the possibility of investment in the land and access to market, and social factors assuring the availability of infrastructure and the accessibility to land that allows farmers to produce at maximum capacity. A number of direct causes are seemingly natural but may have wholly or partly indirectly human causes (bush invasion, forest fires, floods, landslides, and droughts as a result of humaninduced climate change). Behind the direct obvious causes of human-induced land degradation, there often exist other, more deeply rooted, drivers that have to do with population pressure, poverty, lack of markets and infrastructure, poor governance and weak institutional frameworks, inadequate education, etc. Although undoubtedly correct, it is very difficult to prove a cause-and-effect relationship in a statistically significant way and relationships maintained often reflect more opinion than reality. Most of the relationships are based on a few local studies that have little value outside the study area. A good example is the Machakos study [Tiffen et al. 1994], which found a positive correlation between population density and less land degradation. Apparently present day conditions do not confirm these effects were sustainable, nor could they be extrapolated. Some [e.g., Bot et al. 2000] found on a global basis an opposite effect. The conclusion must be that, given that ecosystems produce a range of services and goods including economic and social ones, a cause that affects one service negatively may well affect another positively and a sensible trade-off, depending on the views of the stakeholders, should be reached depending on the local situation.

10.11  THE COST OF LAND DEGRADATION In the wake of the GLASOD study, a hot debate developed on the cost of land degradation. The major alarmist argument made the point that: Soil erosion is a major environmental threat to the sustainability and productive capacity of agriculture. During the last 40 years, nearly one-third of the world’s arable land has been lost by erosion and continues to be lost at a rate of more than 10 million hectares per year. With the addition of a quarter of a million people each day, the world population’s food demand is increasing at a time when per capita food productivity is beginning to decline. [Pimentel et al. 1995]

More recently, a rigorous study on soil erosion and food security and associated costs stated that: Production loss estimates that vary across crops, soils and regions but average 0.3% yr-1 at the global level, assuming that farmers’ practices do not change. These losses correspond to an estimated economic value of $520 million yr−1. Reducing production losses by limiting soil erosion would, therefore, go a long way to attain food security, especially in the developing countries of the tropics and subtropics. [den Biggelar et al. 2003]

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World Soil Resources and Food Security

Note that most of these studies estimated costs of soil erosion, not of land degradation, which may be magnitudes higher when one also considers biomass, water, and biodiversity. Moreover, the studies are largely limited to productivity losses for which there is an overall problem of a lack of consistent relations between soil losses and productivity [Eswaran et al. 2001]. Unless the environmental cost (loss of carbon, decline in water resources, loss of cultural services, etc.) is correctly valued, it is clear that economic valuation results will largely underestimate the costs. Unfortunately, there is no widespread agreement about valuing ecosystem goods and services and, until that is achieved, no progress will be made in correctly estimating the real global or national cost of land degradation. On a more practical local level, Scherr [2003] argued that “Whatever the costs of land degradation, three kinds of contextual information are required.”

1. So long as degradation is reversible at an economically acceptable cost and other investment opportunities are more attractive, prevention is not always preferable, or even cheaper, than cure. 2. Even if the economic impacts of degradation are high, it may not be necessary to take direct policy actions apart from generally supportive measures for the agricultural economy. 3. From a policy perspective it may be wise to invest in soil protection and rehabilitation in areas with the greatest long-term significance for agricultural supply, rural poverty alleviation, or economic growth.

In addition, new economic options for use of degraded lands—growing biofuel crops or for carbon sequestration and carbon trading—will have additional spinoff environmental benefits. Scenarios are very uncertain in this respect, however, in the face of volatile and uncertain markets and the absence of international binding agreements.

10.12  CONCLUSIONS Land degradation is more than an environmental problem alone and should be considered in a holistic way, taking into account all ecosystem goods and services, biophysical as well as socioeconomic. Results should refer to a given time period and solutions should require a full consultation with stakeholders and imply trade-offs. Degraded lands, based on the capacity of the globe’s ecosystem to deliver goods and services, are highly variable. Degraded land occurs most in drylands and steep lands, which deserve special attention. The capacity to deliver ecosystem services is also, generally, significantly less in developing countries compared with industrial nations (Figure 10.7). Degradation of this capacity takes many forms and affects soils, biomass, water, biodiversity, economics, and social services derived from the ecosystem. This decline (degradation) appears to be proportional with the present capacity of the system. In other words, ecosystems with lower capacities decline less than ecosystems with higher capacities.

493

Land Degradation (a)

(b)

Trends of ecosystems services

Trends of ecosystems services

Biomass 100 Social

Biomass

100

Soil

50

Social

0

0

Economic

Water

Biodiversity Country Japan Nepal Spain

Soil

50

LDI 0.31 0.31 0.27

Economic

Water

Biodiversity Country Bangladesh Brazil Burkina Faso

0.56 0.58 0.64

FIGURE 10.7  Comparison between sample countries with low (a) and high (b) trend in ecosystems and services.

However, the impact of this degradation is most felt in areas with large populations and/or high poverty. This implies that, even when starting from a low resource base, the lower rate of degradation in these areas has a much greater impact compared to ecosystems with a higher capacity and a higher rate of degradation, but fewer poor people (Figures 10.9 and 10.10). Agricultural land uses (cropping, livestock-rearing) have a much higher risk to be degrading than nonagricultural ones. Land use and associated inputs and management are indeed the main direct causes of land degradation. Land use itself is determined by natural conditions and cultural and socioeconomic aspects including institutional settings, infrastructure, education, and market availability. There are quite a number of natural factors that cause land degradation, but often they are strongly interlinked, indirectly or directly, with human actions. Consequently, it is difficult to distinguish between the two. At a sub-national level, a harmonized and tested survey methodology developed by WOCAT and LADA is available and could become a harmonized, relatively lowcost way to quickly obtain standardized UNCCD indicators for country reporting on the impact of land degradation. At a local level, various approaches have been promoted. Among them, those based on integrated human–environment considerations, and those developed by LADA that use simplified sampling and socioeconomic surveys, appear to be the most promising. Remote sensing techniques have a definite role to play, particularly in monitoring land degradation, because they provide high resolution information on a continuous timescale. In addition, they are ideal to follow land-cover changes that are linked to land-use changes that are the major cause of degradation. However, until now, no

494

2,250

4,500

9,000 Km

Wastelands or no pop. or no pov.

Geographic coordinates

Water

High

Moderate

N

Low

Very low

FIGURE 10.8  Environmental Degradation Index – EDI. (From Nachtergaele, F.O. et al., Global Land Degradation Information System (GLADIS), beta version. An information database for Land Degradation Assessment at Global Level, 2010.)

World Soil Resources and Food Security

0

495

Land Degradation

None Light Moderate Severe Very severe Wastelands Urban Water

FIGURE 10.9  Percentages of population in degraded area by status of ecosystems services provisions. (a) Water

% of area degraded

Urban Wasteland or no pop Very severe Severe Moderate Light None

High

Medium Poverty

Low

% Population in degraded areas

(b)

Urban Wastelands Very severe Severe Moderate Light None

High

Medium Poverty

Low

FIGURE 10.10  Extent of land degraded by poverty class (a) and population in degraded areas by poverty class (b).

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World Soil Resources and Food Security

unique remote-sensing–based methodology has proven to be able to go much beyond land cover parameters. The cost of land degradation has been hotly debated since the publication of the first global inventory. Given the widely different definitions of land degradation and the limited information on economic losses due to declining environmental services other than productivity, one can only state that the cost of land degradation has a significant impact in most developing countries, given that their overall capacity to generate ecosystem goods and services are significantly less than in most industrial nations. An overall observation concerning global level assessment is that there is an insufficiency of quantitative reliable data, particularly on available water resources and its trends and on economic factors that are often based on statistics of dubious quality. Therefore, apart from the complexity in interpretation highlighted here, the overall reliability of the input data remains another concern.

REFERENCES Bai, Z.G., D.L. Dent, L. Olsson, and M.E. Schaepman. 2008a. Proxy global assessment of land degradation. Soil Use Manag. 24:223–234. Bai, Z.G., D.L. Dent, L. Olsson, and M.E. Schaepman. 2008b. GLADA. Global Assessment of Land Degradation Improvement 1. Identification by remote sensing. LADA/ISRIC/ FAO. LADA technical report N.12. Bot, A.J., F.O. Nachtergaele, and A. Young. 2000. Land resource potential and constraints at regional and country level. World Soil Resources Report #90. Rome: FAO. de Jong, R., S. de Bruin, A. de Wit, M.E. Schapman, and D.L. Dent. 2011. Analysis of monotonic 1 greening and browning trends from global NDVI time-series. Remote Sensing of Environment 115(2):692–702. den Biggelaar, C., R. Lal, K. Wiebe, H. Eswaran, V. Breneman, and P. Reich. 2003. The global impact of soil erosion and productivity II: Effects on crop yields and production over time. Advances in Agronomy 81:49–95. Elwell, H.A., and M.A. Stocking. 1988. Loss of nutrients by sheet erosion is a major cost. Zimbabwe Science News 22:7–8, 83–85. Eswaran, H., R. Lal, and P. Reich. 2001. Land degradation: An overview. In Responses to land degradation. Proceedings of the Second International Conference on Land Degradation and Desertification, eds. E.M. Bridges, I.D. Hannam, L.R. Oldeman, F.W.T. Pening de Vries, S.J. Scherr, and S. Sombatpanit. Thailand: Khon Kaen; New Delhi: Oxford Press. Eswaran, H., P. Reich, and F. Beinroth. 2001. Global desertification tension zones. In Sustaining the global farm, eds. D.E. Stott, R.H. Mohter, and G.C. Steinback. Proceedings of the Tenth Annual ISCO Conference, May 24–29, 1999, Purdue University, West Lafayette, Indiana. Food and Agriculture Organization (FAO). 1978. Report on agro-ecological zones project. World Soil Resources Report 48, Rome: FAO. FAO. 2005. Global forest resources assessment 2005. Progress towards sustainable forest management. Forestry Paper 147. Rome: FAO. FAO. 2008. Aquastat: FAO’s information system on water and agriculture. http://www.fao​.org/ nr/water/aquastat/main/index.stm. Global Land Cover 2000 database. European Commission, Joint Research Centre, 2003. http:// bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php. Hartemink, A., and H. van Keulen. 2005. Soil degradation in sub-Saharan Africa. Land Use Policy 22:1.

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Henao, J., and C. Baanante. 2006. Agricultural production and soil nutrient mining in Africa— Implications for resource conservation and policy development. Muscle Shoals, AL: IFDC. Hoekstra, J.M., T.M. Boucher, T.H. Ricketts, and C. Roberts. 2005. Confronting a biome crisis: Global disparities of habitat loss and protection. Ecol. Letters 8:23–29. International Union for Conservation of Nature and Natural Resources (IUCN). 2006. Red List of Threatened Species. http://www.iucnredlist.org. Lebel, L., J.M. Anderies, B. Campbell, C. Folke, S. Hattfield-Dodds, T.P. Hughes, and J. Wilson. 2006. Governance and the capacity to manage resilience in regional socioecological systems. Ecology and Society 11:19. Liniger, H., G. van Lynden, F. Nachtergaele, and G. Schwilch (eds.). 2009. CDE-FAO-ISRICmapping land degradation and sustainable land management. LADA Technical Report #9. Rome: FAO. McDonagh, J., and S. Bunning. 2009. Field manual for local level land degradation assessment in drylands. LADA Technical Report #11. Rome: FAO. Mortimer, M. 2009. Dryland opportunities, a new paradigm for people, ecosystems and development. IUCN, IIED and UNDP. Gland, Switzerland: IUCN. Nachtergaele, F.O., M. Petri, R. Biancalani, G. van Lynden, and H. van Velthuizen. 2010. Global Land Degradation Information System (GLADIS) version 0.5. An information database for Land Degradation Assessment at Global Level. http://www.fao.org/nr/lada/ index.php?option=com_docman&task=cat_view&gid=26&Itemid=165&lang=en. Nachtergaele, F.O., and M. Petri. 2008. Mapping land use systems at a global and regional scale for land degradation assessment. LADA Technical Report #8. Rome: FAO. Oldeman, L.R., R.T.A. Hakkeling, and W.G. Sombroek. 1990. Global assessment of soil degradation. Wageningen, the Netherlands: International Soil Reference Information Centre. Olson, D.M., and E. Dinerstein. 1998. The global 200: A representation approach to conserving the earth’s most biologically valuable ecoregions. Conserv. Biol. 12:502–515. Pimentel, E., C. Harvey, P. Resosudarmo, K. Sinclair, D. Kurz, M. McNair, S. Crist, L. Shpriz, L. Fitton, R. Saffouri, and R. Blair. 1995. Environmental and economic costs of soil erosion and conservation benefits. Science 24:1117–1123. Sanchez, P.A., S. Ahamed, F. Carre, A.E. Hartemink, J. Hempel, J. Huising, P. Lagacherie, A.B. McBratney, N.J. McKenzie, M. de Lourdes Mendonca-Santo, B. Minasny, L. Montanarella, P. Okoth, C.A. Palm, J.D. Sachs, K.D. Shepherd, T.-G. Vagen, B. Vanlauwe, M.G. Walsh, L.A. Winowiecki, and G.-L. Zhang. 2009. Digital soil map of the world. Science 325:680–681. Scherr, S.J. 2003. Productivity related economic impacts of soil degradation in developing countries. An evaluation of regional experience. In Land quality, agricultural productivity and food security at local, national and global levels, ed. K. Wiebe, 223–261. Cheltenham Glos, UK: Edward Elgar. Sonnevald, B.G.J.S., and D.L. Dent. 2009. How good is GLASOD? J. Env. Manag. 90:274–283. Stoorvogel, J.J., and E.M.A. Smaling. 1990. Assessment of soil nutrient decline in sub-​ Saharan Africa, 1983–2000. Report 28. Wageningen, the Netherlands: Winard Staring Centre-DLO. Tiffen, M., M. Mortimore, and F. Gichuki. 1994. More people, less erosion: Environmental recovery in Kenya. Chichester, UK: Wiley & Sons. United Nations Convention to Combat Desertification (UNCCD). 1994. Elaboration of an international convention to combat desertification in countries experiencing serious drought and/or desertification, particularly in Africa. U.N. Doc. A/Ac.241/27, 33 I.L.M. 1328. Bonn, Germany: UNCCD.

