Todd S. Rosenstock · Mariana C. Ru no Klaus Butterbach-Bahl · Eva Wollenberg Meryl Richards *Editors*

Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

 Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

 Todd S. Rosenstock • Mariana C. Rufi no Klaus Butterbach-Bahl • Eva Wollenberg Meryl Richards Editors

# Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

 *Editors*  Todd S. Rosenstock World Agroforestry Centre (ICRAF) Nairobi , Kenya Klaus Butterbach-Bahl Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research Atmospheric Environmental Research (IMK-IFU) International Livestock Research Institute (ILRI) Nairobi , Kenya Meryl Richards University of Vermont CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) Burlington , VT , USA

 Mariana C. Rufi no Center for International Forestry Research (CIFOR) Nairobi , Kenya

 Eva Wollenberg University of Vermont CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) Burlington , VT , USA

 Gund Institute for Ecological Economics University of Vermont Burlington , VT , USA

 Gund Institute for Ecological Economics University of Vermont Burlington , VT , USA

DOI 10.1007/978-3-319-29794-1

ISBN 978-3-319-29792-7 ISBN 978-3-319-29794-1 (eBook)

Library of Congress Control Number: 2016933777

© The Editor(s) (if applicable) and the Author(s) 2016 . This book is published open access.

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## **Foreword**

 In this book, the author team describe concepts and methods for measurement of greenhouse gas emissions and assessment of mitigation options in smallholder agricultural systems, developed as part of the SAMPLES project. The SAMPLES (Standard Assessment of Agricultural Mitigation Potential and Livelihoods) system adapts existing internationally accepted methodologies to allow a range of stakeholders to assess greenhouse gas (GHG) emissions from different agricultural activities, to identify how these emissions might be reduced (i.e., mitigation), and to provide data through an online dataset that can be used to aid in these efforts.

 The book is divided into three sections: (1) designing a measurement program to allow users to identify what measurements are needed and how to go about taking the measurements, (2) data acquisition, describing how to deal with complex issues such as land use change, and (3) identifying mitigation options, which deals with scaling issues, how to use models, and how to assess trade-offs. Within each section is a series of chapters, written by leading experts in the fi eld, providing clear guidelines on how to deal with each of the issues raised.

 The work was begun at an international workshop in 2012, and the authors have since produced this synthesis. Through this work, the authors provide a comprehensive and transparent system to allow stakeholders to calculate and reduce agricultural GHG emissions, and assess other impacts. Since it builds on established and internationally accepted methodologies it is robust, yet the authors have managed to break down the complex and potentially overwhelming concepts and methods into bitesized chunks. Diffi cult subjects such as inaccuracy and uncertainty are not avoided, yet the authors manage to make these topics accessible and the process manageable.

 Potential users include, but are not limited to, national agricultural research centers, developers of national and subnational mitigation plans that include agriculture, agricultural commodity companies and agricultural development projects, and students and instructors. Anyone with an interest in agriculture, greenhouse gas emissions, and how to minimize these emissions will fi nd the book immensely useful.

Pete Smith

## **Pref ace**

 In October 2011, we faced a problem. We knew that the greenhouse gas (GHG) emissions from smallholder agriculture contributed to climate change and could present a climate change mitigation solution; however, we had no idea by how much. Experts at a workshop on farm and landscape GHG accounting organized by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Food and Agriculture Organization of the UN (FAO) quickly realized that there were few data to support GHG quantifi cation in smallholder systems. Compounding the issue, everyone seemed to use different approaches for estimating emissions and mitigation impacts. This meant that even if data were available they could not easily be compared. We needed to harmonize methods. However, the available measurement protocols typically focused on singular farming activities, such as soil fl uxes or biomass. This contrasted with the realities of diverse smallholder farms, which have multiple greenhouse gas sources and sinks. We needed a more holistic approach that could capture the diversity and complexity of smallholder systems.

 To meet these challenges, workshop participants conceived the idea for the SAMPLES (Standard Assessment of Agricultural Mitigation Potential and Livelihoods) project, which CCAFS initiated in 2012, in collaboration with partners at FAO's Mitigation of Climate Change in Agriculture (MICCA) program, the Global Research Alliance for Agricultural Greenhouse Gas Emissions (GRA), and multiple universities worldwide. The goal of SAMPLES was to increase and improve the availability of data on greenhouse gas emissions and removals in smallholder agricultural systems and to design ways to reduce the cost and improve the quality of future data collection efforts for these systems, especially to quantify the impacts of low emissions practices. SAMPLES has worked toward these objectives through four interrelated activities: (1) global emission hotspot analysis, (2) estimating emissions and potential reductions in a whole-farm context, (3) capacity building around GHG quantifi cation, and (4) policy engagement.

 This volume is the product of 3 years of work toward creating a coherent approach and dataset on smallholder farm emissions and mitigation options. The SAMPLES quantifi cation framework was developed during an expert workshop on GHG quantifi cation held in Garmisch-Partenkirchen, Germany, in October 2012 and hosted by the Karlsrühe Institute of Technology. Following the workshop, authors reviewed the available "best practice" in greenhouse gas quantifi cation methods and in some cases developed new methods to adapt the approach to the research constraints found in developing countries. Methods described herein are based on internationally accepted methods and have been reviewed by experts in the fi eld.

 These guidelines are intended to inform the fi eld measurements of agricultural GHG sources and sinks, especially to assess low emissions development options in smallholder agriculture in tropical developing countries. The methods provide a standard for consistent, robust data that can be collected at reasonable cost with available equipment. They can be used to support improved emissions factors for country inventories, to assess the mitigation impacts of projects, or as methods for scientifi c studies. The accompanying website (http://samples.ccafs.cgiar.org/) provides additional resources such as links to step-by-step guidelines, scientifi c publications, and a database of agricultural emission factors.

 We acknowledge with gratitude the following individuals who helped conceive this volume at a workshop in Garmisch-Partenkirchen, Germany, in October 2012:


 Björn Ole Sander, International Rice Research Institute (IRRI), Philippines Sean Smukler, University of British Columbia, Canada

 Piet van Asten, International Institute of Tropical Agriculture (IITA), Uganda Mark van Wijk, International Livestock Research Institute (ILRI), Costa Rica Jonathan Vayssieres, CIRAD, Senegal


 We also acknowledge the following individuals and organizations that provided feedback on all or part of the guidelines during the review process:

 Juergen Augustin, Leibniz Centre for Agricultural Landscape Research, Germany Rolando Barahona Rosales, National University of Colombia (Medellín), Colombia Ed Charmley, Commonwealth Scientifi c and Industrial Research Organisation, Australia Nicholas Coops, University of British Columbia, Canada Nestor Ignacio Gasparri, National University of Tucumán, Argentina Jeroen Groot, Wageningen University and Research Centre, Netherlands Ralf Kiese, Karlsruhe Institute for Technology, Germany Brian McConkey, Agriculture and Agri-Food Canada Eleanor Milne, Colorado State University, USA Carlos Ortiz Oñate, Technical University of Madrid, Spain David Powlson, Rothamsted Research, UK Philippe Rochette, Agriculture and Agri-Food Canada Don Ross, University of Vermont, USA Sileshi Weldesmayat, World Agroforestry Centre, Kenya Jonathan Wynn, University of South Florida, USA Christina Seeberg-Elverfeldt, German Federal Ministry of Economic Cooperation and Development (BMZ), Germany Marja-Liisa Tapio-Biström, Ministry of Agriculture and Forestry, Finland Kaisa Karttunen, Agriculture and Development Consultant, Finland The Mitigation of Climate Change in Agriculture (MICCA) Program of the United Nations Food and Agriculture Organization.

 This work was undertaken as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is a strategic partnership of CGIAR and Future Earth. This research was carried out with funding by the European Union (EU) and with technical support from the International Fund for Agricultural Development (IFAD). The views expressed in the document cannot be taken to refl ect the offi cial opinions of CGIAR, Future Earth, or donors.

 The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) is supported by Australia (ACIAR), the Government of Canada through the Federal Department of the Environment, Denmark (DANIDA), Ireland (Irish Aid), the Netherlands (Ministry of Foreign Affairs), New Zealand, Portugal (IICT), Russia (Ministry of Finance), Switzerland (SDC), the UK Government (UK Aid), the European Union, and carried out with technical support from the International Fund for Agricultural Development (IFAD).

 Todd S. Rosenstock Mariana C. Rufi no Klaus Butterbach-Bahl Eva Wollenberg Meryl Richards

## **Contents**



## **Contributors**

 **Alain Albrecht** Institute of Research for Development (IRD) , Montpellier , France

 **Piet J. A. van Asten** International Institute of Tropical Agriculture (IITA) , Kampala , Uganda

 **Clement Atzberger** University of Natural Resources (BOKU) , Vienna , Austria

 **Germán Baldi** Instituto de Matemática Aplicada San Luis, Universidad Nacional de San Luis and Consejo Nacional de Ciencia y Tecnología (CONICET) , San Luis , Argentina

 **Lenny van Bussel** Wageningen University and Research Centre , Wageningen , Netherlands

 **Klaus Butterbach-Bahl** International Livestock Research Institute (ILRI) , Nairobi , Kenya

 Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany

 **C. Chang** Commonwealth Scientifi c and Industrial Research Organisation (CSIRO) , Townsville, QLD , Australia

 **Ngonidzashe Chirinda** International Center for Tropical Agriculture (CIAT) , Cali , Colombia

 **Eugenio Díaz-Pinés** Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) , Garmisch-Partenkirchen , Germany

 **Ken E. Giller** Plant Production Systems Group , Wageningen University , Wageningen , Netherlands

 **John P. Goopy** International Livestock Research Institute (ILRI) , Nairobi , Kenya

 **Jonathan Hickman** Earth Institute, Columbia University , New York , USA

 **M. L. Jat** International Maize and Wheat Improvement Centre (CIMMYT) , New Delhi , India

 **R. K. Jat** International Maize and Wheat Improvement Centre (CIMMYT) , New Delhi , India

Borlaug Institute of South Asia , Pusa , Bihar , India

 **P. Kapoor** International Maize and Wheat Improvement Centre (CIMMYT) , New Delhi , India

 **Sean P. Kearney** University of British Colombia , Vancouver , BC , Canada

 **Charlotte J. Klapwijk** Plant Production Systems Group , Wageningen University and Research Centre , Wageningen , Netherlands

International Institute of Tropical Agriculture (IITA) , Kampala , Uganda

 **Shem Kuyah** World Agroforestry Centre (ICRAF) , Nairobi , Kenya

Jomo Kenyatta University of Agriculture and Technology (JKUAT) , Nairobi , Kenya

 **Cheikh Mbow** World Agroforestry Centre (ICRAF) , Nairobi , Kenya

 **Meine van Noordwijk** World Agroforestry Centre (ICRAF) , Bogor , Indonesia

 **David Pelster** International Livestock Research Institute (ILRI) , Nairobi , Kenya

 **Pytrik Reidsma** Wageningen University and Research Centre , Wageningen , Netherlands

 **Meryl Richards** Gund Institute for Ecological Economics, University of Vermont, Burlington , VT , USA

 CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS)

 **Todd S. Rosenstock** World Agroforestry Centre (ICRAF) , Nairobi , Kenya

 **Mariana C. Rufi no** Center for International Forestry Research (CIFOR) , Nairobi , Kenya

 **Gustavo Saiz** Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) , Garmisch-Partenkirchen , Germany

 **Björn Ole Sander** International Rice Research Institute (IRRI) , Los Baños , Philippines

 **Tek B. Sapkota** International Maize and Wheat Improvement Centre (CIMMYT) , New Delhi , India

 **Gudeta W. Sileshi** Freelance Consultant , Kalundu , Lusaka , Zambia

 **Sean M. Smukler** University of British Colombia , Vancouver , BC , Canada

 **David Stern** Maseno University , Maseno , Kenya

 **Clare Stirling** International Maize and Wheat Improvement Centre (CIMMYT) , Wales , UK

 **Philip K. Thornton** CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) , Nairobi , Kenya

 **Nigel Tomkins** Commonwealth Scientifi c and Industrial Research Organisation (CSIRO), Livestock Industries , Townsville , QLD , Australia

 **Katherine L. Tully** University of Maryland , College Park , MD , USA

 **Mark T. van Wijk** International Livestock Research Institute , Nairobi , Kenya

 **Eva Wollenberg** Gund Institute for Ecological Economics, University of Vermont, Burlington , VT , USA

 CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS)

## **Chapter 1 Introduction to the SAMPLES Approach**

 **Todd S. Rosenstock , Björn Ole Sander , Klaus Butterbach-Bahl , Mariana C. Rufi no , Jonathan Hickman , Clare Stirling , Meryl Richards , and Eva Wollenberg** 

 **Abstract** This chapter explains the rationale for greenhouse gas emission estimation in tropical developing countries and why guidelines for smallholder farming systems are needed. It briefl y highlights the innovations of the SAMPLES approach and explains how these advances fi ll a critical gap in the available quantifi cation guidelines. The chapter concludes by describing how to use the guidelines.

## **1.1 Motivation for These Guidelines**

 Agriculture in tropical developing countries produces about 7–9 % of annual anthropogenic greenhouse gas (GHG) emissions and contributes to additional emissions through land-use change (Smith et al. 2014 ). At the same time, nearly 70 % of the

 T. S. Rosenstock (\*) World Agroforestry Centre (ICRAF) , UN Avenue-Gigiri , PO Box 30677-00100 , Nairobi , Kenya e-mail: t.rosenstock@cgiar.org B. O. Sander International Rice Research Institute (IRRI) , Los Baños , Philippines K. Butterbach-Bahl International Livestock Research Institute (ILRI) , Nairobi , Kenya Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) , Garmisch-Partenkirchen , Germany M. C. Rufi no Center for International Forestry Research (CIFOR) , Nairobi , Kenya J. Hickman Earth Institute, Columbia University , New York , USA C. Stirling International Maize and Wheat Improvement Centre (CIMMYT) , Wales , UK M. Richards • E. Wollenberg Gund Institute for Ecological Economics, University of Vermont , Burlington , Vermont , USA CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS)

© The Editor(s) (if applicable) and the Author(s) 2016 1 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_1

technical mitigation potential in the agricultural sector occurs in these countries (Smith et al. 2008 ). Enabling farmers in tropical developing countries to manage agriculture to reduce GHG emissions intensity (emissions per unit product) is consequently an important option for mitigating future atmospheric GHG concentrations.

 Our current ability to quantify GHG emissions and mitigation from agriculture in tropical developing countries is remarkably limited (Rosenstock et al. 2013 ). Empirical measurement is expensive and therefore limited to small areas. Emissions can be estimated for large areas with a combination of fi eld measurement, modeling and remote sensing, but even simple data about the extent of activities is often not available and models require calibration and validation (Olander et al 2014 ). These guidelines focus on how to produce fi eld measurements as a method for consistent, robust empirical data and to produce better models.

 For all but a few crops and systems, there are no measured data for the emissions of current practices or the practices that would potentially reduce net emissions. For crops, signifi cant information has been gathered for irrigated rice systems e.g., in the Philippines, Thailand, and China (Linquist et al. 2012 ; Siopongo et al. 2014) and for nitrous oxide emissions from China where high levels of fertilizer are applied (Ding et al. 2007 ; Vitousek et al. 2009 ). Yet measurements of methane from livestock—a major source of agricultural GHG emissions in most of the developing world—are lacking (Dickhöfer et al. 2014 ). Similarly, little to no information exists for most other GHG sources and sinks. Smallholder farms comprise a signifi cant proportion of agriculture in the developing world in aggregate, as high as 98 % of the agricultural land area in China, for example, yet tend to escape attention as a source of signifi cant emissions because of the small size of individual farms.

 The dearth of empirical data contributes to why most tropical developing countries, all of which are non-Annex 1 countries of the UNFCCC, report emissions to the UNFCCC using Tier 1 methodologies with default emission factors , rather than more precise Tier 2 or Tier 3 methods and country-specifi c emission factors (Ogle et al. 2014 ). However, Tier 1 default emission factors represent a global average of data derived primarily from research conducted in temperate climates for monocultures, which is very different from the complex agricultural systems and landscapes typical of smallholder farms in the tropics. Given our knowledge of the mechanisms driving emissions and sequestration (e.g., temperature, precipitation, primary productivity, soil types, microbial activity, substrate availability), there is reason to believe that these factors represent only a rough approximation of the true values for emissions (Milne et al. 2013 ).

 Field measurement of GHG emissions in tropical developing countries is generally done using methods developed in temperate developed countries. However, multiple factors complicate measurement of agricultural GHG sources and sinks in non-Annex 1 countries and necessitate approaches specifi c to the conditions common in these countries, including heterogeneity of the landscape, the need for low- cost methods, and the need for improving farmers' livelihood and food security.

*Heterogeneous landscapes* . Annex-1 countries are dominated by industrial agriculture, usually monocultures with commonly defi ned practices, over relatively large expanses. The combination of high research intensity and large-scale agriculture in developed countries creates a homogenous, relatively data-rich environment where point measurements of key sources (e.g., soil emissions from corn production in the Midwestern US or methane production from Danish dairy animals) can be extrapolated with acceptable levels of uncertainty to larger areas using empirical and process-based models (Del Grosso et al. 2008 ; Millar et al. 2010 ) .

 In contrast, many farmers (particularly smallholders) in tropical developing countries operate diversifi ed farms with multiple crops and livestock, with fi eld sizes often less than 2 hectares. For example, in western Kenya maize is often intercropped with beans, trees, or both and in regions with two rainy seasons, maize might be followed in the rotation by sorghum or other crops. Exceptions exist of course, such as in Brazil, where industrial farming is well established and farms can be thousands of hectares. Where heterogeneity does exist, it complicates the design of the sampling approach in terms of identifying the boundary of the measurement effort, stratifying the farm or landscape, and determining the necessary sampling effort. Capturing the heterogeneity of such systems, as well as comparing the effects of mitigation practices or agronomic interventions to improve productivity, often demands an impractical number of samples (Milne et al. 2013 ). Methods are needed to stratify complex landscapes and target measurements to the most important land units in terms of emissions and/or mitigation potential.

*Resource limitations* . People and institutions undertaking GHG measurements have different objectives, tolerances for uncertainty, and resources. Cost of research is one of the major barriers faced by non-Annex 1 countries in moving to Tier 2 or Tier 3 quantifi cation methods. Some methods require sampling equipment, laboratory analytical capacity, and expertise that is not available in many developing countries. Furthermore, different spatial scales (e.g., fi eld, farm, or landscape) require different methods and approaches. The chapters in this volume guide the user in choosing from available methods, taking into account the user's objectives, resources and capacity.

*Improving livelihood and food security as a primary concern* . The importance of improving farmer's livelihoods and capacity to contribute to food security though improved productivity must be taken into account in mitigation decision-making and the research agenda supporting those decisions. Measuring GHG emissions per unit area is a standard practice for accounting purposes, but measuring emissions per unit yield allows tracking of the effi ciency of GHG for the yield produced and informs agronomic practices (Linquist et al. 2012 ). This volume considers productivity in targeting measurements and sampling design, along with recommendations for cost-effective yield measurements.

 Improved data on agricultural GHG emissions and mitigation potentials provides opportunities to decision-makers at all levels. First and foremost, it allows governments and development organizations to identify high production, low-emission development trajectories for the agriculture sector. With the suite of farm- and landscape- level management options for GHG mitigation and improved productivity available for just about any site-specifi c situation, there are numerous options to select from. Country- or region-specifi c data allows more accurate comparison of

 **Fig. 1.1** ( **a** ) Total agricultural GHG emissions (GtCO 2 e yr-1) by country (CH 4 and N 2 O only). Data are average of emission fi gures from FAOSTAT database of GHG emissions from agriculture in 2010, EPA global emission estimates for 2010 and national reports to the United Nations Framework Convention on Climate Change (UNFCCC). If a country had not submitted a report to the UNFCC since the year 2000, we used only FAOSTAT and EPA data. ( **b** ) Percent of national emissions that come from agriculture, not including land-use, land-use change and forestry (LULUCF). Data from national reports to the UNFCCC

these options. Second, the prospects of the emerging green economy and potential for climate fi nance will dictate how emission reductions are both valued and verifi ed. Verifi cation, whether for Nationally Appropriate Mitigation Actions (NAMAs) , Nationally Determined Contributions (NDCs), or product supply chain assessments, will require both reasonable estimates of baseline emissions and accurate quantifi cation of emission reductions. Third, economies of tropical developing countries are largely dominated by agricultural production, and this sector contributes a signifi cant fraction to their national GHG budgets (Fig. 1.1 ). Accurate data strengthen the basis for their negotiating position in global climate discussions.

## **1.2 Who Should Use These Guidelines?**

 These guidelines are intended to inform anyone conducting fi eld measurements of agricultural greenhouse gas sources and sinks, especially to assess mitigation options in smallholder systems in tropical developing countries. The methods provide a standard for consistent, robust data that can be collected at reasonable cost with equipment often available in developing countries. They are also intended to provide end users of GHG data with a standard to evaluate methods used in previous efforts and inform future quantifi cation efforts. The comparative analyses found in these chapters are accompanied by the recommended step-by-step instructions for the methods on the SAMPLES website (www.samples.ccafs.cgiar.org).

Potential users of the guidelines include:


#### **Box 1.1 Make Best Use of Limited Resources by Carefully Selecting Practices for Testing**

 GHG measurement is often undertaken with the purpose of comparing mitigation practices. Too often, those practices are chosen randomly or opportunistically, without explicit consideration of their feasibility or mitigation potential. The results of GHG measurement research will be more useful if practices for testing are identifi ed in a systematic way with input from relevant decision-makers. This can be thought of as a process of "fi ltering" options from a laundry list of potentials to a few for further testing.

*Identify the scope of practices for consideration*

 This can be seen as the "boundary" of potential options. Establishing a spatial boundary is a fi rst step; this may be ecological (a watershed) or political (a county). Additionally, it is useful to further narrow the focus to particular agricultural activities or sectors. The criteria for doing so may include:


#### *Identify potential practices*

 Once the geography and scope of the mitigation effort have been established, develop a list of practices that may be applicable. Ideas may come from interviews and surveys of stakeholder groups as well as published literature. The website accompanying this volume includes resources for this purpose.

#### **Box 1.1 (continued)** *Narrow the list of practices for testing*

 Several criteria should be used to narrow the list of practices to a smaller feasible number for fi eld-testing.


## **1.3 How to Use These Guidelines**

 The ten chapters in this volume are grouped into three categories that correspond with the steps necessary to conduct measurement (1) question defi nition, (2) data acquisition and (3) "option" identifi cation (synthesis) (Fig. 1.2 ). Some readers, such as those looking to evaluate mitigation options for an agricultural NAMA, may want to go through each step. Readers interested in measurement methods for a particular GHG source can go directly to the associated chapter.

 **Fig. 1.2** Steps and their results of the SAMPLES approach. Each step yields inputs for subsequent steps, though components within each step are optional and subject to the interest of the inquiry.

#### *Step 1. Question defi nition*

 Question defi nition defi nes the scope, boundaries and objectives of a measurement program. Measurement campaigns may be undertaken for a number of GHG quantifi cation objectives such as developing emission factors, GHG inventories, or identifying mitigation options. The objective has considerable leverage on how and what is measured. In this volume, *Rufi no et al* . (Chap. 2) describes methods for characterizing heterogeneous farming systems and landscapes, identifying the critical control points in terms of food security and GHG emissions in farming systems and landscapes. This characterization of the system generates fundamental information about the distribution and importance of farming activities in the landscape. Though often overlooked, depending on the preferences and priorities of donors or researchers, systems characterization is critical to target measurements to the most relevant areas in a landscape and stratify the landscape to inform sampling design.

#### *Step 2. Data acquisition*

 Data acquisition is the "nuts and bolts" of quantifi cation. It represents the activities that are conducted to measure and estimate GHG fl uxes or changes in carbon stocks. The six chapters that make up this step discuss methods to quantify stocks, stock changes and fl uxes of the major GHG sources and sinks including land-use and land-cover change ( *Kearney and Smukler* Chap. 3), greenhouse gas emissions from soils ( *Butterbach-Bahl et al* . Chap. 4), methane emissions due to enteric fermentation in ruminants ( *Goopy et al* . Chap. 5), carbon in biomass ( *Kuyah et al* . Chap. 6) and soil carbon stocks ( *Saiz and Albrecht* Chap. 7). Methods to measure land productivity under agriculture—an essential input for tradeoff analysis—are treated separately ( *Sapkota et al* . Chap. 8) (Table 1.1 ).

 Each chapter provides a comparative analysis of existing methods for quantifi cation, particularly evaluating methods across three key features—accuracy, scale, and cost (Table 1.2 ). Authors provide recommendations about how to select the optimal measurement approaches appropriate to the technical and fi nancial constraints often encountered in developing countries, supplemented with discussion of the limitation of various methods. A central theme of the chapters is that GHG quantifi cation is inherently inaccurate. The biogeochemistry of the processes that researchers are measuring coupled with the logistical practicalities of research mean that every measurement is only an estimate of the true fl ux. The researcher must therefore understand how different measurement approaches will affect their estimates and tailor measurement campaigns or quantifi cation efforts to characterize the fl uxes necessary to meet program objectives in a transparent and objective way. The resultant data on GHG fl uxes produced from different sources and sinks can then be aggregated for partial or full GHG budgets using the guidelines from Chaps. 9–10.

*Step 3. Estimation of emissions and analysis of mitigation options*

 The fi nal step is to synthesize the results to identify emissions levels and mitigation options.

 Data acquisition in Step 2 may take place at multiple scales, ranging from point measurements of individual farming activities (such as soil carbon measurements) to pixel analysis at various resolutions of land-use and land-cover change. It is then necessary to extrapolate these point measurements of individual features back to scales of interest (fi elds, farms, or landscapes). *Rosenstock et al* . (Chap. 9) describe the three principal ways that this can be accomplished: empirical, process-based models or a combination of both. *Van Wijk et al* . (Chap. 10) provide guidance on approaches to synthesize all the data to produce esti-


 **Table 1.1** Chapters of this volume and their associated IPCC source and sink categories (IPCC 1996 , 2006 )

mates of tradeoffs or synergies in various farm or landscape management activities-for example, activities that support mitigation as well as adaptation to climate change. Tradeoff analysis, though originating in the 1970s, has been developing rapidly due to increase in computing power and advances in theory and modeling frameworks. However, the authors stress that practical analysis has to include stakeholders to integrate their own perspectives and preferences for the analysis to be practically valuable. By developing estimates of GHG fl uxes at relevant scales and analyzing tradeoffs, the approaches detailed in this volume can inform low-emissions development planning.


 **Table 1.2** Examples of measurements options and their accuracy, cost, and scale implications based on analyses in this volume

**Open Access** This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

## **References**


## **Chapter 2 Targeting Landscapes to Identify Mitigation Options in Smallholder Agriculture**

#### **Mariana C. Rufi no , Clement Atzberger , Germán Baldi , Klaus Butterbach- Bahl , Todd S. Rosenstock , and David Stern**

 **Abstract** This chapter presents a method for targeting landscapes with the objective of assessing mitigation options for smallholder agriculture. It presents alternatives in terms of the degree of detail and complexity of the analysis, to match the requirement of research and development initiatives. We address heterogeneity in land-use decisions that is linked to the agroecological characteristics of the landscape and to the social and economic profi les of the land users. We believe that as projects implement this approach, and more data become available, the method will be refi ned to reduce costs and increase the effi ciency and effectiveness of mitigation in smallholder agriculture. The approach is based on the assumption that landscape classifi cations refl ect differences in land productivity and greenhouse gas (GHG) emissions, and can be used to scale up point or fi eld-level measurements. At local level, the diversity of soils and land management can be meaningfully summarized using a suitable typology. Field types refl ecting small-scale fertility gradients are correlated to land

M. C. Rufi no (\*)

C. Atzberger

University of Natural Resources (BOKU) , Peter Jordan Strasse 82 , Vienna 1190 , Austria

G. Baldi

 Instituto de Matemática Aplicada San Luis, Universidad Nacional de San Luis and Consejo Nacional de Ciencia y Tecnología (CONICET) , Ejército de los Andes 950, D5700HHW , San Luis , Argentina

 K. Butterbach-Bahl International Livestock Research Institute (ILRI) , PO Box 30709 Nairobi , Kenya

 Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) , Kreuzeckbahnstr. 19 , Garmisch-Partenkirchen , Germany

#### T. S. Rosenstock World Agroforestry Centre (ICRAF) , PO Box 30677 , Nairobi , Kenya

 D. Stern Maseno University , PO Box 333 , Maseno , Kenya

© The Editor(s) (if applicable) and the Author(s) 2016 15 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_2

Centre for International Forestry Research Institute (CIFOR) , PO Box 30677 Nairobi , Kenya e-mail: m.rufi no@cgiar.org

quality, land productivity and quite likely to GHG emissions. A typology can be a useful tool to connect farmers' fi elds to landscape units because it represents the inherent quality of the land and human-induced changes, and connects the landscape to the existing socioeconomic profi les of smallholders. The method is explained using a smallholder system from western Kenya as an example.

## **2.1 Introduction**

 Little is known about the environmental impact of smallholder agriculture, especially its climate implications . The lack of data limits the capacity to plan for low- carbon development, the opportunities for smallholders to capitalize on carbon markets, and the ability of low-income countries to contribute to global climate negotiations. Most importantly for smallholders, available information has not been linked to the effects on their livelihoods. Many research initiatives aim to close this information gap and will eventually lead to the adoption of mitigation practices in smallholder agriculture. Technically feasible mitigation practices do not necessarily represent plausible options, which are desirable for farmers. A key goal of mitigation in smallholder agriculture is the long-term benefi t to the farmers themselves, achieved either through improved practices or subsidized as part of a global emissions reduction market. This chapter focuses on targeting the measurement of greenhouse gas (GHG) emissions in smallholder systems, as it is expected that this will also correspond to the potential for social impact of mitigation. Here targeting means the process of selecting units of a landscape where scientists or project developers will estimate a number of parameters to assess mitigation potential of land-use practices. Systematic selection of measurement locations ensures that measurements can be scaled up to give meaningful information for implementing mitigation measures.

 Analysis of smallholder agriculture is a challenge because farming takes place in fragmented and diverse landscapes. Various actors may wish to target mitigation actions in this environment, including national and subnational governments who want to meet mitigation goals; project implementers at all levels; communities that wish to access carbon fi nancing; and the research community that wants to contribute meaningfully to climate change mitigation. Although the spatial resolution and coverage of the assessment differ across actors, all face two basic questions related to emissions: how much mitigation can be achieved and where.

 The scientifi c community conducts biophysical research to estimate the potential of soils to sequester carbon, and to estimate emissions of non-CO 2 gases from agriculture, forestry, and other land uses (AFOLU). If estimates of emission reductions are not available, the success of mitigation actions will be unknown. This is mostly the case in projects proposed in low-income countries where information on emissions and carbon sequestration potential is nonexistent or patchy. Most commonly where interventions are proposed, landscapes are considered uniform and equally effective for the mitigation actions promoted.

 Before implementing mitigation projects, all actors should examine the mitigation objectives and use a structured targeting top-down, bottom-up, or mixed- method approach. The scientifi c community should use the same principles to increase the effectiveness of mitigation research, allow for comparability, and fi ll knowledge gaps at critical stages. The targeting of mitigation research projects and the implementation of mitigation actions are typically framed in terms of mitigation potential. Such assessments are carried out at relatively large scale and provide a range of achievable objectives, but do not connect directly with land users' realities. This is often done at an academic level without on-the-ground consultations and ignoring socioeconomic barriers.

 We propose a targeting method using varied sources to support the analysis including geographical information systems (GIS), remote sensing (RS), socioeconomic profi les, and biophysical drivers of GHG emissions. In summary, we introduce a cost-effective method for selecting representative fi elds and landscape units as a basis for estimating GHG emissions, soil carbon stocks, land productivity and economic benefi ts from cultivated soils and natural areas. The objective of this chapter is to guide scientists and practitioners in their decisions to estimate GHG emissions, and to identify mitigation options for smallholders at whole-farm and landscape levels. This is a new area of research that links mitigation science with development, landscape ecology, remote sensing, and economic and social sciences to understand the consequences of land-use decisions on the environment.

The proposed approach is based on the assumptions that:


 To test the method, we used a smallholder system from Western Kenya as an example.

## **2.2 Initial Steps**

 The targeting approach stratifi es landscapes of different complexity into different classes, to identify units that provide estimates of emission reductions representing larger areas. Figure 2.1 shows how a complex landscape can be split—using a

 **Fig. 2.1** Conceptual model of a nested targeting approach. The model indicates ( *dashed boxes* ) the sort of analyses conducted at each level

top- down approach—into smaller units ( *i landscape units* ) that have a common biophysical environment at regional scale. This disaggregation can be done using GIS and RS, assisted by existing secondary data. Landscape units can be further disaggregated into *j farm types* and *k common lands* to describe differences in the ways that individual households and communities access and use the land. The sort of units that link the land-to-land users will vary according to tenure systems in different territories, jurisdictions, and countries (Ostrom and Nagendra 2006 ). This step uses information on incomes, land tenure, and food security. It enables mitigation practices to be designed that are appropriate for heterogeneous rural communities, and where the land can be privately and communally managed. To make a connection with farming activities and ultimately with the level at which mitigation practices are implemented, farms and common lands can be disaggregated into *l fi eld types* and *m land types* . This distinction may fade out in countries where the land is intensively used independently of the tenure system. The identifi ed units can be studied in terms of land productivity, economic outputs, carbon stocks, GHG emissions, and the social and cultural importance of farming activities for rural families.

## **2.3 Top-Down Approach**

 We illustrate the steps to split a complex landscape (of any size) into homogeneous units using GIS and RS information and socioeconomic surveys to study mitigation potential (Fig. 2.1 ). This may be of interest, for example, where a carbon credit project is implemented, or if a district, province, or other authority wishes to assess the mitigation potential of a number of agricultural technologies. Once the landscape boundaries are defi ned, one can disaggregate the complex landscape into different units. If the landscape boundaries are not delineated, the analyst may choose to select an area that is representative of the larger region in order to extrapolate results. The landscape can be analyzed initially using a combination of RS and GIS. We suggest different approaches to disaggregate a landscape and decide where to conduct fi eld measurements.

 After selecting a landscape for assessment and developing a conceptual model of land-use and land-cover (LULC), the simplest method to identify landscape units is the exploration and visual interpretation of satellite imagery, preferably with the best available spatial resolution and observation conditions (e.g., peak of vegetation productivity). LULC classifi cation (using object-based approaches and VHR imagery) and landscape classifi cation (using RS vegetation productivity parameters) are more sophisticated methods of approaching a landscape. With visual interpretation, numerous landscape features can be characterized using physical (e.g., geomorphology, vegetation, disturbance signs) and human criteria (e.g., presence of population, land-use, and infrastructure). This yields relatively large, homogeneous landscape units (e.g., describing the mosaic of LULCs in an area). By comparison, automated LULC classifi cation yields results at a much fi ner spatial scale. In most cases it maps the individual fi elds that make up a landscape. The process of automated LULC mapping involves:


 The two fi rst steps require the composition of the landscape to be characterized (i.e., the areas under each of the fi eld or land types according to Fig. 2.1 ), and their spatial confi guration (i.e., the arrangement of fi eld or land types).

 In landscapes with dominant smallholder agriculture, cultivated land can be easily recognized through the presence of regular plots with homogeneous surface brightness, and minor features such as ploughing or crop lines and infrastructure. In addition, the structural heterogeneity of cultivated areas can be assessed by the geometry of the fi elds (size and symmetry of the shapes), the presence of productive infrastructure and signs of disruption, such as woody encroachment within fi elds. Land under (semi-) natural vegetation can be characterized in terms of vegetation composition (share of trees, shrubs, and grass), signs of biomass removal or the presence of barren areas, and degradation (gullies, surface salt accumulation). Finally, in order to delimit landscape units, all descriptions should be integrated in a holistic manner using, for example, Gestalt-theory (Antrop and Van Eetvelde 2000 ) to identify and digitize potential discontinuities. This simple method has the potential to enhance the quality of broadscale land-use studies, and can be performed using freely available imagery, like Google Earth, supported by online photographic archives such as "Panoramio" or "Confl uence Project " (Ploton et al. 2012 ).

## *2.3.1 Landscape Stratifi cation: An Example from East Africa*

 The Lower Nyando region of Western Kenya, which is dominated by smallholder producers, provides an example of the proposed approach. The CGIAR Program for Climate Change, Agriculture, and Food Security (CCAFS) promotes climate smart agriculture in this area. To develop and test our targeting approach, we used the three methods described above: (1) visual classifi cation using VHR imagery, (2) LULC classifi cation using object-based approaches and VHR imagery, and (3) landscape classifi cation using medium to coarse resolution RS vegetation productivity parameters.

#### **Visual Classifi cation Using VHR Imagery**

 This is a quick and relatively inexpensive visual approach for exploring landscapes. The largest costs are the acquisition of the VHR images. Based on a QuickBird ® image from the dry season (1 December 2008), six landscape classes were identifi ed (Table 2.1 and Fig. 2.2 ). This initial classifi cation can be used to test whether the units are indeed related to soil emissions and mitigation potential. The landscape classifi cation is expected to refl ect differences in land productivity and GHG emissions, because it captures inherent soil and vegetation variability.

 Class delimitation criteria and mitigation opportunities are listed for each class in Table 2.1 . The limits between the classes are determined by spatial changes in the detailed criteria. As expected, these changes can be abrupt or gradual, and the ability or experience of the mapper could lead to variable results.

 The visual delineation may or may not coincide with regional biophysical gradients, as shown by a quick assessment of the topography of Nyando (Fig. 2.3 ). In our case study, the highlands coincided with areas allocated to cash crops, while the lowlands included a continuum from subsistence crops to wooded natural land types. Delineating a landscape on the sole basis of topography may be inaccurate and/or incomplete, yet the use of a digital elevation model (DEM) is an inexpensive option to simplify landscapes.

#### **Land-Use and Land-Cover Classifi cation Using Object-Based Approaches and VHR Imagery**

 The fi ne-scale analysis of actual LULC allows the interface between biophysical and human-induced processes to be captured. The automated methods are more complex than the visual interpretation described previously and require digital processing of remote sensing imagery. VHR satellite imagery with pixel resolution <1 m can be used for semiautomatic (supervised) mapping of LULC in heterogeneous and fi nestructured landscapes with sparse vegetation cover. To make optimal use of the rich information provided by the VHR data, object-based approaches are recommended.


 **Table 2.1** List of visual classes determined for the Nyando study region, Kenya

Compared to pixel-based approaches, object-based approaches permit the full exploitation of the rich textural information present in VHR imagery, as well as shape-related information. They also avoid "salt and pepper" effects when classifying individual pixels. Figure 2.4 summarizes the main steps of such an approach.

 In a similar way to Fig. 2.2 , the landscape is fi rst segmented into small, homogeneous subunits or objects. This process is indicated in Fig. 2.4 as *image segmentation* . Input to this image segmentation is georectifi ed, multilayered very high-resolution (VHR) satellite images. The resulting objects (also called "segments") are groups of adjacent pixels, which share similar spectral properties, and which are different from other pixels belonging to other objects.

 To segment a landscape using VHR satellite images, the so-called segmentation algorithms are used. Contrary to the visual classifi cation approach, objects/segments are

 **Fig. 2.2** Landscape analysis based on a visual inspection of landscape structure of Nyando, Western Kenya. ( **a** – **f** ) Are samples of the territory represented by the original QuickBird ® image (all have the same spatial extent of 500 m). The larger panel on the *right* represents the six meaningful classes of landscape from the visual classifi cation approach. Letters (A, B, C, D, E, and F) show the location of samples in the area (see explanations in Table 2.1 )

 **Fig. 2.3** Topographic characteristics of Nyando region. Altitude (masl) and slope (expressed as percentage) came from the Shuttle Radar Topography Mission (SRTM) digital elevation model (USGS 2004 ). The lines delineating the landscape units of Nyando are the same as in Fig. 2.2

 **Fig. 2.4** Flowchart for object-based supervised classifi cation of VHR imagery. The process yields a detailed LULC map of the area covered by the VHR satellite imagery, as well as information on the uncertainty of the classifi cation outcome for each image object

identifi ed in a fully automated manner. Both commercial and open source solutions exist for this task. Excellent open source solutions are, for example, QGIS (www.qgis. org/), GRASS GIS (grass.osgeo.org/) and ILWIS (www.ilwis.org/).

 **Fig. 2.5** Visualization of important steps of the supervised classifi cation of the Nyando study region. ( **a** ) RGB image of WorldView-2 ® VHR imagery with manually delineated strata, ( **b** ) DEM of the region with strata

 **Fig. 2.6** ( **a** ) In situ information about the land-use/land-cover of training samples for one of the ten strata; the segmented image objects are also visible in *gray* , ( **b** ) classifi cation result based on spectral and textural features of the WorldView-2 ® VHR image for the same stratum

 After segmenting the image into image objects, an arbitrary number of features are extracted for each object. In Fig. 2.4 , this process is labelled as *feature extraction* . Besides spectral features, textural features, as well as shape information, can be extracted. This information is used in a subsequent step to automatically assign each object to one of the user-defi ned LULC classes (process labelled as *Random* ( *RF* ) *forest classifi er* ). To "learn" the relationship between input features and class labels, training samples with known LULC must be provided in suffi cient numbers and quality using a process called *training data extraction* .

 Because the relation between input features and class label may change depending on image location (e.g., related to terrain and elevation), a stratifi ed classifi cation is recommended. For this task, before starting the classifi cation process, the entire scene is (visually) split into a few (larger) regions (or strata) that can be considered homogeneous in terms of land-cover characteristics and the physical setting of the landscape.

 The stratifi cation is usually done just after the automated image segmentation (Fig. 2.4 ). Of course, results from other studies can be used as well (e.g., boundaries shown in Fig. 2.2 ). Figure 2.5a shows the RGB composite of a WorldView-2 image of the Nyando study area, and Fig. 2.5b , the corresponding DEM . In both maps, manually drawn landscape boundaries (strata) are also shown (yellow lines).

 For one of the strata, Fig. 2.6a shows the available reference information obtained from fi eldwork and complemented through visual image interpretation. These training samples are necessary for the RF classifi er to "learn" the relationship between input features and class labels. The resulting object-based classifi cation is shown for this landscape unit in Fig. 2.6b . The object limits (e.g., gray lines in Fig. 2.6a ) have been automatically derived using GRASS GIS.

 For the classifi cation, several algorithms are available (e.g., maximum likelihood classifi er, CART, kNN, etc.). Based on the authors' own and published experience, we exploited a widely used ensemble classifi er called "random forest" (RF) which often yields good and robust classifi cation results (Gislason et al. 2006 ; Rodriguez-Galiano et al. 2012 ; Toscani et al. 2013 ). RF uses bootstrap aggregation to create different training subsets, to produce a diversity of classifi cation trees, each providing a unique classifi cation result. For example, if 500 decision trees are grown inside the RF, one will obtain 500 class labels for each object. The fi nal output class is obtained as the majority vote of the 500 individual labels (Breiman 2001 ). The proportion of votes of the winning class to the total number of trees used in the classifi cation is a good measure of confi dence; the higher the score, the more confi dent one can be that a class is correctly classifi ed. Similarly, the margin calculated as the proportion of votes for the winning class minus the proportion of votes of the second class indicates how sure the classifi er was in their decision. Such confi dence indicators are not readily obtained using visual image interpretation. RF also produces an internal unbiased estimate of the generalization error, using the so-called "out-of-bag" ( OOB ) samples to provide a measure of the input features' importance through random permutation. Classifi cation performance of the entire LULC map can be based on common statistical measures (overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA)) (Foody 2002 ) derived from the classifi cation error matrix, using suitable validation samples. Figure 2.7 shows the resulting LULC map of Nyando obtained with this object-based classifi cation approach and using VHR imagery from WorldView-2 ® .

#### **Landscape Classifi cation Using RS Vegetation Productivity Parameters**

 The two previous approaches are based on static descriptions of the landscape units (or of their constituent elements) in terms of LULC. However, alternative land traits can be explored to determine homogeneous landscape units. A promising alternative is the analysis of vegetation function in terms of the magnitude and temporal

 **Fig. 2.7** LULC map of Nyando from WorldView-2® VHR imagery, using an object-based classifi cation approach

variability of primary productivity (Paruelo et al. 2001 ). We tested this functional analysis in Lower Nyando, using the period 2000–2012. Vegetation primary productivity was assessed through the proxy variable Normalized Difference Vegetation Index (NDVI) . This index has been of great value for biogeographical studies, allowing rough but widespread characterizations of the magnitude and temporal variability of productivity based on homogeneous measurements across wide spatial and temporal extensions and different ecosystems (Lloyd 1990 ; Xiao et al. 2004 ; Sims et al. 2006 ). In this example, we acquired NDVI data from the MODIS (Moderate Resolution Imaging Spectroradiometer) Terra instrument. 1 In this dataset, one image is produced every 16 days, leading to 23 images per year.

 We selected from the 13-years × 23-dates database, only those values indicating good to excellent quality conditions (i.e., pixels not covered by clouds, and with a low to intermediate aerosol contamination). Then, we used the code TIMESAT v.3.1 to reconstruct temporal series (Jönsson and Eklundh 2002 , 2004 ; Eklundh and Jönsson 2011 ). This tool fi ts smoothed model functions that capture one or two cycles of growth and decline per year. We selected an adaptive Savitzky-Golay

<sup>1</sup> Product coded as the MOD13Q1; spatial and temporal resolutions of 250 m and 16 days, respectively from the ORNL "MODIS Global Subsets: Data Subsetting and Visualization" online tool ( http://daac.ornl.gov ).

 **Fig. 2.8** Vegetation functioning depicting an average annual magnitude and seasonality, and interannual variability of primary productivity. ( **a** ) Maximum NDVI, ( **b** ) Intra-annual NDVI CV, ( **c** ) Interannual mean NDVI CV, ( **d** ) slope of the maximum NDVI versus time relationship. *Lines* represent homogeneous landscape units from the visual interpretation of Fig. 2.2

model (Jönsson and Eklundh 2002 ), assuming two vegetation growth cycles per year due to the natural bimodal behavior of rains in the study region. From the reconstructed temporal series (and by means of TIMESAT and the R v.2.15 statistical software), we calculated different functional metrics depicting average annual magnitude (e.g., mean, maximum NDVI) and seasonality (e.g., coeffi cient of variation (CV) of available values, number of growing seasons), and interannual variability (e.g., CV of mean annual values, annual trends) (Baldi et al. 2014 ).

 For the sake of simplicity in the Lower Nyando example, Figure 2.8 presents: (a) NDVI maximum values as a proxy for carbon stocks of cultivated and uncultivated ecosystems; (b) intra-annual CV, describing whether the productivity is concentrated in a short period or distributed evenly through the year; (c) interannual CV of mean annual values, describing long-term productivity fl uctuations; and (d) the slope of the maximum annual NDVI versus time relationship (Paruelo and Lauenroth 1998 ; Jobbágy et al. 2002 ).

 Figure 2.9 shows the entire temporal range for the case of maximum annual values. Combined, structural and functional assessments provide essential information about the quality of the detected fi eld or land types to study GHG mitigation potentials. Likewise, this approach may reveal functional divergences between a single

 **Fig. 2.9** Annual maximum NDVI value for the 2000–2012 period. *Lines* represent homogeneous landscape units from the visual interpretation of Fig. 2.2

 **Fig. 2.10** ( **a** ) Nyando's fi ve classes, based on unsupervised classifi cation from four variables ( **b–e** ) exemplifying functional traits different from those presented in Figs. 2.7 and 2.8 . Bars show averages and standard deviation for each class (depicted by the number and color). *Lines* in ( **a** ) represent homogeneous landscape units from the visual interpretation of Fig. 2.2

fi eld or land type or convergences between different classes as shown in Figs. 2.8 and 2.9 , with strong impacts on cascading ecosystem processes.

 To identify landscape units using only functional information, we integrated functional attributes by applying an unsupervised classifi cation procedure. In contrast with a LULC classifi cation, we do not expect a priori conceptual scheme, both in terms of the number of classes and their identity. Functional classes often have to be split or merged to create a meaningful map, i.e., to show patterns of patches and corridors rather than isolated pixels ("salt and pepper" appearance). Using the unsupervised clustering algorithm ISODATA (Jensen 1996 ), we generated a map delimitating fi ve different classes which reached our pattern-based expectations (Fig. 2.10 ). This approach revealed functional divergences between single farm types or common lands (e.g., western versus eastern cultivated areas dominated by cash crops), and convergences between different classes (e.g., western mixed shrubs and cultivated land versus eastern cultivated areas dominated by cash crops), with potential impacts on cascading ecosystem processes.

 In addition to the landscape analysis, other on-the-ground information is needed for the development of a representative sampling design for smallholder systems before resource-consuming measurements of soil GHG fl uxes or soil carbon and nitrogen stock inventories are implemented. The characterization of farmers' socioeconomic condition is important here, because this also affects resource management. On-farm variations in soil properties, which result from long-term differences in fi eld management, create soil fertility gradients that may justify the use of a fi eld typology.

## **2.4 Bottom-Up Approach**

 For some specifi c landscapes or agricultural systems there may be a wealth of fi eld data that characterize the use of the land at fi eld and farm level. This could include household surveys, soil surveys, productivity and economic assessments. This information comes at the price of laborious and costly data collection, and we encourage scientists and project developers to take advantage of existing fi eld and farm data to inform the targeting of mitigation options at the local level. The analysis of these data informs the selection of fi eld and farm types indicated in Fig. 2.1 , which are the ultimate entry point for deciding where to carry out GHG measurements and identifying mitigation practices. This fi eld-level characterization is especially useful in very fragmented landscapes, where topography, soils and long-term management create strong gradients in soil fertility and water retention capacity, which may lead to differences in emissions potential (Yao et al. 2010 ; Wu et al. 2010 ). We acknowledge that such detailed characterization may not be needed in simple landscapes with few land uses and relatively fl at relief. Expert opinion by soil scientists can help decision-making about the location of fi eld-level assessments.

 We present a method that can be used to link the fi elds and farming practices with the landscape level, and emissions due to agricultural practices with potential for emissions reductions at scale. The method is based on assumptions 2 and 3 presented in Sect. 2.1 : i.e., that the diversity of soils and land management can be meaningfully summarized using a fi eld typology , which connects farmers' fi elds to landscape units representing inherent land quality and human-induced changes . There is evidence that fi eld types can be defi ned on the basis of simple indicators that are correlated to land quality and land productivity. Research in Western Kenya and Zimbabwe shows the relationship between soil quality, intensity of management, and land productivity (Tittonell et al. 2005 , 2010 ; Zingore et al. 2007 ), which we believe are correlated to soil GHG emissions.

 A fi eld typology can be derived a priori using information collected in household surveys. This can help connect fi eld management with farm types, defi ned by livelihood indicators, including food and tenure security. Including these dimensions in the analysis provides an opportunity to link mitigation with food security and poverty to estimate trade-offs and synergies. Such an analysis permits an assessment of the feasibility of mitigation for different farmers and identifi cation of the incentives needed for adoption. Land users can assess and weigh up the livelihood benefi ts of different practices (e.g., income, increased production of food) and the costs of implementing such practices.

 Using the Lower Nyando site , we show how to use household and fi eld surveys to support targeting at a local level and how to link it to the selected landscape. We collected existing information on households and farm management. The lower Nyando site was characterized using the IMPACTlite tool (Rufi no et al. 2012a , b ) that gathered generic data to analyze food security, adaptation, and mitigation in smallholder agriculture. A comprehensive household survey was conducted to characterize household structure, asset ownership, farm production, costs and benefi ts of farming activities, other sources of income generation, and food consumption (Rufi no et al. 2012a , b ). Using the farm household characterization, and to elaborate the fi eld typology, fi elds recorded in the household survey were measured, georeferenced and additional management data were collected. The household survey covered three production systems across the sampling frame of the Kenyan CCAFS site of Nyando (Förch et al. 2013 ), and included 200 households. A fi eld typology was built on the basis of field type scores collected through a survey (see forms in Appendix ). A subsample of fi elds was selected randomly to represent the fi eld types.

## *2.4.1 Field Typology Defi nition*

 The fi eld typology must refl ect inherent soil fertility resulting from soil type and long-term management. The process of defi ning the fi eld typology is dependent on the landscape within which the project works and the sociocultural norms of the farmers. For example, crop diversity may be considered as a sign of productive land in subsistence agriculture systems. Adjusting the weighting to take into account local knowledge is important to link well with ground truths.

 The scores obtained through this process are simply a tool for subdividing fi elds based on easily obtainable data, analogous to a rapid rural appraisal (Dorward et al. 2007 ). It is often justifi able to adjust the weightings based on the data, by identifying the common characteristics of the fi eld types and checking that the subdivisions are

indeed meaningful. Whenever possible the classifi cation should be counter checked against the common sense evaluation of an experienced fi eld offi cer on the ground.

At the Nyando site, we used a number of variables to defi ne a fi eld type score:


 Plots with a score higher than 10 were labelled as fi eld type 1. Those with scores between 4 and 10 were labelled as fi eld type 2, and those with scores lower than 4 were labelled as fi eld type 3. The process of defi ning scores for each variable involved making judgments about correlations and data quality. The end scores were then investigated, defi nitions adjusted and natural cut-off points identifi ed. The identifi cation of natural cut-offs for the fi eld types is a delicate process because the scoring tool is crude enough that one would not expect a substantial difference on the ground between borderline cases. A useful guideline is that borderline cases should not be either under- or overrepresented in any fi eld type .

## **2.5 Combining Top-Down and Bottom-Up: The Basis for Scaling Up**

 The fi eld typology sampled across households represents the diversity of land management practices . If it is combined with a land-use classifi cation, it connects local management with landscape characteristics as indicated in Fig. 2.11 . Provided that land-use units or land classes have been sampled at fi eld-level, or that spatially explicit information is available on the diversity of fi eld types, connecting these two layers may provide a measure of variability on GHG emissions, productivity, and livelihood

 **Fig. 2.11** Conceptual model and products of the nested targeting approach. The model indicates the sort of outputs obtained at each level. The integration of all level measurements conducted at fi eld-level is to be scaled up

indicators. To achieve this, enough fi eld sites have to be selected to represent each landscape class, and must be monitored for GHG emissions, carbon stock changes, production of biomass, and other variables of interest. The number of replicates or fi eld sites to represent a landscape class will depend on within-class heterogeneity, and the resources available for monitoring emissions. An absolute minimum of three replicates per land class is required to estimate biophysical parameters.

 The advantage of selecting replicated fi eld sites that correspond to landscape classes is the possibility to scale up (i.e., to estimate project-level benefi ts and tradeoffs with livelihood indicators). It also provides an opportunity to extrapolate fi ndings to similar environments. In the case of lower Nyando, we combined the fi eld typology derived from a household characterization with the landscape description including fi ve classes or units shown in Fig. 2.10 . " Landscape plots " were selected to represent fi eld types using landscape units where we monitored GHG emissions, analyzed carbon stocks, and estimated productivity and the economics of production. We present here the results of 12 months of monitoring GHG emissions aggregated at fi eld and landscape level (Fig. 2.12 ). The information provided a comprehensive database to estimate emissions potential and trade-offs with other socioeconomic indicators, such as income and land productivity. Additional fi eld sites were added to compensate for areas poorly represented by the household survey and to include natural areas. This can be a serious disadvantage of using secondary data in a bottom-up approach, where householders neglect natural areas such as woodlands or wetlands during interviews. Natural areas were selected from the landscape analysis, where natural vegetation units were mapped.

 **Fig. 2.12** Cumulative annual emissions of CO 2 (Mg C-CO 2 m −2 year −1 ), CH 4 (kg C-CH 4 m −2 year −1 ), and N 2 O (kg N-N 2 O m −2 year −1 ) from 60 different fi elds located in Lower Nyando in Western Kenya split by land class, fi eld type, crop type, and landscape position (Pelster et al. 2015 ).

## **2.6 Conclusions**

 A methodology is presented to target mitigation research at fi eld, farm-, and landscape level. It uses both a top-down and a bottom-up approach to capture local diversity in soils and management practices, and landscape heterogeneity. It enables generic recommendations to be made about scaling up alternative mitigation options. The methods can fi t the purposes of diverse projects, including the targeting of GHG measurement or the testing of carbon sequestration practices. The products generated such as land-use or land class maps and selected fi eld types allow fi eld sites to be selected for monitoring biophysical parameters. Once monitoring of GHG emissions, productivity, and economics are fi nalized, the nested approach suggested here provides a basis for scaling up, which can be achieved using different analytical methods discussed in Chap. 10 of this volume.

## **2.7 Appendix**


Surveyor:


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## **References**


## **Chapter 3 Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change**

#### **Sean P. Kearney and Sean M. Smukler**

 **Abstract** This chapter reviews methods and considerations for quantifying greenhouse gas (GHG) emissions and removals associated with changes in land-use and land-cover (LULC) in the context of smallholder agriculture. LULC change contributes a sizeable portion of global anthropogenic GHG emissions, accounting for 12.5 % of carbon emissions from 1990 to 2010 (Biogeosciences 9:5125–5142, 2012). Yet quantifying emissions from LULC change remains one of the most uncertain components in carbon budgeting, particularly in landscapes dominated by smallholder agriculture (Mitig Adapt Strateg Glob Chang 12:1001–1026, 2007; Biogeosciences 9:5125–5142, 2012; Glob Chang Biol 18:2089–2101, 2012). Current LULC monitoring methodologies are not well-suited for the size of land holdings and the rapid transformations from one land-use to another typically found in smallholder landscapes. In this chapter we propose a suite of methods for estimating the net changes in GHG emissions that specifi cally address the conditions of smallholder agriculture. We present methods encompassing a range of resource requirements and accuracy, and the trade- offs between cost and accuracy are specifi cally discussed. The chapter begins with an introduction to existing protocols, standards, and international reporting guidelines and how they relate to quantifying, analyzing, and reporting GHG emissions and removals from LULC change. We introduce general considerations and methodologies specifi c to smallholder agricultural landscapes for generating activity data, linking it with GHG emission factors and assessing uncertainty. We then provide methodological options, additional considerations, and minimum datasets required to meet the varying levels of reporting accuracy, ranging from low-cost high-uncertainty to high-cost low-uncertainty approaches. Technical step-by-step details for suggested approaches can be found in the associated website.

S. P. Kearney • S. M. Smukler (\*)

University of British Colombia , Vancouver , BC , Canada e-mail: sean.smukler@ubc.ca

<sup>©</sup> The Editor(s) (if applicable) and the Author(s) 2016 37

T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_3

## **3.1 Introduction**

 Land-use and land-cover (LULC) change contributes a sizeable portion of global anthropogenic GHG emissions, accounting for an estimated 12.5 % of carbon emissions from 1990 to 2010 (Houghton et al. 2012 ). Signifi cant demographic and socioeconomic pressures are exerted on carbon storing land uses such as forests in the tropics yet distribution and rates of change (e.g., loss of forests and agricultural intensifi cation) in tropical smallholder landscapes remain very uncertain (Achard et al. 2002 ). Much of this uncertainty stems from the substantial heterogeneity of LULC that exists, often at very fi ne spatial scales, in such landscapes. Even within LULC categories, signifi cant heterogeneity in carbon stocks often occurs as a result of drivers specifi c to smallholder agriculture, such as fallow rotations, uneven canopy age distribution, and integrated crop–livestock systems (Maniatis and Mollicone 2010 ; Verburg et al. 2009 ). These factors result in the need for monitoring strategies different from those developed for more commonly monitored LULC transitions such as large-scale deforestation and urban expansion (Ellis 2004 ). Here we present general considerations and a suite of methods for estimating net changes in GHG emissions that specifi cally address the conditions of smallholder agriculture. In the process we illustrate the relative trade-offs between costs of analysis, precision, and accuracy.

 There are four basic steps required to calculate GHG emissions/ removals from LULC change:


 It is important to note that these steps are not necessarily chronological. For example a baseline scenario could be developed prior to LULC change detection. Accuracy assessments should be done concurrently with each phase of data collection and analysis.

 In order to carry out the above steps, two basic types of data are required, defi ned by the Intergovernmental Panel on Climate Change ( IPCC ) as activity data and emission factors (IPCC 2006 ). Activity data refer to the areal extent of chosen LULC categories, subcategories, and strata and are generally presented in hectares. Emission factors refer to the data used to calculate carbon stocks associated with activity data and are usually presented as metric tons of carbon (or carbon dioxide equivalents) per hectare. Emission factors may not be required for all carbon pools when carbon

stock densities are inventoried directly using fi eld sampling and/or remote sensing techniques. The IPCC Guidelines ( 2006 ) also lay out three tiers of methods used to calculate GHG emissions and reductions, which increase not only in precision and accuracy but also in data requirements and complexity of analysis. Tier 1 requires country-specifi c activity data but uses IPCC default emission factors that can be found in the IPCC Emission Factor Database (IPCC n.d.) and analysis is generally simple and of low cost. Tier 2 uses similar methods to Tier 1 but requires the use of some region- or country-specifi c emission factors or carbon stock data for key carbon pools and LULC categories (more information on key pools can be found in Sect. 3.4.1 ). Tier 3 requires high-resolution activity data combined with highly disaggregated inventory data for carbon stocks collected at the national or local level and repeated over time.

 Collection of data to generate emission factors or calculate carbon stock densities is covered elsewhere in this book. The focus of this chapter is on the generation of activity data and the various methods available to link emission factors and/or carbon stock densities with activity data for estimating total carbon stocks and GHG emissions/removals at the landscape-scale. The following sections provide an overview of the general activities for each of the four steps required to calculate GHG emissions/reductions from LULC change, with a focus on smallholder agriculture landscapes. Trade-offs between uncertainty and cost are addressed and a variety of references—including existing protocols, scientifi c research, and review papers—are cited. Summary tables are presented at the beginning of each section, with a complete table at the end of the chapter (Table 3.8 ).

## **3.2 Determining Change in LULC**

 The IPCC Guidelines ( 2006 ) outline three specifi c Approaches to monitoring activity data (described in detail below). The three Approaches refer to the representation of land area and will infl uence the ability to meet the three IPCC Tiers, which indicate the overall uncertainty of GHG emission/reduction estimates (Table 3.1 ). In general, progressing from Approach 1 to 3 increases the amount of information associated with activity data but requires greater resources. It should be noted that increasing the information contained within activity data does not guarantee a reduction in uncertainty. Accuracy will ultimately depend on the quality of data and implementation of the Approach as much as the Approach itself (IPCC 2006 ). However, progressing from Approach 1 to 3 provides the opportunity for reducing uncertainty and meeting higher Tier requirements.

*Approach 1* uses data on total land-use area for each LULC class and stratum but *without* data on conversions between land uses. The result of Approach 1 is usually a table of land-use areas at specifi c points in time and data often come from aggregated household surveys or census data. Results are not spatially explicit, only allow for the calculation of net area changes and do not allow for analysis of GHG emissions/removals for land remaining within a LULC category or the exploration of


 **Table 3.1** Summary of activities to determine change in LULC at various uncertainty levels

drivers of LULC change. Therefore Approach 1 may not be suitable for carbon crediting under mechanisms such as the Verifi ed Carbon Standard (VCS) or Reducing Emissions from Deforestation and Forest Degradation (REDD+) (see GOFC-GOLD 2014 ).

*Approach 2* builds on Approach 1 by including information on conversions from one LULC class to another, but the data remain spatially non-explicit. This provides the ability to assess changes both into and out of a given LULC class and track conversions between LULC classes. A key benefi t of Approach 2 is that emission factors can be modifi ed (if data are available) to refl ect specifi c conversions from one LULC category to another. For example, forests with a long history of prior cultivation may store less carbon than undisturbed forests of the same age (e.g., Eaton and Lawrence 2009 ; Houghton et al. 2012 ). Such factors cannot be taken into account using Approach 1. The results of Approach 2 can be expressed as a land-use conversion matrix of the areal extent of initial and fi nal LULC categories.

*Approach 3* uses datasets that are spatially explicit and compiled through sampling and wall-to-wall mapping techniques. Remotely sensed data (e.g., imagery from aerial- or satellite-based sensors) are often used in combination with georeferenced sampling such as fi eld or household surveys. Data are then analyzed using geographic information systems (GIS) and can be easily combined with other spatially explicit datasets to stratify LULC categories and emission factors. This can greatly improve the accuracy of emission/removal estimates, especially for large areas, and allows for statistical quantifi cation of uncertainty. Approach 3 can be an effi cient way to monitor large areas. However it may require greater human and fi nancial resources, which could be cost-prohibitive for smaller projects, especially if the spatial resolution of freely available or low-cost imagery is too coarse to detect LULC changes. (See Sect. 3.2.2 for more information about remotely sensed data.)

## *3.2.1 Setting Project Boundaries*

 The extent, location, and objectives of monitoring will all infl uence the appropriate choice of methods for analyzing LULC change and associated GHG emissions and reductions. While activity data may or may not be spatially explicit, the extent (i.e., boundaries) of the area monitored must be explicitly and unambiguously defi ned and should remain the same for all reporting periods. Several factors should be considered when defi ning the extent of the monitoring area.

*Baseline Development and Data Availability* . The availability of existing data (e.g., historical and/or cloud-free satellite imagery, forest inventories, research studies, census data) can determine the area for which a justifi able baseline scenario can be developed and therefore the project extent may need to be adjusted accordingly (Sect. 3.3 ). In some cases, it might be useful to adhere to political divisions rather than geographic boundaries if socioeconomic data are available in political units that do not correspond with geographic boundaries such as a watershed or ecoregion. If a reference region is to be used, it is important to consider whether one of appropriate size and characteristics can be found to match the chosen inventory extent (Sect. 3.3.2 ). For example the reference region may need to be 2–20 times larger than the project area to meet some VCS methodologies (VCS Association 2010 ).

*IPCC Tier Selection* . The inventory area may need to be reduced in order to meet higher IPCC Tier levels. For example, if a spatially explicit inventory (Approach 3) meeting IPCC Tier 3 guidelines is desired, expensive high-resolution satellite imagery and intensive data collection may be required and resource constraints may lead to a smaller inventory area. Meeting a lower IPCC Tier requirement could allow for the use of freely available imagery and/or existing data that could enable monitoring of a larger area.

*Stratifi cation and Variability* . Ideally, inventory data will be collected in such a way as to suffi ciently capture the spatial variability of key stratifi cation variables. Identifi cation of such variables a priori may reveal that it is impractical or fi nancially unfeasible to develop a sampling strategy that can suffi ciently capture variation within the entire area and the extent of the monitoring area may need to be adjusted.

*Policy Levers* . It is important to consider which policy levers exist, at what scale they can be applied and which may be infl uenced by assessment results when determining monitoring boundaries. For example, if regulations affecting land-use are implemented solely along political boundaries, it may not make sense to draw project- monitoring extents around watershed boundaries that may encompass multiple political units with differing regulations or policy options.

## *3.2.2 Data Acquisition*

 Data to estimate areal LULC extents can be acquired through three general sources: existing datasets developed for other purposes, collection of new data through sampling and complete LULC inventories using remote sensing data (Table 3.1 ).

#### **Existing Data**

 Existing datasets can come from national or international sources or from other projects or research activities. Data may be available in a variety of formats and collection dates, and at varying spatial and temporal scales and extents. Time should be taken to identify existing data sources in order to determine what data remain to be collected, at what temporal and spatial scales and to what degree project resources can accommodate these needs. Useful datasets can include historical LULC maps, climate data, biophysical data (e.g., soil or hydrological maps), census or household surveys and political boundaries or administrative units.

#### **Ground-Based Field Sampling Methods**

 Ground-based methods are recommended when existing datasets are incomplete, out of date, or inaccurate and complete spatial coverage with remote sensing techniques is unfeasible or would not be accurate on its own (IPCC 2003 , Sect. 2.4.2). Ground-based sampling can be expensive and time consuming and is generally more appropriate for smaller project areas or when used in a sampling framework over larger areas. Field sampling to help determine LULC areal extents can result in two types of geographic data: biophysical data and socioeconomic data. Biophysical data generally require objective physical measurement of various land attributes (e.g., parcel size, vegetative composition). Ideally these measurements are georeferenced using GPS in order to integrate them with remote sensing data and enable accurate follow-up measurements. Socioeconomic data can be collected using a variety of methods including interviews, surveys, census, questionnaires, and

#### **Box 3.1 Random and Targeted Sampling Methods for Generating LULC Activity Data**

#### *Random Sampling*

 Random sampling is generally done using systematic or stratifi ed sampling methods. Systematic sampling spatially distributes sampling locations in a random but orderly way, for example using a grid. Stratifi ed sampling selects sample sites based on any number of environmental, geographic, or socioeconomic variables to achieve sampling rates in proportion to the distribution of the chosen variables across the inventory extent. Stratifi ed sampling methods (e.g., optimum allocation) can improve the accuracy and reduce costs of monitoring efforts (Maniatis and Mollicone 2010 ) and tools exist to determine the number of sample plots needed (UNFCCC/CCNUCC 2009 ). Ideally sample sites for determination of LULC can be co-located with sites for measuring carbon stocks and GHG emissions, although this may not always be practical or feasible.

#### *Targeted Sampling*

 Targeted sampling refers to the non-random selection of specifi c sample regions based on determined criteria. A common example of targeted sampling is the use of low-cost or free-imagery to identify "hotspots" of active LULC change such as deforestation (Achard et al. 2002 ; De Sy et al. 2012 ). These hotspots, or a randomly selected subset within, can then be selected as sample units for more in-depth monitoring using higher-resolution imagery and/or comprehensive fi eldwork. These data can then be used to train LULC classifi cation algorithms and assess the accuracy of results obtained using medium or coarse resolution imagery. Regardless of the method chosen, sampling should be statistically sound and allow for the quantifi cation of uncertainty .

participatory rural appraisals (e.g., semistructured interviews, transect walks, and other fl exible approaches involving local communities; see Ravindranath and Ostwald 2008 for more information). Socioeconomic data may or may not be georeferenced, depending on the application.

 Both biophysical and socioeconomic data acquired using the methods mentioned above can give a reasonable estimate of the proportions of LULC categories within the inventory area provided sample locations are selected using statistically rigorous methods to maintain consistency and minimize bias. These proportions can then be multiplied by the total land area to generate activity data. Sample locations can be chosen using random or targeted (non-random) methods (Box 3.1 ). Random methods allow for quantifi cation of uncertainties and are therefore generally preferred, but targeted methods may be useful for measuring carbon stocks related to a specifi c event (e.g., a fi re) or calibration of modelling for a specifi c carbon pool (e.g., effects of decomposition on soil carbon) (Maniatis and Mollicone 2010 ).

#### **Remote Sensing Data**

 Complete wall-to-wall LULC inventories are generally carried out using a combination of remote sensing data and fi eld-based sampling. Remotely sensed data come from aerial photography, satellite sensors, and airborne or satellite-based RADAR or LiDAR. Optical sensors are the most commonly used in LULC classifi cation as they provide spectral information in the visible and infrared bands at a range of resolutions and costs (Table 3.2 ). While fi ne (<5 m) or medium (10–60 m) resolution imagery are preferable for accurately monitoring LULC in landscapes dominated by smallholder agriculture, cost of acquisition and/or processing may be prohibitive for projects covering large areas. However, methods exist for nesting high-resolution sampling within coarser resolution wall-to-wall coverage to reduce uncertainty of LULC change analysis across large areas and lower costs (e.g., Achard et al. 2002 ; Jain et al. 2013 ).

 Image processing techniques can be applied to the remotely sensed data to enhance particular land-cover types, or enable more accurate stratifi cation and classifi cation, such as the calculation of the Normalized Difference Vegetation Index (NDVI), developing textural variables (e.g., Castillo-Gonzalez 2009) or principle component analysis (PCA). Imagery can also be classifi ed into land-cover classes enabling easier manipulation in a GIS. Spatial analysis of remotely sensed data combined with environmental and/or socioeconomic variables can also create additional datasets to further enhance classifi cation and stratifi cation. Designating ecological or anthropogenic biomes (Ellis and Ramankutty 2008 ), calculating market accessibility (Chomitz and Gray 1996 ; Southworth et al. 2004 ) and identifying landscape mosaics (Messerli et al. 2009 ) are examples of such user-generated datasets to improve analysis of LULC change and explore drivers of change in smallholder landscapes.