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United Nations Development Programme (UNDP). 2009. Overcoming barriers: Human mobility and development. Human Development Report 2009. New York: UNDP. United Nations Environment Programme. (UNEP). 2007. Global environment outlook 4 (GEO-4). Nairobi, Kenya: UNEP. UNEP. 2008. Carbon in drylands: Desertification, climate change and carbon finance. UNEP/ UADP/UNCCD Tech. Note, CRIC 7. November 3–14, 2008, Istanbul, Turkey. Nairobi, Kenya: UNEP. UNEP/FAO. 1999. Guidelines for land use planning. Rome: FAO. Valentin, C., J.L. Rajkot, and D. Mitja. 2004. Response of soil crusting, runoff and erosion to fallowing in the sub-humid and semi-arid regions of West Africa. Agric. Ecosyst. Env. 104:287–302. Wishmeier, W.H., and D.D. Smith. 1978. Predicting rainfall losses—A guide to conservation planning. Agricultural Handbook No. 573. Washington, DC: USDA. Wessels, K.J., S.D. Prince, P.E. Frost, and D. Van Zyl. 2004. Assessing the effects of humaninduced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sensing of Environment 91:47–67. World Wildlife Fund (WWF). 2006. WildFinder: Online database of species distributions, V. 6 (January). http://gis.wwfus.org/wildfinder/.

Do We Stand 11 Where 20 Years after the Assessment of Soil Nutrient Balances in Sub-Saharan Africa? E. M. A. Smaling, J. P. Lesschen, C. L. van Beek, A. de Jager, J. J. Stoorvogel, N. H. Batjes, and L. O. Fresco CONTENTS 11.1 Nutrient Stocks, Flows, and Balances...........................................................500 11.2 Calculating Nutrient Flows and Balances: Continent, Country, and District (NUTBAL)....................................................................................... 503 11.2.1 Assessment of Soil Nutrient Balances in SSA (1990)....................... 503 11.2.1.1 Mineral Fertilizers (IN1).................................................... 503 11.2.1.2 Manure (IN2)......................................................................504 11.2.1.3 Deposition (IN3).................................................................504 11.2.1.4 Biological N Fixation (IN4)................................................504 11.2.1.5 Sedimentation (IN5)...........................................................504 11.2.1.6 Harvested Product (OUT1).................................................504 11.2.1.7 Crop Residues (OUT2)........................................................504 11.2.1.8 Leaching (OUT3)................................................................504 11.2.1.9 Gaseous Losses (OUT4)...................................................... 505 11.2.1.10 Erosion (OUT5)................................................................ 505 11.2.2 Updating the 1990 Methodology.......................................................506 11.2.2.1 Mineral Fertilizer (IN1)...................................................... 507 11.2.2.2 Organic Inputs (IN2)........................................................... 507 11.2.2.3 Atmospheric Deposition (IN3)............................................ 507 11.2.2.4 N Fixation (IN4)................................................................. 507 11.2.2.5 Sedimentation (IN5)........................................................... 507 11.2.2.6 Harvested Product (OUT1)................................................. 508 11.2.2.7 Crop Residues (OUT2)........................................................ 508 11.2.2.8 Leaching (OUT3)................................................................ 508 499

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11.2.2.9 Gaseous Losses (OUT4)...................................................... 508 11.2.2.10 Erosion (OUT5)................................................................ 508 11.2.3 Applications at District Level............................................................ 511 11.3 Calculating, Monitoring, and Manipulating Nutrient Flows and Balances at Farm and Plot Level (NUTMON).............................................. 512 11.3.1 Farm-NUTMON................................................................................ 512 11.3.2 Calculating Farm-Level Nutrient Balances....................................... 513 11.3.2.1 Inputs–Outputs................................................................... 513 11.3.2.2 Internal Flows..................................................................... 514 11.3.2.3 Farm Income....................................................................... 517 11.3.2.4 Farm Level Heterogeneity.................................................. 517 11.3.3 From NUTMON to MonQI............................................................... 520 11.3.3.1 Joint Learning..................................................................... 520 11.3.3.2 Environment........................................................................ 521 11.3.3.3 Livelihoods......................................................................... 521 11.3.3.4 Agro-Food Chains and Certification.................................. 521 11.4 Studies on Soil Nutrient Dynamics Beyond the NUTMON Family............. 522 11.4.1 Nutrient Balances.............................................................................. 522 11.4.2 C and Nutrient Stocks........................................................................ 525 11.4.3 Integrated Nutrient Management....................................................... 526 11.5 Where Do We Stand, Where Do We Go?...................................................... 527 Acknowledgment.................................................................................................... 531 References............................................................................................................... 531

11.1  NUTRIENT STOCKS, FLOWS, AND BALANCES To feed 9 billion people in 2050, recent estimates indicate that global food production will have to increase by 70% [FAO 2009]. Food security can be realized by expanding the cultivated area and by increasing production per unit land, labor, or capital. Further down the production–consumption chain, increasing efficiency and recycling (including postharvest and waste management) and dietary change also are important. To increase agricultural production, area expansion is still possible, mainly in the range of Ukraine–Russia–Kazakhstan, in sub-Saharan Africa (SSA), and in Latin America, but it will negatively affect the services provided by natural ecosystems. The recent agricultural area expansions in Latin America (soybean for savannah and Amazon forest) and Southeast Asia (oil palm for rainforest and peat lands) clearly show the dilemmas: a booming international market for soy and palm oil and for soy meal as animal feed [Smaling et al. 2008], go at the expense of natural vegetation, plant and animal biodiversity, climatic stability, and above- and below-ground carbon (C) stocks. Hence, raising productivity is the main feasible option on the upstream part of the food security chain. This is still possible in most agricultural systems, but the immediate large increases in cereal production of the Green Revolution will not be repeated easily. As Conway [1997] expressed, “What is needed is a doubly green revolution that raises yields while reducing environmental impact.”

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In the quest for sustainable food production, much attention is given to improved varieties and biotechnology, irrigation, and crop protection measures. Soil fertility management seems of lesser priority. Nonetheless, low soil nutrient stocks and soil nutrient depletion are regarded as fundamental root causes of hunger and poverty [Sanchez 2002]. This is because crop and grass yields and dietary values are reflections of the nutrient status of the soil. Soil organic C (SOC), although not a nutrient, may be regarded as a proxy of the nutrient stocks in tropical soils. In soil fertility/crop production models such as Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), SOC, as a proxy for soil organic matter, comes out as a key soil characteristic explaining crop response to soil fertility [e.g., Janssen et al. 1990; Smaling and Janssen 1993; Samaké 2003]. Estimates of the global mean SOC stocks in the top meter of soil are approximately 110 ton C ha−1, but the mean stock in Africa is only 57–60 tons C ha–1 and in West Africa even less, 42–45 tons C ha−1 [Batjes 2001]. Global amounts of soil nitrogen are estimated at approximately 135 × 109 tons of N for the upper 1 m [Batjes 1996]. A rapid analysis of the ISRIC-WISE (World Inventory of Soil Emission Potentials) database shows that European agricultural soils have an average SOC content that is twice the level of those in SSA [Batjes 2002]. This is not the result of land use history, but largely of differences in soil age and climatic conditions. With the exception of soils of recent volcanic origin, most African agricultural soils are derived from 2 billion-year-old granites, whereas many European agricultural soils are developed in periglacial and holocene sediments, as are many soils of the fertile deltas of Asia. Where there are stocks, there are flows. All soils gain and lose nutrients over time. Fertilizers, for example, represent nutrient inputs applied to realize nutrient outputs in harvested crop parts. Nutrients in sediments in lower reaches of river basins are inputs that relate to nutrients lost further upstream due to erosion. Hence, soil nutrient stocks change due to the combined effect of positive and negative flows. This is true at the scale of individual agricultural plots, but nutrient loss and accumulation occur at all scales, up to a global scale through trade in agricultural commodities. Countries with a net loss of NPK in agricultural commodities correspond to the major food exporting countries—the United States, Australia, and some Latin American countries. In the case of the United States, for example, exports of NPK were 3.1 million tons in 1997 and are expected to reach 4.8 million tons in 2020 [Grote et al. 2005]. West Asia and North Africa, China, and SSA are net importers of NPK in agricultural commodities, but the nutrients imported are commonly concentrated in the cities, creating waste disposal problems rather than alleviating deficiencies in rural soils. Calculating or estimating nutrient flows allows the drafting of a nutrient balance. For SSA, a continental nutrient balance (NUTBAL) study reveals that net flows were negative, i.e., 22 kg N, 2.5 kg P, and 15 kg K are lost annually per hectare over the 1982–1984 period [Stoorvogel and Smaling 1990; Stoorvogel et al. 1993]. This study triggered substantial debate on soil fertility management in SSA and the role of fertilizers, culminating in involvement of many donor agencies, as well as political commitments on fertilizer use at the Africa Fertilizer Summit in Abuja in 2006 [Sanginga and Woomer 2009]. Furthermore, a plethora of nutrient balance studies at different spatial scales emanated inside and out of Africa. Without the

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pretension to be exhaustive, this chapter summarizes 20 years of work on nutrient balances, mainly in SSA, but where appropriate with comparisons to other parts of the globe. An earlier review in Advances in Soil Science addressed NUTBAL and the pros and cons of the input–output approach at the subcontinental scale, where calculations are always based on secondary data and empirical models [Smaling and Oenema 1997]. The model was criticized for its methodological limitations [Faerge and Mahid 2004], and for its limited value for intervention and action [Scoones and Toulmin 1998]. After NUTBAL, the focus shifted to subnational and local scales and from calculating and modeling to measuring and monitoring. The acronym changed accordingly to NUTMON [Smaling and Fresco 1993], with integrated soil fertility management (ISFM) [Sanginga and Woomer 2009; Vanlauwe et al. 2010] and, more broadly, integrated nutrient management (INM), participatory learning and action [Defoer 2002] and resource flow mapping [De Jager 2007] as the actionoriented components. Ten years later, the approach considers other factors than just soil fertility management leading to monitoring for quality improvement (MonQI). It is the successor of the NUTMON field tool, as described by Vlaming et al. [2001], and is a farm monitoring tool that facilitates structured interviews with farmers concerning their daily management of crop and livestock, data entry, data storage and data checking, and data processing and presentation. Currently, MonQI is used by a blend of users from state-of-the-art science toward agencies for certification of niche markets. The timeline of the nutrient balance model development is shown in Figure 11.1. The highlights of the research performed under the NUTBAL and NUTMON umbrellas are described in Chapters 2 and 3. Studies outside the NUTMON family are summarized in Chapter 4. The final chapter provides an assessment of the current state of knowledge and possible future research and development pathways that follow from the analysis.

Use at continental and national levels FARM-NUTMON: Down-scaling to farm level, extension of compartments. Extension and improvement of calculation algorithms. Recoding of software, flexible programming.

Household economics

Tailor-made individual Incorporation of reporting module. optimal soil fertility management, water usage and C management.

Pesticide registration, value chain assessment

1990

1995

2000

2005

2010

FIGURE 11.1  Development of NUTMON family nutrient balance models over the past 20 years.