#### **Spatial Considerations**

 The spatial scale(s) at which data collection and analysis will take place is a key factor to consider when developing a monitoring and analysis program. Changing the scale at which analysis takes place can result in signifi cantly different results, even when using the same dataset. The "optimal" scale of measurement and prediction is project-specifi c and may even vary for different steps of analysis (Lesschen et al. 2005 ). Complementary analysis at multiple scales may further improve accuracy (Messerli et al. 2009 ). A number of factors related to spatial scale should be considered to maintain transparency, and improve accuracy and effi ciency of analysis.

 The fi nest-scale unit of data is called a minimum information unit or minimum mapping unit (MIU or MMU). This is often the size of a small contiguous group of pixels for remote sensing data or the household for census data, although data may only be available aggregated to an administrative unit such as a village or municipality. To qualify for carbon credits, for example under the REDD+ mechanism, MMUs of <1–6 ha are generally required (De Sy et al. 2012 ; GOFC-GOLD 2014 ). In land-


 **Table 3.2** Overview of existing remote sensing data sources (adapted from GOFC-GOLD 2014 )

a Satellite was decommissioned in early 2015 but archived data are available

scapes dominated by smallholder agriculture, individual LULC parcels are often 0.5 ha or smaller. When using remote sensing data, it is preferable to have MIUs (e.g., pixels) that are signifi cantly smaller than the average farm size to avoid mixed pixels that encompass multiple LULC categories. However methods of remote sensing analysis, such as spectral unmixing (Quintano et al. 2012 ) and hierarchical training with very high-resolution imagery (e.g., Jain et al. 2013 ) have been developed to attempt to deal with the issue of mixed pixels in coarser resolution imagery.

 It is important to consider the scale of all available data to avoid mismatches that could lead to data management problems or wasted resources. Depending on the analysis methods used, data may have to be resampled to the coarsest available dataset. For example, it may be unnecessary to acquire a 5 m digital elevation model for stratifi cation if it will be combined with 30 m Landsat data.

#### **Temporal Considerations**

 Several temporal boundaries should be fi xed established during the development of a monitoring methodology.


cover when observed over the long term (Houghton et al. 2012 ). These temporary changes in land-cover (e.g., from annual cropping to secondary forests) can be misinterpreted as afforestation or deforestation depending on the timing of sampling or image acquisition if they are not considered across their entire cycle with suffi ciently frequent measurements (DeFries et al. 2007 ). One approach to account for fl uctuating carbon stocks associated with shifting cultivation is to calculate time-averaged carbon stocks for a given land-use system (Bruun et al. 2009 ; Palm et al. 2005 ).

*Other considerations* . Many studies have found that land-use is often infl uenced by land features. For example, farmers may choose to cultivate areas with fertile, carbon-rich soils (e.g., Aumtong et al. 2009 ; Ellis and Ramankutty 2008 ; Jiao et al. 2010 ) or reduce fallow periods when the soil fertility is high (Roder et al. 1995 ) and leave forests intact only in areas with poor soils. This preferential selection can make it diffi cult to determine that land-use is in fact causing a change in soil carbon stocks, and not the other way around (soil carbon stocks infl uencing land-use). Repeated sampling may be required to observe carbon stock changes resulting directly from LULC conversion (Bruun et al. 2009 ). The effects of prior land-use on future carbon sequestration potential may also be signifi cant (see Eaton and Lawrence 2009 ; Hughes et al. 1999 ). While diffi cult to quantify, these delayed fl uxes can be included when considering LULC transitions (e.g., a forest converted from agriculture may not store the same amount of carbon as a forest converted from a pasture). Finally, complications can arise from temporal mismatching, for example if biophysical or social data are collected in a separate time period from satellite imagery. There may be benefi ts from matching the timing of data acquisition on various factors (Rindfuss et al. 2004 ).

## *3.2.3 LULC Classifi cation and Change Detection*

#### **LULC Category Defi nition**

 Regardless of the Approach used to generate activity data, LULC categories must be clearly and objectively established and LULC categories, subcategories, and strata should be mutually exclusive and totally exhaustive (Congalton 1991 ) with clear defi nitions of transitions from one class to another. (Note that sophisticated analysis methods using non-discrete, probabilistic or "fuzzy" classifi cation do exist (e.g., Foody 1996 ; Southworth et al. 2004 ), but are beyond the scope of this chapter). For example, forests are generally defi ned based on a threshold value of minimum area, height and tree crown cover and the Designated National Authority (DNA) for each country can aid in defi ning LULC category defi nitions (GOFC- GOLD 2014 ). Objective defi nitions are especially important in smallholder landscapes where shifting cultivation and fallow rotations are common and transitions between LULC classes may not be straightforward. Furthermore, since smallholder landscapes often consist of small and heterogeneous land uses, it is possible that sampling points may

fall into more than one LULC category. Systematic, transparent, and objective methods are needed to determine to which LULC category a sampling point belongs (Maniatis and Mollicone 2010 ).

 The IPCC Agriculture, Forestry, and Other Land-Use (AFOLU) Guidelines ( 2006 ) defi ne the following six broad land-use categories:


 These top-level classes were designed to be broad enough to encompass all land areas in a country and allow for consistent and comparable reporting between countries. Monitoring activities can further divide these classes into conversion categories (i.e., Forest Land converted into Cropland, Wetlands converted into Settlements). For REDD+ GHG inventories and Tiers 2 and 3 reporting, it is likely that these top-level classes must be further divided into subcategories and/or stratifi ed to allow for disaggregation of carbon stocks and improved estimation accuracy. Subcategories refer to unique LULCs within a category (e.g., secondary forest, within Forest Land) that impact emissions and for which data are available. Identifi cation of subcategories can greatly reduce uncertainty of carbon stock estimates. For example, Asner et al. ( 2010 ) found that secondary forests held on average 60–70 % less carbon than intact forests in the Peruvian Amazon, and other studies have found similarly large differences in carbon stocks between forest types (e.g., Eaton and Lawrence 2009 ; Saatchi et al. 2007 ), highlighting the importance of forest subclasses. Secondary forests, a signifi cant LULC class in smallholder landscapes, are estimated to make up more than half of tropical forested areas and can be an important source or sink of carbon (Eaton and Lawrence 2009 ; Houghton et al. 2012 ). Therefore, distinguishing between secondary forests, bush-fallows, and undisturbed forests, while often challenging, will likely result in more accurate carbon stock estimates.

 Stratifi cation within LULC categories and subcategories can be based on any number of factors signifi cant to emission estimation such as climate, ecological zone, elevation, soil type, and census data (e.g., population, management practices) (see Stratifi cation, below). Final LULC categories and strata will depend on project location, climate and ecological factors, data availability, analysis capacity, and other factors. Ideally, however, subcategories or strata can be aggregated to correspond with the six broad land-use categories listed above to maintain consistency between country or project inventories. Designation of LULC classes and strata will also depend on the IPCC Approach chosen to represent land-use area data. To meet Approaches 2 and 3, data on conversion between LULC categories and strata must be available, potentially limiting the number of possible subcategories and strata .

#### **LULC Classifi cation, Mapping, and Tabulation**

 Non-spatially explicit methods for collecting activity data (Approaches 1 and 2) result in tables of land area totals by LULC category for a given point in time. Depending on how data are collected, these results can be aggregated to political or geographic boundaries and incorporated into existing maps. The data themselves are not spatially explicit in their disaggregated form and therefore exact patterns of land-use cannot be interpreted within the spatial unit of aggregation (Table 3.1 ). The original data will generally come from LULC surveys, census data, existing maps or a combination of these. Therefore uncertainty associated with Approaches 1 and 2 will depend in large part on the quality of the sampling methods used to collect the original data. Costs could range greatly depending on the size of the project area, availability of existing data, heterogeneity of the landscape, and accessibility, but in general Approaches 1 and 2 can be low-cost options, especially for smaller projects.

 Spatially explicit methods for generating activity data (Approach 3) use a combination of remote sensing and fi eld-based sampling to develop a wall-to-wall classifi ed LULC map with which LULC category areas can be totalled. Wall-to-wall maps provide the opportunity for interpolation between data points using GIS software and the development of spatially explicit polygons and/or individual pixels assigned to various LULC categories. In this manner activity data can be effi ciently calculated, overlaid with ancillary data for stratifi cation, and integrated with emission factors to quantify and analyze GHG emissions/reductions, their spatial variability, and drivers. Many methods exist to classify LULC, but they can be grouped into three main categories: visual interpretation, unsupervised classifi cation, and supervised classifi cation (Box 3.2 ). Additionally, a number of pre- and/or postprocessing steps may also be required to ensure accurate results. Choice of classifi cation methods and image processing will depend on available resources, technical expertise, imagery, location, and available software. Greater detail on specifi c methodologies is presented on the associated website. Whichever methods are chosen for preprocessing, classifi cation, and post-processing, they should be transparent, repeatable by different analysts, and results should be assessed for accuracy (GOFC-GOLD 2014 ).

#### **Stratifi cation**

 Once LULC classes have been identifi ed and imagery classifi ed, stratifi cation by one or more variables may be desirable to improve estimation of carbon stocks, GHG emissions and reductions, and/or baseline development. The primary goal of stratifi cation is to minimize the variability of carbon stock estimates within LULC categories (Maniatis and Mollicone 2010 ). The most basic form of carbon stock stratifi cation is the development of subcategories (e.g., secondary forest versus mature forest; tree crops versus annual crops). Additional datasets and/or more intensive sampling may be required to identify subcategories, which may increase costs,

#### **Box 3.2 General LULC Classifi cation Methods Using Remote Sensing Data**

#### *Visual interpretation*

 The simplest method of LULC classifi cation is visual interpretation. In this method, a person familiar with the landscape and the appearance of LULC classes in remotely sensed imagery, manually interprets and classifi es polygons around different land-covers. This method can be quite accurate but may not be precisely repeatable and can result in high uncertainty if comparisons are made between maps classifi ed by different people. However systematic approaches to visual interpretation can increase accuracy and repeatability (e.g., Achard et al. 2002 ; Ellis 2004 ; Ellis et al. 2000 ).

#### *Unsupervised classifi cation*

 This method is fully automated and classifi cation occurs without direct user intervention, although parameters such as the number of classes to be identifi ed can be set by the user. Unsupervised classifi cation algorithms cluster pixels into spectrally similar classes and very small spectral differences between classes can be identifi ed (Vinciková et al. 2010 ). This method can be useful for exploring the number and distinguishability of potentially identifi able classes.

#### *Supervised classifi cation*

 Supervised classifi cation relies on the training data that is used to calibrate automated or semiautomated classifi cation algorithms. Training data may be obtained through fi eld sampling, separate higher-resolution remote sensing imagery or from within the original image. Ideally training points will be chosen in a statistically rigorous way (e.g., random, stratifi ed, systematic) and spatial and temporal factors should be considered (Sect. 3.2.2 , Spatial Considerations and Temporal Considerations).


#### **Box 3.2 (continued)**

González et al. 2009 ). In pixel-based classifi cation, the pixel is the MIU whereas object-based methods quantitatively group pixels that are spectrally similar and spatially adjacent to create new MIUs representing patches or parcels of homogenous land-covers. Classifi cation is then carried out on individual objects using a combination of spatial and spectral information. Object-based techniques combined with high-resolution imagery have not only been shown to outperform pixel-based methods in highly heterogeneous landscapes (e.g., Moreno and De Larriva 2012 ; Perea et al. 2009 ) but also require extensive technical expertise, time, and specialized GIS software.

• *Other supervised classifi cation techniques* —Additional, relatively complex techniques such as regression/decision trees, neural networks, hierarchical temporal memory (HTM) networks (Moreno and De Larriva 2012 ), and support vector machines (Huang and Song 2012 ) have also shown success in improving classifi cation accuracy in heterogeneous landscapes.

and transparent objective methods should still be used to defi ne subcategories. However, stratifi cation can reduce overall costs if monitoring activities can be targeted toward subcategories in which LULC transitions and carbon stock changes are expected (GOFC-GOLD 2014 ). Further stratifi cation can be done using biophysical (e.g., slope, rainfall, soil type) and socioeconomic (e.g., population) datasets. Combining datasets requires either spatially explicit data (Approach 3) or datasets following Approaches 1 or 2 that have been aggregated to spatially defi ned units such as administrative boundaries. (See Lesschen et al. ( 2005 ) for a good overview on combining datasets for analysis of LULC change in farming systems.)

 Stratifi cation should only be carried out to the degree that chosen strata improve carbon stock estimates and reduce uncertainty. Statistical methods such as multivariate and sensitivity analyses exist to assess the quality of potential strata. Project objectives, timeframe, and the temporal and spatial resolution of available data will also impact the choice of LULC subcategories and strata .

#### **LULC Change Detection**

 When using activity data generated with Approaches 1 and 2, change detection can be as simple as carrying out basic arithmetic to calculate the change in total land area of each LULC class at two or more points in time. Approach 2 will include results on the specifi c transitions observed (e.g., from forest to cropland versus from forest to pasture) and results are generally reported using a land-use conversion matrix (IPCC 2006 ; Ravindranath and Ostwald 2008 ).

 Spatially explicit methods (Approach 3) to detect changes in LULC can be separated into three general categories: post-classifi cation comparison, image comparison approach, and bitemporal classifi cation approach. Post-classifi cation comparison is the most straightforward approach and consists of fi rst conducting separate LULC classifi cations on two or more images and comparing the results to detect change. Post-classifi cation change detection is popular due to the fact that hard classifi cation for single-date imagery is often required for other purposes or preexisting classifi ed images are being used for one or more dates (van Oort 2007 ). One major drawback to this approach is that each image will contain uncertainty stemming from misclassifi cation, which could result in signifi cant errors in the change map from misidentifi cation of LULC change. The image comparison approach attempts to reduce these errors by comparing the two unclassifi ed images and identifying pixel-based change thresholds through methods such as differencing, ratioing, regression, change vector analysis, and principal component analysis (Huang and Song 2012 ). Bitemporal classifi cation goes a step further by analyzing multiple images simultaneously and applying one of a variety of algorithms to produce a fi nal map with change classes in a one-step process (Huang and Song 2012 ). The two latter approaches can be more adept at detecting specifi c changes of interest and more subtle changes (van Oort 2007 ) and may reduce uncertainty in cases where classifi cation accuracy is low.

## **3.3 Developing a Baseline**

 Activity data are monitored at two or more points in time to assess LULC change. However, this change must be compared against a "business as usual" scenario to determine additionality (i.e., to defi ne what would have occurred in the absence of project interventions). Only by comparing observed changes against a well- developed and justifi ed baseline can we be sure that project activities resulted in changes that would not have occurred otherwise. Two general methods exist to develop a comparative baseline of LULC change: the development of a baseline scenario or the monitoring of a reference region.

## *3.3.1 Baseline Scenarios*

 A baseline scenario predicts the LULC changes that would occur within the inventory area in the absence of interventions by creating a "business as usual" scenario from a variety of input data (Table 3.3 ). This scenario can be developed on a project- by- project basis using conditions and information particular to the project (project- specifi c approach) or for a specifi c geographic area, which may extend beyond the project area boundaries (regional baseline approach, also called the performance standard approach). Either approach can be based on historical data and/or logical arguments about economic opportunities that could infl uence future LULC change (Sathaye and Andrasko 2007 ) and examples of both approaches are given in

53


 **Table 3.3** Summary of activities for developing a baseline at various uncertainty levels

Table 3.4 . The project-specifi c approach is often based on logical arguments where the baseline scenario is identifi ed as the scenario facing the fewest barriers (Greenhalgh et al. 2006 ). This approach requires the development of multiple scenarios for the project area and requires economic-related data to evaluate which is most likely to occur. The regional baseline approach uses time-based estimates to project future carbon stock changes. This approach may require more GHG-related and spatially explicit data to enable quantitative analysis of trends in LULC change and GHG emissions/removals (Greenhalgh et al. 2006 ). The regional approach can result in more credible and transparent baselines and reduce costs when multiple projects are proposed within the same region (Brown et al. 2007 ; Sathaye and Andrasko 2007 ). An example of a potentially very useful dataset for identifying historical trends of forest-related disturbances is the high-resolution global forest change map recently published by Hansen et al. ( 2013 ).

 Modelling future LULC changes based on historical and current data can be done using solely historical trends in percent change in land area or by incorporating drivers of LULC change into predictive models. Projection of historical LULC change trends requires reliable activity data for at least two points in time, preferably at the beginning and end of the historical period. Drivers used in modelled baselines can be simple metrics (e.g., population growth) to meet Tiers 1 and 2, or a more complex combination of spatially explicit biophysical and socioeconomic factors to meet Tiers 2 and 3. Drivers can greatly improve baseline development by capturing periodic fl uctuations or variations across a landscape that may not be captured using trend analysis (Sathaye and Andrasko 2007 ). For example historical deforestation



trends may not continue into the future if certain thresholds have been reached or land-use determinants such as road networks have changed (Chomitz and Gray 1996 ). Incorporating such factors into models can improve trend prediction and many different models exist to analyze the infl uence of drivers and set baselines (e.g., Brown et al. 2007 ). Reporting should describe the model and drivers in detail and the chosen model should be transparent, include empirical calibration and validation processes and generate uncertainty estimates (Greenhalgh et al. 2006 ).

 To qualify for carbon crediting under the VCS, Clean Development Mechanism (CDM), REDD+ or other mechanisms, the baseline must generally be justifi ed using investment, barrier and/or common practice analysis (Greenhalgh et al. 2006 ;Tomich et al. 2001 ; VCS Association 2012 ). In other words, barriers to the LULC changes sought by project activities or policies must be identifi ed to show that insuffi cient incentives exist to achieve the desired LULC changes without intervention. Ideally multiple scenarios will be developed and evaluated to determine which is the most credible and conservative baseline choice. Several temporal considerations also exist related to both the historical period used to generate a baseline scenario and the period for which the baseline is projected forward. Historical data should be as relevant as possible to the projected period and major events (e.g., hurricanes, fi res) and policy changes (e.g., protected area designations) should be considered when acquiring historical data. A narrative approach exploring the story behind historical LULC dynamics can further reveal relationships between observed changes and the forces driving them (Lambin et al. 2003 ). The validity period for the baseline (i.e., for how many years the baseline is considered valid and accurate) should also be taken into account. Experience from other projects suggests that an adjustable baseline approach is preferable. A common approach is to set a fi xed baseline for the fi rst 10 years, at which point it is evaluated and adjusted as needed (Brown et al. 2007 ; Sathaye and Andrasko 2007 ; VCS Association 2014 ).

## *3.3.2 Reference Regions*

 An alternative to developing a baseline scenario for the project area is to monitor a separate reference region, a common approach among Voluntary Carbon Standard (VCS) methodologies (e.g., VCS Association 2010 and others). The reference region should be suffi ciently similar to the project area to conclude that the trajectory of LULC change observed in the reference region would also have occurred within the project area in the absence of project activities. While exact requirements for identifi cation of a reference region vary, in general the reference region must be signifi cantly larger than and demonstrably similar to the inventory area. In order to demonstrate similarity, key variables must be compared which may include landscape features (e.g., slope, elevation, LULC distribution), ecological variables (e.g., rainfall, temperature, soil type) and socioeconomic conditions (e.g., population, land tenure status, policies, and regulations) (see VCS Association 2010 ). Transparent comparison procedures must be developed to set comparative thresholds for the reference region (e.g., average slope of the reference region shall be within 10 % of the average slope of the inventory area).

 Monitoring a reference region may be a cost-effective option for small projects that can easily identify an area similar to the project area. However larger projects, or projects working in a unique biophysical or sociopolitical environment, may fi nd it diffi cult to locate an appropriate reference region, or may fi nd it cost-prohibitive to monitor one.

## **3.4 Calculating Carbon Stock Changes**

 In order to estimate GHG emissions and removals, carbon stock densities must be quantifi ed for each LULC category subclass and/or stratum. Carbon stock densities may come from default values, national datasets, scientifi c studies or fi eld sampling and are generally given as tons of carbon per hectare (Mg C ha −1 ) for individual or combined carbon pools (Table 3.5 ).

## *3.4.1 Key Carbon Pools*

 The IPCC Guideline s ( 2006 ) defi ne fi ve carbon pools: living aboveground biomass, living belowground biomass, deadwood, litter and soil organic matter (SOM). In the case that data are not available for all carbon pools, key pools can be identifi ed based on their relative expected contribution to total carbon stock changes caused by possible LULC transitions. Thresholds are developed to delimit the minimum contribution of total emissions from a pool to be defi ned as "key." For example, a threshold could be created stating that only pools representing more than 10 % of total carbon stocks are considered key. Therefore it is possible that some pools will be key for certain LULC classes but not for others. Identifying key pools can help target monitoring and modelling efforts to minimize uncertainty and is required under IPCC reporting.

## *3.4.2 Initial Carbon Stock Estimates*

 Calculation of initial carbon stocks can be done in several ways ranging from the use of simple arithmetic to running complex models. The simplest method is to assign a single carbon stock density value (or range of values) to each LULC category and multiply this value by the total area of each category. This method can be used with activity data associated with any of the three Approaches. It is relatively straightforward and potentially low-cost, but may introduce high levels of uncertainty as it assumes that there is no variability of carbon stocks within LULC categories.

 Uncertainty can be reduced by taking into account additional drivers of carbon stocks beyond just LULC categories. This can be done through stratifi cation (Sect. 3.2.3 ) and/or modelling. Modelling approaches require data on carbon stocks and rates of change, which can be obtained from default emission factors, scientifi c research, or fi eld measurements. Additional biophysical (e.g., slope, rainfall, soil type) and socioeconomic (e.g., population) datasets may also be needed. A variety


 **Table 3.5** Summary of activities for calculating carbon stock changes from LULC change at various uncertainty levels

of models such as PROCOMAP, CO 2 FIX, CENTURY, ROTH, and others exist with a range of complexity and data requirements. (See Ravindranath and Ostwald 2008 for a good comparison of several models.)

## *3.4.3 Monitoring Carbon Stock Changes*

 Carbon stock changes are estimated using one of two general methods: one processbased and the other stock-based. The process-based method estimates the net additions to, or removals from, each carbon pool based on processes and activities that result in carbon stock changes, such as tree harvesting, fi res, etc. The stock-based method estimates emissions and removals by measuring carbon stocks in key pools at two or more points in time.

#### **Process-Based Method**

 The process-based method (sometimes called the gain-loss, IPCC default or emission factor method) estimates gains or losses of carbon in each pool by simulating changes resulting from disturbance or recovery (Houghton et al. 2012 ). Changes in LULC drive process-based models, and carbon stocks are re-allocated based on observed or modelled LULC change. Gains are a result of carbon accumulation from the atmosphere (e.g., in tree biomass) or transfers from another pool (e.g., from biomass to SOC via decomposition). Losses are attributed to transfers to another pool or emissions to the atmosphere as CO 2 or other GHGs (IPCC 2006 , Volume 4, Chap. 2). Additional emission factors can be developed for emitting activities that do not necessarily affect the fi ve carbon pools identifi ed by the IPCC. These include, for example, direct emissions from livestock, farm equipment or the production of nonfood products. Models and emission factors used in process- based methods can vary in complexity and potentially meet any Tier requirements. IPCC default factors can be used to achieve Tier 1 reporting requirements whereas country-specifi c or locally derived research data combined with more complex modelling approaches are required to meet Tier 2 and 3 requirements.

#### **Stock-Based Method**

 The stock-based method (also called the bookkeeping, stock-difference, or stockchange method) combines ground-based and/or remotely sensed data of measured carbon stocks with data on changes in the total land area of each LULC class between two or more points in time. For stock-based methods, carbon stock changes are measured independently of LULC change and are then multiplied by the total area of each LULC class and stratum. Process-based methods model carbon stock changes based on LULC changes. Depending on the spatial resolution of data, conversions might be required to arrive at a carbon density (Mg C ha −1 ) that is then combined with activity data to estimate total emissions/removals. Typically, country- specifi c information is required for use with the stock-based method and resource requirements for data collection may be greater than process-based methods unless appropriate datasets already exist. Stock-based methods often meet at least Tier 2 requirements, provided activity data were generated according to Approach 2 or 3.

## **3.5 Assessing Accuracy and Calculating Uncertainty**

 In order to qualify for carbon crediting under mechanisms such as VCS, CDM, and REDD+, fi nal reporting of GHG emissions/removals associated with LULC change must include uncertainty estimates (Maniatis and Mollicone 2010 ). Uncertainty should be reported as the range within which the mean value lies for a given probability (e.g., a 95 % confi dence interval) or the percent uncertainty of the mean value, each of which can be calculated from the other (IPCC 2003 ). Errors will be introduced at every level of data collection. Analysis and assessment of accuracy and uncertainty should be carried out for each step. Not only is this important for reporting purposes, it can provide valuable information to project managers to determine which steps contain the greatest sources of uncertainty, thereby encouraging cost-effective monitoring (e.g., Smits et al. 1999 ).

 In this chapter we focus on estimating uncertainty associated with the collection of activity data, detection of LULC changes, and linking of emission factors and/or carbon stocks. Methods for assessing uncertainty related to the production of emission factors and measurement of carbon stocks (e.g., calculating soil carbon in a forest) are discussed elsewhere.

## *3.5.1 LULC Classifi cation Accuracy Assessment*

 When remote sensing data are used to develop wall-to-wall LULC maps, two types of error exist: errors of inclusion (commission errors) and errors of exclusion (omission errors). Accuracy should be assessed using a statistically valid method, the most common method being statistical sampling of independent higher-quality validation sample units (e.g., pixels, polygons, sites) for comparison against classifi ed sample units (Congalton 1991 ) (Table 3.6 ). These validation samples can be taken from fi eld observations, additional higher-resolution remote sensing imagery, or can be visually identifi ed from within the original image provided they are independent from those used during training. As with the selection of training data, validation sampling should be done in a statistically sound and transparent manner. Stratifi ed or proportional sampling techniques may be desirable to improve accuracy and reduce costs. When using fi eld-based sampling to analyze current imagery, validation data should be collected as close to the time of image acquisition as possible, ideally at the same time as training data. Including farmers or other community members in the data collection process can be an effective way to estimate past LULC for classifi cation and validation of historical imagery, while at the same time empowering stakeholders and addressing conservation issues (e.g., Sydenstricker-Neto et al. 2004 ).

 The accuracy of classifi ed sample units compared against "real-world" validation sample units can be presented in an error matrix, also called a confusion matrix. This helps visualize errors, identify relationships between errors and LULC categories, and calculate indices of accuracy and variation (Congalton 1991 ). Classifi cation accuracy refers to the percentage of sample units correctly classifi ed and can be calculated as commission and omission errors for each LULC class as well as an overall accuracy for all classes (Table 3.7 ). These classifi cation accuracies can then be used as an uncertainty estimate to discount carbon credits associated with LULC change. For example, to maintain conservativeness of carbon credit estimates the VCS Association VM0006 ( 2010 ) uses the smallest accuracy of all maps as a discount factor for carbon credits. In the hypothetical example from Table 3.7 , this would result in carbon credits being discounted by 25 % (multiplied by a discount factor of 0.75). Representing accuracy using an error matrix also provides an opportunity to assess which LULC categories are most often confused. For example, cropland in smallholder landscapes


 **Table 3.6** Summary of activities for assessing accuracy and calculating uncertainty at various uncertainty levels

 **Table 3.7** Hypothetical error matrix showing the number of pixels mapped and validated (groundtruthed) by LULC class. Values in bold highlight the number of correctly mapped pixels and the row and column totals, which are used to calculate producer's and user's accuracy


is often misclassifi ed due to small farm sizes and its resemblance to bare soil (due to minimal refl ectance from young crops) or secondary forests (due to intercropping with tree species commonly found in secondary forests) (e.g., Sydenstricker-Neto et al. 2004 ). Other accuracy indicators include the kappa coeffi cient or KHAT statistic, root mean squared error (RMSE), adjusted *R*<sup>2</sup> , Spearman's rank coeffi cient and others (Congalton 1991 ; Jain et al. 2013 ; Lesschen et al. 2005 ; Smits et al. 1999 ).

## *3.5.2 LULC Change Detection Accuracy Assessment*

 The accuracy of LULC change detection can be assessed using methods similar to those used to validate single scene LULC classifi cation, but additional considerations exist. When making post-classifi cation comparisons using two independently classifi ed images, the accuracy of each individual classifi cation should be assessed in addition to the accuracy of the change image. It is usually easier to identify errors of commission in change products because often only a small proportion of the land area will have experienced change, and often within a limited geographic area (GOFC-GOLD 2014 ). Unique sampling methodologies may therefore prove more cost-effective to validate the relatively rare event of changes in LULC within an image (Lowell 2001 ). A transition error matrix can be used to report the accuracy with which transitions between LULC categories are detected. This allows for assessment of uncertainty for each transition (e.g., forest to cropland, forest to grassland) and for partitioning of uncertainty attributable to the change detection process versus classifi cation (van Oort 2007 ).

## *3.5.3 Uncertainty Associated with Estimating Carbon Stocks*

 Uncertainty estimates should be developed for key carbon pools within each LULC category. Uncertainty of carbon stocks using the stock-based method will be related to sampling. The process-based method will contain uncertainty estimates derived from scientifi c literature, model accuracy or other sources. Factors such as the scale of aggregation, stratifi cation variables, and the spatial or temporal considerations discussed above can all infl uence the uncertainty associated with integrating carbon stocks and activity data.

## *3.5.4 Combining Uncertainty Values and Reporting Total Uncertainty*

 Combining uncertainty estimates for activity data, LULC change detection and emissions factors or carbon stocks can be done several ways, ranging from simple error propagation calculations (Tier 1) to more complex Monte Carlo simulations, also called bootstrapping or bagging (Tiers 2 and 3). Several approaches exist for calculating error propagation. For example, different equations are recommended if input data are correlated (e.g., the same activity data or emission factors were used to calculate multiple input factors that are to be summed) or if individual uncertainty values are high (e.g., greater than 30 %) (GOFC-GOLD 2014 ; IPCC 2003 ). Monte Carlo simulations select random values within probability distribution functions (PDF) developed for activity data and associated carbon stock estimates to calculate corresponding changes in carbon stocks. The PDFs represent the variability of the input variables and the simulation is undertaken many times to produce a mean carbon stock-change value and range of uncertainty (see IPCC 2003 and citations within for more detailed information on running Monte Carlo simulations). Simulation results can be combined with classifi cation accuracies to compute uncertainties for each pixel. This allows exploration of the variation of accuracy by LULC class or stratum, and where to target future measurements to achieve the greatest reductions in overall uncertainty (Saatchi et al. 2007 ). Generally speaking, Monte Carlo simulations require greater resources than error propagation equations, but both methods require quantitative uncertainty estimates for activity data, LULC changes, and carbon stocks.

## **3.6 Challenges, Limitations, and Emerging Technologies**

 Monitoring LULC change and associated GHG emissions/reductions in a costeffective manner remains a challenge in heterogeneous landscapes such as those dominated by smallholder agriculture. Monitoring change in management within LULC categories can be even more challenging, yet management is often a key component of smallholder carbon projects. Technologies are emerging to directly monitor carbon stocks (namely aboveground biomass), which could overcome some of these challenges. For example LiDAR shows promise for accurate direct estimation of vegetation structure, aboveground biomass, and carbon stocks (Goetz and Dubayah 2011 ; Goetz et al. 2009 ). While direct measurement methods are generally still in the research phase and may be cost-prohibitive for most projects, they may prove especially useful for smallholder settings as they can improve accuracy by removing the error associated with misclassifi cation of LULC, a potentially large source of uncertainty in heterogeneous landscapes. In the end, it is diffi cult to recommend a single methodological approach to monitoring LULC in smallholder landscapes as optimal methods will depend on the project area, size, available resources, time period, interventions, and other factors. An overall summary of the general methods discussed in each section of this chapter is presented in Table 3.8 . Time should be taken to assess these methods and their associated trade-offs, read the relevant key references and stay abreast of emerging remote sensing options to identify the most appropriate methodology for specifi c project conditions.


64



66

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## **References**


## **Chapter 4 Quantifying Greenhouse Gas Emissions from Managed and Natural Soils**

#### **Klaus Butterbach-Bahl , Björn Ole Sander , David Pelster , and Eugenio Díaz-Pinés**

 **Abstract** Standard methods for quantifying GHG emissions from soils tend to use either micrometeorological or chamber-based measurement approaches. The latter is the most widely used technique, since it can be applied at low costs and without power supply at remote sites to allow measurement of GHG exchanges between soils and the atmosphere for fi eld trials. Instrumentation for micrometeorological measurements meanwhile is costly, requires power supply and a minimum of 1 ha homogeneous, fl at terrain. In this chapter therefore we mainly discuss the closed chamber methodology for quantifying soil GHG fl uxes. We provide detailed guidance on existing measurement protocols and make recommendations for selecting fi eld sites, performing the measurements and strategies to overcome spatial variability of fl uxes, and provide knowledge on potential sources of errors that should be avoided. As a specifi c example for chamber-based GHG measurements we discuss sampling and measurement strategies for GHG emissions from rice paddies.

E. Díaz-Pinés

© The Editor(s) (if applicable) and the Author(s) 2016 71 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_4

K. Butterbach-Bahl (\*)

International Livestock Research Institute (ILRI) , Old Naivasha Rd. , P.O. Box 30709 , Nairobi , Kenya

Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) , Kreuzeckbahnstr. 19 , Garmisch-Partenkirchen , Germany e-mail: K.Butterbach-Bahl@cgiar.org

B. O. Sander International Rice Research Institute (IRRI) , Los Baños , Philippines

D. Pelster International Livestock Research Institute (ILRI) , Nairobi , Kenya

Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) , Garmisch-Partenkirchen , Germany

## **4.1 Introduction**

 Microbial processes in soils, sediments, and organic wastes such as manure are a major source of atmospheric greenhouse gases (GHG). These processes create spatially as well as temporally heterogeneous sources or sinks. Consequently, a thorough understanding of the underlying processes and a quantifi cation of spatiotemporal dynamics of sinks and sources are the bases for (a) developing GHG inventories at global, national, and regional scales, (b) identifying regional hotspots and (c) developing strategies for mitigating GHG emissions from terrestrial, specifi cally agricultural systems.

 At the ecosystem scale, biosphere– atmosphere fl uxes of CO 2 , CH 4 , and N 2 O are bi-directional, i.e., what is observed is a net fl ux of production and consumption processes (e.g., CO 2 : photosynthesis and autotrophic and heterotrophic respiration; CH 4 : methanogenesis and methane oxidation; N 2 O: nitrifi cation and de-nitrifi cation as source processes and de-nitrifi cation as a sink process). The same is true for soil– atmosphere exchange processes , though, with regard to CO 2 , often only respiratory fl uxes are measured.

 Approximately 2/3 of all N 2 O emissions are linked to soil and manure management (Fowler et al. 2009 ; IPCC 2013). For CH 4 as well, soils and organic wastes strongly infl uence atmospheric CH 4 concentrations. It is estimated that wetland and paddy soils represent approximately 1/3 of all sources for atmospheric CH 4 (Fowler et al. 2009 ). On the other hand, well-aerated soils of natural and semi-natural ecosystems—and to a lesser extent soils of agroecosystems—are sinks for atmospheric CH 4 , removing approximately 20–45 Tg yr −1 of CH 4 from the atmosphere (Dutaur and Verchot 2007 ), which corresponds to approximately 6–8 % of all sinks for atmospheric CH 4 (Fowler et al. 2009 ). For CO 2 , soils are a major source due to autotrophic (plant root) and heterotrophic (microbial and soil fauna breakdown of organic matter) respiration. However, at the ecosystem scale, soils can act as net sinks as well as sources for CO 2 , since at this scale plant primary production (CO 2 fi xation from the atmosphere by photosynthesis), litter input to soils as well as respiratory fl uxes are considered. It is well established that soils to a depth of 1 m globally store approximately three times the amount of carbon currently found in the atmosphere (Batjes 1996 ; IPCC 2013). Thus, land use and land management changes, as well as changes in climate affect plant primary production and fl uxes of litter to the soil and soil organic matter mineralization dynamics. This can either result in a mobilization of soil C and N stocks, or, with adequate management, turn soils into C sinks. The latter is an essential process for removal of atmospheric CO 2 and climate protection and has been called the " recarbonization " of our terrestrial ecosystems (Lal 2009 ).

 Due to the mostly microbiological origin of soil, sediment, and organic waste GHG emissions, changes in environmental conditions directly affect the exchange of GHG between terrestrial systems and the atmosphere (Butterbach-Bahl and Dannenmann 2011 ). Changes in temperature affect enzyme activities, while changes in redox conditions—as infl uenced by soil aeration fl uctuations as a consequence of

 **Fig. 4.1** General recommendations for chamber placement, gas sampling, gas concentration measurements, and measurement of auxiliary parameters for static chamber soil GHG fl ux measurements. ( *Note* : text in *italic* are additional measurements/parameters which might be worthwhile to observe)

changes in soil moisture—can favor sequentially different microbial processes. For example, fi eld irrigation and fl ooding as a standard management for rice paddies results in anaerobic soil conditions, thereby slowing down and stopping aerobic decomposition processes, while sequentially initializing a series of microbial processes that use elements and compounds other than oxygen as an electron acceptor: fi rst NO 3 − (denitrifi cation), followed by SO 4 − and Fe 3+ and Mn 3+/4+ reduction, before fi nally CH 4 is produced as a product of organic matter degradation under strictly anaerobic conditions by methanogens (Conrad 1996 ).

 Environmental conditions not only change naturally across days, seasons, and years as a consequence of diurnal and seasonal temperature and rainfall regimes, but also due to management of agricultural (forest with regard to plantations) land, as was explained above with the example of fl ooding of paddy fi elds. Changes in environmental conditions affect the activity of the microbial community as well as that of plants, and consequently, the associated GHG production and consumption processes. Thus, GHG emissions from soils show a rather pronounced temporal variability on short (diurnal) and longer (days to weeks and years) timescales (e.g., Luo et al. 2012 ). Moreover, environmental conditions also vary spatially because soil conditions, plant cover, land management and thus, nutrient availability, soil aeration and microbial community composition, also change across micro- (e.g., soil matrix) to landscape and continental scales. As a result, GHG fl uxes also vary considerably across spatial scales, making it necessary to develop a solid sampling strategy to target measurement sites, i.e., determine which sites are representative for the landscape one would like to work in, to estimate GHG fl uxes and develop strategies to mitigate those emissions. Targeting (Chap. 2 of these guidelines) is a cornerstone to allow meaningful upscaling to landscape and higher spatial scales. But targeting already starts at the measurement site, since decisions have to be made about where (and when) to place chambers for fl ux measurements (Fig. 4.1a ).

 This chapter does not aim to provide a cookbook of how to measure soil and GHG fl uxes. Plenty of work has been published on this topic, fi lling bookshelves and libraries (see e.g., Table 4.1 ). Here, we provide guidance to the relevant literature and highlight potential problems that might come up when designing a GHG measurement program (Fig. 4.1 ) rather than explain the sampling procedures in detail. We also provide examples of how to overcome problems in the context of GHG measurements for smallholder systems.

## **4.2 What Technique Is Most Suitable for Measuring Biosphere–Atmosphere Exchange Processes of GHGs?**

 The two most commonly used techniques for measuring fl uxes between terrestrial ecosystems and the atmosphere are: (a) enclosure-based (chamber) measurements (manual or automated) and (b) micrometeorological measurements (e.g., eddy covariance or gradient methods), or a combination of both (Denmead 2008 ). The choice of the measurement technique itself is largely driven by resource investment, demand, and by the research question.

## *4.2.1 Micrometeorological Measurements*

 Use of micrometeorological techniques requires homogenous fi elds with a signifi cant fetch (>1 ha) that should not be infl uenced by buildings, trees, slopes, etc. Land use, land management, vegetation, and soil properties should be homogeneous for the direct fetch area, but also for the wider area. Typically these techniques are applied in fl at terrain with large, homogeneous land use, such as pasture, grassland, maize, or wheat monocrops, forests, or tree plantations. Capital costs of micrometeorological measurements of GHG fl uxes are high, since the required sensors (3D wind fi eld, fast-response gas analyzers) plus auxiliary instruments (meteorological station, mast, etc.) for fl ux measurements at one site, cost around 60,000–80,000 USD for CO 2 and energy fl uxes alone. Adding other components, such as CH 4 (open path sensors are available) and N 2 O (requiring laser spectroscopy instruments),



(continued)


76


77

(continued)



(continued)


**Table 4.1** (continued)

80


a Recommended reading

requires a signifi cant additional investment in instruments, starting from 30,000 to 40,000 USD per gas. Energy supply for the instruments (if not only focused on open path CO 2 /H 2 O/CH 4 technology) is another constraint that should be considered. The two most prominent global networks for multi-site and multi-species observations of biosphere–atmosphere-exchange of GHGs using micrometeorological methodologies are the National Ecological Observatory Network (NEON) in the USA (http://neoninc.org/) and the Integrated Carbon Observation Network (ICOS) in Europe (http://www.icos-infrastructure.eu/?q=node/17). Both networks offer information, processing tools for calculating fl uxes and experts for providing support for designing, establishing, and running micrometeorological measurements.

 Micrometeorological techniques for assessing GHG exchange are not recommended for smallholder systems due to the complexity of land uses and land management, small-scale gradients in soil fertility, and complex crop rotations with intercropping (Chikowo et al. 2014 ).

 Some literature for a fi rst reading on micrometeorological techniques is listed in Table 4.1 .

## *4.2.2 Chamber Measurements*

 This technique allows measurements of GHG fl uxes at fi ne scales, with chambers usually covering soil areas <1 m 2 , and are thus much better suited for smallholder farming systems. They can be operated manually or automatically (Breuer et al. 2000 ). Chamber measurements are rather simple and therefore the most common approach for GHG measurements since they allow gas samples to be stored for future analysis and, with the exception of automated systems, they do not require power supply at the site. In contrast with micrometeorological approaches, chambers are suitable for exploring treatment effects (e.g., fertilizer and crop trials) or effects of land use, land cover, or topography on GHG exchange. However, care must be used in order to obtain accurate data, since installation of the chamber disturbs environmental conditions and measured fl uxes might not necessarily refl ect fl uxes at adjacent sites if some precautions are not considered (see Sect. 5.2.1 below).

 There are two types of chambers: dynamic and static chambers. For dynamic chambers the headspace air is exchanged at a high rate (>1–2 times the chamber's volume per minute) and fl uxes are calculated from the difference in gas concentrations at the inlet and outlet of the chambers multiplied by the gas volume fl ux, thereby considering the area which is covered by the chamber (Butterbach-Bahl et al. 1997a , b ). Static chambers are gas-tight, without forced exchange of the headspace gas volume, and are usually vented to allow pressure equalization between the chamber's headspace and the ambient air pressure (e.g., Xu et al. 2006 ). The volume of the "vent tube" should be greater than the gas volume taken at each sampling time.

 Two situations call for using dynamic chambers: fi rst, when measuring reactive gas fl uxes such as soil NO emissions, and when there is a need to minimize the bias of changes in headspace air concentrations on the fl ux (Butterbach-Bahl et al. 1997a , b ). The second point is important, as signifi cant deviations of chamber headspace gas concentrations from ambient air concentrations affect the exchange process between soils and the atmosphere itself, since the fl ux at the soil–atmosphere interface is the result of simultaneous production and consumption processes. For example, if N 2 O concentration in the chamber headspace is much higher than atmospheric concentrations, microbial consumption processes are stimulated. Moreover, since emissions are mainly driven by diffusion and gas concentration gradients, signifi cant increases/decreases in headspace concentrations of the gas of interest will slow down/accelerate the diffusive fl ux. Both mechanisms fi nally result in a deviation of the fl ux magnitude from undisturbed conditions (Hutchinson and Mosier 1981 ). It is important to be aware of this, though for practical reasons it is partly unavoidable because the precision of the analytical instruments used for gas fl ux measurements, such as electron capture detectors (ECDs) and gas chromatography, is insuffi cient to allow for dynamic chamber measurements. However, there are methods to cope with this problem, such as using non-linear instead of linear models to calculate fl uxes as measured with static chamber technique (e.g., Kroon et al. 2008 ; Table 4.1 ), using quantum cascade lasers (QCLs) in the fi eld (fast box; Hensen et al. 2006 ) and in general by minimizing chamber closure time as much as possible. Chamber closure time is dictated not only by the magnitude of the gas fl ux but also by the chamber height. Therefore, in agricultural systems where plants need to be included for representative measurements, it is suggested to use chambers which can be extended by sections according to plant growth (Barton et al. 2008 ).

 Static chambers are usually mounted on a frame which should be inserted (approximately 0.02–0.15 m) at least a week before fi rst fl ux measurements to overcome initial disturbances of soil environmental conditions due to the insertion of the frame. Once the chamber is closed gas-tight on the frame, headspace concentrations start to change, either increasing if the soil is a net source (e.g., for CO 2 —Fig. 4.2 ), or decreasing if the soil is functioning as a net sink (e.g., CH 4 uptake by upland soils). For accurate calculation of gas fl ux, a minimum of four gas samples from the chamber headspace across the sampling interval (e.g., 0, 10, 20, 30 min following closure) is recommended (Rochette 2011 ).

 Gas fl ux measurements with static and dynamic chambers have been described extensively and Table 4.1 provides an overview of recommended literature, while Fig. 4.1 indicates important considerations when using chamber methodology. Static chambers can not only be used for measurement of soil N 2 O and CH 4 and CO 2 respiratory fl uxes, but also for measuring net ecosystem exchange of carbon dioxide. The latter requires the use of transparent chambers and consideration of corrections for photosynthetically active radiation and temperature inside and outside the chamber (Wang et al. 2013 ).

#### **Chambers and Changes in Environmental Conditions**

 Closing a chamber gas-tight from the surrounding environment immediately affects a number of boundary conditions. The pressure inside the chamber might differ from outside, because when chambers are gas-tight and exposed to sunlight, the temperature of the headspace air increases so that air pressure inside in the chamber

 **Fig. 4.2** Theoretical evolution of the concentration of a gas being emitted from the soil upon use of a static chamber. Concentration of the gas above the soil surface ( *black line* ) remains at a relatively constant level; at the moment when the chamber is closed ( *left arrow* ), the concentration in its headspace begins to rise. Along the closing period of the chamber, several gas samples are taken ( *black squares* ) and subsequently the concentration is determined, e.g., by use of gas chromatography. Right after opening the chamber ( *right arrow* ) concentration above soil surface returns to atmospheric background levels. Soil GHG emissions are most commonly calculated from the linear increase of the headspace gas concentration during the chamber closing period ( *red line* ), the volume of the chamber, the area of the soil covered by the chamber, as well as air temperature, air pressure, and molecular weight of the molecule under investigation (see e.g., Butterbach-Bahl et al. 2011 ). It should be noted that changes in gas concentration upon chamber closure can signifi cantly deviate from linearity, showing, e.g., saturation effects. In all cases it should be tested if non-linear fl ux calculation methods do not fi t the better observed changes in chamber headspace concentrations with time (see e.g., Pedersen et al. 2010 )

increases too. Both factors affect the gas exchange between the soil and the air. Thus, chambers should be heat insulated and opaque (except for the determination of net ecosystem respiration; see Zheng et al. 2008a , b ) and a vent should be used (see Hutchinson and Livingston 2001 ) to equilibrate pressure differences between ambient and headspace air. Upon chamber closure of transparent non-insulated chambers exposed to direct sunlight, headspace temperature might increase by 10–20 °C within 20 min. Insulated chambers will also show a slight increase in soil headspace temperature. This affects microbial as well as plant respiratory activity. Therefore, minimizing closure times is necessary not only to minimize the effects of changing headspace gas concentrations on diffusive fl uxes as described above, but to minimize temperature changes as well as (Table 4.1 ). One should therefore calculate the minimum fl ux that can be detected with the analytical instrument to be used and adjust the closure time accordingly. If possible, limit closure time to a

 **Fig. 4.3** The concept of gas pooling. ( **a** ) Gas pooling across chambers for a given sampling time, ( **b** ) gas sample mixing within the syringe, ( **c** ) transfer of the gas sample to a vial, ( **d** ) four vials for four sampling times and fi ve chambers, ( **e** ) air sample analysis via gas chromatography (for further details see Arias-Navarro et al. 2013 )

maximum of 30–45 min. If automated chamber systems are used, change positions weekly or at 2-week intervals to minimize effects on soil environmental conditions, in particular soil moisture. Chambers have been shown to reduce soil moisture even if they open automatically during rainfall (Yao et al. 2009 ).

#### **Chambers and Spatial Variability of GHG Fluxes**

 Soil environmental conditions change on a small scale due to differences in (a) bulk density resulting from machine use or livestock grazing, (b) texture as a consequence of soil genesis, (c) management (rows, inter-rows, cropping), (d) temperature (plant shading), (e) soil moisture (e.g., groundwater distances or as an effect of texture differences), (f) soil organic carbon (heterogeneous distribution of harvest residues) or (g) rooting depth and distribution (with effects on soil microbial diversity, activity, and distribution) (see Fig. 4.1a ). For example, urine or feces dropping by livestock on rangeland or manure application to cropland has been shown to increase spatial and temporal variability of fl uxes, since at plot scale not every patch responds equally to increased availability of substrate for microbial N and C turnover processes due to small-scale differences in soil properties, soil environmental conditions, and microbial activity and diversity. Overcoming spatial variability effects on GHG fl uxes is a major challenge, specifi cally for highly diverse smallholder systems. The problem can be addressed by proper sampling design (Fig. 4.1 ) (see e.g., Davidson et al. 2002 ) or by using the gas pooling technique (Arias-Navarro et al. 2013 ) (Fig. 4.3 ).

 Proper sampling design in this context requires fi rstly that the landscape should be stratifi ed into a number of separate categories. This stratifi cation needs to include geophysical information as well as management activities. Also, in order to understand the drivers of the management decisions, it is critical to collect the political and socioeconomic climate of the various farms. The sampling approach can then concentrate measurement activities on emission hotspot and leverage points to capture heterogeneity and account for the diversity and complexity of farming activities (Rosenstock et al. 2013 ).

 The gas pooling technique is similar to what is usually done for soil or water analyses. The principal idea of gas pooling is to generate a composite air sample out of the headspace of several chambers (Fig. 4.3 ). The chamber headspace is sampled at least four times across the closure period as is usually done, but gas samples at time 0, 10, 20, or 30 min are combined for several chambers of each individual sampling time (Arias-Navarro et al. 2013 ). As a consequence, information on the spatial variability is lost, but can be regained if on some sampling days, fl uxes of the chambers are measured individually. This technique allows installation of a signifi cantly higher number of chambers without increasing the amount of gas samples to be analyzed.

## **4.3 Measurement of GHG Fluxes in Rice Paddies**

 Due to its importance as a source for atmospheric CH 4 we specifi cally discuss measurement of GHG fl uxes in rice paddies in more detail. Unlike other fi eld crops, rice is usually grown in fl ooded fi elds. The standing water creates anaerobic conditions in the soil that allows growth of a certain class of microorganisms ( *methanogenic archaea* ) that use simple carbon compounds (e.g., CO 2 or acetate) as electron donors and produce methane in anaerobic respiration. Methane oxidation , on the other hand, does occur but only in the uppermost mm of fl ooded paddy soil or in the rhizosphere—due to radial O 2 losses of rice roots (Butterbach-Bahl et al. 1997a , b )—and during unfl ooded periods. Since methanogenic archaea are extremely sensitive to oxygen and immediately stop CH 4 production while stimulating CH 4 oxidation, drainage of rice fi elds is an attractive mitigation option.

 Methane is the most important GHG in rice production systems and has some implications on the chamber design and sampling time. Nitrous oxide emissions are generally low in fl ooded fi elds but increase with drainage. However, this increase in N 2 O emissions does not offset the mitigation effect that dry fi eld conditions have on CH 4 emissions (Sander et al. 2014 ).


 **Table 4.2** Overview of recommended minimum requirements for closed chamber sampling in rice paddy and for measurements of fi eld GHG fl uxes from upland arable fi elds

 These recommendations have been synthesized from prior chamber measurement protocols (see Table 4.1 ) and amended or modifi ed on basis of expert judgments. For further details see also Fig. 4.1

 Overall, requirements for GHG measurements in fl ooded rice production systems (dominated by CH 4 emissions) are partly different from measurement in upland systems , which has some important implications on the chamber design and general sampling procedure (Table 4.2 ).

## *4.3.1 Rice Chamber Design and General Procedure (See Also Table 4.2 )*

 Methane that is produced in the soil has three different emission pathways to the atmosphere: (1) diffusion through the water layer, (2) ebullition (bubbling), and (3) transport through the aerenchyma of the rice plants. The largest share of emitted methane (up to 90 %) is in fact transported through the rice plant itself (Wassmann et al. 1996 ; Butterbach-Bahl et al. 1997a , b ), which makes it indispensable to include rice plants into the closed chamber (→ chamber height >1 m). This also applies to any measurements of wetland GHG fl uxes, since plant- mediated transport is of critical importance here as well. The chamber base (the part of the chamber that remains in the soil during the whole growing season) should be installed at least 1 day (better a week or more) before the start of the sampling campaign and should not be higher than ~20 cm (with 10 cm below and 10 cm above soil surface) in order to minimize an effect on plant growth. To account for variability within the fi eld, each chamber should include at least 4 rice plants or 4 "hills" in a transplanted system and an area of average plant density in a seeded system, resulting in a chamber area of ≥0.16 m 2 . Note that due to the fl ooded fi eld conditions, the chamber base in rice systems should have holes (~2 cm above soil surface) to allow water exchange between the chamber inside and the fi eld. This hole or holes must be closed before sampling in case irrigation water level falls and the hole(s) is above the water layer.

 Movement in the wet paddy soil can potentially cause gas bubbles to evolve and impede undisturbed gas sampling. Therefore, installation of boardwalks in the fi eld is highly recommended. Exposure to high air temperatures and high solar radiation often characterize rice paddies and so it is in especially crucial to ensure that the plants inside the chambers are not damaged by heat stress during sampling. Therefore, the chamber material should be refl ective or white or the chamber should be equipped with proper insulation. Since the gas volume in the closed chamber changes due to temperature increase and samples being taken, chambers should have a vent to allow equilibration with outside air pressure.

## *4.3.2 Time of Day of Sampling*

 Methane emissions typically follow a distinct diurnal variation following changes in soil temperature (Neue et al. 1997 ), i.e., low emissions during night time that increase after sunrise, peak around noon to early afternoon and decrease again thereafter. Therefore the timing of gas sampling is of great importance in order to measure as close as possible to a time representing a daily average fl ux rather than at times leading to over or underestimation of fl uxes. Minamikawa et al. ( 2012 ) found that methane fl uxes around 10 a.m. were closest to the daily mean CH 4 fl ux in temperate regions. Similar assumptions are likely valid for tropical and subtropical regions. However, we recommend measuring region-specifi c diurnal emission patterns at least three times during the growing season of rice and based on the observed diurnal pattern to decide on the best sampling time. Alternatively, measuring diurnal soil temperature profi les at 5-cm depth can provide reasonable estimations of the time of day with mean methane emission because soil temperature and CH 4 fl ux are closely related.

## *4.3.3 Sampling Frequency*

 The precision of cumulative seasonal GHG emissions largely depends on the sampling frequency. Minamikawa et al. ( 2012 ) found that sampling once a week for fl ooded rice in temperate regions resulted in an accurate estimation of total emissions. Buendia et al. ( 1998 ) proposed a more fl exible sampling schedule of 10-day intervals in the beginning of the growing season, 20-day intervals in the middle and 7-day intervals at the end of the season in tropical environments and came up with similarly accurate seasonal emission estimates.

 It is important to note that more frequent sampling is necessary during dry periods of rice cultivation as methane emissions from paddy soils with a high clay content show a sharp peak when drainage is applied (Lu et al. 2000 ) and nitrous oxide emissions increase during dry periods (Jiao et al. 2006 ). In order to have complete fl ux information of an area, some gas samples should also be taken between two cropping seasons.

## **4.4 Analytical Instruments Used for Chamber Measurements**

 When using the static chamber approach, several analytical instruments can be used for determining GHG concentrations in the sample air, either directly in the fi eld or, following storage of headspace gas samples in vials or gas-tight syringes, at a later time in the laboratory. The latter always requires that the gas-tightness of the vials/ syringes is tested regularly.

## *4.4.1 Gas Chromatography*

 Instruments used for gas sample analysis rely on different operational principles. Gas chromatography ( GC ) is the most commonly used analytical technique when determining GHG concentrations in gas samples from chambers (e.g., Keller et al. 1986; Kiese and Butterbach-Bahl 2002; Kelliher et al. 2012). Usually, 1–3 mL of air sample is injected into the gas chromatograph and the different compounds are separated on an analytical column (e.g., Hayesep N for N 2 O, 3 m, 1/8″) for detection with various detectors. For N 2 O a 63 Ni Electron Capture Detector (ECD) is commonly used. The ECD should be operated at between 330 and 350 °C, since the N 2 O sensitivity is highest and the cross-sensitivity to CO 2 is lowest in this range. However, there is still a cross-sensitivity to CO 2 if N 2 is used as sole carrier and purge gas (Zheng et al. 2008a , b ; Wang et al. 2010 ). No cross-sensitivity exits if Argon/CH 4 is used as carrier gas or if the ECD cell is purged with a gas mixture of 5 % CO 2 in N 2 (Wang et al. 2010 ). Another possibility to eliminate the cross-sensitivity of N 2 O and CO 2 is to use a pre-column fi lled with Ascarite (coated NaOH), which scrapes the CO 2 from the gas-stream. However, pre-columns need to be changed frequently (approximately 2-week intervals) due to saturation and capturing of air sample moisture.

 Another critical point is that if gas chromatographs with ECD are used for concentration measurements, the signal to concentration ratio might deviate from a linear response if—in the case of N 2 O—sample air concentrations are signifi cantly >700 ppbv. Therefore, a check of the linearity of the signal to concentration ratio should be done for each instrument and gas under consideration.

 For CH 4 a fl ame ionization detector (FID) is normally used and, if a methanizer is introduced before the detector, CO 2 can also be measured with a FID (or more standard: use of a thermal conductivity detector for CO 2 ).

## *4.4.2 Spectroscopic Methods*

 Spectroscopic methods are becoming more and more prominent for measuring GHG fl uxes between soils and the atmosphere by static chamber technique. A specifi c example is photoacoustic spectroscopy (PAS), with instruments being miniaturized to make them suitable for direct fi eld use, e.g., allowing direct measurements of changes in chamber headspace N 2 O, CH 4 , or CO 2 concentration with time following chamber closure (e.g., Leytem et al. 2011 ). PAS technique, as every spectroscopic method, is based on the principle that GHGs absorb light at a specifi c wavelength, here in the infrared spectra. The absorption is thereby directly linked to the concentration (Beer-Lambert law) and in the case of PAS, the absorption of the light or energy is converted into an acoustic signal, which is measured by a microphone. For chamber measurements in the fi eld, the PAS instrument is usually connected to the chamber in a closed loop so that the air from the apparatus exhaust is returned to the chamber avoiding underpressure or dilution.

 PAS instruments are becoming popular as an alternative to GC-technique due to portability, low maintenance, and ease-of-operation (Iqbal et al. 2012 ). In principle, commercially available PAS instruments, such as INNOVA (Lumasense Technologies) require a yearly calibration only and are "plug-and-play" instruments ready to be used in the fi eld. However, because GHGs and water vapor have multiple absorption bands across the measuring spectra, such instruments are prone to interferences. Recently, Rosenstock et al. ( 2013 ) showed that for INNOVA instruments N 2 O concentration measurements were non-linearly affected by water content and CO 2 . Comparable results were already reported by Flechard et al. ( 2005 ), though only a few researchers have noted the problems that might be associated with the use of PAS. The manufacturers claim that the INNOVA software accounts for cross interferences, but corrections do not seem to work suffi ciently while testing several instruments (Rosenstock et al. 2013 ). Furthermore, there is also evidence that ambient air temperature affects the electronics and thus, the reliability of measured GHG concentrations (Rosenstock et al. 2013 ), when using PAS under fi eld conditions. Specifi cally for N 2 O, measured concentrations varied up to 100 % depending on environmental conditions (Rosenstock et al. 2013 ). Also the precision and accuracy of CH 4 measurements seems to be rather low, with deviations in concentration of nearly 400 % for calibration gases (Rosenstock et al. 2013 ). As it stands now, it is advisable to question the use of INNOVA instruments for CH 4 as well as for N 2 O measurements in particular by using the instrument for simultaneous measurements of multiple gas species.

 Other techniques may include tunable diode lasers (TDL), quantum cascade lasers (QCL), Fourier transform infrared spectroscopy (FTIR) or cavity ring-down spectroscopy (CRDS). Instruments using these spectroscopic techniques usually operate under high vacuum and, thus, a continuous air fl ow through the instrument is required. Therefore, instruments need to be at the study site and physically connected to chambers. Though these instruments are still quite expensive (e.g., compared to GC) they are becoming more and more robust and suitable for fi eld applications. However, a constant (use of UPS is suggested) main power supply is still needed and checks for cross-sensitivity should be a standard procedure in the laboratory .

## *4.4.3 Auxiliary Measurements*

 As described earlier in this chapter, spatiotemporal patterns of GHG fl uxes are closely linked to changes in environmental conditions (see also Fig. 4.1 ). Therefore, GHG fl ux measurements are rather useless if environmental parameters such as soil and vegetation properties and management are not monitored at the same time, since these factors signifi cantly affect fl uxes. This necessarily also includes the quantifi cation of soil C and N stocks, as for example application of animal manure to arable fi elds and rangeland has been shown to signifi cantly increase soil carbon stocks (Maillard and Angers 2014 ), which need to be considered when calculating the GHG balance of a given system. Moreover, since GHG fl ux measurements are expensive and can't be repeated everywhere, models need to be developed, tested, and fi nally used for estimating fl uxes at landscape, regional, and global scale as well as for exploring mitigation options at multi-year scales or for predicting climate change feedbacks on biosphere–atmosphere exchange processes. Comprehensive datasets, including both fl ux measurements and detailed information on soil and vegetation properties and management are prerequisites for model development and testing. Surprisingly such datasets are still scarce, because either fl ux measurements do not meet the required measuring standards or the needed auxiliary measurements and site information are not monitored or reported.

 Since responsibilities for GHG fl ux and auxiliary measurements are often split between collaborators, there is a need to clarify personal responsibility of data provision prior to the start of measurements. Rochette and Eriksen-Hamel ( 2007 ) reviewed published N 2 O fl ux data and developed a minimum set of criteria for chamber design and methodology. According to their evaluation of 365 studies, there was low to very low confi dence in reported fl ux values in about 60 % of the studies due to poor methodologies or incomplete reporting. Thus, it is necessary to improve not only the quality of fl ux measurements, but also the reporting of soil and vegetation properties and management. See Fig. 4.1 for suggested variables for measurement.

## **4.5 Conclusions**

 Micrometeorological or chamber-based techniques can be used for the quantifi cation of biosphere–atmosphere exchange processes of GHGs. In view of the diversity and patchiness of land uses and land management associated with smallholder agriculture, chamber-based methods, specifi cally the closed (static) chamber approach, is recommended. Overcoming spatial and temporal variability of fl uxes remain an issue, and should be addressed by a well- designed sampling scheme including landscape targeting of measuring sites (see Rufi no et al. this book), targeting of chamber placement at fi eld and plot scale (Fig. 4.1 ), running of at least 3–5 replicates per plot to address small- scale variability (and possibly use of the gas pooling technique, Fig. 4.3 ), fl ux measurements in weekly intervals over a period of at least 1 year and detailed documentation of environmental conditions and fi eld activities (Fig. 4.1 ). This will ensure that all data can fi nally be used for modeling and upscaling. Quality control and quality assurance remains an issue at all steps, also with regard to gas analytics. Probably the most effi cient way for a researcher to familiarize him- or herself with gas fl ux measurement techniques is a longer stay with a recognized research group.

 **Acknowledgments** This work was fi nanced by the SAMPLES project of the Climate Change, Agriculture and Food Security (CCAFS) research program of CGIAR Centre and Research Programs. Eugenio Diaz-Pínés received additional funding from the EU-InGOS project.

 **Open Access** This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

## **References**


 Barton L, Kiese R, Gatter D, Butterbach-Bahl K, Buck R, Hinz C, Murphy DV (2008) Nitrous oxide emissions from a cropped soil in a semi-arid climate. Glob Chang Biol 14:177–192

Batjes NH (1996) Total carbon and nitrogen in the soils of the world. Eur J Soil Sci 47:151–163


## **Chapter 5 A Comparison of Methodologies for Measuring Methane Emissions from Ruminants**

#### **John P. Goopy , C. Chang , and Nigel Tomkins**

 **Abstract** Accurate measurement techniques are needed for determining greenhouse gas (GHG) emissions in order to improve GHG accounting estimates to IPCC Tiers 2 and 3 and enable the generation of carbon credits. Methane emissions from agriculture must be well defi ned, especially for ruminant production systems where national livestock inventories are generated. This review compares measurement techniques for determining methane production at different scales, ranging from in vitro studies to individual animal or herd measurements. Feed intake is a key driver of enteric methane production (EMP) and measurement of EMP in smallholder production systems face many challenges, including marked heterogeneity in systems and feed base, as well as strong seasonality in feed supply and quality in many areas of sub-Saharan Africa.

 In vitro gas production studies provide a starting point for research into mitigation strategies, which can be further examined in respiration chambers or ventilated hood systems. For making measurements under natural grazing conditions, methods include the polytunnel, sulfur hexafl uoride (SF 6 ), and open-path laser. Developing methodologies are briefl y described: these include blood methane concentration, infrared thermography, pH, and redox balance measurements, methanogen population estimations, and indwelling rumen sensors.

J. P. Goopy (\*)

C. Chang

N. Tomkins

International Livestock Research Institute (ILRI) , Old Naivasha Rd. , P.O. Box 30709 , Nairobi , Kenya e-mail: j.goopy@cgiar.org

Commonwealth Scientifi c and Industrial Research Organisation (CSIRO) , Townsville , QLD , Australia

Commonwealth Scientifi c and Industrial Research Organisation (CSIRO), Livestock Industries , Townsville , QLD 4811 , Australia

<sup>©</sup> The Editor(s) (if applicable) and the Author(s) 2016 97 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_5

## **5.1 Introduction**

 Fermentation processes by rumen microbes result in the formation of reduced cofactors, which are regenerated by the synthesis of hydrogen (H 2 ) (Hungate 1966 ). Accumulation of excessive amounts of H 2 in the rumen negatively affects the fermentation rate and growth of some microbial consortia. Methanogens therefore reduce carbon dioxide (CO 2 ) to methane (CH 4 ) and water (H 2 O) thereby capturing available hydrogen (McAllister et al. 1996 ). It is predicted that total CH 4 emissions from livestock in Africa will increase to 11.1 mt year −1 by 2030, an increase of 42 % over three decades (Herrero et al. 2008 ). Production increases and effi ciencies in the livestock sector are seen as complementary outcomes if enteric methanogenesis can be reduced. While mitigation strategies are focused on manipulation of nutritional factors and rumen function, animal breeding programmes for selecting highly effi cient animals that produce less enteric CH 4 might also be useful. Regardless of the mitigation strategy imposed, any reduction in enteric methane production (EMP) must be quantifi ed and for this to be achieved, accurate baseline emissions data are essential.

 This chapter reviews the existing and developing methodologies for gathering accurate data on ruminant methane production under a wide range of production systems. The principles of using predictive algorithms based on dietary, animal and management variables are considered here for modelling smallholder livestock emissions, but not in detail. Predictive models have been considered in detail elsewhere (Blaxter and Clapperton 1965 ; Kurihara et al. 1999 ; Ellis et al. 2007 , 2008 ; Charmley et al. 2008 ; Yan et al. 2009 ). Major techniques are highlighted at different levels—in vitro, animal, herd and farm scale—and their advantages and disadvantages, including implementation in practice, are discussed. These methodologies can be used to support mitigation strategies or quantify total national livestock emissions.

## **5.2 Indirect Estimation**

## *5.2.1 In Vitro Incubation*

 The amount of gas released from the fermentation process and the buffering of volatile fatty acids (VFAs) is related to the kinetics of fermentation of a known amount of feedstuff (Dijkstra et al. 2005 ). Several systems have been developed for measuring in vitro gas production, varying considerably in complexity and sophistication. Menke et al. ( 1979 ) describes a manual method using gastight syringes, which involves constant registering of the gas volume produced. More recently others have described a system using pressure transducers (Pell and Schofi eld 1993 ; Theodorou et al. 1994 ; Cone et al. 1996 ). Variants of this system are now available as proprietary systems (RF, ANKOM Technology ® ) using radiofrequency pressure sensor modules, which communicate with a computer interface and dedicated software to record gas pressure values.

 The basic principle of the in vitro technique relies on the incubation of rumen inoculum with a feed substrate under an anaerobic environment in gastight culture bottles. Gas accumulates throughout the fermentation process and a cumulative volume is recorded. Gas volume curves can be generated over time. To estimate kinetic parameters of total gas production, gas production values are corrected for the amount of gas produced in a blank incubation and these values can be fi tted with time using a nonlinear curve fi tting procedure in GenStat (Payne et al. 2011 ) or other suitable software. Headspace gas samples are taken to analyze the gas compositions and determine actual CH 4 concentrations, typically by gas chromatography. A "quick and dirty" alternative is to introduce a strongly basic solution, such as NaOH into the vessel, which will cause the CO 2 to enter the solution. The remaining gas is assumed to be CH 4 .

 Gas is only one of the outputs of microbial fermentation, and the quality of the information derived can be improved by also considering substrate disappearance and production of VFAs (Blümmel et al. 2005 ).

## *5.2.2 Estimation from Diet*

 EMP can be estimated from intake and diet quality (digestibility). A number of algorithms can be used to do this, although estimates of emissions can vary by 35 % or more for a particular diet (Tomkins et al. 2011 ). Diet quality can be inferred from analysis of representative samples of the rations or pasture consumed, but where intake is not measured, estimation of EMP faces considerable challenges. Models which estimate intake based on diet quality or particular feed fractions assume ad libitum access, and in situations where animals are corralled without access to feed overnight, the validity of this assumption is likely violated (Jamieson and Hodgson 1979 ; Hendricksen and Minson 1980 ). In such a case, intake can be inferred from energy requirement (Live Weight (LW) + Energy for: LW fl ux; maintenance + lactation and pregnancy + locomotion) using published estimates (such as National Research Council) to convert physical values into energy values and so infer intake of the estimated diet. If this method is chosen, multiple measurements are required to capture changes in these parameters, as well as seasonal infl uences on feed availability and quality. Where possible, estimates made using this methodology should be validated by measurements in respiratory chambers.

## **5.3 Direct Measurement**

## *5.3.1 Open-Circuit Respiration Chambers*

 Models to estimate national and global CH 4 emissions from sheep and cattle at farm level are mostly based on data of indirect calorimetric measurements (Johnson and Johnson 1995 ). Respiration chambers are used to measure CH 4 at an individual animal level. Their use is technically demanding, and only a few animals can be monitored at any one time (McGinn et al. 2008 ). However, these systems are capable of providing continuous and accurate data on air composition over an extended period of time.

 Although the design of chambers varies, the basic principle remains the same. Sealed and environmentally controlled chambers are constructed to house test animals. All open-circuit chambers are characterized by an air inlet and exhaust, so animals breathe in a one-way stream of air passing through the chamber space. Air can be pulled through each chamber and, by running intake and exhaust fans at different speeds, negative pressure can be generated within the chamber. This is to ensure that air is not lost from the chamber (Turner and Thornton 1966 ). However, CH 4 can still be lost from chambers that are imperfectly sealed (down the concentration gradient), so gas recovery is an essential routine maintenance task. Thresholds for chamber temperature (<27 °C), relative humidity (<90 %), CO 2 concentration (<0.5 %), and ventilation rate (250–260 L min −1 ) have been described (Pinares-Patiño et al. 2011 ), but may vary in practice. It is very important, however, to ensure that test animals remain in their thermoneutral zone while being measured, or intake is likely to be compromised. Some chambers may be fi tted with air-conditioning units, which provide a degree of dehumidifi cation and a ventilation system. This ensures that chambers can be maintained at constant temperature (Klein and Wright 2006 ) or at near-ambient temperature to capture normal diurnal variance (Tomkins et al. 2011 ). Choices about temperature are governed by technical resources and experimental objectives. Feed bins and automatic water systems may also be fi tted with electronic scales and meters to monitor feed and water intake.