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11.2  C  ALCULATING NUTRIENT FLOWS AND BALANCES: CONTINENT, COUNTRY, AND DISTRICT (NUTBAL) Inspired by Frissel [1978], Pieri [1985], and Van der Pol [1992], the subcontinental NUTBAL study adopts a clearly defined nutrient balance and quantified nutrient flows. It constitutes the basis for many subsequent nutrient balance studies as shown by several reviews [FAO 2003; Schlecht and Hiernaux 2004; Cobo et al. 2010]. In this chapter, the original 1990 NUTBAL methodology is summarized, followed by an overview of the updates up to the latest version described by Lesschen et al. [2007].

11.2.1  Assessment of Soil Nutrient Balances in SSA (1990) The initial NUTBAL study [Stoorvogel and Smaling 1990] assesses the state of soil nutrient depletion in SSA for 1982–1984 with a projection for 2000. It provides data on the net balance of the macronutrients N, P, and K from the rootable soil layer on a country basis. Production figures (1982–1984) and projections (2000) for major crops per country were provided by the UN Food and Agriculture Organization (FAO). These statistics are further specified for six largely climate-based land/water classes (LWC): low, uncertain, and good rainfall areas, problem areas, and naturallyflooded and irrigated areas. In addition to this, three broad soil fertility classes are used: low, medium, and high. Soil fertility dynamics is captured by five inputs (IN) and five outputs (OUT) as shown in Figure 11.2. The various model components, and underlying assumptions, are detailed below. 11.2.1.1  Mineral Fertilizers (IN1) The FAO database contains information on actual total fertilizer consumption per crop per country for 1982–1984 and projections for 2000. However, these data are not specified per LWC. Hence, literature data on the regional distribution of fertilizers within a country is used, or weighting factors are used for each land-use system (LUS). Animals IN2 Manure IN1 Mineral fertilizers IN3 Deposition IN4 Biological N fixation IN5 Sedimentation

Crops

Soil organic and mineral N, P, & K

OUT2 Crop residues OUT1 Harvested products OUT3 Leaching OUT4 Gaseous losses OUT5 Erosion

FIGURE 11.2  Nutrient flows to and from the soil. (From Stoorvogel, J.J., et al. Fert. Res., 35, 227–235, 1993. With permission.)

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11.2.1.2  Manure (IN2) NUTBAL only considers soil nutrient balances of arable land and does not consider the balance of extensive grazing lands. Two forms of manuring are distinguished: 1) manure collection from stables, kraals, and other storage places, and application to arable fields prior to planting, with fixed quantities (0, 500, 1000, or 1500 kg ha−1) per land use system, and 2) on-the-spot manuring by livestock feeding on crop residues with interaction with OUT2. 11.2.1.3  Deposition (IN3) Input by dry deposition, which mainly occurs in West Africa under the influence of the Harmattan dust storms, is determined by an interpolation of available measurements. For wet deposition, a regression with mean annual rainfall is carried out on the basis of literature data. 11.2.1.4  Biological N Fixation (IN4) Based on information from literature, three stipulations are presented, depending on total N demand by crops: 1) for symbiotic N fixation in leguminous species, 2) for chemoautotrophic N fixation in wetland rice and 3) for nonsymbiotic fixation. 11.2.1.5  Sedimentation (IN5) For the naturally flooded LWC, it is assumed that the nutrient balance is in equilibrium due to sedimentation. For the irrigated area LWC, the nutrient content of the irrigation water is also considered as an input factor. Based on literature, an annual input of 10 kg N per ha is assumed. 11.2.1.6  Harvested Product (OUT1) Based on literature, average values for the nutrient content of each crop are compiled (in kg nutrient per ton harvested product). In order to obtain an estimate of OUT1, these data are combined with FAO production figures. 11.2.1.7  Crop Residues (OUT2) An estimate of the amount of crop residues removed from the arable field is obtained from the literature. For each LUS, i.e., a unique combination of crop and LWC, a removal factor is assigned: complete removal of residues (e.g., used for fuel, roofing, or manufacturing), or incomplete (e.g., grazing or burning). Average values of the amount of nutrients in crop residues per harvested ton are compiled. 11.2.1.8  Leaching (OUT3) Leaching is a significant loss mechanism for some nutrients. Based on a literature review and expert consultations, the following regression equation is developed for N:

OUT3N = 2.3 + (0.0021 + 0.0007 × F ) × R + 0.3 × (IN1 + IN2) – 0.1 × U,

in which F is a soil fertility class (1 - low; 2 - moderate; 3 - high), R is rainfall (mm), and U is total nutrient uptake by the crop.

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11.2.1.9  Gaseous Losses (OUT4) Nitrogen losses through denitrification are expected to be highest in wet climates, on highly fertilized and clayey soils. Ammonia volatilization is linked to the amount of mineral and organic fertilizer and plays a role in alkaline environments. However, such soils are not very common in SSA, and therefore volatilization and denitrification are not treated separately. Based on scarce literature data the following regression equation is developed for N:

OUT4 = Base + 2.5 × F + 0.3 × (IN1 + IN2) – 0.1 × U,

in which Base is a constant value covering relative wetness of the soils specific for LWCs. 11.2.1.10  Erosion (OUT5) Soil loss estimates are based on the LUS descriptions within the LWCs. Additionally, a nutrient content is assigned to each soil fertility class. As the finest soil particles are the first to be dislodged during erosion, an enrichment factor is established, which is set at 2.0 for all three nutrients. As topsoil erodes, the roots of crops start to enter layers that were previously beyond the root zone. Hence, part of what is lost on top is gained at the bottom of the soil profile. These contributions are set at 25% percent of the calculated losses for P and K. The NUTBAL study presents N, P, and K balances for LUS and LWCs for most countries in SSA; negative balances are observed throughout the subcontinent (Figure 11.3). Densely populated regions in the Rift Valley (Kenya, Ethiopia, Rwanda, and Malawi) have the most negative values, owing to high ratios of cultivated land to

Low Moderate High Very high

FIGURE 11.3  Nitrogen depletion in sub-Saharan Africa. Low < 10; moderate 10–20; high 20–40; very high > 40 kg ha−1 yr−1. (After Stoorvogel, J.J., and E.M.A. Smaling, Assessment of Soil Nutrient Depletion in Sub-Saharan Africa: 1983–2000. Report 28, Winand Staring Centre, Wageningen, the Netherlands, 1990; Stoorvogel, J.J., et al. Fert. Res., 35, 227–235, 1993.)

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total arable land, relatively high crop yields (OUT1) and soil erosion (OUT5), as well as relatively high nutrient stocks. For SSA as a whole, the nutrient balances for 1982–1984 and projections for 2000 are −22 and −26 kg N ha−1 yr−1; −2.5 and −3.0 kg P ha−1 yr−1; and −15 and −19 kg K ha−1 yr−1, respectively. The projection for 2000 being more negative is partly attributed to optimistic FAO estimates for crop production in 2000 and the expected decrease in fallow areas in 2000.

11.2.2  Updating the 1990 Methodology The FAO, who commissioned NUTBAL in 1990, also facilitated an overhaul of the approach [FAO 2004]. The approach is broader and includes stocks, flows, and balances at macrolevel (national), mesolevel (district or province), and microlevel (village or farm). Nutrient balances are calculated at different levels in Ghana, Kenya, and Mali with important cash crops. The methodology can be applied to all SSA countries by using continental GIS maps and FAOSTAT data. The calculation is performed for N, P, and K based on averaged data for the period 1997–1999. The updated TABLE 11.1 Changes in Calculation of Nutrient Stocks and Flows When Moving from NUTBAL to NUTMON Land use systems Nutrient stocks IN1: mineral fertilizer IN2: organic inputs IN3: atmospheric deposition

IN4: nitrogen fixation IN5: sedimentation OUT1: crop products OUT2: crop residues OUT3: leaching OUT4: gaseous losses OUT5: erosion

The spatial distribution of land use systems is modeled through a biophysical land suitability assessment based on Ecocrop WISE database with soil nutrient concentrations per soil type of the 1:5,000,000 FAO soil map [Batjes 2002] Fertilizer use data per crop [IFA/IFDC/FAO 2000] and total consumption from FAOSTAT (Reference) Livestock density maps [Wint et al. 2000] in combination with literature data on nutrient contents Dry deposition: Improved map for Harmattan deposition (source) New regression on nutrient concentrations and IIASA rainfall map [Leemans and Cramer 1991] Percentage of leguminous crop production and related to rainfall [Leemans and Cramer 1991] Sedimentation calculated using the LAPSUS model [Schoorl et al. 2002] No changes No changes New regression model based on review by De Willigen [2000] New regression model based on data from IFA/FAO [2001] Erosion calculations using the LAPSUS model [Schoorl et al. 2002]

Source: Stoorvogel, J.J. and Smaling, E.M.A., Assessment of Soil Nutrient Depletion in Sub-Saharan Africa: 1983–2000. Report 28, Winand Staring Centre, Wageningen, the Netherlands, 1990; FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, 2004; Lesschen, J.P., et al. Nutr. Cycl. Agroecos., 78, 111–131, 2007.

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methodology is based on NUTBAL, but with a substantial number of improvements. Although LWCs in NUTBAL are to some extent spatially explicit, the revised version allows taking into account the spatial variation in soils, climate, and nutrient balances within a country. Updated procedures to calculate nutrient flows are used (Table 11.1). Nutrient stocks are quantified for each soil unit instead of using three soil fertility classes. A disaggregation procedure is developed to create a land–use map for all SSA countries, which shows the most likely crop distribution at a grid resolution of 1 km. The methodology is based on the principles of qualitative land evaluation, where land qualities are matched with land-use requirements to assess the suitability of land for a given use [FAO 1976]. The methodology involves three key steps: 1) identification of land units with similar topography, climate, and soil conditions; 2) matching properties of the land units with crop requirements; and 3) disaggregating harvested areas from FAOSTAT over the land units. The final land-use map is combined with other spatial data needed for the nutrient balance calculation. 11.2.2.1  Mineral Fertilizer (IN1) The FAOSTAT database provides figures for total fertilizer consumption per country. Data of the fertilizer use per crop studies [IFA/IFDC/FAO 2000] are used to derive the fractions of the total fertilizer consumption per nutrient for each crop. 11.2.2.2  Organic Inputs (IN2) Livestock density maps are available for the major livestock classes, i.e., cattle, small ruminants, and poultry [FAO 2000]. The livestock densities are multiplied by the excre­tion per animal per year and the nutrient content of the manure, for which updated figures are established. These amounts are corrected for country-dependent differences in management for both grazing and application of manure from storage. 11.2.2.3  Atmospheric Deposition (IN3) Updated factors for nutrient contents in both rainfall and dust are used, and the IIASA rainfall map is used for wet deposition [Leemans and Cramer 1991]. Based on several literature sources and windstream patterns, a new interpolated map of Harmattan dust is used for dry deposition. 11.2.2.4  N Fixation (IN4) For symbiotic N fixation, updated factors are used. For wetland rice, a fixed amount of 15 kg N ha−1 yr−1 is assumed for N fixation by cyanobacteria. For the nonsymbiotic N fixation and N fixing trees, a regression equation is developed based on annual rainfall. 11.2.2.5  Sedimentation (IN5) This flow consists of two parts: input of nutrients by irrigation water, and input of sediment as a result of erosion. For the nutrient input by irrigation water, the worldwide map of irrigation areas [Döll and Siebert 2000] is combined with the assumptions about nutrient content and amount of irrigation water of NUTBAL. The input by sedimentation is calculated by the LAPSUS model [Schoorl et al. 2002], which provides a feedback between IN5 and OUT5.