 Change in O 2 , CO 2 , and CH 4 concentrations is measured by sampling incoming and outgoing air, using gas analyzers, infrared photoacoustic monitors, or gas chromatography systems (Klein and Wright 2006 ; Grainger et al. 2007 ; Goopy et al. 2014 b). The other essential measurement is airfl ow, over a period of either 24 or 48 h. The accuracy and long-term stability of the measurements are dependent on the sensitivity of the gas analyzers used and the precision of their calibration. Chambers are directly calibrated by releasing a certain amount of standard gas of known concentration to estimate recovery values (Klein and Wright 2006 ). Measurement outcomes are also infl uenced by the environmental temperature, humidity, pressure, incoming air composition, and chamber volume. The larger the chamber, the less sensitive the measurements are to spatial fl uctuations, as the response time is dependent on the size of the chamber and the ventilation rate (Brown et al. 1984 ). The calibration of the gas analyzers must be accurate and replicable for long-term use.

 One constraint of this technique is that normal animal behavior and movement are restricted in the respiration chambers. Animals benefi t from acclimatization in chambers prior to confi nement and measurement, in order to minimize alterations in behavior, such as decreased feed intake (McGinn et al. 2009 ). However, there is clear evidence that this will happen in a small proportion of animals, regardless of training (Robinson et al. 2014 ) and this should be borne in mind when interpreting data. Using transparent construction material in chamber design allows animals to have visual contact with the other housed animals.

 There are high costs associated with the construction and maintenance of opencircuit respiration chambers. The need for high performance and sensitive gas analyzers and fl ow meters must be considered in design and construction. Only a few animals can be used for measurements within chambers at any one time (Nay et al. 1994 ). Nevertheless, respiration chambers are suitable for studying the differences between treatments for mitigation strategies, and continue to be regarded as the "gold standard" for measuring individual emissions.

## *5.3.2 Ventilated Hood System*

 The ventilated hood system is a simplifi cation of the whole animal respiration chamber, as it measures the gas exchange from the head only, rather than the whole body. Moreover, it is an improvement on face masks as used by Kempton et al. ( 1976 ), because gas measurements can be generated throughout the day and animals are able to access food and water.

 Modern ventilated hood systems for methane measurements have been used in Japan, Thailand (Suzuki et al. 2007 , 2008 ), USA (Place et al. 2011 ), Canada (Odongo et al. 2007 ) and Australia (Takahashi et al. 1999 ). Fernández et al. ( 2012 ) describes a mobile, open-circuit respiration system.

 The ventilated hood system used by Suzuki et al. ( 2007 , 2008 ) consists of a head cage, the digestion trial pen, gas sampling and analysis, behavior monitoring, and a data acquisition system. Similarly to whole animal chambers, it is equipped with a digestion pen for feed intake and excreta output measurements. An airtight head cage is located in front of the digestion pen and is provided with a loose fi tting sleeve to position the animal's head. Head boxes are provided with blowers, to move the main air stream from the inlet to the exhaust. Flow meters correct the air volume for temperature, pressure, and humidity. Air fi lters remove moisture and particles from the gas samples, which are sent to the gas analyzers (Suzuki et al. 2007 ). The mobile system of Fernández et al. ( 2012 ) contains a mask or a head hood connected to an open-circuit respiration system, which is placed on a mobile cart.

 The ventilated hood system is a suitable method under some circumstances, especially where open-circuit chambers are not viable. A critical limitation of the hood system is that extensive training is absolutely essential to allow the test animals to become accustomed to the hood apparatus. Thus while it can be used to assess potential of feeds, it is not suitable for screening large numbers of animals. A further consideration is that hoods capture only measurements of enteric methanogenesis and exclude the proportion emitted as fl atus.

## *5.3.3 Polytunnel*

 Polytunnels are an alternative to respiration chambers, and operation and measurements are somewhat simpler. Methane emissions from individual or small groups of animals can be acquired under some degree of grazing. This allows test animals to express normal grazing behavior, including diet selection over the forages confi ned within the polytunnel space (Table 5.1 ). They have been used in the UK to measure


 **Table 5.1** Techniques for estimation of methane emission from livestock


(continued)


**Table 5.1** (continued)


(continued)



CH 4 emissions from ruminants under semi-normal grazing conditions. Murray et al. ( 2001 ) reports CH 4 emissions from sheep grazing two ryegrass pastures and a clover–perennial ryegrass mixed pasture using this methodology. Essentially polytunnels consist of one large infl atable or tent type tunnel made of heavy duty polyethylene fi tted with end walls and large diameter ports. Air is drawn through the internal space at speeds of up to 1 m 3 s −1 (Lockyer and Jarvis 1995 ). In general they are used where emissions from fresh forages are of interest because animals can be allowed to graze a confi ned area of known quality and quantity. When the available forage is depleted the tunnel is moved to a new patch.

 Air fl ow rate can be measured at the same interval as the CH 4 or can be continuously sampled at the exhaust port (Lockyer 1997 ). Micropumps may be used to pass the exhausted air to a dedicated gas analyzer or a gas chromatograph (GC) (Murray et al. 2001 ). Data from all sensors can be sent to a data logger, which captures fl ow rate, humidity, and temperature within the tunnel, and gas production from the livestock. Samples of the incoming and exhaust air can be taken as frequently as necessary, depending on the accuracy required. The samples can be either taken manually or by an automatic sampling and injection system.

 The polytunnel system requires frequent calibration to assure a good recovery rate, which is performed using the same principle as the chamber technique. Methane measurements can be collected over extended periods of time. Fluctuations occur due to changes in animal behavior, position relative to the exhaust port, internal temperature, relative humidity, and grazing pattern of the animal: eating, ruminating, or resting (Lockyer and Jarvis 1995 ; Lockyer and Champion 2001 ). The polytunnel is suitable for measuring CH 4 emissions under semi-normal grazing conditions. It has been reported that the polytunnel method gives 15 % lower readings of CH 4 concentration compared to the respiration chamber method, suggesting that animals actually consume less in the polytunnel. This requires further investigation. Recovery rate is high in both systems: 95.5–97.9 % in polytunnels, compared to 89.2–96.7 % in chambers (Murray et al. 1999 ). With an automated system, measurements can be performed with high repeatability. The system is portable and can be used on a number of pastures or browse shrubs, though again its utility is limited by the inability to capture feed intake.

## *5.3.4 Sulfur Hexafl uoride Tracer Technique*

 The sulfur hexafl uoride (SF 6 ) technique provides a direct measurement of the CH 4 emission of individual animals. This technique can be performed under normal grazing conditions, but can also be employed under more controlled conditions where intake is measured and/or regulated.

 The SF 6 principle relies on the insertion of a permeation tube with a predetermined release ratio of SF 6 into the rumen, administered by mouth (Johnson et al. 1994 ). Air from around the animal's muzzle and mouth is drawn continuously into an evacuated canister connected to a halter fi tted with a capillary tube around the neck. Johnson et al. ( 1994 ) provide a detailed description of the methodology.

 The duration of collection of each sample is regulated by altering the length and/ or diameter of the capillary tube (Johnson et al. 1994 ). Several modifi cations have since been reported with specifi c applications (Goopy and Hegarty 2004 ; Grainger et al. 2007 ; Ramirez-Restrepo et al. 2010 ). Most recently Deighton et al. ( 2014 ) has described the use of an orifi ce plate fl ow restrictor which considerably reduces the error associated with sample collection and should be considered in preference to the traditional capillary tube fl ow restrictors. At completion of sample collection the canisters are pressurized with N 2 prior to compositional analysis by gas chromatography. Enteric CH 4 production is estimated by multiplying the CH 4 /SF 6 ratio by the known permeation tube release rate, corrected for actual duration of sample collection, and background CH 4 concentration (Williams et al. 2011 ), which is determined by sampling upwind ambient air concentration. Williams et al. ( 2011 ) emphasized the importance of correct measurement and reporting of the background concentrations, especially when the method is applied indoors. CH 4 is lighter (16 g mol −1 ) than SF 6 (146 g mol −1 ) and will therefore disperse and accumulate differently depending on ventilation, location of the animals, and other building characteristics.

 This method enables gas concentrations in exhaled air of individual animals to be sampled and takes into account the dilution factor related to air or head movement. The high within- and between-animal variation is a signifi cant limitation of this method. Grainger et al. ( 2007 ) reported variation within animals between days of 6.1 % and a variation among animals of 19.7 %. Pinares-Patiño et al. ( 2011 ) monitored sheep in respiration chambers simultaneously with the SF 6 technique. They reported higher within (×2.5) and between (×2.9) animal variance compared to the chamber technique, combined with a lower recovery rate (0.8 ± 0.15 with SF 6 versus 0.9 ± 0.10 with chambers). These sources of variation need to be taken into account in order to determine the number of repeated measures necessary to ensure accurate results. Moate et al. ( 2015 ) describes the use of Michaelis–Menten kinetics to better predict the discharge rate of capsules, which should reduce error associated with estimating discharge rates. It should also prolong the useful life of experimental subjects through the improved predictability of discharge rates over much longer intervals.

 The SF 6 technique allows animals to move and graze normally on test pastures. This makes the method suitable for examining the effect of grazing management on CH 4 emissions (Pinares-Patiño et al. 2007 ) but it does so at a cost. The SF 6 method is less precise, less physically robust (high equipment failures), and more laborintensive than respiration chamber measures.

## *5.3.5 Open-Path Laser*

 The use of open-path lasers combined with a micrometeorological dispersion method can now be used to measure enteric methane emissions from herds of animals. It therefore facilitates whole-farm methane measurements across a number of pastures.

 The open-path laser method for whole-farm methane measurements is already in use in Canada (McGinn 2006 ; Flesch et al. 2005 , 2007 ), Australia (Loh et al. 2008 ; McGinn et al. 2008 ; Denmead 2008 ; Tomkins et al. 2011 ), New Zealand (Laubach and Kelliher 2005 ) and China (Gao et al. 2010 ). Methane concentration measurements are performed using one or more tuneable infrared diode lasers mounted on a programmable and motorized scanning unit (Tomkins et al. 2011 ). The tuneable infrared diode laser beams to a retro refl ector along a direct path, which refl ects the beam back to a detector. The intensity of the received light is an indicator of the CH 4 concentration (ppm) along the path. In an optimal situation there should be at least one path for each predominant wind direction: one path upwind (background CH 4 ) and multiple paths downwind (CH 4 emission) of the herd. This method assumes that the herd acts as a surface source or, when individual animals can be fi tted with GPS collars, individual animals are treated as point sources.

 Regardless of application, the CH 4 concentration is calculated as the ratio of the external absorption to internal reference-cell absorption of the infrared laser beam as it travels along the path (Flesch et al. 2004 , 2005 ). Methane concentration and environmental indicators such as atmospheric temperature, pressure, and wind direction and speed are continually measured and recorded using a weather station (Loh et al. 2008 , 2009 ). Data—including GPS coordinates of the paddock or individual animals from a number of averaging time periods—can be merged using statistical software. After integrating, WindTrax software (Thunder Beach Scientifi c, Nanaimo, Canada) uses a backward Langrangian Stochastic (bLS) model to simulate CH 4 emissions (g day −1 per animal), by computing the line average CH 4 concentrations with atmospheric dispersion conditions.

 The data integrity of the open-path laser method is highly dependent on environmental factors and the location of test animals. Flesch et al. ( 2007 ) described several criteria to determine data integrity using the open-path laser method. These criteria are based on wind turbulence statistics, laser light intensity, *R*<sup>2</sup> of a linear regression between received and reference waveforms, surface roughness, atmospheric stability, and the source location (surface or point source). Invalid data can be generated as a result of misalignment of the laser, unfavourable wind directions, surface roughness or periods in which the atmospheric conditions (rain, fog, heat waves, etc.) are unsuitable for applying the model (Freibauer 2000 ; Laubach and Kelliher 2005 ; Loh et al. 2008 ). To optimize the positioning of the equipment, these meteorological and physical aspects of the experimental site must be taken into account (Flesch et al. 2007 ; Loh et al. 2008 , 2009 ). Moreover, the measurement area is restricted by the length of the laser paths when using a surface source approach. It is important to defi ne the herd location, as uneven distribution of the herd results in miscalculations of the CH 4 concentration. Tomkins et al. ( 2011 ), comparing open- circuit respiration chambers with the open-path laser technique, reported estimated CH 4 emissions using the bLS dispersion model of 29.7 ± 3.70 g kg −1 dry matter intake (DMI), compared to 30.1 ± 2.19 g kg −1 DMI measured using open-circuit respiration chambers.

 The open-path laser method does not interfere with the normal grazing behavior of the cattle and is noninvasive. Spatial variability is taken into account in these measurements, as the method can simulate gas fl uxes over a large grazing area. Moreover, the tuneable diode laser is highly sensitive and has a fast response to changes in CH 4 concentration, with detection limits at a scale of parts per trillion (McGinn et al. 2006 ). The labor intensity is low, although the equipment requires continuous monitoring. This method is expensive, which refl ects not only the requirement for sensitive and rapid-response instruments to analyze CH 4 concentration, but also the requirement to capture micrometeorology data. Diurnal variations due to grazing and rumination pattern, pasture composition, and individual variation need to be considered in planning experimental protocols to prevent over- or undercalculation of the total emission. Furthermore, DMI determination is not very accurate as this is based on predictive models using the relationship between LW and LW gain, following assumption of the ARC ( 1980 ).

## **5.4 Short-Term Measurement**

 While most assessments of enteric methane emissions are focused on daily methane production (DMP), or the derivative, daily methane yield (MY), there is an increasing impetus to estimate the emissions of large numbers of animals in their productive environment. This is driven both by the demand for data to establish genetic parameters for DMP and to verify mitigation strategies or GHG inventories. This area is discussed only briefl y here, as there is currently limited scope for the application of these technologies in sub-Saharan Africa. The area has been ably reviewed by Hegarty ( 2013 ).

## *5.4.1 Greenfeed* **®** *Emission Monitoring Apparatus*

 Greenfeed **®** is a patented device (Zimmerman and Zimmerman 2012 ) that measures and records short-term (3–6 min) CH 4 emissions from individual cattle repeatedly over 24 h by attracting animals to the unit using a "bait" of pelleted concentrate. By being available 24 h day −1 potential sampling bias is reduced and the technique has been shown to provide comparable estimates to those produced both by respiratory chamber and SF 6 techniques (Hammond et al. 2013 ). However, a signifi cant limitation of the technique is the requirement to supply an "attractant" to lure the animal to use the facility, consisting of up to 1 kg of concentrate pellets per day. This will certainly affect DMP and may also alter VFA profi les or the overall digestibility of the diet. Attempts to use energy neutral attractants, such as water have proven equivocal (J Velazco, personal communication).

## *5.4.2 Portable Accumulation Chambers*

 Portable accumulation chambers ( PAC ) consist of a clear polycarbonate box of approximately 0.8 m 3 volume, open at the bottom and sealed by achieving close contact with fl exible rubber matting. Methane production is measured by the increase in concentration that occurs while an animal is in the chamber for approximately 1 h. PACs were designed to screen large numbers of sheep, variously to identify potentially low and high emitting individuals and to develop genetic parameter estimates in sheep populations. This technique initially showed close agreement with respiratory chamber measurements (Goopy et al. 2009 , 2011 ). Subsequent investigations demonstrated such measurements to be moderately repeatable in the fi eld and to have potential for genetic screening of animals (Goopy et al. 2015 ). Longer-term comparisons of PAC measurements and respiratory chamber data, however, suggest that these two methods may be measuring quite different traits and further investigation is required before committing signifi cant resources to PAC measurements (Robinson et al. 2015 ).

## *5.4.3 Application of CH 4 :CO 2 Ratio*

 Madsen et al. ( 2010 ) proposed using the ratio of CH 4 :CO 2 in exhaled breath to assess EMP in ruminants. This method requires knowledge about the intake, energy content, and heat increment of the ration consumed. Haque et al. ( 2014 ) applied this method, using a fi xed heat increment factor. Hellwing et al. ( 2013 ) regressed opencircuit chamber measurements of DMP in cattle against estimates calculated using CH 4 :CO 2 ratios and found them to be only moderately correlated ( *R*<sup>2</sup> = 0.4), which suggest this method is unsuitable for precision measurements.

## *5.4.4 Spot Sampling with Lasers*

 Spot measurements of methane in the air around cattle's mouths have been made using laser devices to provide short-term estimates of enteric methane fl ux (Chagunda et al. 2009 ; Garnsworthy et al. 2012 ). These estimates are then scaled up to represent DMP — requiring an impressive number of assumptions to be met to satisfy such scaling. Chagunda and Yan ( 2011 ) have claimed correlations of 0.7 between laser and respiratory chamber measurements, but this claim is based on the laser apparatus measuring methane concentrations in the outfl ow of the chambers, rather than from the animals themselves.

## **5.5 Emerging and Future Technologies**

## *5.5.1 Blood Methane Concentration*

 This methodology relies on enteric methane being absorbed across the rumen wall, transported in the blood stream to the pulmonary artery and respired by the lungs. The jugular (vein) gas turnover rate of enteric SF 6 (introduced by an intraruminal bolus) and CH 4 has been used to determine the respired concentrations and solubility of these gases (Ramirez-Restrepo et al. 2010 ). The solubility coeffi cients and CH 4 concentrations are determined by gas chromatography, comparing the peak area of the sampled gases with standards. Variances in CH 4 and SF 6 blood concentrations may be related to the methodology, or may occur because these gases are not equally reabsorbed. This requires further investigation. Sampling can be logistically challenging and labor-intensive and it is important to recognize that this method provides little more than a "snapshot" of methane concentration at the time of sampling.

## *5.5.2 Infrared Thermography*

 Montanholi et al. ( 2008 ) have examined the use of infrared thermography as an indicator for heat and methane production in dairy cattle. No direct relationship was reported, however, between temperature in any specifi c part of the body and methane production.

## *5.5.3 Intraruminal Telemetry*

 The use of a rumen bolus to measure methane in the liquid phase is logistically possible and small changes (<50 μmol L −1 ) in CH 4 concentrations could be detectable (Gibbs 2008 ). Low pH and redox potential have been correlated with decreased CH 4 concentrations, and a pH and redox sensor have been developed to suit a rumen bolus by eCow Electronic Cow Management at the University of Exeter, UK (www.ecow.co.uk). This technology is still in its exploratory stages but the application of a rumen bolus to measure CH 4 in the rumen headspace has been patented (McSweeney, personal communication.) and could theoretically provide accurate CH 4 concentration estimates for large numbers of free grazing animals.

## *5.5.4 Quantitative Molecular Biology*

 Gibbs ( 2008 ) examined the correlation between the numbers of methanogens and CH 4 production in short time intervals. Results from real-time polymerase chain reaction (PCR) suggest that increased CH 4 production is related to increased methanogen metabolic activity rather than increased population size.

## **5.6 Summary**

 EMP is a complex trait, involving animal physiology and behavior, plant factors, and animal management. Although there are many techniques available to estimate EMP, all have limitations. The appropriateness of a technique is strongly infl uenced by its intended purpose and the degree of precision required. It is important to recognize that while more sophisticated in vitro techniques can provide robust information about the fermentative, and hence, methanogenic potential of feeds, they do not truly represent in vivo fermentation, nor do they account for feed intake, and will be of limited predictive use for animals grazing heterogeneous pastures. If intake is unknown it will diminish the utility of established models, especially when assumptions regarding *ad libitum* intake are violated. Lasers, infrared, and SF 6 techniques can all be used to measure EMP of animals at pasture. However, all are technically fastidious and in situations where intake is unknown, cannot be used to determine emissions intensity. Respiration chambers, while requiring signifi cant capital to construct and technical skill to operate, provide precise and accurate measurements of EMP on known feed intake. Whilst there are justifi ed criticisms surrounding reproducibility of EMP at pasture and evidence of changed feeding behavior in some cases, respiration chambers remain the most accurate method of assessing EMP in individual animals.

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## **References**


## **Chapter 6 Quantifying Tree Biomass Carbon Stocks and Fluxes in Agricultural Landscapes**

 **Shem Kuyah , Cheikh Mbow , Gudeta W. Sileshi , Meine van Noordwijk , Katherine L. Tully , and Todd S. Rosenstock** 

 **Abstract** This chapter presents methods to quantify carbon stocks and carbon stock changes in biomass of trees in agricultural landscapes. Specifi cally it assesses approaches for their applicability to smallholder farms and other tree enterprises in agricultural landscapes. Measurement techniques are evaluated across three criteria: accuracy, cost, and scale. We then recommend techniques appropriate for users looking to quantify carbon in tree biomass at the whole-farm and landscape scales. A basic understanding of the carbon cycle and the concepts of biomass assessment is assumed.

## **6.1 Introduction**

 Trees and woody biomass play an important role in the global carbon cycle. Forest biomass accounts for over 45 % of terrestrial carbon stocks, with approximately 70 % and 30 % contained within the above and belowground biomass, respectively (Cairns et al. 1997 ; Mokany et al. 2006 ). Not all trees exist inside forests, however.

 Jomo Kenyatta University of Agriculture and Technology (JKUAT) , Nairobi , Kenya e-mail: s.kuyah@cgiar.org

 C. Mbow • T. S. Rosenstock World Agroforestry Centre (ICRAF) , UN Avenue-Gigiri , PO Box 30677-00100 , Nairobi , Kenya

 M. van Noordwijk World Agroforestry Centre (ICRAF) , Bogor , Indonesia

 K. L. Tully University of Maryland, College Park , MD , USA

© The Editor(s) (if applicable) and the Author(s) 2016 119 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_6

S. Kuyah (\*)

World Agroforestry Centre (ICRAF) , UN Avenue-Gigiri , PO Box 30677-00100 , Nairobi , Kenya

G. W. Sileshi Freelance Consultant , 5600 Lukanga Road , Kalundu , Lusaka , Zambia


 **Table 6.1** Typical precision for various quantifi cation uses

Trees feature prominently in agricultural landscapes globally. Almost half of all agricultural lands maintain at least 10 % tree cover (Zomer et al. 2014 ) (Table 6.1 ). Despite widespread distribution, tree outside forests ( TOF ) are an often neglected carbon pool and little information is available on carbon stocks in these systems or their carbon sequestration potential (de Foresta et al. 2013 ; Hairiah et al. 2011 ).

 The ubiquity and use of trees in agricultural landscapes is signifi cant for smallholder farmers' livelihoods and modifying local climate (van Noordwijk et al. 2014 ), but it also contributes to global climate change mitigation (Nair et al. 2009 , 2010 ). Even when planted at low densities, the aggregate carbon accumulation in trees can help fi ght climate change because of the large spatial extent covered (Verchot et al. 2007 ; Zomer et al. 2014 ). Such trees are estimated to accumulate 3–15 Mg ha −1 year −1 in aboveground biomass alone (Nair et al. 2010 ), a non-trivial amount when compared to other carbon sinks such as soil. Simultaneously, trees diversify diets, reduce soil erosion, and expand market opportunities for smallholder farmers (Van Noordwijk et al. 2011 ). Thus, trees in agricultural landscapes offer opportunities to mitigate climate change and improve smallholder livelihoods (Kumar and Nair 2011 ). The synergy between climate adaptation and mitigation through trees in agricultural lands is now receiving explicit attention (Duguma et al. 2014 ).

 Despite the signifi cant advances in assessment methods, quantifying carbon stocks and fl uxes at different spatial scale is still challenging. Although National Forest Inventories ( NFIs ) are supposed to provide such guidelines, they are well developed only in the Northern hemisphere. Most NFIs also do not include trees outside forests (TOF) and until recently TOF have been poorly defi ned (de Foresta et al. 2013 ; Baffetta et al. 2011 ). Hence sampling designs that can be consistently applied to both forests and TOF are lacking while ideally national biomass estimates should include carbon estimates of both forests and TOF. Most NFIs (except Sweden and Canada) do not include explicit TOF categories (de Foresta et al. 2013 ).

 The dearth of consistent methodology and a new interest to integrate trees in farming systems in global biomass assessments (de Foresta et al. 2013 ) is catalyzing efforts to generate data on biomass and carbon specifi c for trees on farmland. This, however, comes with the challenge to rapidly develop and standardize methods for biomass assessment, obstacles in the forestry community has been grappling with for decades. Forest-based methodologies can be adapted for some applications. However, TOF present unique issues. To begin with, tree stands in agricultural landscapes typically show irregular shapes when compared to those in more dense forest stands. The geometry of tree stands on farmland is particularly plastic, sensitive to local environmental conditions (Harja et al. 2012 ), and human management (Dossa et al. 2007 ; Frank and Eduardo 2003 ). Tree management (pruning, coppicing, lopping, etc.) may violate assumptions of the available allometries, which were developed based on physiological relationships (e.g., mass and diameter at breast height (DBH) ) observed in forests and plantations (Kuyah et al. 2012a ). The impact of local edaphic conditions on tree growth combined with the diversity of uses and agroecological conditions complicates the construction of a coherent database to represent carbon and biomass estimation equations (BEMs) for farmland trees. The consequence is a scarcity of data and a fragmented understanding of the role trees on farms may play in climate and development discussions.

 With more attention paid to farm forestry, agroforestry, and expansion of the agricultural frontier in many countries, quantifi cation of biomass in trees in agricultural landscapes is receiving greater attention. There is a growing interest in the assessments of carbon stocks and sequestration for carbon monitoring and reporting needs, but also as a way to evaluate agricultural interventions (Thangata and Hildebrand 2012 ). In the following sections, we discuss general considerations of measurement accuracy, cost, and scale when quantifying and discuss the two predominant quantifi cation approaches for biomass and carbon in trees on farms.

## **6.2 Accuracy, Scale, and Cost**

 Accurate estimates of changes in C stocks are required and uncertainties should be reduced as much as is practical (IPCC 2003 ). Yet, uncertainty depends strongly on scale and the costs of high accuracy plus high spatial resolution must be weighed against the benefi ts of farmer incentive schemes that need such information, as opposed to cheaper solutions that meet accuracy targets by spatial aggregation, e.g., to a 1 km 2 scale (Lusiana et al. 2014 ). Methodological limitations and random as well as systematic errors associated with quantifi cation of biomass of trees on farms guarantee uncertainties in estimates. A large degree of uncertainty exists in estimations of C stocks and fl uxes at the local, regional, and global scale. Some of the uncertainty results from the lack of consensus on defi nitions, inconsistencies in methods, and assumptions leading to widely differing results even among similar studies (Sileshi 2014 ). These variations are mainly a result of lack of a common framework for sampling. Uncertainty in C estimation should be addressed to establish the reliability of estimates and provide a basis of confi dence for decisionmaking, particularly where comparisons (e.g., with baseline results) are involved. Identifi ed uncertainties can be quantifi ed through statistical methods such as error propagation (Chave et al. 2004 ). Uncertainties in biomass quantifi cation result from six primary sources in the quantifi cation process: (1) the level of detail in the method used, (2) the complexities of the systems and landscapes being modeled, (3) sampling error, (4) measurement error, (5) model errors, and (6) the inconsistency in


 **Table 6.2** Comparison of approaches and techniques in terms of scale, cost, and accuracy

estimating and reporting biomass components (Chave et al. 2004 ; IPCC 2003 ). Available biomass and carbon estimates for trees on farms vary considerably and associated measures of uncertainty in the estimates (e.g., standard errors and confi dence intervals) are often not reported.

 There is a potential mismatch between the scale at which measurements are made and the scale at which information is required for policy and programmatic development. Different methodologies allow quantifi cation of carbon stocks at various spatial and temporal scales, ranging from plot to landscape scale and shorter and longer time horizons. Here again, the method used depends on the available funds and accuracy required. Field sampling methods destructive (i.e., harvesting trees, drying, and weighing biomass) or non-destructive (i.e., use of BEMs) are affordable and applicable for only a limited number of sites (Table 6.2 ). Remote sensing is practical and effective for mapping aboveground biomass in expansive remote areas, e.g., at regional scale.

 The cost of carbon quantifi cation depends on the method chosen, a choice that is determined by the scale of measurement and desired level of accuracy. The methods presented here vary in their degree of robustness, allowing for trade-offs between accuracy, cost, and practical viability for smallholder systems (Table 6.2 ). The key is to determine information that can be obtained at relatively low cost but still produces estimates within an acceptable level of accuracy. Destructive measurements are known to be costly in terms of resources, effort, and time, and are not permitted for rare or protected species. Modeling with BEMs is therefore an expedient way of estimating carbon both from fi eld inventories or remote sensing. Obtaining fi eld inventories is expensive, slow, and impractical in large areas. Ground-based measurements of tree diameters are therefore often combined with predictive models to estimate carbon stocks in small areas that can be upscaled. The costs on fi eld inventories and analytical methods are greatly infl uenced by the sampling design used and the minimum number of measurement required for a particular method. For both modeling with BEMs and remote sensing, costs can be greatly reduced and effi ciencies of labor and time achieved by adopting multipurpose sampling sites or procedures. For example, the sites could be designed to take measurements for carbon quantifi cation, and also provide data for biodiversity analyses or assessment of vegetation and soil properties. An example is the Land Health Surveillance Framework, designed to cost-effectively enable measurement and monitoring of carbon in a given landscape over years (Vågen et al. 2010 ). Regarding the models, simple power-law models with DBH alone are less expensive to develop and use compared to parameter-rich models. This is because DBH measurements can be easily obtained at low cost compared to specialized equipment required for height or crown area measurements. Remote sensing can greatly reduce the time and cost of collecting data over large areas, particularly for highly variable, widely spaced, and hard-to-access areas (Wulder et al. 2008 ). However, remote sensing approaches such as airplane-mounted LiDAR instruments are still too costly and technically demanding. And while remote-sensing instruments can estimate proxies that can also be converted into biomass using statistical models; additional expenses will be incurred on fi eld data for calibration/validation, which are also prone to errors. This is because there is no remote-sensing instrument that can presently measure tree carbon stocks directly (Gibbs et al. 2007 ).

## **6.3 Quantifi cation of Five Carbon Pools of Representative Plots**

 Tree biomass can be estimated using direct (destructive) or indirect (non- destructive) approaches (Pearson et al. ( 2005 ) or GOFC-GOLD ( 2011 ) for methods, models, and parameters widely used). Direct methods require felling of trees and weighing the component parts. Destructive sampling provides the best data for building BEMs, generating inventory for estimating biomass, and providing requisite information for validating indirectly estimated biomass (Brown 1997 ; Gibbs et al. 2007 ). By contrast, indirect methods (e.g., BEMs and remote sensing) use readily measurable proxies, such as DBH, crown area, or vegetation indices that are then converted into biomass based on statistical relationships established by destructive sampling (Brown 2002 ; Bar Massada et al. 2006 ). Unfortunately, most algorithms and regressions relating remotely sensed data to biomass increase precision, not accuracy. Therefore, it is important to make ground measurements to increase the accuracy of BEMs and remotely sensed data.

 Cost considerations require that estimates of carbon stocks and stock changes on farms and landscapes be based on representative samples from land uses and covers and measurement of proxy variables rather than quantifying biomass on every farm or pixel and destructive sampling of trees, respectively. Indirect measures and statistical models only approximate biomass with a precision subject to the representativeness of the models to local conditions. That latter consideration is particularly salient for smallholder situations in tropical developing countries. Models have largely been constructed on data not collected in the tropics and little in Africa (Hofstad 2005 ; Henry et al. 2011 ) and even fewer data and BEMs are available for trees on farms. Applying equations to data with size range beyond the one that was used in building the equations can lead to high levels of bias and poor estimates of biomass. Biomass—and carbon—estimates by indirect methods will therefore always be inaccurate. Qualitatively, at least, the direct linkage between tree architecture, as modifi ed by farm management, and fractal branching models that generate allometric equations suggests ways to make adjustments where major branches or parts of the crown are missing from trees (Hairiah et al. 2011 ; MacFarlane et al. 2014 ).

 The cost and time of destructive measurement make it impractical for most uses. Therefore, this discussion focuses on indirect quantifi cation methods. Indirect quantifi cation of four IPCC identifi ed biomass carbon pools (aboveground biomass, belowground biomass, deadwood, and litter) involves a series of steps (1) stratifi cation/identifi cation of the target areas, (2) measurement of proxies for biomass, (3) calculating biomass/carbon (4) scaling to whole-farms and landscapes (Fig. 6.1 ). This highlights the need to recognize two aspects to the uncertainty of carbon estimation: the fi rst aspect is plot level—how good are measurements of biomass in the fi eld? Do they account for belowground biomass, dead biomass, soil carbon, hollow trees, and smaller trees e.g., those <10 cm diameter? How good are we at converting wood volumes into total aboveground biomass? The second aspect of uncertainty is converting plot-level measurements across space, either through modeling or with satellite data.

 **Fig. 6.1** Mixed-method approach to fi ve-pool carbon estimates for farms and landscapes

## *6.3.1 Selecting Plots*

 Quantifi cation at farm and landscape scales requires extrapolation from data gathered from relatively small plots to larger areas. Extrapolation is necessary because it is prohibitively expensive to measure every tree on every farm or throughout the landscape. With stratifi cation, we aim to quantify the biomass and carbon at a few representative locations and then use data on the frequency of their occurrence to calculate total biomass at larger spatial extents. It is therefore critically important that the sample is representative of the larger area and farm/ landscape features of interest and an estimate of the frequency of occurrence of the feature of interest is possible (Brown 1997 ). A stratifi ed random sampling approach can be employed to guide sample selection ranging from remote sensing to household surveys. For building BEMs, a randomized pre-sample of trees can be generated from an inventory with respect to a stratifi ed diameter class and trees for destructive sampling chosen through a blind selection without tree species association. For inventories, stratifi cation by topographic features, management infl uence, and age classes are likely to produce more homogenous strata from which sample units could be selected. Age is essential particularly where lifecycle analysis is involved. In rotational plantations this is easy to implement, but in many land use systems derived from natural vegetation by selective retention of trees (e.g., shea or baobab trees in many savanna systems), regeneration pattern need to inform the sample selection. In systems with "internal regeneration," similar to natural forest with a gap renewal cycle, the age of the most frequent tree diameter class can be used to reconstruct a time-averaged carbon stock at the land use system level (Hairiah et al. 2011 ). We refer you to Chap. 2 of this manual and the references therein to determine an appropriate method for stratifying the sample. The remainder of this discussion assumes the availability of representative plots and knowledge of the relative distribution of different features or land use classes in the geographic space of interest.

## *6.3.2 Measurements of Proxies for Tree Biomass*

 Tree biomass is estimated from ground- based inventory data, remote sensing, or a combination of the two. Researchers and project developers tend to rely on BEMs, which calculate tree biomass based on easily measured dimensions based on the idea that standard relationships occur such as the diameter to mass or height to mass (West 2009 ) or root-to-shoot (Cairns et al. 1997 ; Mokany et al. 2006 ). Because of the variations in tree characteristics among ecological conditions, particularly in agricultural landscapes, and the need to account for biomass in all plant parts, it is ideal to use locally developed equations or develop BEMs at a local scale (Henry et al. 2011 ). Where local BEMs are not available, there are two other options. First, volume equation and inventory data arising from commercial interest valuing the stock of wood resources in forests may be available in many developing countries (Hofstad 2005 ; Henry et al. 2011 ). However, this approach provides data primarily on merchantable wood, leaving out components such as branches, twigs, and leaves, yet in some species these components constitute a signifi cant amount, about 3 %, of the total aboveground biomass (Kuyah et al. 2013 ). The second option is to use the pantropical models (e.g., Chave et al. 2005 ). However, these are broadly derived, based on a large dataset and stratifi ed by region or climatic conditions. The defi nition of climatic regimes is not intuitive and direct application of these models could give biased estimates if applied across the board, particularly in agricultural landscapes where trees face multiple stresses (Kuyah et al. 2012a ; Sileshi 2014 ).

 BEMs require the measurement of tree dimensions such as DBH, basal area, height, or crown dimensions. Presuming measurements are conducted with care, accurate biomass estimates are best obtained by measurements of each parameter. However, certain measurements (e.g., height) are diffi cult to obtain accurately in the fi eld by non-destructive methods and hence including this parameter in models may introduce error into the biomass estimates, by a mean of 16 % (Hunter et al. 2013 ). Furthermore, complete datasets are in many cases not necessary to provide a reasonable estimate of biomass because inclusion of all parameters only moderately increases the accuracy of the total estimate. For example, inclusion of DBH alone provided an estimate within 1.5 % of the actual biomass measured in an agricultural landscape of Western Kenya (Kuyah and Rosenstock in review), which agrees with most studies (Cole and Ewel 2006 ; Basuki et al. 2009 ; Bastien-Henri et al. 2010 ). Given the complexities and potential errors in measuring other parameters (i.e., diffi cult terrain or dense foliage when measuring height), the need for specialized tools (e.g., hypsometer or clinometer for height), or destructive measurements (e.g., wood density), the use of DBH alone appears cost-effective and robust for most purposes (Sileshi 2014 ).