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11.2.2.6  Harvested Product (OUT1) The NUTBAL approach is used with updated crop production statistics. 11.2.2.7  Crop Residues (OUT2) The NUTBAL approach is followed with updated crop and country-dependent removal factors. 11.2.2.8  Leaching (OUT3) A new regression model for N is used:

OUT3N = (0.0463 + 0.0037 × (R/(C × L))) × ((IN1 + IN2) + D × NOM − U),

in which C is clay content in the topsoil (%), R is rainfall (mm), L is the layer thickness or rooting depth (m), D is the decomposition rate (set at 1.6 % per year), and NOM is the amount of N in soil organic matter (kg N ha–1). This N-leaching regression model is based on 43 measurements and accounted for 67% of the variance [De Willigen 2000]. The equation is slightly edited for perennial crops to prevent overestimation of N leaching. 11.2.2.9  Gaseous Losses (OUT4) A new regression model is used:

OUT4 = (0.025 + 0.000855 × R + 0.01725 × (IN1 + IN2) + 0.117 × SOC) + 0.113 × (IN1 + IN2),



in which SOC is the organic C content (%). The equation is based on a larger data set [IFA/FAO 2001] and consists of one regression model for the N2O and NOx losses through denitrification, and a direct loss factor for volatilization of NH3. The regression model has a R2 of 0.70. 11.2.2.10  Erosion (OUT5) To assess nutrient loss by erosion, the LAPSUS model [Schoorl et al. 2002] is used. This model simulates erosion and sedimentation at the landscape scale, which has several advantages: quantitative data is generated, erosion is considered at the landscape scale, and sedimentation is taken into account. Main input data of the LAPSUS model are the topographical potentials derived from a digital elevation model [USGS 1998] and rainfall surplus, derived from the rainfall map [Leemans and Cramer 1991]. Other input data are soil depth and erodibility, which are based on the soil map [FAO/UNESCO 1997], and a land cover map [USGS et al. 2000]. With these inputs the model simulates runoff and erosion–sedimentation for 1 year at a 1 km2 resolution. The loss or gain of nutrients is calculated by multiplying the erosion or sedimentation by the soil nutrient contents and an enrichment factor. Based on additional literature the enrichment factor is adjusted to 2.3 for N, 2.8 for P, and 3.2 for K. The revised NUTBAL study includes nutrient balance maps as shown in Figure 11.4 for Mali. Whereas the 1990 NUTBAL study indicates N depletion of 8 (1983)

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N depletion (kg/ha) > 100 50–100 20–50 5–20 0–5 Positive No data

N W

E S

FIGURE 11.4  Example of the spatially explicit nitrogen balance for Mali. (Data from FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities. FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004. With permission.)

to 11 (2000) kg ha−1 yr−1, the updated approach shows large differences within the country. The N balance is mainly positive in the central part of Mali, where rice and fallow are the main land uses, while the northern border of the agricultural zone, with mainly millet and sorghum cultivation, shows severe depletion. Projected N flows for Ghana, Kenya, and Mali are summarized in Figure 11.5. In Kenya, the input of mineral and organic fertilizer is relatively important, whereas 15

Ghana Kenya Mali

10

Nitrogen (kg/ha)

5 0 –5

–10 –15 –20 –25

IN1

IN2

IN3

IN4

IN5 OUT1 OUT2 OUT3 OUT4 OUT5 Nitrogen flows

FIGURE 11.5  Nitrogen flows for Ghana, Kenya, and Mali. (Data from FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities. FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004. With permission.)

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Ghana has a greater input by atmospheric deposition because of Harmattan dust. Outflows by leaching and gaseous losses are somewhat greater in Kenya where more mineral fertilizers are used. Most apparent are the N-losses due to widespread water erosion in Kenya. According to the updated NUTBAL study, Kenya has the greatest nutrient depletion for N and K, followed by Ghana and Mali. For P, Ghana and Mali show slightly negative balances, while the P balance for Kenya is neutral (Table 11.2). Kenyan farmers apply 30,000 tons of P with mineral fertilizer (IN1), which is 15 times that applied in Ghana and five times that applied in Mali. A comparison with the results of the 1990 NUTBAL study shows that the latter’s projections for 2000 is in agreement with the projections from the updated NUTBAL, particularly for Ghana and Mali. For Kenya, nutrient depletion is about  25% less severe compared to the projections for 2000 by the NUTBAL study. The 1990 NUTBAL study has no detailed uncertainty analysis, although uncertainty in the projections can be great due to the assumptions and simplifications used. This has been remedied in the revised version. Uncertainty in the nutrient balances can be attributed to various biases and errors. It is usually smaller for farmgate balances (dealing mainly with IN 1, IN2, and OUT1) than for soil surface balances that consider leaching and gaseous losses [Oenema et al. 2003]. Lesschen et al. [2007] apply the revised NUTBAL methodology [FAO 2004] to Burkina Faso, and this includes an uncertainty assessment. Cross and spatial correlations between the various sources of uncertainty and their scale-dependency are taken into account. In the case of Burkina Faso, the error margin in the projected soil nutrient balance is −20 (±15) kg N ha−1 yr−1, −3.7 (±2.9) kg P ha−1 yr−1, and −15 (±12) kg K ha−1 yr−1. Overall, however, uncertainty associated with the soil nutrient balances is relatively low, compared to uncertainties of all input data. According to the uncertainty analysis, most LUSs are being depleted in soil nutrients.

TABLE 11.2 Comparison between the Nutrient Balance Calculations of Stoorvogel and Smaling and the FAO FAO [2004]

Stoorvogel and Smaling [1990]

1997–1999 Ghana Kenya Mali

1982–1984

2000

N

P

K

N

P

K

N

P

K

−27 −38 −12

−4 0 −3

−21 −23 −15

−30 −42 −8

−3 −4 −1

−17 −29 −7

−35 −46 −11

−4 −1 −2

−20 −36 −10

Source: Stoorvogel, J.J. and Smaling, E.M.A., Assessment of Soil Nutrient Depletion in Sub-Saharan Africa: 1983–2000. Report 28, Winand Staring Centre, Wageningen, the Netherlands, 1990; FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004. Note: Values for the year 2000 are projections carried out in 1990.

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TABLE 11.3 Nutrient Balance for Nkawie District, Ghana Area Crop Cassava Maize Plantain Cocoa All crops

N

(ha) 11 838 11 455 11 725 48 493 110 262

P

K

(kg/ha) −68.3 −32.4 −8.7 −3.2 −18.0

−9.6 −6.3 −0.3 −0.1 −1.9

−59.0 −20.3 −35.6 −8.5 −20.3

Source: FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004.

11.2.3  Applications at District Level The updated NUTBAL study includes an assessment of nutrient balances at district levels in Ghana, Kenya, and Mali for major cash crops. Tables 11.3 and 11.4 show the nutrient balances for the four most important crops in Nwakie District (Ghana) and Embu District (Kenya), as an example. It is apparent that these cash crops have much better nutrient balances than the food crops. Figure 11.6 shows the dominant role of cotton in the N balance of the Koutiala region of Mali. Soil N outputs under sorghum and millet are smaller than total N outputs under cotton, even though the latter receives large inputs through fertilizers. However, in the common rotational systems, millet and sorghum scavenge on the fertilizer applied to cotton in the preceding year. The study shows that it is possible to construct a nutrient balance at district level, also offering entry points for farming and land-use strategies.

TABLE 11.4 Nutrient Balance of the Tea-Coffee-Dairy Zone, Embu District, Kenya Area Crop Maize Beans Coffee Tea All crops

N

(ha) 5 143 2 748 8 813 1 092 20 678

P

K

(kg/ha) −174.2 −142.0 −39.1 −16.3 −95.6

−31.2 −25.9 −7.6 −1.4 −14.9

−73.0 −23.8 −7.3 −2.3 −33.1

Source: FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004.

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Cotton Millet Sorghum

Nitrogen (kg/ha)

30 20 10 0

–10 –20 –30

IN1

IN2

IN3

IN4

IN5

OUT1 OUT2 OUT3 OUT4 OUT5

Nutrient flows

FIGURE 11.6  Nitrogen flows for the three major crops, Koutiala Region. (Data from FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities. FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004. With permission.)

11.3  C  ALCULATING, MONITORING, AND MANIPULATING NUTRIENT FLOWS AND BALANCES AT FARM AND PLOT LEVEL (NUTMON) 11.3.1  Farm-NUTMON The farm and plot levels offer new dimensions to nutrient balance research. Nutrient stocks and flows can be calculated and estimated, but also monitored. Next, internal flows between farm and plot compartments can play a large role, in addition to the inputs and outputs of Figure 11.2. The internal flows become visible by partitioning farms and plots into several compartments or activity levels such as household, plots, livestock, feedstocks, and redistribution units (stables, compost heaps, latrines, etc.). The conceptual framework is shown in Figure 11.7. By using internal farm compartments, different farming strategies can be evaluated and best practices identified. Such best practices can be labeled INM and offer concrete options for action. INM technologies often combine one or more of the following categories: • Adding nutrients to the system (increasing INs), such as the application of mineral fertilizers and amendments, concentrates for livestock, organic inputs from outside the farm, and N-fixation in wetland rice and by leguminous species • Saving nutrients from being lost from the system (decreasing OUTs), such as erosion control, keeping crop residues inside the farming system, and planting deep-rooting species to reduce leaching losses • Recycling the volume of nutrients within the system so as to maximize nutrient use efficiency and system productivity (improving routing of internal flows)

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IN 1,2,3,4,5

Farm PPU

OUT 1,2

OUT 2,3,4

IN 1,2

RU IN 1,2

IN 2

SPU

Stock

Household

OUT 1,2

LEGEND External flows IN 1 IN 2 IN 3 IN 4 IN 5

in flows

mineral fertilizer organic manure wet + drt deposition biological N-fixation sedimentation

OUT 1 OUT 2 OUT 3 OUT 4 OUT 5 OUT 6

out flows

Internal flows

crop products crop residues animal products fresh manure FYM/compost household waste

harvested crop parts crop residues leaching denitrification water erosion human feces

OUT 1,2

OUT 6

FIGURE 11.7  Nutrient inflows, outflows, and internal flows used in NUTMON. PPU = primary production unit; SPU = secondary production unit; RU = redistribution unit; FYM = farmyard manure. (Data from Van den Bosch, H., et al., Agric. Ecos. Envir., 71, 62–80, 1998; Vlaming, J., et al., Monitoring Nutrient Flows and Economic Performance in Tropical Farming Systems (NUTMON)—Part 1: Manual for the NUTMON-Toolbox, Alterra, Wageningen, the Netherlands, 2001. With permission.)

Also, an economic component can be added to the nutrient monitoring, as IN1, IN2, OUT1, and OUT2 can be expressed in monetary units. Moreover, nutrient management is brought inside the wider domain of farm household production and consumption strategies. The resulting monitoring framework is known as NUTMON. The different features of NUTMON are described in a suite of papers [Smaling and Fresco 1993; Van den Bosch et al. 1998; De Jager et al. 1998a, 1998b], that form part of a larger series of documented nutrient balance studies [Smaling 1998; Smaling et al. 1999], and a characterization of major SSA farming systems on the basis of nutrient stocks, flows, and INM technologies [Smaling and Dixon 2006]. NUTMON has, since its inception, been used to i) calculate input–output balances [e.g., Van den Bosch et al. 1998], ii) understand actual farm practices under diverging agroenvironmental conditions [e.g., Van Beek et al. 2009], iii) perform impact assessments of interventions [e.g., De Jager 2007], iv) guide registration and certification procedures [e.g., De Jager 2007], and v) support joint learning and participatory research with different (groups of) stakeholders [e.g. Onduru et al. 1999, 2001].

11.3.2  Calculating Farm-Level Nutrient Balances 11.3.2.1  Inputs–Outputs To demonstrate differences in farm nutrient management, the results of five major NUTMON studies are brought together (Table 11.5). All studies in the table refer to mixed smallholder farming systems of, on average, 3 ha, but the objectives and monitoring periods of the projects differed. Table 11.6 provides the average nutrient balances recorded in the projects listed in Table 11.5. The results follow from a database

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TABLE 11.5 Characteristics of Major NUTMON Projects Project LEINUTS

NUTSAL

VARINUTS

Objective Identification of potentials of low-external input and sustainable agriculture to attain productive and sustainable land use in Kenya and Uganda Assessment and monitoring of nutrient flows and stocks and development of appropriate nutrient management strategies for semiarid areas in Kenya Spatial and temporal variation of soil nutrient stocks and management in sub-Saharan African farming systems

PIMEA INMASP

INM to attain sustainable productivity increases in East African farming systems

Main Cropping System

Monitoring Period

Maize, coffee, vegetables

1997

Maize, peas, sorghum, beans

1999, 2000

Maize, coffee

1997

Beans, barley, wheat Cassava, sorghum, millet, maize

2001 2002

Source: FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004.

that contains approximately 500 farm records. Each site is characterized by net farm income (NFI), i.e., the gross margins of all farm activities, excluding off-farm labor and income. Hence, the NFI is an indicator of farm profitability. In Table 11.6, NFIs are converted to US$ at the time of monitoring and cover one growing season. The range is considerable, and includes two sites with negative NFI, in the low-potential areas of Mbeere (Kenya) and Pallisa (Uganda). Next, full and partial nutrient balances are given. Full nutrient balances refer to the sum of all inputs minus the sum of all outputs. Partial balances only cover the easy-to-quantify flows IN1, IN2, OUT1, and OUT2. They are clearly incomplete from a biophysical standpoint, but more accurate than a full balance and offering a basis for strategic farm decisions. The partial balance is generally regarded as an indicator for the efficiency of nutrient use, whereas the full balance is an indicator for soil nutrient depletion. Figure 11.8 shows the breakdown of the full N balance for each site. Highest losses of N were estimated for erosion and leaching, but the uncertainties in these estimations are high. On the input side, most entries are easy to quantify and hence considered relatively accurate. 11.3.2.2  Internal Flows Strategic management of the interactions between the crop and livestock compartments, or the PPU and SPUs (Figure 11.7), is common in many farming systems, and a major INM technology. Grass, fodder crops, and crop residues are eaten by animals, whereas manure is returned to the grasslands and crop fields. Figure 11.9

Project

Country

LEINUTS LEINUTS NUTSAL2 NUTSAL NUTSAL NUTSAL NUTSAL Varinuts3 Varinuts Pimea4 Pimea INMASP5 INMASP INMASP INMASP INMASP INMASP INMASP INMASP INMASP

1

Kenya Uganda Kenya Kenya Kenya Kenya Kenya Kenya Burkina Faso Ethiopia Ethiopia Uganda Uganda Uganda Ethiopia Ethiopia Kenya Kenya Kenya Kenya

District Nyeri Pallisa Machakos Makueni Makueni Mwingi Kajiado Embu Manga Teghane GoboDeguat Wakiso Pallisa, Chelekura Pallisa, Akadot Solkua Wache Mbeere, Munyaka Kiambu, Kibichoi Kiambu, Ngaita Mbeere, Kamugi

No. of Farms 19 15 29 19 17 13 8 16 31 8 11 28 10 21 20 14 31 31 16 32

NFI (US$ farm−1) 172 147 28 89 107 28 478 581 943 1038 765 4131 886 –243 831 1013 184 272 1019 −933

Nfull

Pfull

Kfull

Npart

Ppart

Kpart

−10.4 −2.6 −50.0 1.1 −15.8 −17.2 −28.7 3.4 −23.5 −121.8 −0.7 −8.2 −23.7 −2.6 −3.7 −8.8 –45.6 111.5 −66.2 −11.6

3.1 36.7 5.9 −1.7 −1.3 −0.8 −2.1 7.2 8.2 −64.3 0.5 0.1 −0.4 2.3 0.0 −12.4 −8.4 18.6 −2.1 3.8

2.5 16.9 −28.8 −5.4 −1.8 −0.6 −3.7 19.7 −3.3 −36.2 7.5 −0.4 −7.6 −0.1 1.9 −28.2 −3.8 155.4 −9.2 −8.2

−1.4 12.2 68.0 36.7 4.2 0.3 0.2 32.7 36.6 −1.4 9.8 0.3 −0.1 4.7 5.8 0.6 9.9 178.3 31.7 4.3

−0.3 1.8 41.8 13.0 1.0 0.2 0.6 7.9 9.3 −0.2 1.0 0.1 1.2 −0.3 0.5 1.1 2.1 26.6 7.3 6.2

−0.7 16.3 44.8 8.5 8.0 0.2 1.2 25.2 4.9 −2.5 10.2 0.2 −3.9 −0.6 7.7 −1.9 3.2 173.2 12.4 0.9

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Source: 1Onduru, D.D., et al., Exploring New Pathways for Innovative Soil Fertility Management in Kenya. Managing Africa’s Soils, No. 25, IIED, London, 2001; 2Gachimbi, L.N., et al., Land Use Policy, 22, 13–22, 2005; 3Onduru, D.D., et al., Participatory Research of Compost and Liquid Manure in Kenya. Managing Africa’s Soils, No. 8, IIED, London, 1999; 4 De Jager, A.H., et al., Internat. J. Sustain. Agric., 3, 189–205, 1999; 5 Van Beek, C.L., et al., Agricultural, Economic and Environmental Performance of Four Farmer Field Schools in Kenya, INMASP Project Reports No. 18, Alterra, Wageningen, the Netherlands, 2005. Note: NFI = Net farm income. The subscripts full and part refer to full balances including hard to quantify flows and partial balances consisting only of easy to quantify flows, respectively (see text), and are expressed in kg ha−1 season−1. The upper seven sites were monitored twice, the others once.

20 Years after the Assessment of Soil Nutrient Balances

TABLE 11.6 Overview of Datasets of Previous NUTMON Studies (1997–2002) and Main Results at Farm Level

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Embu Manga Teghane GoboDeguat Matuu Kiomo Kibwezi Kasikeu Enkorika Pallisa Kabarole Nyeri Machakos Wakiso Chelekura Siakago Ngaita Kibichoi Gachoka Wache Solkua Akadot –300

–200

–100

0

Mineral fertilizer Organic fertilizer Inflow grazing Atmospheric deposition

100 kg N ha–1

200

Biological N fixation Harvest crop products Removed crop residues Outflow grazing

300

400

Leaching Gaseous losses Erosion Human feces

FIGURE 11.8  Breakdown of full N balances for the NUTMON project sites.

and Table 11.7 (for each project), show the magnitude of the N flows from crop to livestock and vice versa, averaged for the dataset of Table 11.6. Clearly, the flow of N from crops to livestock exceeds the flow of N from livestock to crop. At the same time, external inputs of N were greater for the livestock compartment than for the crop compartment. In other words, more N is imported as concentrates and fodder from external markets than N imports through fertilizers. However, exports of N 9 13

b

Crop 14 a

39 Livestock Farm 8 24

External (markets)

FIGURE 11.9  Average N flows between crop and livestock at farm level and interactions with external compartments (markets, kg farm−1).

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20 Years after the Assessment of Soil Nutrient Balances

TABLE 11.7 Internal N Flows (kg) between PPUs and SPUs, Averaged per Project Project INMASP LEINUTS NUTSAL PIMEA Varinuts

From Crop to Livestock 9 55 61 6 37

From Livestock to Crop 3 19 28 540) NSTOCK Ntot/Nstock N_IN1 N_IN2 N_IN2B N_IN3 N_IN4 N_OUT1 NTOT NFI NPART N_OUT2 N_OUT2B N_OUT3 N_OUT4 N_OUT5 N_OUT6 MARKETSH TLUNO NOHHM

NSTOCK Ntot/Nstock N_IN1 0.3 N_IN2 0.2 0.4 N_IN2B 0.2 N_IN3 –0.2 N_IN4 0.3 N_OUT1 –0.5 –0.3 NTOT –0.3 0.7 0.2 –0.4 –0.3 NFI –0.4 0.2 –0.3 –0.3 NPART 0.2 0.2 0.7 0.5 0.6 0.3 N_OUT2 0.3 N_OUT2B –1.0 –0.2 –0.5 N_OUT3 –0.2 0.7 –0.4 –0.4 0.9 –0.3 N_OUT4 –0.5 0.3 –0.3 –0.4 –0.4 0.8 –0.2 –0.2 0.7 N_OUT5 –0.3 0.2 –0.4 –0.3 0.6 0.3 0.5 N_OUT6 –0.2 0.3 0.4 MARKETSH TLUNO –0.5 0.2 0.2 0.2 –0.2 0.3 0.3 –0.2 –0.4 –0.2 NOHHM –0.4

Source: FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004. Note: NStock refers to the total N content in the upper 30 cm of soil; Ntot is the total N balance; NFI is the net farm income; Npart is the partial N balance; Marketsh is an indicator for the market orientation of the farm; TLUNO is number of tropical livestock units at farm level; and NOHHM is the total number of household members.

TABLE 11.8b Pearson’s Correlations for P Fluxes (p < 0.0001, n > 540) NFI MARKETSH TLUNO NOHHM PSTOCK PTOT PPART P_IN1 P_IN2 P_IN2B P_IN3 P_OUT1 P_OUT2 P_OUT2B P_OUT5 P_OUT6

NFI

MARKETSH TLUNO NOHHM PSTOCK PTOT –0.8 PPART 0.3 0.2 P_IN1 0.3 0.9 P_IN2 0.2 0.5 0.2 P_IN2B 0.2 P_IN3 –0.2 0.2 P_OU –0.2 P_OUT2 P_OUT2B –0.2 –0.9 P_OUT5 –0.8 –0.3 P_OUT6 –0.4 –0.3 –0.2 0.2 0.2

Source: FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004.

Characteristic Altitude (m) Rainfall (mm) Mean temp (°C) Main land use Main soil types Total soil N (g kg−1) N balance (kg ha−1 yr−1) P balance (kg ha−1 yr−1) K balance (kg ha−1 yr−1)

AEZ1

AEZ2

AEZ3

AEZ4

AEZ5

1 770 1 750 16.8 Tea/dairy Andosol/Nitisol 7.5 −143 −4.0 −11.7

1 590 1 400 18.2 Tea/coffee/dairy Nitisol 6.4 −197 −11.6 −30.3

1 320 1 200 20.2 Coffee/maize Nitisol 4.4 −143 −3.6 −31.2

980 900 21.4 Tobacco/food crops Luvisol 2.1 −30 8.8 1.8

830 800 22.6 Livestock/shifting cultivation Lixisol 0.9 −27 −1.9 6.6

20 Years after the Assessment of Soil Nutrient Balances

TABLE 11.9 Land Characteristics and Nutrient Balances of Agroecological Ones in Embu District, Kenya

Source: FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, Rome, FAO, 2004.

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TABLE 11.10 Partial N Balance for Three Soil Fertility Management Classes in Two Villages, Southern Mali M’Peresso Number of farms IN1 IN2 OUT1 OUT2 Partial balance

Noyaradougou

Class 1

Class 2

Class 3

Class 1

Class 2

Class 3

3 15.9 23.8 21.6 19.4 –1.3

10 16.4 16.4 18.8 13.3 0.7

7 13.4 14.5 17.3 13.1 –2.5

8 42.9 11.5 28.2 17.3 8.9

5 42.1 8.1 24.0 15.0 11.2

7 40.6 11.8 22.6 17.3 12.5

Source: Kanté, S., Gestion de la Fertilité des Sols par Classe d’Exploitation au Mali-Sud. PhD thesis, Wageningen University, the Netherlands, 2001; FAO, Scaling Soil Nutrient Balances—Enabling Mesoscale Approaches for African Realities, FAO Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome, 2004.

detail for the farms monitored during the VARINUTS project in Embu, Kenya [FAO 2004]. In each agroecological zone (AEZ), three farms were monitored. Although the choice of farms does not pretend to be fully representative of each AEZ, it is clear that the wetter AEZs in the sloping areas with little fallow land have the more negative nutrient balances, but also have the larger N stocks. In Southern Mali, partial nutrient balances were calculated for two villages where cotton, millet, and sorghum were the major crops (Table 11.10). Villagers grouped each other into three classes related to nutrient management. M’Peresso had the higher livestock density (0.36 vs. 0.12 TLU ha−1), explaining the higher IN2 values. Noyaradougou compensates this by higher fertilizer use. M’Peresso has average soil N stocks of 600 kg ha−1 in the upper 40 cm of soil, whereas Noyaradougou has 900 kg ha−1 [Kanté 2001; FAO 2004].

11.3.3  From NUTMON to MonQI In the past years, the scope of application of NUTMON has been broadened and the model renamed MONitoring for Quality Improvement (MonQI). At present, when the MonQI toolbox is applied, generally nine consecutive steps are taken, as shown in Figure 11.10. To facilitate the choice for a certain application, four different profiles were determined, which consist of different sets of software modules. 11.3.3.1  Joint Learning This profile is used in situations where the main objective is to relate social empower­ ment and action learning. The main feature is that it uses the reporting module, which allows farmers to compare their farm performance with the results of other members of a group and/or the average of a group. The tool produces reports per individual farm, as well as at the group or village level. Data are collected by farmers

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20 Years after the Assessment of Soil Nutrient Balances 2. Training of enumerators by MonQl R&D team

3. Check and modify background data to local conditions

1. Adapt MonQl configuration to project objectives

4. Farm interviews using structured questionnaires

5. Data entry

9. Follow up activities

8. Data interpretation and reporting

6. Data debugging 7. Data processing

FIGURE 11.10  The nine steps of the MONQI methodology.

in their own language, using the units of measure they are accustomed to. This profile is used in the INMASP project (Table 11.6) and demonstrates the impact of providing quantitative data (in charts) to stimulate farmers to learn from each other [De Jager et al. 2008]. 11.3.3.2  Environment The environment profile is used when the objective is to understand environmental impacts of smallholder agriculture through the leaching of nutrients and pesticides. It uses the pesticide monitoring and evaluation module and hard to quantify estimation of nutrient losses. Currently, this profile is mostly used in China and Vietnam to identify agricultural practices with risks for environmental trade-off effects [Peeters et al. 2007]. 11.3.3.3  Livelihoods The livelihoods profile is mostly used to assess food security, household assets, and income. This profile was used after the tsunami of 2004 to monitor asset development and the impact of aid. Another example of this profile is the application of MonQI as an early warning indicator in Somaliland for food insecurity through monitoring asset developments of smallholders. 11.3.3.4  Agro-Food Chains and Certification The agro-food chain profile is used for tracking and tracing the use of inputs (most often pesticides) or monitoring on-farm production management in the agro-food chain with the objective to meet quality standards of the EU or for certification for niche markets. This profile is currently used for the certification of watermelon in

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Vietnam and to monitor adherence to a private-sector-set code of conduct in the floriculture sector in Ethiopia. In a study in Vietnam, for example, in order to get detailed and quantitative information on pesticide use on the watermelon variety Happy Sweet, farm management was monitored in detail using an additional module called MonQI-P on environmental risks associated with the use of pesticides [Peeters et al. 2007]. In MonQI-P, the tailor-made reporting tool of profile 1 is further extended with calculations of recorded amounts of pesticides into active ingredients. In this way, the pesticide protocol of the buying company can be compared with farmers’ field operations against the background of international health and sanitation standards and export quality restrictions. The MonQI profiles overlap and, through the flexible activation of modules, mixed profiles are also possible. A mixed profile is used for monitoring sustainability of commercial tea production in Kenya. In the coming years, further modifications of the software are foreseen such as the monitoring of C and water management and optimal resource distribution under limited availability (Figure 11.1).