 At landscape scales, ground-based inventories are typically too resourceintensive to complete. Instead, crown area—which can be measured by remote sensing—is increasingly being tested for estimating aboveground biomass (Wulder et al. 2008 ; Rasmussen et al. 2011 ; Fig. 6.2 ). Two issues complicate widespread application of remote sensing and crown areas. First, crown area is not as strongly correlated with biomass as DBH . This may be particularly important for trees on farms that show irregular growth patterns due to variable environmental conditions (e.g., near red/far red light interception, availability of soil nutrients) or management by farmers (e.g., limb collection for fi rewood). For example, (Kuyah et al. 2012b ) show crown area measurements alone grossly misrepresent standing stocks of carbon, by about 20 % relative to diameter estimates. It is therefore important to calibrate remotely sensed crown area estimates with fi eld measured DBH to improve the accuracy of measurements. Second, remote sensing of crown areas for trees outside of forests requires high-resolution imagery to differentiate small features such as individual trees on farms. Typically, Quickbird images with sub-m resolution are best suited for this task but cost ~15 USD per km. Without suffi cient

 **Fig. 6.2** Delineation of TOF crowns by remote sensing using sub-meter resolution Quickbird imagery (Gumbricht unpublished)

resolution, it is not possible to identify trees and may lead to underestimation of biomass. Unfortunately, the price of the satellite imagery increases in parallel with the resolution and the specialized skills necessary to process the imagery limits many applications of this technique outside of the research arena at this time. Despite the challenges, crown area allometry is likely the most promising approach to transform our ability to capture information on aboveground biomass stocks, potentially for relatively low total costs in the future (Gibbs et al. 2007 ; Wulder et al. 2008 ).

 Field measurements and remote sensing generate estimates of aboveground biomass. Though most of the carbon in trees is contained in aboveground biomass, a signifi cant fraction can be found in the four other major carbon pools: belowground biomass, litter, deadwood, and soils. Soil carbon is discussed in Chap. 7 (Saiz and Albrech this volume) and thus we restrict this brief discussion to the other three pools. For almost all applications, belowground biomass will be estimated by allometric relationships based on DBH or prescribed root-to-shoot ratios. We are quite skeptical of the accuracy of general root-to-shoot ratios for estimation of belowground biomass as the growth patterns are sensitive to water availability and may range from 10:1 in moist conditions versus 4:1 in arid conditions (IPCC 2003 ). Recent destructive experiments suggest that DBH may be a better predictor than root-to-shoot ratio for trees on farms but again require inventories to establish DBH . Global studies show that belowground biomass (BG) is isometrically related to aboveground biomass (AG) (Hui et al. 2014 ; Cheng and Niklas 2007 ); i.e., BG = *a* (AG). If one can correctly estimate ' *a* ', we believe estimating BG from AG using allometric method may be better than using shoot-to-root ratios.

 Consideration of litter and deadwood deserve unique attention for trees on farm. Litter might be assumed to be in equilibrium with growth and thus ignored in biomass estimation especially on farmland. Deadwood might also be treated in the same way given most will be collected for fi rewood or in slash and burn agriculture, fi re will consume most of it. A case can be made that the relative limited size of these pools justifi es such treatment for most cases, especially when considering decadal timescales. In cases when litter and deadwood need to be estimated, measurements using small nested plots or an independent sampling design will be required. For litter, the information collected is total mass per unit area but for dead wood, depending on the size, one can measure total mass or estimate volume that can be used for mass calculation if wood density is known (Pearson et al. 2005 , 2007 ).

## *6.3.3 Calculating C Stocks and Fluxes*

 Until now, we have been discussing the quantifi cation of biomass stocks in a small plot area. Oftentimes, however, researchers and project developers are more interested in the change in carbon, accumulation or loss, with various practices or land use change. So here we consider methods to quantify rates of change in woody biomass.

#### **Time-Averaged Carbon Stock for Different Land Uses**

 Carbon stocks in trees generally accumulate slowly over time. Often it is therefore most appropriate to analyze the changes over multiple years or decadal time scales. On longer time scales it is possible to analyze the average change (per annum or a given time interval) for the lifecycle of the land use or farming system (see Fig. 6.3 , for example). Stock change accounting assesses the magnitude of change carbon stored between two or more ecosystems that share a reference state. This approach is desirable because it allows a researcher to substitute space for time, overcoming the challenges of returning to measure the same location/land use/trees twice. Researchers locate farming systems existing in the landscape that have already been transformed from other land use systems. Carbon stocks calculated from the different systems can then be compared to provide a relative estimate of changes over time. Characteristically, the changes are standardized to changes per year. This approach assumes that carbon stock changes results from land use change/ management and changes in carbon stocks are linear over the time period examined. This latter assumption negates the temporal dynamics of carbon. Yet, time averaged carbon stock presents a snapshot picture about the relative annual fl ux and cumulative impacts.

 **Fig. 6.3** Time-averaged aboveground carbon and total soil carbon (0–20 cm). *Source* : Hairiah et al. ( 2011 )

#### **Annual Changes: Growth Rates, Dendrochronology, Repeated Measurements**

 Though rarely quantifi ed, examining annual changes in biomass carbon in trees on farm is important when calculating whole-farm GHG balances, that is, when calculating the global warming potential or global warming intensity of the system. Unfortunately, the growth rates of tropical tree species are only known for a small sample of commercially viable timber species and the remaining knowledge gap greatly limits the ability to map or model carbon stock changes. There are typically few options to gain information about annual stock changes in the absence of published growth rates: repeated measurements, biomass expansion factors (BEFs) , and dendrochronology.

 Repeated measurement of the same tree species is an option to create information on growth rates or annual changes in carbon stocks. Repeated measurements must be cautious to return precisely to the same tree/stand and the same measurement of the tree. Because repeated measurement relies on exact locations to document what can sometimes be small changes, this method is sensitive to observational and measurement errors as well as anomalies in growth patterns on the tree selected. Furthermore, repeated measurements can typically only be performed on a limited number of trees. Thus again, tree selection, to account for heterogeneity and minimize sampling artifacts, is critical. Though not without uncertainty, repeated measurement do provide a non-destructive approach to quantify short-term changes in carbon stocks.

 BEFs are another approach of using exiting stand volume data from previous forest studies to assess carbon density. BEF bundles two aspects, a conversion of volume to mass and an inclusion of ignored trees foliage, small branches not accounted in commercial volume assessment. The BEF is a conversion factor that calculates biomass based on traditional commercial volume data (Brown 1997 ).

 The use of dendrochronology is an emerging fi eld of application of tree ring for biomass assessment for individual tree growth. The method is based on the formation of annual rings in many tropical trees in areas with one distinct dry season. Often, this seasonality induces cambial dormancy of trees, particularly if these belong to deciduous species (Brienen and Zuidema 2005 ). Annual tree rings provide growth information for the entire life of trees and their analysis has become more popular in tropical forest regions over the past decades (Soliz-Gamboa et al. 2010 ). It is demonstrated that tree-ring studies is a powerful tool to develop highresolution and exactly dated proxies for biomass accumulation over time in individual trees (Mbow et al. 2013 ). In addition to annual increment of biomass, tree-ring analysis helps characterize climate–growth relationship between tree growth and rainfall in certain periods of the year and how this translates into tree productivity information that is central to carbon sequestration assessment (Mbow et al. 2013 ). Basically the use of such method implies the application of allometric models on diameter over bark on individual rings measured during the tree lifetime (Gebrekirstos et al. 2008 ). Important information can be collected using tree ring: (1) growth rate—average annual diameter increment-of-individual species to reconstruct long-term growth of trees and estimate productivity of trees; (2) age–diameter relationships which are required in carbon projections; (3) limiting factors of tree growth such as long time drought or severe fi res.

## *6.3.4 Scaling to Whole-Farms and Landscapes*

 The fi nal step involves aggregating the data on carbon stocks or stock changes into whole-farm and landscape-scale estimates. The precise scaling methods applied somewhat depend on the types of data collected and the equations used. However, scaling plot measurements will generally proceed in the following steps:


 Because the principal scaling approach relies on similarity-based relationships (e.g., allometric equations) that are scale invariant, the same steps are equally relevant for whole-farms or landscapes, irrespective of the spatial extent. Furthermore, since the results are expressed in CO 2 equivalent ha −1 it is possible to integrate these measures with those from other GHG sources and sinks such as soil carbon or trace gas emissions from soils.

## **6.4 Additional Sources of Information**

 Because of the interest in forest inventories, there are countless sources of information available to help appropriately select and apply various techniques. Table 6.3 tabulates what we feel are the key sources of information, and links to specifi c protocols can be found on the website (http://www.samples.ccafs.cgiar. org/protocol/Biomass).

#### **Table 6.3** Annotated key sources of information

 Brown S (1997) Estimating Biomass and Biomass Change of Tropical Forests: a Primer. (FAO Forestry Paper—134). Food and Agriculture Organization of the United Nations (FAO), Rome, Italy

 This report describes multiple methods for estimating biomass density, including one of the fi rst comprehensive descriptions of methods for destructive biomass estimation. The report includes biomass estimates for different tropical countries based on forest type and climate. Supplementary tables report wood density for different tree species across tropical Asia, America, and Africa

West PW (2009) Tree and Forest Measurement. 2nd edition. Springer, Heidelberg, Germany

 The primary audience for this book is undergraduate forestry students, practicing foresters, and landholders. As such, it introduces the techniques of tree and forest measurement with particular attention paid to non-destructive (allometric) approaches. This book provides a step-by-step description of how to measure trees as well as their component parts and then scale to the stand or population

 One hundred years of tree-ring research in the tropics-a brief history and an outlook to future challenges. Dendrochronologia 20:217-231

 This article describes the history of tree-ring analysis in the tropics. Tropical dendrochronology is hotly debated primarily because the consistent intra-annual temperatures of tropical systems do not produce the same tree-ring pattern we observe in temperate tree-rings. Worbes discusses the progress in and applications of tropical tree-ring research. One such application that we would like to highlight is the potential to use tree-rings to evaluate individual tree growth and thus track biomass accumulation through time

(continued)

#### **Table 6.3** (continued)

 Mbow C, Chhin S, Sambou B, Skole DL (2013) Potential of dendrochronology to assess annual rates of biomass productivity in savanna trees of West Africa. Dendrochronologia 31:41-51

 This article describes the application of dendrochronology to assess biomass in individual savanna species in Southern Senegal. The materials and methods section provides a comprehensive description of the steps involved. Note that the destructive sampling was implemented in this study and may not be suitable for some situations

 Gibbs HK, Brown S, Niles JO, Foley JA (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2:13

 Using REDD (reductions in emissions from deforestation in developing countries) as a backdrop, this review paper discusses a range of methods available to estimate nationallevel forest carbon stocks using both ground-based and remotely sensed measurements of particular forest characteristics which can be converted into estimates of national carbon stocks using allometric relationships. This is the fi rst article to report a complete set of national-level carbon stock estimates

**Open Access** This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

## **References**


allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145(1):87–99


## **Chapter 7 Methods for Smallholder Quantification of Soil Carbon Stocks and Stock Changes**

#### **Gustavo Saiz and Alain Albrecht**

**Abstract** Smallholder agricultural systems in tropical and subtropical regions may have significantly contributed to greenhouse gas (GHG) emissions over the past number of decades. As a result, these systems currently offer large GHG mitigation potentials (e.g., soil organic carbon (SOC) sequestration), which can be realized through the implementation of good management and sustainable agricultural practices. In this chapter we synthesize current available methodologies designed to assess SOC stocks and stock changes. From this analysis, it becomes apparent that the design and subsequent implementation of any quantification and monitoring scheme envisaged for studies focusing solely on the soil component greatly differs from those developed for whole ecosystem accounting, not just in its approach, but also in the amount of resources needed to implement it within a given degree of accuracy. We provide analyses and recommendations on methods specifically dealing with quantification and assessment of SOC at both the individual farm and the landscape scale in smallholder agricultural systems.

## **7.1 Introduction**

Agricultural activities are responsible for about one-third of the world's greenhouse gas (GHG) emissions and this share is projected to grow, especially in developing countries (IPCC 2007). Indeed, smallholder agricultural systems are highly dynamic and heterogeneous environments that may have significantly contributed to GHG emissions over the past number of decades (Berry 2011). Furthermore, these systems traditionally suffer from severe soil organic matter (SOM) depletion due to intense decomposition following soil ploughing, the

G. Saiz (\*)

© The Editor(s) (if applicable) and the Author(s) 2016 135

Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstrasse 19, 82467 Garmisch-Partenkirchen, Germany e-mail: gustavo.saiz@kit.edu

A. Albrecht Institute of Research for Development (IRD), Montpellier, France

T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_7

removal of most of the aboveground biomass during harvest, and the enhanced soil erosion inherent to those activities. Yet, they may also offer large mitigation potentials through the implementation of good management and sustainable agricultural practices, particularly through improvements in land-use management, as nearly 90 % of IPCC-identified technical potential lies in enhancing soil carbon sinks (Lipper et al. 2011).

A number of methodologies are currently available for the quantification of carbon stocks in terrestrial ecosystems, varying widely in terms of accuracy, scale, and resources needed for their implementation (e.g., Pearson et al. 2005; Ravindranath and Ostwald 2008; Hairiah et al. 2010). Table 7.1 offers a comparative analysis of methods for quantification of soil organic carbon (SOC) stocks and changes with regard to level of accuracy, scale, resources demanded, and land covers considered. While nearly all the schemes feature soil as a component of the total carbon pool, the number of methods specifically designed to assess SOC stocks and stock changes are considerably more limited. This is despite the wide acknowledgement that many ecosystem services are strongly correlated with SOC levels, and their huge importance for sustaining local livelihoods. The design and implementation of any quantification and monitoring methodology for studies focusing solely on the soil component may greatly differ from those developed for whole ecosystem accounting, not only in approach or the accuracy but also in necessary resources. Therefore, it is justified to develop methods that can effectively deal with soil carbon quantification and monitoring for a given accuracy within the available budget. In the present work we focus on the soil component and provide analyses and recommendations for methods to quantify SOC in smallholder agriculture in tropical environments.

The SOC inventory in a given soil profile is controlled by the complex interaction of many factors, including climate, soil texture, topography, fire frequency, land use, and land management (Bird et al. 2001; Saiz et al. 2012). These drivers exert contrasting influences on SOC stocks at different spatial scales. At the local scale, biotic factors and management activities play a fundamental role in affecting the quantity and quality of carbon inputs and decomposition processes, while at larger scales the variation in SOC stocks is mainly controlled by topographic, edaphic, and climate-related factors (Wynn and Bird 2007; Allen et al. 2010; Saiz et al. 2012). Ultimately, an increase in SOC levels at a given site may occur either through the reduction of factors promoting SOM mineralization and lateral exports (e.g., erosion), and/or by increasing SOM inputs and enhancing stabilization mechanisms (e.g., physical protection of SOM through stable aggregates).

Given the inherent high spatial variability of SOC, accurate quantification and monitoring of SOC stocks and stock changes is a complex task even in relatively homogeneous ecosystems. This complexity is further exacerbated in smallholder environments by the existence of multiple land use activities occurring at various management intensities. Moreover, sources of uncertainty and suitable levels of precision and accuracy differ when working at the landscape scale as opposed to the farm scope because biogeochemical processes affecting SOC dynamics operate and interact at different spatial scales (Veldkamp et al. 2001; Milne et al. 2013). **Table 7.1** Comparative analysis of methods for smallholder quantification of SOC stocks and changes with regard to level of accuracy, scale, resources demanded, and land covers considered




*Notes*: LMH (low, medium, high)

 aIf remote sensing available bSpecifically designed for paddock (generally an extensive rangeland) cNot designed for sites with signs of significant erosion

dOnly areas with climate smart agricultural activities considered

Therefore, efficient sampling designs are needed across smallholder agricultural systems to ensure that SOC stocks and stock changes can be detected at various scales for a given accuracy and at minimum costs (Milne et al. 2012; Singh et al. 2013). Chapters 2 and 3 in this book provide some critical discussions on sampling designs specific to smallholder contexts. These chapters deal with systems characterization and targeting, and determination GHG emissions and removals associated with land use and land cover change.

In the present work, we propose an integrated field-based approach for small household systems that encompasses estimates of SOC stocks and stock changes both at farm and landscape scales over a wide range of land use management intensities.

## **7.2 Quantification of Soil Carbon Stocks**

## *7.2.1 Sampling Design: Stratification of the Project Area*

While the establishment of a geographical extent for quantification of SOC stocks and stock changes at the farm level can be straightforward, it is not the case for smallholder landscape assessment. The landscape concept may be defined by a geographic or ecological boundary, which often includes a mosaic of land covers and land uses that are managed in several different ways by the multiple stakeholders involved. In this context, Chap. 2 in this book provides recommendations for stratifying the landscape according to its agricultural productivity, economic outputs, potential GHG emissions, and social and cultural values. A SOC quantification scheme could integrate with such a stratification approach at the landscape level.

Herein, we describe the methods specifically dealing with quantification and assessment of SOC at both the individual farm and the landscape scale in smallholder agricultural systems.

#### **Farm Level**

Intensive work conducted over the past decade in smallholder agricultural systems in sub-Saharan Africa has demonstrated the existence of within-farm variability of soil fertility and related soil properties (Prudencio 1993; Carsky et al. 1998; Tittonell et al. 2005a, b, 2013). A common feature of these farming systems is the existence of strong gradients of decreasing soil fertility with increasing distance from the homestead, which mainly occur as a result of differential resource allocation driven by the farmer. This spatial gradient must be taken into account when designing SOC sampling strategies in these agricultural systems, and more so considering that previous work has also identified strong correlations between yields, soil quality indicators, land use management, and the distance from the homestead (Tittonell et al. 2005b, 2013). On the other hand, the presence of either annual or perennial vegetation on a given land use may have a strong impact on SOC stocks, as they significantly determine both the quantity and quality of organic matter inputs into the soil (Guo and Gifford 2002; Saiz et al. 2012). Therefore, distance from the homestead and land use classified by the presence of annual or perennial vegetation, are the main criteria to use in order to categorize field types for the purpose of soil sampling. Accordingly, fields are classified into home gardens, close-distance, middistance, and remote fields following a similar procedure as in Tittonell et al. (2005b). These areas may contain several land uses, and as it may not be feasible to sample all of them, priority should be given to the actual representativeness of the land uses being considered. Therefore, sampling should be preferentially done in the largest fields provided that management activities with potentially heavy impact on SOC stocks, such as manure additions or recurrent burning of stubble, are roughly comparable between the different land uses. However, this assumption may not hold quite true in these farming systems, and thus it is worth noting that if land use management needs to be adequately quantified, then the sampling effort may need to be increased quite considerably. Nonetheless we hypothesize that, on the whole, soil sampling across a spatial gradient may partially account for the effect of land management intensities along the farm, given that such activities are also likely to occur along the same gradient.

#### **Landscape Level**

Assessment of SOC stocks at the landscape scale can be done following a spatially stratified randomized sampling design, as this will allow for a more optimum areal coverage and unbiased assessment of sample mean, variance, and estimation variance of the sample mean. At the landscape level, the stratification can be done either through: (a) ancillary data, or (b) geographic coordinates, which may include the use of a systematic grid over the project area (de Gruijter et al. 2006).

Stratification through *ancillary variables* requires the establishment of discrete strata on which selected factors affecting SOC stocks show some degree of uniformity. Once the study boundaries have been defined, the use of remote sensing in combination with geophysical and management information may provide an effective means to stratify the target area (Ladoni et al. 2010). Such stratification needs to be performed considering, at minimum: available soil classifications, soil texture, landform information, topographic position, land cover, land use, management history, fire records, and obvious soil erosion/deposition processes. The initial stratification should be conducted in a hierarchical order whereby the factor that exerts the strongest influence on SOC stocks is ranked first, and other factors with less influence on SOC are subsequently assigned (e.g., a classical ranking approach might be climate, soil texture, land cover and management, etc.). The VCS module (VMD0018) provides detailed methodology on how to implement and adapt the stratification to the needs of the sampling process. Ideally, the number of samples to be measured in each stratum should be determined as a proportion of the area and the variance observed for that particular stratum. For this, a pilot soil sampling can be conducted which would serve a double purpose: to obtain an initial estimate of the variance for each stratum and serve as a training exercise for technicians who will be involved in subsequent sampling (MacDicken 1997). Nonetheless, it is likely that in smallholder systems, a stratum defined by biophysical factors may still be made up of land parcels managed in highly contrasting ways. Indeed, land management could account for more variation in SOC stocks at the landscape/ regional level than either soil types or land use. Under such circumstances, there may be a need to stratify into a greater number of land use categories to account for land use management practices between farm tenancies (Bell and Worrall 2009). Consequently, the number of samples needed to account for spatial patterns and uncertainty in a highly heterogeneous environment can quickly become impractical due to the cost and time associated with sample collection, preparation, and analyses. To avoid this, *spatially stratified systematic sampling* approaches such as the one employed by the Land Degradation Surveillance Framework (LDSF; Aynekulu et al. 2011; Vågen et al. 2015) are easier to establish and monitor, and therefore may be a cost-effective alternative to provide a representative landscape estimate of SOC stocks and their changes. Moreover, the resulting sampling locations are spatially dispersed across the study area, but the range of variation in SOC stocks is not as effectively covered as with the stratification by ancillary variables. Therefore, the user should make his/her own choice depending on the available resources and the degree of accuracy required. We advocate the stratification by ancillary variables. However, in the case of very large heterogeneous regions, we recommend the implementation of a spatially stratified systematic sampling. It is worth stressing that while both stratification approaches (spatial and using ancillary variables) can yield relatively accurate information about SOC stocks at the landscape level, they lack proper accounting at the farm scale unless specific sampling strategies within a given household are further implemented.

The number of plots required to estimate SOC stocks in each stratum depends on the desired precision, often set at ±10 % of the mean at 90 or 95 % confidence level. The number of plots per stratum can be ascertained through the relationship described by Snedecor and Cochran (1967); See specifics in the detailed methodology section (Appendix A).

An initial soil sampling campaign should be conducted to establish baselines that can be used as references to monitor changes in SOC stocks. The level of precision required for a SOC inventory will undoubtedly influence the number of plots to be sampled, which will have necessarily a very strong impact on the cost associated with fieldwork and soil processing. Indeed, the largest component of the total cost incurred in SOC surveys corresponds to soil sampling and preparation (Aynekulu et al. 2011). Except for the case of surveys in which extremely large numbers of samples are collected (>2000), the actual cost of soil analyses is relatively low compared to the total expenditure derived from the collection and preparation of samples. Withal, and in order to minimize the number of samples to be analyzed, an extensively applied method is the bulking (pooling) of samples collected within a plot at the same depth interval. This procedure has been shown to be a cost-effective technique for smoothing out local heterogeneity and for achieving robust local and regional estimates of SOC inventories (Bird et al. 2004; Wynn et al. 2006; Saiz et al. 2012).

The specific objectives of the study shall ultimately dictate the sampling priorities, which combined with the available resources, will determine the methodology and sampling intensity to apply.

## *7.2.2 Sample* **Collection**

(*See also the Simplified Protocol for this purpose in* Appendix B)

Ideally, samples undergoing analyses should be as representative as possible of the area of interest. To help with this, samples can be combined to provide a single representative composite sample, but there should be at least several composite samples per selected plot to provide an estimate of variance. Therefore, we propose to take three soil samples (which will be subsequently pooled by depth interval before analyses) at four locations in each plot. A plot will correspond to a given field and land use within each selected farm. The initial sampling location will roughly be allocated at the center of the field, with three replicates laid out according to a pattern of three axes separated 120° with respect to an initial axis pointing north. The replicates will be selected along these axes at approximately mid-distance between the center of the field and its boundaries. The final sampling locations will be georeferenced using a GPS, and notes should be taken about the sampling location with regard to the proximity of perennial vegetation (i.e., shrubs, trees, etc.), and any other relevant information such as presence of rock outcrops. Unless very intensive sampling is required in a given particular field, then the low analytical load proposed at the field scale (four composite samples) does not allow for proper intercomparison of small-scale intercropping, or for comparison between furrows and ridges. Therefore, sampling should be systematically allocated at the same ploughing feature (e.g., furrow).

Previous to any sampling surface litter will be removed by hand. Soil samples will then be collected at **0–10** and **10–30**cm depth intervals making use of a steel corer. This procedure will allow for determinations through the retrieval of a single soil core of both OC abundance and accurate soil bulk density (SBD) at each depth interval. Accurate determination of SBD in the topsoil layers is particularly critical given that it is at these shallow locations where SBD shows the largest variability and significantly large quantities of OC are stored. Nevertheless, it is important to note that while the use of a steel corer may be a feasible procedure in many arable lands as a result of both soil being regularly disturbed and stones being progressively removed over the years, the use of a soil auger may be necessary to collect samples in stony or very hard soils. Indeed, impenetrable layers permitting, soil sampling at **30–50**cm needs to be carried out individually at each of the four sampling locations. In this case, replication at each sampling location is avoided because of the considerable extra time and effort that would be required. Section 7.2.4 explains the different procedures that can be used to calculate SOC stocks.

## *7.2.3 Sample Preparation and Analytical Methods*

#### (*See also the Simplified Protocol for this purpose in* Appendix B)

Once in the laboratory, samples are weighed in their sealed bags, clumps broken by hand and then oven dried at 40 °C to constant weight. Thereafter, an aliquot of each sample will be oven dried at 105 °C for 4 h which will allow for the calculation of SBD, while the remainder of the samples will then be dry sieved to 2 mm and gravel and root content >2 mm determined by weight.

Standard methods of soil carbon analysis such as dry combustion or wet oxidation are extensively used in SOC studies as they provide optimum quality results. Moreover, elemental (dry) combustion appliances can be coupled to mass spectrometers to provide stable isotopic carbon signatures of SOM, which broadens the possibilities for better assessing soil carbon dynamics (Bird et al. 2004). However, the elemental combustion technique is resource-demanding and may be impractical or too expensive for large sets of samples and for continuous monitoring (Aynekulu et al. 2011; Batjes 2011). Nonetheless, the amount of time required to estimate SOC stocks and the sampling and analytical costs can be greatly reduced by employing emerging techniques for in situ estimation of SOC. Among such techniques the one that has been most widely used, and thus tested, is the Infrared Reflectance Spectroscopy, either at the Near or Mid-infrared reflectance spectroscopy (NIRS or MIRS), which once calibrated can provide rapid accurate SOC estimates (Shepherd and Walsh 2002, 2007; Aynekulu et al. 2011). Despite its usefulness and versatility, it is still necessary that a significant proportion of samples (i.e., 20 %) covering the projected range of SOC values for a given inventory are analyzed using standard SOC analytical procedures. This will in turn offer the necessary calibration set to confidently apply either MIRS or NIRS to the total set of samples. The use of remote spectroscopy on airborne or satellite-mounted sensors can also provide spatially distributed and resource-efficient measurement of SOC content (Ladoni et al. 2010). However, these techniques still require simultaneous ground observations to allow for proper calibration, and there are several major challenges associated with data accuracy (Croft et al. 2012; Stevens et al. 2006).

## *7.2.4 Quantification of SOC Stocks*

There are different approaches to account for soil carbon stocks and stock changes, and they all aim at providing a measure of mass of SOC per unit ground area.

The *spatial coordinate* approach calculates stocks considering the amount of carbon contained within a given volume of soil, which is defined by the sampled area and the depth referenced to the surface level. With this approach, the average SOC stock for a given depth interval (d) is calculated according to the following formula:

$$
\mu\_{\rm d} = \mathbf{BD}\_{\rm d} \times \mathbf{OC}\_{\rm d} \times \mathbf{D} \times \left(1 - \mathbf{gr}\right) / 10 \,\mathrm{s}
$$

where:

*μ*d is SOC stock (Mg OC ha−1) BDd is soil bulk density (g cm−3) OCd is the concentration of OC in soil (<2 mm; mg OC g−1soil) *D* is soil depth interval (cm) gr is fractional gravel content, the soil fraction >2 mm

However, the amount of soil contained within a given volume (SBD) may change as a result of swelling and/or compaction caused by land use change or management. Under those circumstances, sampling to a fixed depth from the surface (spatial coordinate approach) will result in different amounts of soil mass being sampled for the same volume, while the soil C concentration per unit dry soil mass might not have changed. This can lead to errors in the interpretation of changes in SOC storage following disturbance.

The determination of SOC stocks can also be achieved through *cumulative* or *material mass coordinate* approach, which consists of collection and quantification of all the soil mass in a given depth interval. The use of *cumulative mass coordinate* approach is widely used to correct for differences in bulk density that may have been caused by land use change or agricultural practices. Moreover, the adoption of this method may improve our ability to make comparative measurements across time, treatments, locations, and equipment (McKenzie et al. 2000; Gifford and Roderick 2003; Wuest 2009). Furthermore, since sampling by mass avoids potential biases derived from varying bulk density caused by land use change or agricultural practices, it is often regarded as the method of choice for SOC monitoring over time (see McKenzie et al. 2000 and Gifford and Roderick 2003 for detailed guidance on the method). Nonetheless, compared to soil coring, this method requires additional effort and skill. In the cumulative mass approach, depth varies such that each sample contains the same dry mass per unit ground area. Gifford and Roderick (2003) provide in-detail explanations and examples on how to determine SOC stocks using this methodology. Briefly, the method involves coring a bit deeper than the nominal depth involved (e.g., 55 cm for a required 50 cm depth) and each full soil core is then divided into several segments. We recommend sampling at 10, 30, 50, and 55 cm in those cases where coring may be feasible in order to compute for SBD and be able to interconvert between the spatial coordinate and the cumulative mass coordinate approach.

Another method that has been recommended to quantify SOC stock changes is the *equivalent soil mass* approach (Ellert and Bettany 1995; Lee et al. 2009). It consists of correcting for differences in SBD through the calculation of the mass of SOC in an equivalent soil mass per unit area (i.e., the heaviest soil layer is designated as the equivalent mass, against which to calculate the thickness of the soil that is required to obtain such mass). However, its implementation is even more difficult than the coordinate mass approach (McBratney and Minasny 2010).

Regardless of the method used to quantify SOC stocks, the provision of SBD data is of great importance so as to understand and interpret SOC dynamics (Gifford and Roderick 2003). In the case of soil augering, the calculation of SBD can be achieved by sand-filling the auger-hole volume. Alternatively, one can use soil density rings, which are orthogonally inserted onto the wall of a dugout soil pit. These are however highly time consuming as well as demanding tasks, and hence they should be limited to cases in which coring is not possible.

## *7.2.5 Scaling SOC Stocks to Landscape and Whole Farms*

There is a lack of standardized methodologies to scale up SOC stocks from a point source (pedon) to regional (landscape) and larger spatial scales. In this work, the scaling up of SOC stocks at the landscape scale is achieved through the proposed spatially stratified randomized sampling design. Accordingly, the average SOC stock for a given stratum is calculated as follows:

$$\mu\_{\rm su} = \frac{1}{n} \sum\_{i=1}^{n} \gamma\_i;$$

where:

*μ*st is the mean SOC stock for stratum st

*yi* represents each calculated SOC stock in that stratum

*n* is the number of observations in that stratum (see Appendix A for detailed calculations on the number of plots required in each stratum)

The *variance* in SOC stocks for a given stratum is calculated according to the following formula:

$$
\sigma\_{\rm su}^2 = \frac{1}{n-1} \sum\_{i=1}^n \left( \mathbf{y}\_i - \boldsymbol{\mu}\_{\rm su} \right)^2;
$$

where:

*σ* is the SOC stocks variance

*yi* represents each calculated SOC stock in that stratum *μ*st is mean SOC stock associated with the stratum st *n* is the number of observations in that stratum

The *average* SOC stock for the area of study (landscape) is calculated considering both the mean SOC stock obtained for each stratum and the area occupied by each stratum. Therefore, the calculation is as follows:

$$\mu = \frac{\sum\_{h}^{H} a\_h \times \mu\_h}{A};$$

where:

*μ* is the mean SOC stock *ah* is the area of the stratum *h μh* is mean SOC stock associated with the stratum *h A* is the total area of the study

The average *standard error* in SOC stocks for the area of study (landscape) is calculated according to the following formula:

$$\text{SE} = \sqrt{\sum\_{h=1}^{H} \left(\frac{a\_h}{A}\right)^2 \times \frac{\left.S\_h^{\ast^2}\right|}{a\_h}};$$

where:

SE *is the standard error for the entire population ah* is the area of the stratum *h Sh* is the variance of stratum *h A* is the total area of the study

Scaling SOC stocks from a few point source measurements (fields) to the whole farm necessarily requires a series of assumptions unless all fields within the farm are sampled (which may be highly unpractical). Here, it is assumed that the center and perimeter of each field are georeferenced so that the field's surface area can be determined. In the proposed scheme, samples within a given farm should be taken along the previously described land use intensity gradient (i.e., home gardens, close-distance, mid-distance, and remote fields) at their most spatially representative fields. If for a given section (i.e., close-distance fields), there is an occurrence of individual fields with annual and perennial vegetation (crops or trees), and the area of the smaller field is at least half the size of bigger field, then sampling should be conducted at both fields. The *average* SOC stock for the selected farm is then calculated considering both the mean SOC stock obtained for each section and the area occupied by each section. The calculation procedure is similar to the one described for the landscape scale, and it simply replaces strata by sections.

Uncertainties in SOC stock assessments vary according to the scale and the spatial landscape unit. Goidts et al. (2009) demonstrated that scaling up field scale measurements to the landscape level increases the coefficient of variation of SOC estimates. However, the same work showed that such uncertainty may be smaller than errors associated to local spatial heterogeneity and analytical procedures.

## **7.3 Quantification of Soil Carbon Stock Changes**

The determination of the sampling intensity required to demonstrate a minimum detectable difference in SOC stocks over time has been the subject of numerous studies (Garten and Wullschleger 1999; Conen et al. 2004; Smith 2004). The

actual number of samples to detect SOC differences for different degrees of statistical confidence will be directly dependent on the background level that the study requires (i.e., the detectable difference in SOC relative to the stock baseline estimated in the first inventory). Moreover, considering the inherent natural variability of soil properties, the demonstration of small changes in SOC stocks may often require the collection of an impractically large number of samples (Garten and Wullschleger 1999), whose costs may quickly overrun any financial benefit derived from a potential increase in SOC levels. Therefore, different approaches have been used to monitor SOC stock changes, which invariably represent a compromise between accuracy and cost. Table 7.2 shows a comparison of methods used to monitor SOC stock changes classified according to the level of accuracy, scale, and resources demanded.

## *7.3.1 Repeated measurements*

A further classification is made on the basis of the measurement domain (where the analyses take place).

#### **Laboratory-Based Analyses**

These are the most widely used techniques, which involve physical collection and subsequent processing of soil samples (see Sect. 7.2.3). The standard methods used for soil carbon analysis are dry combustion, wet oxidation, and the use of reflectance spectroscopy, which is increasingly being used over the past number of years as an effective way to optimize time and analytical costs. However, some controversy still exists about the compatibility of data derived from different spectroradiometers (Reeves 2010), and there is still a need for collection and analyses by conventional techniques of a significant proportion of samples to allow for calibration of the entire sample set.

#### **In Situ Analyses**

While lab-based analyses provide high-quality results, they are resource-demanding and may be impractical or too expensive for continuous monitoring of SOC (Aynekulu et al. 2011; Batjes 2011). The implementation of SOC analyses in the field by means of portable spectroscopy allows for the assessment of a much larger number of sampling locations compared to that offered by lab-based methods, as the former is a fast, cost-effective, and non-destructive technique. However, its accuracy is lower than that provided by conventional methods, since there are issues related to soil surface conditions such as soil moisture and surface roughness, which


**Table 7.2**Comparison of methods for monitoring SOC stock changes with regard to level of accuracy, scale, and resources demanded

*Notes*: LMH (low, medium, high)

aSee text for limitations and assumptions needed when using modeling bEmission factors provided by IPCC Good Practice Guidelines to calculate changes in SOC stocks have been widely used, but due to its inherent generality is an extremely coarse instrument for proper quantification of changes in small household systems, and it is mentioned here for reference purposes only

cThe use of remote spectroscopy at the farm scale is strongly limited by the resolution of the available imagery

dProper simulation of SOC dynamics at the landscape and farm scale may be possible provided it is conducted on sites with accurate management information

may affect the spectral signal. Therefore, there is a need to conduct a statistical calibration before each field campaign in order to achieve an acceptable level of accuracy (Stevens et al. 2006).