11.4  S TUDIES ON SOIL NUTRIENT DYNAMICS BEYOND THE NUTMON FAMILY Soil fertility dynamics can be studied through expert judgment by farmers when no quantitative assessments are available using soil color, workability, previous harvests, etc. Vernacular names then often shed light on the perception and appreciation of soil quality differences [e.g., Stroosnijder and van Rheenen 2001]. Remarkably, on a world scale, the Global Assessment of Human-Induced Soil Degradation (GLASOD) is also largely based on expert judgment [Oldeman et al. 1991]. The NUTMON approach is semiquantitative, using an input–output model with different terms, providing a net balance for the system under study. A considerable number of nutrient balance research papers not linked to NUTMON have been published over the past 20 years, and are summarized in Section 11.l. Calculating and assessing changes in soil fertility do not necessarily require a nutrient balance approach. Changes can also be measured or assessed by sampling soils over certain time intervals (chronosequence), or by sampling soils at the same time but in different LUSs (biosequence). Results of these approaches are given in Section 11.2. A set of cases on the manipulation of nutrient stocks and balances (INM) is given in Section 11.3.

11.4.1  Nutrient Balances At the national level, the Lesschen et al. [2007] study covers Burkina Faso, with a nutrient balance of −20, −3.7, and −15 kg N, P, and K ha−1 yr−1, respectively. Folmer et al. [1998] estimated soil fertility loss in Mozambique by combining land units (based on soil fertility, precipitation, and erosion) and land-use types (characterized by crops, scale, and occupation percentage of the land) into LUSs. On average, nutrient balances for 1997 were estimated at −33, −6.4, and −25 kg N, P, and K ha−1 yr−1. Haileslassie et al. [2005] assessed soil nutrient depletion and its spatial variability for Ethiopia and its regional states. At the national level, full nutrient balance results indicated an annual

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20 Years after the Assessment of Soil Nutrient Balances 200

N balance

100 0 –100

Scale n s P 132 11 F 133 10 VW 6 4 DR 86 4 N 251 6 C 13 3

–200

P balance

40 20 0 Scale n s P 106 10 F 133 9 VW 6 4 DR 81 3 N 251 6 C 13 3

–20 –40

200

K balance

100 0 –100

Scale n s P 46 5 F 127 9 VW 3 2 DR 86 4 N 251 6 C 13 3

–200 –300 –400

P

F

VW DR Main spatial scale

N

C

FIGURE 11.11  Nutrient balances at different spatial scales in Africa. P = plot; F = farm; VW = village and watershed; DR = district and region; N = nation; C = continent. Only data expressed as kg ha−1 year−1 and derived from full nutrient balances studies were plotted for the comparison. The number of observations (n) and studies (s) per category are shown in the rectangles. (Data from Cobo, J.G., et al., Agric. Ecos. Envir., 136, 1–15, 2010. With permission.)

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depletion rate of 122 kg N ha−1, 13 kg P ha−1, and 82 kg K ha−1. Soil nutrient stocks in all regional states were decreasing with the exception of areas under permanent and vegetable crops. Soil erosion was the major cause of nutrient depletion in Ethiopia, accounting for 70%, 80%, and 63% of the N, P, and K losses, respectively. For Asia, results are different. Surface N balances for China’s crop production systems were estimated using statistical data collected from 1980 to 2004 at the national and provincial scales and from 1994 to 1999 at the county level. The total cultivated land in China had a positive N balance of 143 kg ha−1 yr−1 in 2004, which is expected to increase to 169 kg ha−1 yr−1 in 2015. The N balance surplus in the more developed southeastern provinces was the largest [Sun et al. 2008]. Pathak et al. [2010] studied nutrient balances for the different states of India. Removals of N, P, and K by major agricultural crops in the country were 7.7, 1.3, and 7.5 Mt, respectively, but yet there were positive balances of N (1.4 million tons) and P (1.0 million tons), and a negative balance of K (3.3 million tons) for the 2000–2001 season. Cobo et al. [2010] recently published a comprehensive overview for 57 peer-reviewed studies in Africa. Most nutrient balances are calculated at the plot and farm scales and generated in East Africa. The analysis confirms the trend of nutrient mining in Africa for N and K, where more than 75% of selected studies have negative balances, whereas for P, 56% of the studies show negative mean balances (Figure 11.11). Other significant results derived from the analysis of the dataset are 1) LUSs of wealthier farmers mostly present higher N and P balances than systems of poorer farmers; 2) plots located close to homesteads also show higher balances than plots located relatively further away; and 3) partial nutrient balances are significantly higher than full balances calculated for the same systems. The data do not reveal a major trend in the magnitude of N, P, and K balances by increasing the spatial scale from the plot to the continental level. This is in apparent contradiction to Haileslassie et al. [2007], Schlecht and Hiernaux [2004], and Onduru and Du Preez [2007] who claim a trend of increasingly negative nutrient balances with increasing scales of observation. However, a limitation of the results in Figure 11.11 is the diversity of farming systems assessed and the inclusion of sublevels within main scales, which could increase the variability. The review of Cobo et al. [2010] illustrates the high diversity of methods used to calculate nutrient balances and highlighted the main pitfalls, especially in the case of upscaling nutrient flows and balances. According to the authors, the major generic problems are the arbitrary inclusion and exclusion of flows from the calculations, short evaluation periods, difficulties on the setting of spatialtemporal boundaries, inclusion of lateral flows, and the linking of the balances to soil nutrient stocks. Main challenges during scaling-up are related to the type of aggregation and internalization of nutrient flows, as well as issues of nonlinearity and spatial variability, resolution, and extent, which have not been properly addressed yet. Urban and periurban agriculture have nutrient balances that can differ largely from those in rural areas. Grote et al. [2005] point out that urban environments are sinks of nutrients through the large-scale import of food stuffs. The waste that is created with it can be usefully recycled back into the agricultural system, but can also pose overuse and health problems [Drechsel and Kunze 2001]. A recent study in Niamey (Niger) shows positive partial nutrient balances in ten vegetable gardens of 290–1133 kg N ha−1, 125–223 kg P ha−1, and 312–351 kg K ha−1 [Diogo et al. 2010].

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For Asia at the field level, Regmi et al. [2002] did a nutrient balance assessment, based on long-term rice-rice-wheat trials in Central Terai, Nepal, and found nutrient balances of 3, −4, −12 (control treatments), 29, 23, −62 (NPK), and 96, 33, −16 (manure) kg ha−1 yr−1 for N, P, and K, respectively. Even in an intensive high-input system, K withdrawal is apparently not matched by stocks and inputs, and is the major cause of the slight but consistent yield declines that were observed throughout the 20-year period. Dobermann et al. [1996a, 1996b] provide accounts of P and K balances in intensive wetland rice systems in Southeast Asia, and also find negative K balances in most systems. Finally, the use of nutrient balances is not restricted to agricultural systems. Various studies applied the concept to natural ecosystems [e.g., Stoorvogel et al. 1997; Klinge et al. 2004].

11.4.2  C and Nutrient Stocks Various studies of SOC stocks and changes exist for SSA at a national level, including Kenya [Batjes 2004], Senegal [Batjes 2001; Woomer et al. 2004], Congo Republic [Schwartz and Namri 2002], and Central Africa [Batjes 2008] using soil survey legacy data and conventional GIS-based mapping approaches. Other studies apply dynamic, process-based models to assess changes in SOC subsequent to defined changes in land use and management [Tschakert et al. 2004; Kamoni et al. 2007; Tan et al. 2009]. SOC stocks for Senegal, for example [Batjes 2001], range from 10 tons C ha−1 for an Arenosol under sparse vegetation to 72 tons for a Ferralsol under forest, whereas the highest value was 300 tons C ha−1 for a Fluvisol under rice and short grassland. This study accounts for differences in bulk density and gravel content, with soil type and land-use management. Alternatively, Windmeijer and Andriesse [1993] report average SOC content for the West African Equatorial Forest, Guinea savanna, and Sudan savanna to be 24.5, 11.7, and 3.3 g kg−1, respectively. Pieri [1989] mentions annual loss rates of SOC content in cultivated fields in Senegal (3.2%– 7.0%), Burkina Faso (1.5%–6.3%), and Chad (0.5%–2.8%). At the district and province levels, a survey by the Burkina Faso Bureau National des Sols (unpublished) for the southwestern Houet Province reports 11 to 30 tons C ha −1 , and 1000 to 2500 kg N ha−1. In the Sanmatenga Province, on the Mossi Plateau, these values are 17 to 25 ton C ha−1, and 1100 to 1700 kg N ha−1 [Stroosnijder and Van Rheenen 2001]. In the Embu District in Kenya, total N content ranges from approximately 7.5 g kg−1 on the tea areas on the higher slopes of Mount Kenya to 0.9 g kg−1 in the semiarid lowlands to the east [Smaling et al. 2002]. In Zimbabwe, SOC stocks under reference woodlands are largest (53.3 t ha−1) in a red clay soil, followed by a granitic sand (22.8 t ha−1), and a Kalahari sand (19.5 t ha−1) [Zingore et al. 2005]. When looking at chronosequences and biosequences in SSA, Hartemink [2003] mentions that SOC contents under Nigerian forest were 26–35 g kg−1, against 13–19 g kg−1 for cocoa fields of 10–55 years of age, and that SOC equilibrium under cocoa is below that observed for soils under natural forest. Data from Cameroon [Kanmegne 2004] and Madagascar [Brand and Pfund 1998] show soil N losses as high as 3000 and 5000 kg ha−1 when forest is converted to agricultural land. Prolonged fallow [Nye and Greenald 1960] can help restore soil C stocks, albeit seldom to the original level observed under natural vegetation. Samaké et al. [2005] mention SOC values

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in unfertilized bush fields in the Malian Dogon between 1.9 (1 year fallow) and 3.4 g kg−1 (7 years fallow), and total N of 0.17 and 0.25 g kg−1. For land under 3–5 years of millet cultivation, SOC values were 1.5 and 0.14 g kg−1. In terms of distance from the homestead, values ranged from 1.0 to 5.5 g kg−1 C and from 0.12 to 0.23 g kg−1 N, when 2000 m vs. 50 m away from the homestead. Bado et al. [1997] found that 10 years of continuous maize cultivation in West Burkina Faso led to SOC levels of 1.9 g kg−1 (no fertilizer) and 3.6 g kg−1 (N fertilizer + manure), but fell short of the initial fertility of 4.5 g kg−1. Corresponding values for N were 0.17, 0.27, and 0.29 g kg−1. Pieri [1989] found annual loss rates of SOC in cultivated fields in Senegal (3.2%–7.0%), Burkina Faso (1.5%–6.3%), and Chad (0.5%–2.8%). In Asia, Maskey et al. [2001] show that SOC in Nepal declines upon cultivation. In the upper 30 cm, uncultivated land in the Central Hills had 12.6 g kg−1, lands cultivated for 50 years 6.9 g kg−1. For the Terai lowlands, bordering India, these values were 7.9, 5.9, and 4.0 g kg−1. The picture in China is mixed. A countrywide survey of SOC dynamics in trial sites in China (>1000 observations), monitored between 1985 and 2006, reveals that for dryland crops, average SOC went up from 10.1 to 10.8 g kg−1, and in paddy rice from 15.7 to 17.4 g kg−1 [Pan et al. 2010]. A long-term experiment in Northeastern China [1990–2005] shows that N stocks (0–20 cm) under control plots went down from 2625 to 2068 kg ha−1, under N (150 kg ha−1) from 2396 to 2192 kg ha−1, under N + manure (205 kg ha−1) from 2429 to 2455 kg ha−1, and under NPK from 2577 to 2302 kg ha−1 [Qiang Ma et al. 2010]. In a long-term double cropping (maize, wheat) experiment on the red clays of Southern China, SOC contents under the control treatment were stable: 8.5 (1991–1998) to 8.7 g kg−1 (1999–2006), and increased under NPK (300-53-104) from 9.2 to 10.3, and under 60 tons ha−1 pig manure from 10.9 to 14.4 g kg−1. Soil N, however, declined over these periods, apart from the treatments that included manure [Zhang et al. 2009]. For Brazil, Bustamante et al. [2006] estimate that soils of the Cerrado region have an average stock of 117 ton C ha−1. Comparing the dynamics of SOC in a long-term experiment in the South of Brazil, Mielniczuk et al. [2003] estimate that no-tillage reduced the decomposition rate from 3.2% to 1.7% yr−1. In the Cerrado region, Silva et al. [1994] found SOC to drop by 41% on clayey and by 80% on sandy soils after 5 years of cultivation. However, Freitas et al. [2000] and Roscoe and Buurman [2003] did not observe changes in the SOC stocks of a clay soil after 25 years of maize–bean successions with conventional tillage. Lilienfein and Wielcke [2003] report no significant changes in C content of a Ferralsol Oxisol after 12 years of maize–soybean rotation under conventional tillage. A recent, extensive review of SOC changes under different land management systems in the Brazilian savanna is given by Batlle-Bayer et al. [2010].

11.4.3  Integrated Nutrient Management Implementation of so-called best practices can help sustain and improve SOC levels. Such practices should be directed at increasing inputs of organic matter into the soil and on decreasing organic matter decomposition. They typically include a judicious combination of various practices such as organic residue and fallow management, water conservation and management, soil fertility management including

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use of chemical fertilizers, organic manures, and liming, and introduction of agroecologically adapted crop and plant species, including agroforestry [Batjes and Sombroek 1997; Paustian et al. 1998; Bruce et al. 1999b; Lal 2008]. Nonetheless in SSA, examples of successful INM in a research environment abound, but the exact spread of the successes is limited [Gabre-Madhin and Haggblade 2004]. Zougmoré et al. [2002] show in erosion-prone central Burkina Faso that stone rows alone failed to completely stop soil fertility decline, but in a later study, Zougmoré et al. [2003] find that stone rows and Andropogon grass strips together reduced runoff by 59%. Adding fertilizer N or organic N caused an even stronger reduction of runoff by 67% and 84%, respectively, next to a strongly positive effect on crop yields by fertilizer (65%) or compost and manure (142%). Stone rows alone or grass strips alone improved water storage but not yields, but the combination of stone rows + compost was the best treatment giving 2300–2800 kg ha−1. Planting pits (zaï) can turn hardened ironstone plateaus into relatively productive land again while reducing runoff. Experimentation in Yatenga (central Burkina Faso) gave 200 kg ha−1 sorghum with pits alone, 700 kg ha−1 with dry dung, 1400 kg ha−1 with mineral fertilizers, and 1700 kg ha−1 with both dung and fertilizers [Reij and Thiombiano 2003]. Microdosing of fertilizer also holds promise. Twomlow et al. [2010] present a review of 1200 pairedplot trials in Zimbabwe consistently showing that microdosing of 17 kg N ha−1 can increase grain yields by 30%–50% across a broad spectrum of soil, farmer management, and seasonal climate conditions. In order for a household to make a profit, farmers need to obtain 4–7 kg of grain for every kg of N applied, but they commonly obtain 15–45 kg. The benefits of leguminous species in farming systems are widely known [e.g., Giller 2001; Sanginga and Woomer 2009]. An African reality is that smallholder farmers tend to spread risks in land management. This can manifest itself in multiple cropping systems, multistory cropping, or integrated crop–livestock systems. As to soils, farmers tend to cherish some parts of their fields at the expense of others, which is also a way to spread drought risks. Samaké et al. [2005] found that N stocks (0–15 cm) in concentric rings, at 10–200 m from homesteads in the Malian Dogon were 600 kg ha−1, whereas in the outer rings, 500–2000 m away from the homesteads, it was only 300 kg ha−1. Similar spatial soil fertility variation-driven farm nutrient management was found by Prudencio [1993] in Burkina Faso, Elias et al. [1998] in Ethiopia, Zingore et al. [2007] in Zimbabwe, Ebanyat et al. [2009] in Uganda, and Vanlauwe et al. [2006] and Tittonell et al. [2008] in West Kenya. Combination of spatial management with INM technologies lead Giller et al. [2006] to conclude that spatial patterns of resource use in SSA are consistent across different farming systems. Farmers preferentially allocate manure, mineral fertilizers, and labor to fields close to the homestead, resulting in strongly negative soil fertility gradients away from the homestead. Giller et al. [2006] also state that livestock in SSA farming systems are the central means of concentration of nutrients, which is confirmed by the NUTMON analysis in Chapter 3.

11.5  WHERE DO WE STAND, WHERE DO WE GO? The NUTBAL study [Stoorvogel et al. 1993] and its improved version [FAO 2004; Lesschen et al. 2007] reveal that nutrient balances in SSA have negative values for N,

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P, and K (Table 11.2). This means that, on average, soil fertility on the subcontinent declines. On top of that, C and nutrient stocks in the soils of SSA are only 60% of the estimated global average. Comparing the N depletion projection of NUTBAL in 2000 (26 kg ha−1 yr−1) with the Batjes [2001] estimate of African SOC stocks (60,000 kg ha−1) suggests, when taking an approximate C/N ratio of 12, an annual N loss of 0.5% of the total N stocks in soils. The NUTBAL studies raise awareness of soil fertility problems at a macrolevel (FAO, research community, donor community), and they show in which regions nutrient depletion is most severe. It is the mesolevel studies, however, that provide clearer options for national policies, public services, and private sector investment, i.e., through the development of vertical supply chains and establishment of agro-input dealer outposts. The district examples in Ghana, Kenya, and Mali, for example, show the positive impact of cash crops on the nutrient flows and balances (Figure 11.6; Tables 11.3 and 11.4). At the microlevel, NUTBAL was transformed into a farm-level monitoring tool: NUTMON. The tool has helped to not only better understand differences between African farming systems and their nutrient stocks and balances, but also increased insight into farmers’ soil fertility and farm management strategies. Many MSc and PhD students, inside or outside the African Network for Soil Biology and Fertility (http://webapp.ciat.cgiar.org/tsbf_institute/africa.htm) have used NUTMON over the past 20 years [e.g., Vanlauwe et al. 2002; Bationo 2004; Bationo et al. 2007; Sanginga and Woomer 2009]. The NUTMON studies showed a wide range of N, P, and K balances as well as related net farm income (Table 11.6). Out of 20 projects, 80% have a negative (full) balance for N, 40% for P, and 70% for K. The visualization of internal flows shows that, on average, the traffic of nutrients between crops and livestock is strongly in favor of the latter (Figure 11.9; Table 11.7). Analyzing 57 nutrient balance studies performed at the microlevel in SSA, Cobo et al. [2010] finds that 75% have a negative balance for N and K, and 56% for P (Figure 11.11). NUTMON allows manipulation of soil fertility by integrating INM technologies such as zaï, use of compost pits, biomass transfers, contour planting, and improving the efficiency within crop-livestock systems. Visualizing internal flows within the farm systems has been key at the microlevel, whereas this is all inside the black box at the macrolevel and mesolevel. Many INM technologies are potentially successful, and are known to have been adopted. The further rapid spreading of such successes should be a key priority. Still, going by analyses of success stories in African agriculture and natural resource management, more has been achieved on improved varieties (maize, rice), eradication of diseases (cassava viruses, rinderpest), and improved market opportunities (horticulture, floriculture) than through INM technologies [Gabre-Madhin and Haggblade 2004]. Successes in the microdosing of fertilizer and improved land management are notable [Reij and Smaling 2008], but there is no major leap ahead yet in fertilizer and manure use, and INM in general. As periurban systems show positive nutrient balances, even in SSA, it may be worthwhile to improve agricultural areas that are not too distant from major cities in such a way that they become breadbaskets, benefiting from fertilizers (IN1), city waste (IN2), crop rotations that include leguminous species (IN4), conservation tillage (less OUT3 and OUT4 and maintenance of SOC), erosion control (less OUT5), in relatively large-scale management units.

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NUTMON has meanwhile grown into MONQI, which allows for the inclusion of producer strategies within a real-world context of priority setting at the farm level, risk management, reliance on nonfarm income including remittances, realities of changing market prices, and other policy considerations. Smallholder farmers play a key role in rural development and various public and private interventions are in progress to assist smallholders in optimizing farm management (increase productivity and income), in the sustainable use of natural resources (water, pest, and soil fertility management), in improving market linkages, in diversifying sources of income, and in reducing risks. The need for actual farm management information and monitoring change and impacts as well as for generating information to assist learning and innovation processes in smallholder enterprises will increase and the relevance for further development of MONQI remains high. Uncertainties play a large role when estimating balances on the basis of a set of inputs and outputs. Lesschen et al. [2007] determined the uncertainties in calculating the nutrient balance for Burkina Faso, based on the improved version of NUTBAL [FAO 2004]. Uncertainty is, of course, not restricted to NUTBAL and SSA. A special issue of the European Journal of Agronomy [2003, 20] describes how nutrient balances in different EU countries are used to gain understanding and input for agricultural and environmental policies and measures. At this advanced level, a range of shortcomings and uncertainties exist [Öborn et al. 2003]. The development of a mineral bookkeeping system (MINAS) at farm level in the Netherlands, for example [Hanegraaf and Den Boer 2003], was not deemed solid enough by the European Commission and had to be replaced by a system of maximum fertilizer and manure application per unit of agricultural land [Schröder and Neeteson 2008]. Also, the comparison of soil C and nutrient stocks is hampered by uncertainties and by a lack of information on sampled soil depth and bulk density, which would allow transformation of data provided on a per soil mass basis to a (preferred) per hectare/volume basis. Batjes [1996], for example, estimated that world SOC stocks are approximately 700 × 109 ton for the upper 30 cm, but for the upper 1 m, values were about twice this amount and, for the upper 2 m, even 2400 × 109 ton C was reported. Overall, opportunities exist for increasing SOC in the soil, thereby also contributing to atmospheric C mitigation, but these are constrained by the available knowledge and access to resources; hence, the need for mechanisms that pay for environmental services [Ringius 2002] such as improved soil C and soil water management. In Asia, nutrient balances at the national level are estimated to be positive for N, but negative for K. Field studies confirm these findings. Reasons include the much higher and often subsidized use of (N and P) fertilizers in Asia and the relatively large percentage of nutrient-balanced irrigated systems. This review also shows that in China and India, average nutrient balances are positive for N and P [Sun et al. 2008; Pathak et al. 2010]. Under conservation agriculture in Latin America, nutrient balances can also remain relatively neutral. In SSA, the examples given in Chapter 4 show that conversion of forest leads to massive nutrient losses, whereas continuous cultivation only keeps SOC, at best, at 75% of its original value under high applications of fertilizers and manure. Asia and Latin America have two more advantages over SSA. In Asia, production per unit land has increased largely as a result of improved rice production methods (breeding, mechanization, pest control,

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and irrigation). In parts of Latin America, farms are often so large that production per unit labor can be maximized through large-scale mechanization and using other economies of scale. However, it is feared that the smallholders’ systems in mountain areas of the Andes and Central America experience strongly negative balances similar to Africa, although inherent soil fertility is higher on average. Trials in rice-based systems in Asia also show that (too) large applications of N have two negative externalities, i.e., a small percentage of the N in urea is actually taken up by the crop, whereas the remainder stays in the soil or is lost as OUT3 or OUT4, and the withdrawal of soil K and other nutrients will be much larger than in a situation where little urea is applied. Also, soils with high N but low P and K stocks may have quite negative N balances. Application of N fertilizers will then not be a sensible option until the soil N, P, and K ratio is more in balance with cropuptake ratios. Hence, a nutrient balance close to zero is preferred, but the road toward achieving that can be different and relates to a target or ideal soil fertility [Janssen and De Willigen 2006]. Finally, although awareness of nutrient balances has increased among agricultural scientists, the subject still features marginally in most debates about sustainable agricultural development. As a result, the great missing link between scientific nutrient balance assessments and agricultural policy remains unaddressed. Few countries, particularly in SSA, have developed comprehensive policies, including subsidies, credit, and marketing, to promote increased fertilizer use and INM. This remains of serious concern in view of even the most optimistic projections of nutrient needs to feed the world population in 2050. Initiatives such as the release of, and active participation of, key partners in the Web site and discussion platform on www.africafertilizer.org are highly necessary. Also, a next generation of fertilizers is in the making through the IFDC-triggered Virtual Fertilizer Research Center, aimed at tailoring type and amount of fertilizer to soil characteristics and crop needs so as to maximize fertilizer use efficiency and value–cost ratios. Welltrained agro-input dealers can play a key role in acceptance of fertilizers, also by women and first-time users. This is testified to by much of the work IFDC and TSBF-CIAT have done in SSA. Banking on fertilizers alone is, however, not the way ahead for resource-poor farmers. Not only can the price be prohibitive, the way it is offered on the market (in 50-kg bags), substandard product quality, and the risk of late availability during the growing season make resource-poor farmers look for broader INM options. The entire manipulation, including the components offered in the NUTMON toolbox, is geared at realizing high OUT1 at higher and more efficient use of inputs and the clever management of internal flows. This approach addresses the increased productivity, efficiency, and recycling spelled out in Chapter 1, and includes the add, save, and recycle components of INM introduced in Chapter 3. Nonetheless, a concerted effort is required to raise the visibility of soil nutrients as an essential ingredient in sustainable agriculture. Little political action has been taken so far, apart from pledging support during the 2006 Africa Fertilizer Summit, where it was agreed to raise fertilizer use in SSA to 50 kg ha−1 (from the current 80% of the required increase in food production. These are also the regions where the credible data on soil resources are not available. Planning for sustainable development requires a thorough understanding of the state-of-the-soils to choose an appropriate land use and identify RMPs. Oldeman [1998] estimated the productivity loss for the period between World War II and 1990 at 12.7% for croplands, and 8.9% for combined croplands and pasturelands. The estimated loss on a regional basis for croplands ranged from 3.2% for Oceania, to 36.8% for Central America, and to 25% for Africa (Table 12.1). These estimates were based on the national average yield levels and the areal extent of degradation based on the Global Assessment of Human-Induced Soil Degradation (GLASOD) methodology. There are two databases of global assessments of soil and land degradation (Table 12.2). The third database on land desertification and degradation in arid climates is not discussed in this chapter. The GLASOD methodology estimated soil degradation in 1990 at 1.964 billion ha (Bha). The regional estimates included 747 million ha (Mha) (38% of the world total) in Asia, 494 Mha (25%) in Africa, 306 Mha (15.6%) in Latin America and the Caribbeans, 218 Mha (11.2%) in Europe, 103 Mha (5.2%) in Australia and the Pacific, and 96 Mha (5.0%) in North America. Another global study in 2008 estimated land area affected by degradation at 2.63 Bha (overall 3.5 Bha). The regional estimates, recalculated from the national level data provided by Bai et al. [2008], indicated degradation of 626 Mha (23.8%) in Asia, 523 Mha (19.9%) in Africa, 468 Mha (17.8%) in Latin America and the Caribbeans, 446 Mha (16.9%) in North America, 355 Mha (13.5%) in Europe, and 216 Mha (8.1%) in Australia and the Pacific (Table 12.2). The data, in which regional totals of area affected by degradation do not add up to the global total, show a global increase of 34% even with the conservative estimate of 2.63 Bha (Table 12.2). The 2008 estimates by Bai et al. indicated an increase in area affected by land degradation over that reported in 1990 by Oldeman et al. for all regions except in Asia. There is an important distinction between the terms land and soil. The latter is only one of the components of land, another being vegetation. The change in area under land degradation was −16.2% in Asia, 5.9% in Africa, 52.9% in Latin America and the Caribbeans, 364.6% in North America, 109.7% in Australia and the Pacific, and 62.8% in Europe (Table 12.2). The estimates in 2008 were based on the vegetation index, and were also related