#### **Remote Spectroscopy**

The use of reflectance spectroscopy on airborne or satellite-mounted sensors provide high temporal resolution and allow for an improved representation of the spatial variation of SOC in a cost-efficient manner (Ladoni et al. 2010; Croft et al. 2012; Stevens et al. 2006). Nonetheless, there are still major constraints with regard to using this technique as a plausible method to detect SOC stock changes. Croft et al. (2012) highlight some of these limitations, which include: the comparatively higher analytical uncertainty than that obtained from conventional or ground-based reflectance spectroscopy; the high spatiotemporal variability of soil surface conditions that can affect the spectral signal (e.g., soil moisture, vegetation or crop residue cover, differences in soil surface roughness, etc.); the spatial uncertainties associated with instrument spatial resolution and SOC spatial heterogeneity; and the need for atmospheric correction and simultaneous ground data collection to calibrate and validate the output of such studies. Furthermore, remote spectroscopy can only use the reflectance of bare surface to measure soil properties and is not able to detect vertical gradients in SOC within the topsoil (Stevens et al. 2006). Finally, there is a dearth of studies using remote spectroscopy in arid or semi-arid regions, which host a large amount of small household farming systems. In these environments SOC contents are typically low and the interference with other soil properties (e.g., CaCO3 or CaSO4 contents) may change the spectral behavior of soil considerably, which could have further detrimental effects on the performance of the remote sensing techniques (Ladoni et al. 2010). Withal, the detection limit of these techniques is still too high to use them for SOC stock change studies (Stevens et al. 2006). To make these techniques fully operative, additional efforts must be taken to decrease the detection limit.

## *7.3.2 Modeling*

Compared to measuring techniques that require the implementation of repeated measurements to quantify SOC stock changes, the use of process-based models (e.g., DNDC, Roth-C, Century) have obvious advantages in terms of resources demanded. Moreover, models can provide relatively fast and inexpensive simulations of SOM dynamics at different spatiotemporal scales. However, such simulations are based on a number of assumptions that will necessarily result in very large uncertainties of the estimates obtained. Here, we briefly describe some of the main weaknesses of models that could potentially be used to quantify SOC stock changes within the context of small household agricultural systems in tropical environments.

#### **Assumption of Stable Conditions**

Most SOM dynamic models assume stable conditions in SOM pools prior to modeling how factors like management or climate change affect their dynamics. However, the vast majority of small household systems in the tropics are not necessarily in steady state conditions. In the tropics, large tracts of land under current agricultural practices have been covered by natural ecosystems not much longer than a generation ago, but in many cases this would only be a few decades or even just some years ago (Houghton 1994; FAO and JRC 2012). Because of this, current SOM dynamics will still be highly influenced by past vegetation. Therefore, the assumption of stable conditions in those systems is likely to result in gross inaccuracies. While the influence of past vegetation might of course be modeled, this would be done at the expense of bringing on further uncertainty to the results, as this impact is likely to vary with the type of vegetation, time since conversion, landscape position, soil type, etc.

#### **Coupling Erosion Processes**

Quite a significant number of small household systems are established on slopes of varying degrees, with farms being increasingly established on steep marginal land as a result of population pressure. Moreover, cropped fields may be void of vegetation for some time during the year, or in some cases, the entire year (fallow). The combination of those factors makes soil erosion a highly significant factor, which may naturally lead to lateral transfers of SOM. Again, coupling a soil erosion model to a SOM dynamic one can be attempted, but the resultant application would need to be parameterised for the wide array of heterogeneous conditions existing between farm managements, the different land uses, soil types, etc., all of which may undoubtedly produce an even greater source of uncertainty.

#### **Existence of Contrasting SOM Dynamics Between Crops**

Small household systems are highly dynamic in terms of the crops being used (C3 plants such as legumes and napier grass; and C4 plants such maize and sorghum) whose presence and abundance may vary between years within the fields of a given farm. There is increasing evidence that C3 and C4 vegetation have a strong influence on SOM processes, see for instance Wynn and Bird (2007) and more recently Saiz et al. (2015). Besides inherent microbial processes and material (biomass) recalcitrance, these dynamics are highly influenced by soil texture through their effect on abiotic properties. Therefore, vegetation may exert very strong effects on SOC stocks, which traditional SOM dynamic models are not yet able to simulate.

In summary, models can provide very useful indications about trends of SOM levels with respect to changes in climate and/or management, and they can do so at high spatiotemporal resolutions and at a fraction of the cost of those using repeated measurements (Table 7.2). However, the uncertainties associated to the estimates are currently too large to use them as a verifiable tool to demonstrate SOC stock changes, particularly in these highly heterogeneous systems. At the very least, models require high-quality data gathered at different time intervals for proper parameterisation, and this is still an important aspect clearly lacking for these grossly understudied tropical systems (Rosenstock et al. 2016, Chap. 9).

## *7.3.3 Monitoring Frequency and Recommendations*

While IPCC (2003) and IPCC (2006) recommend 5- and 10–20 year monitoring intervals respectively, a relevant sampling interval suited to site-specific conditions can be ascertained by using models of SOC dynamics to plan both the frequency and intensity of subsequent surveys for determining SOC stock changes (Smith 2004). However, modeling of highly heterogeneous environments such small household agricultural systems in tropical systems is a challenging task, which is unlikely to provide a single answer with regard to when and how intensively different sites should be measured to detect significant changes in SOC stocks. Alternatively, estimation of changes in SOC over shorter periods could be achieved through the measurement of changes in particular soil carbon fractions (e.g., particulate organic matter) given that these are more sensitive to changes than total carbon in the bulk soil (Six et al. 2002). While this is a rather useful qualitative assessment of SOC sequestration it does not reflect the overall SOC stock changes that should be simultaneously assessed, thus increasing the overall cost and sampling effort. Furthermore, the implementation of a SOM fractionation procedure requires specific laboratory equipment (i.e., sonicator) and access to relatively expensive consumables (i.e., heavy liquid; Wurster et al. 2010).

We recommend adopting a strategy similar to the one proposed by Lark (2009), which suggests sampling only a proportion of the initial baseline sites in any one stratum. This strategy purposely focuses efforts in those locations likely to show the larger differences in SOC stocks over a fixed term (i.e., 10-year period). Thereafter, the strata that show a large change could then be sampled more intensively. Locations likely to show the larger changes in SOC stocks will normally include fields affected by intensive management, those having changed land use since the last survey, and the ones presenting recent signs of land degradation. We also advise pairing sampling locations in space as this may allow for a more effective detection of SOC changes in time (Ellert et al. 2007), and a sampling scheme consistent with that used in the first round of sampling. Furthermore, collection of samples should be routinely conducted at roughly the same time of the year, and in between relevant agricultural practices (i.e., harvesting, fertilization, etc.). Further information about quantifying SOC over time is given in the Appendix A.

We would like to conclude this section on SOC stock changes stressing that the only way to detect reliable signals and early trends in soil monitoring schemes is to improve the overall measurement quality (precision and bias) and to shorten the measurement periodicity (Desaules et al. 2010). However, the labor, analytical costs, and time needed to achieve a given sensitivity might overrun the potential monetary benefits derived from a hypothetical increase in SOC levels. As an illustrative case, Smith et al. (2001) indicate that between 10 and 20 samples should be collected to detect a 15 % change in SOC stocks in a relatively homogeneous system (<25 % coefficient of variation). Moreover, special attention should also be placed on the issue of permanence as most of the new SOC fixed as a result of improved management activities is in a labile form (particulate organic carbon), and thus, it is highly prone to be lost back to the atmosphere in a relatively short timeframe if conditions changed. Therefore, emphasis should be placed on promoting sustainable agricultural practices, as these will bring both economic and environmental benefits to the farmers in the medium term. Enhanced SOC sequestration may indeed be one of those benefits, but in our view it should not be the purpose of grand resource-demanding monitoring schemes, especially if the time elapsed between surveys has not been long enough (i.e., at least 10–20 years). Bearing this in mind, and even considering that at present proper simulation of SOM dynamics is very limited in small household systems because of the scarcity of high-quality data, modeling still represents an alternative that, provided high-quality data was available, could be applied across broad spatiotemporal scales in a cost-effective manner. Therefore, we propose the establishment of permanent monitoring sites across a gradient of management qualities (from highly intense to poor management scenarios) in the geographical area of interest to serve as reference sites to generate data that can be used for model parameterization and validation for farming practices under small household conditions.

## **Appendix A: Methodology for Quantification of Soil Carbon Stocks and Carbon Stock Changes**

## *Number of Plots Required*

The number of plots required to estimate SOC stocks in each defined stratum depends on the desired precision, often set at ±10 % of the mean at 90 or 95 % confidence level. In the case of strata defined by ancillary variables, the number of plots per stratum can be ascertained through the relationship described by Snedecor and Cochran (1967);

$$m = \left(\frac{t\_\alpha \ S}{D}\right)^2;$$

where:

*tα* is Student's t with degrees of freedom at either 0.95 or 0.90 probability level *S* and *D* are the standard deviation and the specified error limit respectively for values obtained from an initial assessment of the stratum

On the other hand, and for the case of a given area stratified by geographical coordinates or ancillary variables, the number of plots required could be determined using a slightly modified relationship (Pearson et al. 2005; Aynekulu et al. 2011);

$$\mathbf{n} = \frac{\left(N \times \mathbf{S}\right)^2}{N^2 \times D^2};$$

$$\frac{N^2 \times D^2}{t\_\alpha^{\cdot^2}} + \left(N \times \mathbf{S}^2\right)^2$$

where:


The resultant number of plots can be further allocated into a number of defined strata by using:

$$\mathbf{n}\_h = \frac{\mathbf{N}\_h \times \mathbf{S}\_h}{\sum\_{h=u}^L \mathbf{N}\_h \times \mathbf{S}\_h} \times n;$$

where:

*Nh* is the area of the stratum *h Sh* is the standard deviation of stratum *h L* is the number of strata *n* is the total number of plots

In the cases where the confidence interval exceeds ±10 % with 90 % confidence, the user may undertake one of three actions (VCS module VMD0018): (a) restratify according to any significant correlation observed between the sample variance to geographic or other factors, (b) Increase the number of plots, and (c) set lower confidence intervals, increasing thus the estimates uncertainty. The determination of the number of plots to be sampled in each stratum as a proportion of both its area and the observed variance may certainly be an efficient approach. Adding to this efficiency, it can also be expected that the number of plots required for determination of SOC stocks for a given stratum defined by ancillary variables may be significantly small compared to the ones needed in the less homogeneous strata defined by geographical coordinates.

With regard to the number of samples required to demonstrate a given minimum detectable difference in SOC stocks over time the reader is referred to Garten and Wullschleger (1999), Conen et al. (2004) and Smith (2004) for sound descriptions of the methods and equations used. Finally, a very recent report by Chappell et al. (2013) provides excellent advice on a generic monitoring design to detect changes in SOC, which includes illustrative examples with step-by-step calculations.

## **Appendix B: Simplified Protocol for Taking and Processing Soil Samples, Adapted for the SAMPLES Project**

This protocol covers both the soil sampling procedure and sampling processing and assumes the plots to be sampled have already been pre-selected.

## *Soil Sampling*

Soil samples are collected in four different locations within the plot of choice to account for the inherent heterogeneity of SOC. Start roughly at the center of the plot/subplot (replicate 1) and establish the other three replicates laid out according to a pattern of three axes separated 120° with respect to an initial axis pointing north. Make sure the other three replicates are set up at a prudent distance from the edges of the plot/subplot (+5 m if possible) to avoid any boundary effects, but do try to cover ground. The final sampling locations will be georeferenced using a GPS.

It is assumed that a stainless steel corer, a soil auger, and/or a spade will be used for retrieving the samples. All samples will be placed in labeled zip-lock bags. It is very important that the bags are clearly labeled with a permanent marker. Always a good idea to label them immediately after you take the sample otherwise they may get mixed up (if a marker is not around, write it in a paper and put it inside of each bag). A good labeling should mention at the very least:


Then in the same bag and line in big clear letters following the example given it should say: *DCR-3* (*10–30*)

## *Detailed Sampling Procedure*

In the case of the 0–10 and 10–30 cm intervals, three individual samples within 1 m radius will be collected. This is done to better account for local heterogeneity, which is particularly pronounced at this shallow depth. Subsequently samples from the same location and depth interval are pooled to minimize analytical costs.

## **0–10 cm**


#### **10–30 cm**


#### **30–50 (55) cm**


scratching/ sampling from the bottom once the hole has been finished. Take roughly the same amount of soil material along the targeted depth interval, as you do not want to take most of your sample at a concentrated point. It would be good to have a graduated ruler or stick with depth marks.

In general, also consider the following:


## *Soil Bulk Density Determinations*

In all cases, calculation of SBD should include fractions >2 mm. So before any sieving takes place the following should be done:

As soon as possible, and certainly before 2 days after collection from the field, let the samples air-dry (after opening and rolling down bags) in a rain-protected location. It is always a good idea to progressively (each day) break the soil clumps with your fingers while the bags are being dried (but be gentle or you may break the bag). A bit everyday is the best, otherwise you will find handling of samples much harder in the coming days, and will have to use a hammer. Also, avoid crosscontamination between samples by doing it from the outside of the bag (gently squeezing it with your fingers). When an oven becomes available, put the bags inside at 40 °C. After a number of days, when samples are seemingly dry (5–7 days will be safe—but of course it all depends on initial moisture content), take them out of the oven and **weigh each sample** (including the plastic bag) but wait about half an hour after the samples have been taken out to do this weighing.

After this weighing, take an aliquot of each sample and place them in labeled paper bags (about ~ ¼, of the total sample, **but weigh how much exactly before you put them inside the oven**). Dry them at 105 °C for 24 h. As before, **weigh all the samples after about half an hour after they were taken out of the 105° oven**. Once the weights of these aliquots have been recorded you can throw this material away.

In total **you should have three weights for each sample** (i.e., total soil weight, sample before oven dried at 105 °C, sample after oven dried at 105 °C). This will allow for proper calculation of SBD.

In general, also consider the following:


## *Sample Processing*

**Sieving:** The remaining of each sample dried at 40° (most of it) needs to be weighed again and sieved to 2 mm. Gravel and root content >2 mm will be weighed separately. Therefore we will get the fractions of coarse roots and gravel. But first remove carefully all large clumps with a rolling pin (bakery). Removing the soil from the bag to break up any clumps is very time consuming and may lead to gross errors. Therefore, it is good practice to progressively break clumps from outside of the bag as the sample dries. After sieving, you should have three weighs for each sample (bag) in total (i.e., total soil weight, roots>2 mm, and gravel (>2 mm)).

**Pooling/bulking:** There are numerous ways of pooling, and the final choice depends on the purpose and load of work that can be undertaken. The methods explained below are just two ways that lead to fewer analyses to be undertaken and cover two different purposes:

	- • Use the same weight for all the replicates (20 or 30 g), and put them together in a bowl or tray. Do not use the entire sample from each bag! Keep them as back ups.
	- • Mix them a bit always with clean, dry hands (10 s should be alright).
	- • Put the mixture in a new bag with the same code as before but indicating "Bulk" at the end.
	- • If the aim is to also get **a "master soil sample" 0–30 cm** for subsequent analyses (texture, mineralogy, organic matter fractionation, ECEC, etc.) then from the previous bulked bags the weights that need to be put together are calculated as follows:

First the average bulk density for the Master (BDM) is calculated:

$$\mathbf{BDM} = \left[ \mathbf{BD}\_{\left(0-10\right)} \times \left(1/3\right) \right] + \left[ \mathbf{BD}\_{\left(10-30\right)} \times \left(2/3\right) \right],$$

Then to obtain about 90 g of Master sample, proceed as follows:

$$\begin{array}{l} \mathbf{30} \text{ g} \times \mathbf{BD}\_{\left(0^{-10}\right)} / \mathbf{BDM} \\\\ \mathbf{60} \text{ g} \times \mathbf{BD}\_{\left(10^{-30}\right)} / \mathbf{BDM} \end{array}$$

These weights are put in a separate bag, which is to be called "master" with same code as before and indicating (0–30) at the end of the labeling.

2. Sometimes it may be necessary to have an extra bag with about 20 g of Master soil (0–30) that will be used for soil textural analyses. Take about 20 g from this bag and put them into a small bag with the same coding indicating that is for "texture."

**Powdering:** If powdering is needed, then proceed as follows:


#### 7 Methods for Smallholder Quantification of Soil Carbon Stocks and Stock Changes


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## **References**


## **Chapter 8 Yield Estimation of Food and Non-food Crops in Smallholder Production Systems**

**Tek B. Sapkota, M.L. Jat, R.K. Jat, P. Kapoor, and Clare Stirling**

**Abstract** Enhancing food security while contributing to mitigate climate change and preserving the natural resource base and vital ecosystem services requires the transition to agricultural production systems that are more productive, use inputs more efficiently, are more resilient to climate variability and emit fewer GHGs into the environment. Therefore, quantification of GHGs from agricultural production systems has been the subject of intensive scientific investigation recently to help researchers, development workers, and policy makers to understand how mitigation can be integrated into policy and practice. However, GHG quantification from smallholder production system should also take into account farm productivity to make such research applicable for smallholder farmers. Therefore, estimation of farm productivity should also be an integral consideration when quantifying smallholder mitigation potential. A wide range of methodologies have been developed to estimate crop yields from smallholder production systems. In this chapter, we present the synthesis of the state-of-the-art of crop yield estimation methods along with their advantages and disadvantages. Besides the plot level measurements and sampling, use of crop models and remote sensing are valuable tools for production estimation but detailed parameterization and validation of such tools are necessary before such tools can be used under smallholder production systems. The decision on which method to be used for a particular situation largely depends on the objective, scale of estimation, and desired level of precision. We emphasize that multiple approaches are needed to optimize the resources and also to have precise estimation at different scales.

International Maize and Wheat Improvement Centre (CIMMYT), G-2, B-Block, National Agricultural Science Centre (NASC) Complex, Dev Prakash Shastri Marg, New Delhi 110 012, India e-mail: T.Sapkota@cgiar.org

International Maize and Wheat Improvement Centre (CIMMYT), New Delhi, India

C. Stirling International Maize and Wheat Improvement Centre (CIMMYT), Wales, UK

© The Editor(s) (if applicable) and the Author(s) 2016 163 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_8

T.B. Sapkota (\*) • M.L. Jat • P. Kapoor

R.K. Jat Borlaug Institute of South Asia, Samastipur, Pusa, Bihar 848125, India

## **8.1 Introduction**

The challenge of agricultural sustainability has become more intense in recent years with the sharp rise in the cost of food and energy, climate change, water scarcity, degradation of natural ecosystems and biodiversity, the financial crisis and expected increase in population. With increasing demands for food and agricultural products, intensification of smallholder production system becomes increasingly necessary. Recently, agricultural technologies that increase food production sustainably while offering climate change adaptation and mitigation benefits–collectively known as climate smart agricultural (CSA) practices-have been the subject of scientific investigation. CSA practices are designed to achieve agricultural sustainability by implementation of sustainable management practices that minimize environmental degradation and conserve resources while maintaining high-yielding profitable systems, and also improve the biological functions of the agroecosystems. However, simultaneous quantification of productive, adaptive, and mitigative production systems is still scant and scattered.

Understanding the greenhouse gas (GHG) fluxes between agricultural fields and the atmosphere is essential to know the contribution of farm practices to GHG emissions. However, quantification of GHGs from agricultural production systems in smallholder systems is meaningless if the livelihood effects of those activities are ignored (Linquist et al. 2012). As farm productivity is inextricably linked to food security of smallholder farmers in developing countries, the importance of productivity must be taken into account in mitigation decision-making and the GHG research agenda supporting those decisions. Most of the GHG emission studies, so far, highlight the emission reduction potential of particular activities without paying due attention on yield and livelihood benefits for smallholder production (Rosenstock et al. 2013). The benefit of smallholder production systems, in terms of reduced emissions and increased carbon sequestration should, therefore, be assessed taking household benefits such as resilience led-productivity enhancement and input use efficiency in due consideration. In this chapter, we focus on comparative analysis of yield estimation methods from field to landscape level under smallholder production practices.

## **8.2 Crop Productivity Estimation**

Various methods have been developed for quantifying production and productivity of agricultural systems at research plot level and also for agricultural statistics at regional and national level. However, as agricultural production systems are changing to address new challenges, for example, CSA practices, the yield estimation methods developed and tested for a particular production system may not adequately reflect the yield for new production systems. For example, the standard crop cut method using sampling frames may create significant bias and error if applied to crops planted in raised beds in row geometry.

Standardization of crop yield estimation methods, particularly in the context of smallholder production system at various scales (field, farm to landscape scale) helps not only to obtain accurate agricultural statistics but also in assessing suitability of low-emission agricultural practices under various production environments. Accurate yield estimation allows trade-off analysis on crop yield and emission reduction of particular production practices thereby helping appropriate mitigation decision-making without compromising smallholder livelihoods and rural development (Rosenstock et al. 2013). This is particularly important in the context that a significant proportion of developing countries have expressed an interest in GHG mitigation in the agriculture sector (Wilkes et al. 2013). Here, we present various yield estimation methods followed by comparative analysis of those methods at various scales i.e., from field to landscape level.

## *8.2.1 Crop Cuts*

Estimating crop yield by sampling a small subplot within cultivated field was developed in the 1950s in India (Fermont and Benson 2011) and rapidly adopted as the standard method of crop yield estimation, known popularly as the crop cut method. In this method, yield in one or more subplots is measured and total yield per unit area is calculated as total production divided by total harvested area in the crop cut plot or subplot. The number of subplots and area of each subplot to be selected for yield estimation through crop cuts depends on the resources available and the level of precision required in the estimation. In practice, 1–5 subplots of 0.25–50 m2 are used for yield estimation. In on-farm research conducted by CIMMYT, use of a 0.5 m by 0.5 m sampling frame overestimated the wheat yield by more than two times as compared to 1 m2 or larger sampling frame (Fig. 8.1). This finding suggests that when estimating crop yield by using crop cut method, the size of sampling plot should be at least 1 m2 . In the field with variable crop performance, it is advisable to use even larger sampling frame or increase the number of subplots to be harvested for yield estimation. For better result, the person throwing the sampling frame in the field should be blindfold. Alternatively, a person independent of the research or demonstration should throw the sampling frame in the field to minimize the bias.

## *8.2.2 Farmers' Survey*

Estimating crop production through farmers' interviews involves asking farmers to estimate or recall the yield for an individual plot, field, or farm. It can be done before harvesting (estimate) or after harvesting (recall). Before harvesting, farmers are asked to predict what quantity they expect to harvest. Farmers will base their predictions of expected yield on previous experiences, by comparing the current crop performance to previous crop performances. Singh (2013) argue that yield estimation surveys following this method should be made at maximum crop growth stage. This helps enumerators/extension worker to verify the farmer's response by visual observation of the crop. Postharvest estimations are commonly made at the

**Fig. 8.1** Estimated grain yield of wheat by harvesting the subplot of different size

farmer's house or at the site where the harvest is stored in order for the enumerator to cross-check the estimates with the harvested products. Postharvest surveys should be carried out as soon as farmers harvest the crop, although Erenstein et al. (2007) reported that farmers can recall yield for up to three-to-six previous seasons.

To estimate the crop yield, production data obtained from farmer recall or prediction require division by the plot area from which the crop was or will be harvested. This introduces an additional source of error. To remove this error source, Fermont et al. (2009) obtained a direct estimate of average crop yield by asking farmers to estimate the number of local harvest units they would have obtained from a wellknown unit of land, often the farm compound, if it had been planted to a specific crop.

## *8.2.3 Estimating Crop Yield by Using Grain Weight (Test Weight)*

Estimating crop yield by using pre-estimated test weight is one of the easiest and quickest methods which can be used in a number of situations and farm conditions. This is similar to the crop cut method but does not require harvesting and subsequent weighing of the sampled area. Using a sampling frame, count the number of


**Table 8.1** Thousand grain weight of some example crops

earheads/pods in 1 m2 area at least five to seven times within a plot whose yield is to be determined and calculate average number of heads/pods per meter square area. Similarly, count the number of grains in 20–25 heads/pods and take the average. The yield of the crop can then be determined by using the following formula. The 1000-grain weight can be taken from previous data or from published figures (Table 8.1).

$$\text{Yield Mg ha}^{-1} = \frac{\text{\# grains per head} \times \text{\# heads per m}^2}{100} \times \frac{1000 - \text{grain weight} \left(\text{g}\right)}{1000}$$

The 1000-grain weight of crops is influenced by many factors such as genotype, management, and environment. Therefore, care should be taken to use appropriate 1000-grain weight value based on the variety grown and the growing condition. Estimation accuracy, regardless of the method, depends on the accuracy of observations taken in the field. Counts of grain per head and heads per square meter area must be accurate and taken randomly at enough locations (at least 5) to provide an average of the whole field.

## *8.2.4 Whole Plot Harvest*

Harvesting the entire field to determine crop yield is normally done in trial plots, excluding one or more boundary lines that may not reflect the tested treatment due to boundary effects. This method can be employed in experimental or demonstration plots. It can also be used to estimate yield from small-scale farmers' fields if farmers are willing to cooperate but is too costly for larger samples of farmers. The complete harvest method is considered the most accurate and often used as a standard for comparing effectiveness and accuracy of other methods. Crops that have a defined maturity date, such as cereals or legumes with a determinate growth habit, can be harvested in a single operation whereas crops with staggered maturity such as banana, cassava, and legumes or with an indeterminate growth habit like common bean, cowpea, and mungbean require multiple harvests per plot. In many cases, a farmer gathers all his/her produce from his/her land in one place, threshes there and take home the produce after weighing. In such cases, it is easy to estimate the yield by dividing the total yield by the total area cultivated by the farmer.

## *8.2.5 Sampling for Harvest Unit*

This is similar to yield estimation through whole plot harvest except that only a few samples out of the total harvest are weighed. In this method, the number of units, such as sacks, baskets, bundles, is counted after the farmer harvests his/her plot. A number of harvest units are then randomly selected and weighed to obtain an average unit weight. Total harvest of the plot is obtained by multiplying the total number of units harvested by the average unit weight. Crop productivity can then be calculated by dividing total production by the area from where the production came from. Ideally, sampling of harvest units is done just before storage and includes a measurement of the moisture content of the harvested product (Casley and Kumar 1988). This method can be used on larger samples than is possible with crop-cut or whole-plot harvest method. However, the crops must be harvested all at once for this method to be applicable.

An alternative method which requires the physical threshing of only a small sample to estimate yield, biomass, and other yield-related parameters has been developed by Castellanos-Navarrete et al. (2013). This is rather a simple procedure that dramatically reduces the labor and large-scale threshing required to obtain reliable yield and associated yield-related parameters. The methodology can also be used for any situation and any cereal crop. It can be readily applied for on-farm research situations where samples are taken in the field and then transported back to a central point for threshing. Harvest should be done as soon after physiological maturity as possible. Here, after harvesting the crop from sample harvest area, 50–200 tillers are selected randomly for fresh and dry biomass weight, grain weight, and test weight. The yield and yield-related parameters are then determined by using the relationship of the determined parameters and the harvest area. Step-by-step procedures for yield estimation following this method can be found in Castellanos-Navarrete et al. (2013).

## *8.2.6 Expert Assessment*

Sometimes crop yield is estimated by summarizing the opinions of field agronomists, extension agents, and researchers (Dumanski and Onofrei 1989). These experts are often able to estimate crop production or yield by visually assessing the crop condition, such as color, plant vigor, plant density, in the field. This is known as eye assessment. Eye assessment can be combined with field measurement and empirical formulas, collectively known as the expert assessment method. The expert assessment method can be applied on a relatively large scale as compared to the crop-cut method but on a smaller scale than the farmer's estimate. However, eye estimation of crop yield requires not only practical but also technical familiarity with the yield potential of different varieties of crops in different environments. Therefore, accuracy of the yield assessment, in this method, will strongly depend on the level of expertise of the personnel involved in the assessment. Care should be taken not to use extension worker as expert for yield estimation in their own work area as they may be biased to demonstrate their own work (Casley and Kumar 1988).

## *8.2.7 Crop Cards*

The crop card method is a refined version of the farmer recall procedure to obtain more reliable harvest estimates for crops with an extended harvest period or multiple harvests, such as cassava, banana, cowpea, sweet potato. As farmers may have problems in accurately remembering the amounts they harvested over time from one or several plots, this method helps them by keeping the written record of all plots. In this method, each farmer in a survey is given a set of crop cards where he/she records the quantity of crop in each harvest, which can then be added up to calculate the total harvested yield. However, this may be challenging to use in smallholder production contexts of developing countries due to high illiteracy rates and lack of adequate manpower for regular monitoring (Ssekiboobo 2007).

## *8.2.8 Crop Modelling*

Crop modelling is widely used to estimate average biological yields in the conditions of smallholder farmers. Empirical–statistical crop models establish a relationship between yield and environmental factors from long-term datasets and use the established relationship to predict crop yield at regional or national levels based on environmental data (Park et al. 2005). Empirical crop growth models are relatively simple to develop, but these models cannot take into account the temporal changes in crop yields without long-term field experiments (Jame and Cutforth 1996). Furthermore, the derived functional equation is locally specific, and it is thus difficult to extrapolate to other areas unless environmental conditions are similar. Many of such models embody a number of simplifications. For example, weeds, diseases, and insect pests are assumed to be controlled, and there are no extreme weather events such as heavy storms.

Process-based crop models, on the other hand, estimate crop yield on the basis of daily gains in biomass production by taking into account all known interactions between physiological processes and environmental conditions (Sawasawa 2003). Because process-based models explicitly include plant physiology, agroclimatic conditions, and biochemical processes, these models are able to simulate both temporal and spatial dynamics of crop yields and thus have higher extrapolation potential than empirical models.

## *8.2.9 Allometric Models*

Allometric models are mathematical relationships between plant morphological characteristics and crop yield. The morphological characters can be measured on a selected number of plants which then can be used to predict biological yield in field. Allometric models should be based on variables that can be quantified easily using rapid, inexpensive, and non-destructive methods of data collection (Fermont and Benson 2011). For bananas in Uganda, Wairegi et al. (2009) found that a multivariate model using girth of the pseudo-stem at base and at 1 m, the number of hands, and the number of fingers gave a robust prediction of bunch weight. Tittonell et al. (2005) used plant height and ear length to predict maize yields in western Kenya. In cereal crops, the number of tillers per unit area, ear or spike length, number of grains per spike, and 1000-grain weight—commonly known as yield attributing characters—can be determined and used to estimate the crop yield. Data collection is one of the prerequisites of this method although data collection may be less labor intensive than with the crop cut method.

## *8.2.10 Remote Sensing*

Use of remote sensing to estimate the biological crop yield is being explored in many countries and likely will become the basis of agricultural statistics in the future (Zhao et al. 2007). Crop yield estimation using remote sensing is based on the principle of spectral reflectance of green plants, which can be captured in satellite images as spectral data, and depends on the state, structure, and composition of the plant. The spectral data can be used to construct several vegetation indices such as normalized difference vegetation index (NDVI) which indicates the green biomass that can be used as proxy indicator of the yield (Prasad et al. 2006). The limitation in the use of satellite images to estimate crop yields of smallholder farmers is that the resolution of available satellite imagery (pixel size) is not sufficiently detailed to capture the variability of crops and crop performance in smallholder fields, which often are less than 0.1 ha in size and sometimes intercropped (Fermont and Benson 2011). In India, for example, vegetation indices from satellite images show only a moderate correlation (*R*<sup>2</sup> between 0.45 and 0.54) with crop cut data (Singh 2013).

## **8.3 Critical Analysis and Comparison of Yield Estimation Methods with Regards to Cost, Scale, and Accuracy**

A comparison of the wide range of methodologies to estimate crop production in terms of their cost effectiveness, suitability for different scales from field to landscape and sources of errors or bias is presented in Table 8.2. A strong advantage of


**Table 8.2** Comparison of various methods of crop production estimation with regard to their cost-effectiveness, scale, and accuracy

the crop-cut method is that the area of the cut is known and thus does not introduce an error into the final yield computation. It has been a standard method for yield estimation recommended by organizations such as the Food and Agriculture Organization of the United Nations for years. However, crop cuttings may suffer from serious limitation due to heterogeneity of crop conditions within farmers' plots. In crop cuts, enumerators have the tendency not to sample locations with poor crop stand, leave border areas where crop yield is generally lower than in the middle of the plot and include the plant falling at the edge of sampling frame. A study done in Bangladesh found that even with best-educated enumerators, crop-cut estimates exceeded actual yield by 20 % whereas farmers' estimates of production were lower (Diskin 1999). Further, crop cut only estimates biological yield without taking into account postharvest losses and is therefore unable to estimate economic yield, which is of most interest to farmers. All these tendencies contribute to upward bias when extrapolating results to a larger area. Further, using a large weighing balance to weigh smaller quantities from crop cuts may sometimes introduce measurement errors. This method is costly and time-consuming, and not suitable for heterogeneous crop performance (typical of smallholder production systems) and staggered harvesting as this is a one-point-in-time measurement.

The farmers' estimation method does not require laborious measurements, and therefore this method is time- and cost-efficient and is suitable for estimation at larger scales. For years, it was assumed that farmers' estimates were too subjective and unreliable and when differences appeared between crop cut and farmers' production estimates, it was attributed as farmers' error. However, research in 1980s suggested that farmers' estimation may be just as accurate as crop cut, at least for determining total farm production (Murphy et al. 1991). However, literacy levels of farmers and nonstandard harvest units pose serious drawbacks in its use in smallholder production systems of developing countries. Farmers may use part of their produce as in-kind payment to their labor which they may not count in their estimate, leading to underestimation. Further, many farmers consciously over- or underestimate in the case of suspected benefits such as food aid or penalties such as taxes (Diskin 1999). Expert assessment can be relatively error-free if the same team of experts can be used throughout the study (Rozelle 1991). However, finding a large number of experts with required practical and technical experience to estimate relative performance of different crops/ varieties under different environments is a challenge to employ this approach at larger scales. Furthermore, both farmer's estimation and expert assessment are subjective and amenable to several non-sampling errors. Therefore, it is advisable to combine these methods with other methods for better estimation of crop yield.

The advantage of whole plot harvest method is that it is almost bias-free since all sources of possible errors and biases associated with crop cut or farmers' estimate are eliminated when the entire field is harvested. However, this involves a large volume of work to obtain robust estimates of yield at landscape level. Sampling of harvest units can be used on larger samples than is possible with crop-cut or whole plot harvest method. However, this method is unsuitable for crops with staggered harvesting.

Use of crop cards can be combined with farmers' estimate for crops with multiple harvesting and staggered ripening. However, this is again very labor intensive and cannot be employed for large-scale surveys. Further, use of local unit of measurement by different farmers may introduce error in estimation. Use of allometric methods is limited to a certain number of crops such as banana and maize. In developed countries, purchasers' records or crop insurance data may be used for crop yield estimation but this method may not be suitable in the context of smallholder production in developing countries.

Crop modelling and remote sensing are cost-effective methods of yield estimation which can be employed at large scales fairly accurately although empirical models fail to capture landscape heterogeneity and process-based models need rigorous parameterization, calibration, and validation before they can be used for large-scale estimation.

## **8.4 Conclusion**

Precise estimation of crop yield in smallholder agriculture is challenging because of highly heterogeneous crop performance within a plot, continuous planting and intercropping or mixed cropping to meet various household requirements. Staggered ripening of many crops with an extended harvest period and planted area not being equal to harvested area further complicates the issue of crop yield determination in smallholder farmers' condition. A wide range of methodologies have been developed to estimate crop yields in the smallholder production systems, each with advantages and disadvantages. This review has primarily considered the application of these methodologies to cereal cropping systems, but the methodologies can be adapted to other cropping systems as well. A choice of method depends on the objective and desired level of precision, scale of estimation, and available resources. For example, whole plot harvesting may be suitable for small-scale detailed studies at plot level whereas for large-scale survey at regional level combination of crop cut, farmer's estimation and expert assessment may be used. Use of crop models and remote sensing may be appropriate for agricultural statistics, provided adequate parameterization of models is done and imagery at sufficiently fine resolution to capture the variability of crops and their performance in smallholder fields is available.