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TABLE 12.1 Average Cumulative Loss of Productivity from Cropland Region Africa Asia South America Central America North America Europe Oceania World

Cropland

Crop and Pastureland

25.0 12.8 13.9 36.8 8.8 7.9 3.2 12.7

14.2 8.9 6.7 14.5 5.8 9.0 3.2 8.9

Source: Oldeman, L.R., Soil degradation: A threat to food security. ISRIC Report 98/01. Wageningen, the Netherlands: ISRIC, 1998.

to NPP. However, higher estimates reported by Bai et al. are partly due to the fact that they assessed degradation of land rather than of soil. To complicate the already complex situation, the terms degradation and desertification are also used widely and interchangeably. Regardless of the diverse but related approaches and use of different terminology, the reliability/credibility of the data were not extensively checked by validation through ground truthing. Yet, it is important that estimates are validated on a pilot scale for principal soils in major biomes or ecoregions. The major drawback, which creates skepticism and the crying wolf syndrome, is the lack of validation and of establishing the cause–effect relationship with productivity and other ecosystem

TABLE 12.2 Global Assessment of Soil Degradation in 1990 and 2006 Degrading Area (Mha) Region Asia Africa Latin America and Caribbeans North America Australia and Pacific Europe Total a

1990 [Oldeman et al. 1990] 747 494 306 96 103 218 1964

2008 [Bai et al. 2008]

Percent Change

626 523 468 446 216 355 2634 (3606)a

−16.2 5.9 52.9 369.6 109.7 62.8 34.1

The regional total, computed form the national statistics published by Bai et al. [2008], do not add up to the global total. The discussion in the text and the ratio shown in the last column are based on the lower estimates based on the regional total.

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services. Improving the data on the state-of-the-soils is essential for acceptance of the risks of soil degradation by policymakers and planners, which is necessary to implement any long-term soil restoration programs.

12.3  ADVERSE IMPACTS ON AGRONOMIC PRODUCTION The data on extent and severity of soil degradation must be related to quantity and quality of agronomic production. The term quantity implies change in mean crop yields and total production at regional, national, and global levels. Crop yield, being a function of management and inputs, must be assessed for a range of technological options (i.e., from traditional to improved and innovative). Any masking impact of RMPs on degradation-induced declines in crop yields must be quantified. Soil degradation, especially that caused by the depletion of SOM content and deficiency of macronutrients and micronutrients, is also related to malnutrition in humans and the overall decline in public health. Indeed, soil degradation affects food security directly by reducing crop yields and decreasing agronomic production, and indirectly by reducing the nutritional value of the agricultural produce (protein content, concentration of micronutrients such as Zn, Cu, I, Se). Other indirect effects of soil degradation on human health are those related to pollution of soils, air, and water. Thus, both direct and indirect effects of soil degradation must be quantified for major cropping and farming systems on principal soils and representative agroecosystems. Soil degradation also exacerbates the adverse impacts of extreme events related to climate change by natural variability or human-induced factors. For example, frequency and duration of drought are accentuated by soil degradation due to erosion, salinization, or elemental imbalance. Among the three types of drought (meteorological, hydrological, and agronomical), soil degradation exacerbates the adverse effects of an agronomic drought. The latter is attributed to an increase in the loss of water by runoff and evaporation, and a decrease in plant available water capacity (AWC) in the root zone (Figure 12.1). In contrast to drought, extreme events may also reduce agronomic yields by inundation (or flooding) that causes anaerobic environments in the root zone. These impacts need to be quantified through implementation of properly designed long-term studies. Nutrient mining (refer to Chapter 11 by Smaling et al.) is a major problem in subSaharan Africa and also in South Asia. The adverse impact of the negative nutrient budget, assessed for soil-specific situations under different levels of nutrient inputs, must be assessed for a range of crops and cropping systems. The goal is to quantify the vulnerability of agronomic production to soil degradation by different processes under a range of management options. Agronomic yield can also be related to use efficiency of inputs (i.e., fertilizer, water, energy) under different severities of soil degradation. Agronomic production also affects farm income, poverty, and social well-being. The so-called poverty trap tightened by soil degradation must be quantified, especially for the resource-poor farmers of sub-Saharan Africa, South Asia, and other developing countries. To be credible, the statistics on the extent and severity of soil degradation must be related to some quantifiable and verifiable indicators.

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12.4  A  NOMALY BETWEEN THE DATA ON SOIL DEGRADATION AND AGRONOMIC PRODUCTION The estimates of the land area affected by degradation indicate an increase in the extent of degradation at regional and global scales between 1990 and 2008. All other factors remaining the same, there should be a corresponding decline in agronomic production. Yet, there has been an increase in crop yields and total cereal and food production globally and in most regions (except sub-Saharan Africa). The data in Table 12.3 show that between 1990 and 2008, the global average cereal yield increased by 27% from 2783 kg/ha to 3539 kg/ha, and total cereal production increased by 28% from 1974 million Mg/yr to 2521 million Mg/yr. Thus, there is an apparent anomaly and discrepancy in the data on the extent of degradation and agronomic production. This discrepancy, in a critical need of an objective assessment, seemingly indicates the following:

1. All other factors have not been the same between 1990 and 2008 because of increases in inputs (i.e., irrigation, fertilizers, improved varieties) and differences in cropping systems. 2. The data on soil degradation are not credible and do not reflect the local and regional levels. 3. The data sets are not comparable because of differences in terminology and methodology (i.e., land vs. soil).

Providing that the areal extent of degradation has increased since 1990, there must be a strong “masking” impact of RMPs on productivity of degraded soils. If the use of inputs (i.e., fertilizers, irrigation, tillage, new varieties) can alleviate the soil­related constraints exacerbated by erosion or nutrient depletion, then there is an urgent need to revisit the definition of the term soil degradation. Rather than including the categories of light and moderate, estimates may only focus on strong or even very strong categories. Assuming that the estimates of soil degradation are limited to strong and very strong categories, ground truthing and field measurements of crop yields for a range of management scenarios are crucial. TABLE 12.3 World’s Total Cereal Production and Average Cereal Yield Total Cereal Production

Average Cereal Yield

Year

(106 Mg/yr)

(%)

(kg/ha)

(%)

1992 2005 2008

1974 2268 2521

100 115 128

2783 3286 3539

100 118 127

Source: FAOSTAT. Rome: FAO, 2010. http://faostat.fao.org/site/567/Desktop​Defauly​.aspx?pageID=567.

Research Needs for Credible Data on Soil Resources and Degradation

545

12.5  RESEARCH NEEDS In view of the argument presented in Section 12.4, there is a strong need to im­plement  coordinated, multidisciplinary, and interinstitutional studies on assessing: (1) the area affected by strong or very strong degradation processes; (2) severity of degradation in relation to the on-site and off-site impact; and (3) the impact on agronomic production, use-efficiency of inputs, and long-term sustainability. The methodology and terminology used must be standardized and used uniformly across the world. Such an evaluation must be done once every decade so that long-term trends can be established. Some researchable priorities include the following:



1. Assessing degradation on a soil basis (rather than a regional or national basis) in relation to the global data base published by FAO/UNESCO, USDA, ISRIC and other organizations. 2. Using remote sensing and geostatistical techniques to extrapolate the data to regional and global scales. 3. Quantifying loss in agronomic production and economic returns under different managements, cropping systems, and input scenarios. 4. Evaluating the impact of soil degradation on the nutritional quality of produce. 5. Determining the social impact of soil degradation on farm income, standards of living, education of children (especially of girls), and social and gender equity. 6. Relating soil degradation to environmental factors such as the quality of natural waters (surface and groundwater), biodiversity, emission of greenhouse gases, ecosystem C budget, etc. 7. Establishing long-term trends in the vegetation cover (ground cover) through remote-sensing techniques, and relating these to the area affected by soil degradation. 8. Standardizing the methodology of assessment of degradation and terminology (soil vs. land; degradation vs. desertification), and developing soil type and use-specific indicators of soil quality. 9. Developing channels of communication with policymakers on short- vs. long-term and on- vs. off-site effects of soil degradation, and creative relevant policy interventions to reversing the trends and restoring degraded soils. The strategy is to enhance awareness among leaders and policymakers. 10. Encouraging appropriate changes in education curricula for primary, middle, and secondary schools to enhance awareness among students during the formative stages of their development.

12.6  CONCLUSIONS Soils are among the most basic resources essential to the existence and well-being of all terrestrial life; therefore, preserving, restoring, and improving soil resources is important. In this regard, the data on state-of-the-soils is important for planning long-term and sustainable use of soil resources. It is thus important to create and

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strengthen a credible database on the extent and severity (degree) of soil degradation, establish the cause–effect relationship, standardize the methods of assessment of the areal extent and temporal changes and terminology, evaluate adverse impacts on agronomic production for a range of management scenarios, and identify indicators of soil quality in relation to the land use. The strategy is to establish long-term interdisciplinary and multi-institutional studies on the pilot scale for principal soils and major ecoregions. World soils have the capacity to feed current and future populations and provide other essential ecosystem services, providing that soil resources are judiciously used with site-specific RMPs that maintain and restore soil quality. Identifying longterm management options necessitates credible data on the state-of-the-soils and its impact on productivity and environment quality. The data on soil degradation must be validated against ground truth measurements, and agronomic information on productivity, nutritional quality, and economic well-being.

ACRONYMS AWC Bha C ECEC GLASOD MBC Mha NPP RMPs SOC SOM

Available water capacity Billion ha Carbon Effective cation exchange capacity Global Assessment of Human-Induced Soil Degradation Microbial biomass carbon Million ha Net primary production Recommended management practices Soil organic carbon Soil organic matter

REFERENCES Bai, Z.G., D.L. Dent, L. Olsson, and M.E. Schaepman. 2008. Proxy global assessment of land degradation. Soil Use & Management 24:223–234. FAO. 2010. FAOSTAT. Rome: FAO. http://faostat.fao.org/site/567/DesktopDefauly.aspx?​ pageID=567 (accessed December 15, 2010). Oldeman, L.R. 1998. Soil degradation: A threat to food security. ISRIC Report 98/01. Wageningen, the Netherlands: ISRIC. Oldeman, L.R., V.W.P. Van Engelen, and J.H.M. Pulles. 1990. The extent of human-induced soil degradation. In World map of the status of human-induced soil degradation, and explanitory note, 2nd ed., eds. L.R. Oldeman, R.D.A. Hakkleling, and W.G. Sombroeck. Wageningen, the Netherlands: GLASOD, ISRIC.