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## **References**


## **Chapter 9 Scaling Point and Plot Measurements of Greenhouse Gas Fluxes, Balances, and Intensities to Whole Farms and Landscapes**

**Todd S. Rosenstock, Mariana C. Rufino, Ngonidzashe Chirinda, Lenny van Bussel, Pytrik Reidsma, and Klaus Butterbach-Bahl**

**Abstract** Measurements of nutrient stocks and greenhouse gas (GHG) fluxes are typically collected at very local scales (<1 to 30 m2 ) and then extrapolated to estimate impacts at larger spatial extents (farms, landscapes, or even countries). Translating point measurements to higher levels of aggregation is called *scaling*. Scaling fundamentally involves conversion of data through integration or interpolation and/or simplifying or nesting models. Model and data manipulation techniques to scale estimates are referred to as scaling methods.

In this chapter, we first discuss the necessity and underlying premise of scaling and scaling methods. Almost all cases of agricultural GHG emissions and carbon (C) stock change research relies on disaggregated data, either spatially or by farming activity, as a fundamental input of scaling. Therefore, we then assess the utility of using empirical and process-based models with disaggregated data, specifically concentrating on the opportunities and challenges for their application to diverse smallholder farming systems in tropical regions. We describe key advancements needed to improve the confidence in results from these scaling methods in the future.

T.S. Rosenstock (\*) World Agroforestry Centre (ICRAF), PO Box 30677-00100, UN Avenue-Gigiri, Nairobi, Kenya e-mail: t.rosenstock@cgiar.org M.C. Rufino Center for International Forestry Research (CIFOR), Nairobi, Kenya N. Chirinda International Center for Tropical Agriculture (CIAT), Cali, Colombia L. van Bussel • P. Reidsma

Wageningen University and Research Centre, Wageningen, Netherlands

K. Butterbach-Bahl International Livestock Research Institute (ILRI), Nairobi, Kenya

Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, Garmisch-Partenkirchen, Germany

© The Editor(s) (if applicable) and the Author(s) 2016 175 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_9

## **9.1 Introduction?**

Agricultural systems are a major source of atmospheric greenhouse gas (GHG) emissions, contributing approximately 30 % to total anthropogenic emissions if land use change is included (Vermeulen et al. 2012). To better target interventions aimed at reducing GHG emissions from agricultural systems, there is a need for information on GHG balances and the GHG intensity of agricultural products (e.g., emissions per unit product) at levels where livelihood and environmental impacts occur and land management decisions are being made. However, even in smallholder farming systems where decisions are taken on fields and farms that are usually less than 1 ha, this decision scale is substantially greater than the scale at which changes in GHG fluxes take place or are measured, often that of micrometers and meters (Butterbach-Bahl et al. 2013). The factors regulating nitrous oxide (N2O) generation in agricultural fields illustrate this point. At the scale of soil aggregates—mm in size–soil moisture affects oxygen available to microbes, driving denitrification (the conversion of NO3 − to N2O principally by facultative anaerobic bacteria). Meanwhile, soil moisture, influenced by the percentage of water filled pore space, is regulated by precipitation and soil tillage—events determined at a larger spatial extent. Furthermore, heterogeneous distribution of decomposing residues from the previous harvest may lead to formation of denitrification and N2O hotspots at the cm scale, thereby triggering changes in the magnitude and spatial variability of fluxes even at plot scale (Groffman et al. 2009). Consequently, land-based mitigation actions require a lower resolution of information than that needed to explain the processes driving GHG emissions at the soil–plant–atmosphere interface.

GHG fluxes are typically measured at locations or "points," intended to be representative of a larger area. Independent of source, sink or molecule, GHG measurements—for example chamber measurements of fluxes—are conducted on only a fraction of the area or a few of the landscape units because of costs and complexity (Rufino et al. 2016; Butterbach-Bahl et al. 2016). When attempting to understand landscape or regional GHG fluxes or consider mitigation options, it is therefore necessary that these point measurements be translated to larger extents where effective and meaningful mitigation actions can be taken.

"Scaling" GHG flux measurements underlies GHG accounting (e.g., national inventories), and forms the basis for policy analysis (e.g., marginal abatement cost curves), development strategies (e.g., low emission development), and even simple testing of mitigation options (e.g., comparing impacts of one practice versus an alternative). Thus, it is important to understand basic principles and terminology that pertain to scales and scaling, to avoid confusion in discussions and analysis. *Scale* refers to the spatial or temporal dimension of a phenomenon (van Delden et al. 2011; Ewert 2004). *Scaling* refers to the transfer of information between scales or organizational levels (Blöschl and Sivapalan 1995). *Scaling methods* refer to tools required to accomplish scaling. This chapter is concerned with understanding the theory and practice behind scaling methods as applied to GHG measurements and impacts.

## **9.2 Scaling Methods**

Most scaling methods are grounded in ecological hierarchy theory. Hierarchy theory provides a conceptual framing for scaling in that it structures systems as nested levels of organization (Holling 1992). Components are arranged within higher levels; for example, a field is part of a farm that can be thought of as part of a landscape; moreover, these different components are spatially heterogeneous areas of interacting patches of ecosystems (Fig. 9.1). Scaling methods rely on this conceptual framing to infer relationships between attributes and to translate values derived from point measurements into estimates across scales.

Scaling methods can be categorized into two groups: (1) manipulation of input or output data or (2) manipulation of models (Volk and Ewert 2011). Approaches that manipulate data are extrapolation, interpolation, (dis)aggregation, or averaging sampled input data (i.e., point measurements) to generate estimates at larger scales (Table 9.1). National Greenhouse Gas Inventories that use IPCC default Tier 1 emission factors (IPCC 2006) are an example of a scaling method that uses a data manipulation approach, namely disaggregation and aggregation. Agriculture is disaggregated into farming activities and their extents (e.g., size of cattle population or tons of nitrogen (N) fertilizer applied) for which a coefficient or empirical model derived from point measurements of the relationship between that activity and GHG fluxes (i.e., empirical model) is then used to calculate emissions at national or subnational levels. Data manipulation approaches are among the simplest approaches to implement, especially in regions and for production conditions where data are sparse. However, since data manipulation approaches generally neglect heterogeneity in GHG emissions and underlying physicochemical and biological processes, estimates may not represent observed fluxes well at the site level. However, in most cases for developing countries, the accuracy of using such methods is unknown because there are insufficient data to evaluate the variation of source events (input data) or the accuracy of outputs. The ability to generate accurate estimates at larger temporal or spatial scales by manipulating data depends on (1) representative sampling of the disaggregated GHG source/sink activities and (2) the availability of a

**Fig. 9.1** Illustration of a nested hierarchy. Regions (East Africa) can be disaggregated to landscapes (natural forest, communal lands, and agriculture) to farms (mixed crop–livestock) to fields (cabbages) (Photos: Authors; CCAFS; Google Maps 2015)


**Table 9.1** Conceptual framework of select scaling methods based on Ewert et al. (2011). Reprinted with permission.

Based on Ewert et al. (2011)

reasonable model—empirical or process-based—to scale input data. Recently, novel approaches for disaggregation of national, landscape, or farm components such as stratification by socioecological niches using a combination of household surveys and remote sensing and stratification by agroecological conditions using existing climate, soils, and management information have been evaluated to improve estimates because of the better representation of the heterogeneity found in plots, fields, farms, and landscapes (Hickman et al. 2015; Rufino et al. 2016).

The alternative to manipulating data is to modify existing models to be relevant at larger spatial scales. This has been successfully done for national-scale soil C monitoring in the United States, where an integrated data collection and biogeochemical process-based model (DAYCENT) estimates changes in soil C stocks (Spencer et al. 2011). However, other examples for agricultural GHG impact assessments remain scientific exercises (see Perlman et al*.* 2013 for national scale N2O assessment). Approaches to manipulate models change the model structure to account for the availability and resolution of input data and to make them computationally tractable. Reformulation of model structure (not creating new models) can result in a reduction of parameters (e.g., macroecological models of functional traits) or simplified model functional forms (e.g., empirical equations derived from multiple runs of process-based models). An important consideration is that scaling by modifying models introduces uncertainty: uncertainty in the quality and quantity of input data, uncertainty of datasets used to test models, and uncertainty related to model structure and parameters in the revised models.

Theory supporting the manipulation of data and models as well as potential errors/uncertainties in outcomes is reviewed in the integrated assessment literature (e.g., Ewert et al*.* 2011; Volk and Ewert 2011). The process of selecting representative sampling points by various stratification methods (e.g., spatially, land cover, farming activity, etc.) are covered in Chap. 2 and measurement techniques for various fluxes and productivity are covered in Chaps. 3–8. Here we discuss the two methods most commonly used to *scale up* point measurements of disaggregation/ aggregation data: empirical and process-based models.

Empirical models are usually relatively simple statistical functions constructed based on the relationship between occurrence of activities or external events, farming or rainfall for example, and monitored responses in the magnitude and temporal and spatial variability of GHG fluxes. By contrast, process-based ecosystem models are built upon our current theoretical understanding of the physicochemical and biological processes underlying GHG emissions. They represent current understanding of complex processes and the interactions of C, N, and water cycling at the ecosystem scale to simulate the mechanisms that control GHG fluxes. However, process models need detailed input information and have numerous parameters describing key ecosystem processes and some of the algorithms are still empirical and represent apparent flux responses rather than the underlying processes. Unlike empirical models that require calibration each time they are used, one assumes that the simulated processes are universal and, thus, that are based on a number of site tests, they might be applied at sites with a different agroecological regime for which they have not previously been calibrated, although calibration of specific parameters might still be required. In the following, we briefly describe these two approaches, their applicability for smallholder systems, representation of the landscape units, technical demands of the process, and sources of uncertainty.

## **9.3 Using Empirical and Process-Based Models with Disaggregated Data**

## *9.3.1 Empirical Models*

Empirical models for scaling GHGs are based on statistical functions that relate land management "activities" such as extent of a land cover type, amount of fertilizer applied, or the number of heads of livestock to changes in GHG emissions or C sequestration. Carbon stock changes, and GHG fluxes can then be calculated based on two types of input data: (1) that describes the occurrence of activities (the socalled "activity data") and (2) the average effect that an activity has on a nutrient stock or flux in question ("emission factors") (Eq. (9.1)).

$$\text{GHG} = \sum\_{i}^{n} A\_{i} \, ^{\*}\text{EF}\_{i} \tag{9.1}$$

where

*GHG* equals the stock (mass) or flux (rate: mass per unit time), sequestration or balance in units of C, N, or an integration of the two (CO2 eq)

*A* represents the extent (area) over which an activity occurs

*EF* is an emissions factor (e.g., a constant rate relative to the specific activity: mass per unit time per unit area)

Summation of GHG fluxes or stock changes across *N* activities (sources/sinks) generates a cumulative balance for the selected area. This approach is analogous to a linear aggregation scaling method based on measurements or estimated values.

The most widely applied empirical models for scaling GHGs are contained within the IPCC Guidelines for Greenhouse Gas Accounting (IPCC 2006). The IPCC Guidelines define global (Tier 1) and, sometimes regional (Tier 2) emission factors for GHG sources and sinks such as the methane produced by enteric fermentation per head of cattle or the amount of nitrous oxide resulting from the application of nitrogenous fertilizers. Persons interested in GHG quantification can multiply these values and use the provided equations with locally relevant data on farm and landscape management activities to generate estimates of individual sources and sinks or cumulative GHG balances. Application of emission factors and empirical models is the foundation of national GHG inventories and data (Tubiello et al. 2013) and is becoming more common for landscape GHG accounting including *ex-ante* climate change mitigation project impact assessments (Colomb and Bockel 2013).

IPCC Tier 1 default emission factors are based on both empirical data and expert opinion. In some cases, emissions factors are derived from analysis of 100 s or even 1000 s of measurements of the source activity and the rates of emissions. For instance, IPCC default emissions factor for nitrous oxide emissions from N fertilizer use (%) are based on the database of nearly 2 000 individual measurements from studies conducted around the world (Stehfest and Bouwman 2006). Distribution of the studies they are taken from is however biased toward measurement campaigns conducted in Europe and North America. Other emission factors are estimated based on very limited data (e.g., single values for carbon stocks in agroforestry systems) or expert opinion (e.g., emission factor for methane emission from enteric fermentation is based on modeled results, not measurements, for Africa) (IPCC 2006). Global default emission factors are published in the National Guidelines for Inventories while other regionally relevant emission factors are available in the IPCC Emissions Factor database, peer-reviewed literature and in the future will be made available through the SAMPLES web platform.

Empirical models are typically thought to generate reasonable approximations of GHG fluxes at higher levels of organizations and large spatial extent (Del Grosso et al. 2008), presuming the activity data are well constrained. This is because it is thought that at large scales such as across countries, the departure of actual fluxes from average emissions factor values will average out with aggregation of multiple land units. However, for any local scale—farms for example, where local environmental and management heterogeneity of conditions are not well represented in the global datasets, applying empirical models and emissions factors may represent a significant departure from actual fluxes.

The relevance of using empirical models for farm-scale estimates of GHG balances is untested and perhaps spurious, especially for farming systems in developing countries. IPCC guidelines using Tier 1 default factors were not designed for this purpose. Tier 1 approaches were intended to be used when the source activity was relatively inconsequential to total GHG budgets, perhaps contributing less than 5 % of the total (IPCC 2006). Furthermore, significant variations in GHG flux rates occur between point locations due to edaphic mechanisms that control biological emission processes. Because observations of GHG fluxes for tropical smallholder farming systems are scarce or nearly missing in available databases, Tier 1 default factors may considerably misrepresent flux rates for such systems. In view of the low use of N fertilizers in sub-Saharan Africa it is therefore not surprising that many of the N2O fluxes currently being measured there are 1/3 to 1/2 of those estimated using the Tier 1 IPCC emission factors (Hickman et al*.* 2014; Shcherbak et al. 2014). A comprehensive evaluation of Tier 1 emission factors relating to GHG impacts measured in tropical regions is currently lacking. Despite these concerns and the uncertainty of the results, disaggregation of whole farms into component activities and applying available empirical models remains a way to estimate relative impacts of smallholder farming activities at the whole-farm level (Seebauer 2014), as well as understand emission hotspots and the research gaps.

Emissions from livestock production in the tropics, namely from enteric fermentation and manure management, present their own challenges due to data scarcity (Goopy et al. 2016). Similarly to soil fluxes, emissions from both sources are poorly constrained and according to the review by Owen and Silver (2015) data for dairy manure management are limited in Africa and extremely scarce for other systems (Predotova et al. 2010). Yet in many countries, these sources are thought to be substantial contributors to total GHG budgets (Gerber et al. 2013).

Besides poorly constrained emission factors, an additional issue (and arguably most important) is limited knowledge of farm management practices (*A* in Eq. (9.1)), which limits the use of empirical relationships and models to calculate fluxes. Many developing countries have poorly defined record keeping and reporting schemes about organic and inorganic fertilizer use, manure management, crop rotations, and other activities, so there is limited information on the extent of land management decisions (Ogle et al. 2013). This adds another source of uncertainty (in addition to emission factors themselves). Valentini et al. (2014) reported that estimates of the extent of various land cover types in Africa can be from 2.5 to 110 % different, depending on the data source, either using inventory sources or satellite imagery. Other evidence from data collection methods suggests that the uncertainty in farm management practices is similar to that of emissions, 30–80 % (Fig. 9.3, Seebauer 2014). New practices have been developed to help developing countries better represent the activities in their agricultural landscapes (Tubiello et al. 2013) and many institutions such as the US Environmental Protection Agency train government personnel in developing countries to co-compile inventories. However, problems with the data quality itself remain. Incentives to improve and standardize data collection and archiving efforts are limited.

Simplicity and transparency are the largest benefits of using data (dis)aggregation techniques and empirical models for scaling GHG estimates. The models represent relationships that are easy to understand and implement, which makes them accessible to next users without requiring much technical expertise. This has led to the creation of a wide range of GHG calculators such as the Cool Farm Tool and EX-ACT (see Colomb and Bockel 2013 for a review). These tools make it possible for non-specialists to perform calculations and generate estimates of GHG balances with relatively little data or effort. It is still unknown, however, whether the estimates produced by such tools provide robust values—either in terms of absolute or relative changes between different practices (Fig. 9.2).

## *9.3.2 Process-Based Models*

Empirical models are only one way to scale measured data. Process-based models are also used. For example, Bryan et al. (2013) averaged household data for seven counties and four agroecological zones in Kenya used a process-based model to predict changes in methane emissions from enteric fermentation and revenue with improved feeding practices (Table 9.2). Process-based models consist of equations implementing current scientific understanding of the mechanisms determining system properties. Even though microbial and physicochemical processes involved in GHG emissions from soils are implemented in various biogeochemical models, equations are often based on empirical observations or represent apparent changes in production rates or microbial activity due to, for example, changes in environmental conditions such as changes in moisture and temperature. Thus, models describe a system consisting of components such as soil physics and energy fluxes,

**Fig. 9.2** Uncertainty of activity data inputs into a whole-farm accounting approach used in Western Kenya (Seebauer 2014). Uncertainty depends on the farm activity in question and ranges from 10 to 20 % for crop residue inputs up to greater than 80 % with on-farm tree biomass. Data were collected by survey and colors represent different farm types

vegetation biomass development, or soil microbial C and N turnover and their interactions, which are represented by the equations describing states and rates at different points in time (temporal resolution). Process-based GHG models are designed to run at source scale (e.g., site or animal) after being calibrated based on observed relationships in controlled experiments and monitoring data. Because the equations represent principal microbial, biogeochemical and physicochemical processes underlying ecosystem–atmosphere exchange processes and the emission of GHGs, the models can be suitable to simulate GHG dynamics under diverse environmental and management conditions, even conducting "what if" scenario type of experiments. The robustness of process-based models has made them a widely used predictive tool in global change studies and they might be suitable as well to account for fine scale heterogeneity in the farming context, which is not possible with current empirical models. However, process-based models need to be tested for their ability to represent GHG under specific conditions to have confidence in their predictions. This is an involved process, which restricts their utility for sites and systems outside the range of the available calibration data. Until process-based models have been adapted, calibrated, and evaluated to account for diversity and complexity characteristic of smallholder farming, their use for GHG quantification at the whole-farm level in mixed systems, such as the crop–livestock systems of Africa, remains a challenge, requires a tight coupling of sectorial models and a whole system understanding, and implies significant uncertainty.


**Table 9.2** Geographically averaged input data was used to run a process-based model (RUMINANT) to predict changes in emissions and revenues with changing diets under two scenarios (Bryan et al. 2013)

The accuracy of a process-based model is related to errors due to model structure (model parameter uncertainty) or errors due to the accuracy of data inputs (input uncertainty). Errors related to model structure are based on incomplete understanding and knowledge of the fundamental relationships that are driving GHG production and consumption processes in soils, variation in ways to describe underlying processes, and fluxes at the soil–atmosphere interface and the representation of them in the model. These errors can be quantified statistically by comparing the model's predicted GHG fluxes to measured GHG fluxes, with correlation coefficients for instance. Errors related to input uncertainty occur because the input data describing a particular system is not well known. This may be particularly problematic in developing countries when the detailed climate, soils, and land use data are not available at a high degree of resolution. Input uncertainty can be estimated using Bayesian calibration and Monte Carlo simulations (see for example Van Oijen et al. 2011; Rahn et al. 2011).

Process-based models are available for the majority of biological GHG sources and sinks but tend to be limited to one type of source or sink. For instance, CENTURY, DAYCENT, and LandscapeDNDC (Giltrap et al. 2010; Haas et al. 2013) were developed to simulate biomass production and soil processes, including simulation of soil GHG fluxes and soil C/N stock changes. Process-based models are also available to simulate CH4 emissions from livestock but have so far mainly been applied in the United States and in Europe (Thornton and Herrero 2010; Rotz et al. 2012; Duretz et al. 2011). These models are reasonable when evaluating the soil carbon sequestration potential at large scales or emissions of N2O from monoculture fields (Babu et al. 2006), or changes in herd management (Pathak et al*.* 2005; Bryan et al. 2013; Perlman et al. 2013) but perhaps less so when trying to characterize the GHG impacts of smallholder systems at the whole farm level or for landscape-scale accounting.

Smallholder farming systems comprise multiple types of farming activities, often combining trees, animals, and crops in interconnected systems. Human management alters nutrient flows, potentially mitigating or exacerbating emissions from parts of the system; applying sectoral process-based models to whole farms therefore may oversimplify the complex interactions taking place (Tittonell et al*.* 2009). As of yet, few modeling approaches have been adapted for farm-level modeling of GHG impacts in mixed crop–livestock systems (Schils et al*.* 2007; Groot et al. 2012; Del Prado et al. 2013) and to our knowledge none have been applied to smallholder conditions of tropical developing countries.

To facilitate the widespread use of process-based models, as a first step the models need to be tested for most locations dominated by smallholder farming, which requires the availability of respective test datasets. Data on site-specific factors such as soil properties, cropping sequences, and fertilizer use are required; information which is often unavailable in many developing countries. In terms of enteric fermentation, the challenge is both a lack of information on animal numbers, species, and breeds, feeding regimes, as well as the quality of feeds and forages even though the models are based on the presumption that the chemical reactions that occur in the rumen are fairly standard and tend to go to completion. However, emission factors and rates currently available which have been obtained so far, don't consider that livestock production in developing countries often involves periods of severe undernutrition with feed qualities being far lower than tested in experiments in OECD countries. It is obvious that there is a great need to generate data that can be used for model parameterization and evaluation for smallholder conditions. Until now, only limited information has been available to independently assess the validity of the emission models for developing country conditions, casting doubt on the reliability of results generated from process-based models.

## **Conclusion**

The complexity and scale that is characteristic of smallholder farming and the general lack of data presents significant challenges for scaling GHG emissions with much certainty. Significant efforts and investments are needed to improve systems representation so that the data collected are used to improve either empirical or process-based models. Moreover, conducting detailed monitoring campaigns can address the challenge of complexity and heterogeneity, and provide data that can be used to scale up representative systems with greater confidence.

Besides concerns over accuracy, technical demands in terms of data availability and model testing all limit the utility of using process-based models as a scaling method for GHG fluxes in agricultural systems of tropical developing countries at this time. However, given the costs of monitoring programs, it becomes an imperative to establish programs that can adapt and improve process-based models for quantification as they provide a means to test hypotheses of mitigation options and GHG accounting. This will require a number of investments in monitoring of smallholder practices of field and livestock management, scientific capacity building, and GHG measurements to evaluate the models for smallholder conditions. We estimate that a 10-year program of targeted and iterative measurements and modeling—those for key sources and sinks spanning heterogeneous conditions—is needed before use of process-based models becomes a viable solution for widespread GHG quantification in smallholder systems at either farm or landscape scales. In the meantime, models can be parameterized and tested well for farm and landscape situations, albeit time and resource intensive, but the limitations need to be recognized by those using the models and more importantly those using the model outputs.

**Open Access** This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

## **References**


## **Chapter 10 Methods for Environment: Productivity Trade-Off Analysis in Agricultural Systems**

 **Mark T. van Wijk , Charlotte J. Klapwijk , Todd S. Rosenstock , Piet J. A. van Asten , Philip K. Thornton , and Ken E. Giller** 

 **Abstract** Trade-off analysis has become an increasingly important approach for evaluating system level outcomes of agricultural production and for prioritising and targeting management interventions in multi-functional agricultural landscapes. We review the strengths and weakness of different techniques available for performing trade-off analysis. These techniques, including mathematical programming and participatory approaches, have developed substantially in recent years aided by mathematical advancement, increased computing power, and emerging insights into systems behaviour. The strengths and weaknesses of the different approaches are identifi ed and discussed, and we make suggestions for a tiered approach for situations with different data availability. This chapter is a modifi ed and extended version of Klapwijk et al. (2014).

 M. T. van Wijk (\*) International Livestock Research Institute , Old Naivasha Rd., P.O. Box 30709 , Nairobi , Kenya e-mail: m.vanwijk@cgiar.org

 C. J. Klapwijk Plant Production Systems Group , Wageningen University and Research Centre , Wageningen , Netherlands

 International Institute of Tropical Agriculture (IITA) , Kampala , Uganda e-mail: l.klapwijk@cgiar.org

 T. S. Rosenstock World Agroforestry Centre (ICRAF) , Nairobi , Kenya

 P. J. A. van Asten International Institute of Tropical Agriculture (IITA) , Kampala , Uganda

 P. K. Thornton CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) , Nairobi , Kenya

 K. E. Giller Plant Production Systems Group , Wageningen University , Wageningen , Netherlands

© The Editor(s) (if applicable) and the Author(s) 2016 189 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1\_10

## **10.1 Introduction**

 Trade- offs , by which we mean exchanges that occur as compromises, are ubiquitous when land is managed with multiple goals in mind. Trade-offs may become particularly acute when resources are constrained and when the goals of different stakeholders confl ict (Giller et al. 2008 ). In agriculture , trade-offs between output indicators may arise at all hierarchical levels, from the crop (such as grain versus crop residue production), the animal (milk versus meat production), the fi eld (grain production versus nitrate leaching and water quality), the farm (production of one crop versus another), to the landscape and above (agricultural production versus land for nature). An individual farmer may face trade-offs between maximizing production in the short term and ensuring sustainable production in the long term. Within landscapes, trade-offs may arise between different individuals for competing uses of land. Thus trade-offs exist both within agricultural systems, between agricultural and broader environmental or sociocultural objectives, across time and spatial scales, and between actors. Understanding the system dynamics that produce and change the nature of the trade-offs is central to achieving a sustainable and food-secure future.

 In this chapter we focus on how the complex relationships between the management of farming systems and its consequences for production and the environment here represented by greenhouse gas (GHG) emissions—can be analyzed and how trade-offs and possible synergies between output indicators can be quantifi ed. For example, an important hypothesis is that by increasing soil carbon sequestration in agricultural systems, farmers can generate a signifi cant share of the total emission reductions required in the next few decades to avoid catastrophic levels of climate change. At the same time, increasing soil carbon sequestration also increases soil organic matter, which is fundamental to improving the productivity and resilience of cropping and livestock production systems, and thereby a potential win–win situation is identifi ed. However, it is debatable whether these win–win situations exist in reality. An important constraint for this hypothesis is the lack of organic matter like crop residues on many smallholder mixed crop–livestock systems, to serve both as feed for livestock and as an input into the soil in order to increase soil organic matter. This organic matter could be produced through the use of mineral fertiliser or intensifi cation of livestock production, but both of these have negative consequences for GHG emissions, probably offsetting the gains made in soil organic matter storage. It therefore seems likely that to achieve maximum impact on smallholders' food production and food security, environmental indicators have to be compromised. However, good quantitative insight into these compromises is still lacking.

 Trade-off analysis has emerged as one approach to assessing farming system dynamics from a multidimensional perspective. Although the concept of trade-offs and their opposite—synergies—lies at the heart of several recent agricultural researchfor- development initiatives (Vermeulen et al. 2011 ; DeFries and Rosenzweig 2010 ), methods to analyze trade-offs within agroecosystems and the wider landscape are nascent (Foley et al. 2011 ). We review the state of the art for trade-off analyses, highlighting important innovations and constraints, and discuss the strengths and weaknesses of the different approaches used in the recent literature.

## **10.2 The Nature of Trade-Off Analysis**

 Trade-offs are quantifi ed through the analysis of system-level inputs and outputs such as crop production, household labour use, or environmental impacts such as greenhouse gas emissions. The outcomes that different actors may want to achieve, in and beyond the landscape, need to be defi ned at different time and spatial scales. Understanding these desired outcomes, or different stakeholders' objectives, is a necessary fi rst step in trade-off analysis.

 We illustrate the key concepts and processes of trade-off analysis with a simple example that has only two objectives: farm-scale production and an environmental impact on greenhouse gas emissions. Once the objectives have been defi ned, the next step is to identify meaningful indicators that describe these objectives. The indicators form the basis for characterizing the relationships between objectives (Fig. 10.1 ). The shape of the trade-off curve gives important information on the severity of the trade-off of interest. Is it simply a straight line, like the central curve (Fig. 10.1a )? Is the curve convex (i.e. the lower curve), which means strong tradeoffs exist between the indicators); or concave (i.e. the upper curve), which means the indicators are independent of each other and the trade-offs between the indicators are quite 'soft'? The shape of the trade-off curve represents different functional relationships and can be assessed by evaluating farm management options; in our example, each point could represent a method and level of mineral fertiliser application (Fig. 10.1b ). The position of each option in the trade-off space describes its outcomes in terms of the two indicators, productivity and environmental impact. Based on this information, a 'best' trade-off curve can be drawn (Fig. 10.1c ). In trade-off analyses the researcher will be interested in which system management interventions result in which type of outcome of the different objectives (Fig. 10.1d ).

 Once the best (observed or inferred) trade-off curve has been identifi ed, various system management interventions can be studied to assess the extent to which they contribute to the desired objectives (Fig. 10.1d ). This analysis determines whether so-called 'win–win' solutions are possible, where the performance of the system can be improved with regard to both objectives. Alternatively, does improvement in one objective automatically lead to a decrease in system performance for another objective (Fig. 10.1e )? Possible threshold values can be identifi ed once the shape of the trade-off curve is known. For example, do productivity thresholds exist, above which the environmental impact increases rapidly? In some situations, it may be possible to alter the nature of the trade-off between production and environmental impact through the exploration of new management interventions (Fig. 10.1f ), thereby redefi ning the 'best' trade-off curve.

## **10.3 Research Approaches and Tools**

 Trade-offs are typically much more complex with more dimensions and objectives than indicated by the simple two-dimensional examples presented in the previous section. A wide variety of tools and approaches have been developed to account for

 **Fig. 10.1** Key concepts of trade-offs and their analysis of a simple two-objective example (for explanation see text) EI = environmental impact, P = production. ( **a** ) Shape, ( **b** ) outcomes of management options, ( **c** ) trade-off and possibility for synergies, ( **d** ) strategies (interventions) and outcomes, ( **e** ) thresholds, ( **f** ) can trade-offs be alleviated

diverse situations. The most suitable approach depends on the nature and scale of the problem to be addressed, the trade-offs involved, and the indicators available. We assess fi ve widely applied approaches: (1) participatory methods ; (2) empirical analyses; (3) econometric tools; (4) optimization models, and (5) simulation models. These fi ve approaches overlap often and can help generate complementary knowledge. Consequently, trade-off analyses will often utilize several methods simultaneously or iteratively.

 The concept of *participatory research* originally highlighted the need for the active involvement of those who are the subject of research, or for whom the research may lead to outcome changes. In recent times, the notion has expanded to acknowledge that change in researchers' assumptions and perceptions may be required to achieve desired outcomes that are attractive to farmers (Crane 2010 ). Participatory approaches, such as fuzzy cognitive mapping (Murungweni et al. 2011 ), resource fl ow mapping, games and role-playing, are powerful ways to identify actor-relevant objectives and indicators, although the scope of farmer knowledge and perceptions within scientifi c research can be constraining in some situations, particularly in times of rapid change (Van Asten et al. 2009 ). There are many examples of participatory approaches (Gonsalves 2013 ) that could be or are used to assess trade-offs. Participatory approaches usually generate qualitative data and so, although they may not be well suited for quantifying trade-offs, they provide critically important information to support quantitative tools, for example through the development of participatory scenarios (DeFries and Rosenzweig 2010 ; Claessens et al. 2012 ). However, despite the participatory nature of these approaches, the assessment of trade-offs often remains researcher-driven.

 Quantitative assessment of trade-offs requires *empirical* or experimental approaches to generate data on the behavior of the system under different conditions. Trade-off curves can be drawn on the basis of experimental measurements of indicators, such as the removal of plant biomass for fodder and the resulting soil cover, which is a good proxy for control of soil erosion (Naudin et al. 2012 ). Statistical techniques such as data envelope analysis (Fraser and Cordina 1999 ) or boundary line analysis (Fermont et al. 2009 ) can be used to quantify best possible trade-offs between indicators in empirical datasets (e.g. Fig. 10.1c ). Related to these empirical approaches are *econometric tools* : these use large datasets as the basis of statistical coeffi cients that defi ne the input–output relationships of system level outcomes (e.g. Antle and Capalbo 2001 ). Developments combine biophysical and socioeconomic aspects of the system, and use farm-level responses to quantify consequences at a regional level (Antle and Stoorvogel 2006 ). Empirical and econometric approaches are powerful in the sense that outcomes of various system choices can be explored using the existing variability in system confi guration and performance. However, the inference space of the analysis is constrained to the dataset collected and is therefore not suitable for predicting outcomes outside the ranges of the original data.

 Empirical approaches cannot be used to assess indicators that are diffi cult to measure directly; therefore, they are often combined with *simulation models* to obtain an overview of overall system performance. Simulation models allow the dynamic nature of trade-offs to be explored, where outcomes can differ in the short or long term (Zingore et al. 2009 ). System performance, expressed quantitatively in terms of outcomes represented by different indicators, can be used as an input for *optimization* approaches such as mathematical programming (MP). MP fi nds the best possible trade-off through multicriteria analysis and can assess whether this trade-off curve can be alleviated through new interventions. MP has a long history (e.g. Hazell and Norton 1986 ) and is among the most extensively used trade-off application in land use studies (e.g. Janssen and Van Ittersum 2007 ). This is despite its inherent limitation, that land users do not always behave according to economic rationality and optimise their behaviour. Techniques have been developed recently to solve non-linear MP problems and integrate across levels, linking farms and regions through markets and environmental feedbacks (e.g. Laborte et al. 2007 ; Roetter et al. 2007 ; Louhichi et al. 2010 ).

 Inverse modelling techniques use non-linear *simulation models* directly to perform multiobjective optimization without the intermediate step of MP. Furthermore, with the identifi cation of the appropriate model outputs, system behaviour can be assessed across different temporal and spatial scales and feedbacks taken into account, which is often a weak part of MP models. The complexity of agroecosystems and the large number of potential indicators can hamper effi cient applications of this computationally intensive method. But advances in computer power have resulted in several applications in farming systems research, going from farm to landscape (Groot et al. 2007 , 2012 ; Tittonell et al. 2007 ).

 The various approaches to trade-off analysis each have key strengths and weaknesses and combining approaches may provide enhanced opportunities for a realistic, relevant, and integrated assessment of systems (Table 10.1 ). For example, in many cases, participatory approaches are needed to defi ne meaningful objectives and indicators, but are not suitable to reliably quantify the trade-offs associated with possible interventions. Empirical and econometric approaches can be used to quantify the current state of the overall agricultural system. In many cases, however, simulation models are needed to quantify indicators that are diffi cult to measure (for example, effects of management on longer term productivity) and to explore options beyond the existing system confi gurations and boundaries (Table 10.1 ). Optimization can be used to assess the potential for synergies and alleviation of trade-offs, but has limited applicability when sociocultural traditions and rules play a key role (Thornton et al. 2006 ).

 It is clear that for trade-off analyses combinations of techniques are needed. Multicriteria analysis is an example of such an integrated approach , in which participatory and optimization methods are combined: the weighting of the individual criteria in goal programming models is done together with the stakeholders, and by changing these weights with the stakeholders a trade-off analysis is performed (e.g. Romero and Rehman 2003 ).

## **10.4 A Tiered Approach**

 The discussion above demonstrates that for fully integrated trade-off analyses different approaches should be combined. However, in many cases data availability will not allow such elaborate analyses. The techniques discussed in the previous


 **Table 10.1** Strengths and weaknesses of the different approaches for analysing trade-offs in agricultural systems

*Act* actual or current use in the scientifi c literature, *Pot* potential usefulness of technique

section not only have different strengths and weaknesses, but also different data demands. Typically, empirical and econometric approaches are highly data-demanding, and therefore time-consuming and expensive, whereas participatory approaches can provide essential information about system functioning after only a few welldesigned discussion panels and targeted questionnaires. Simulation and optimization models can be, in terms of data demand, anywhere between these extremes. Their data demand is highly determined by model setup and complexity.

 An example of a tiered approach in which researchers move from quick initial data analyses to more complex, data demanding, modelling exercises is the fourstep approach used by Van Noordwijk and his team at ICRAF (Meine van Noordwijk, personal communication; see also Tata et al. 2014 for the fi rst three steps; Villamor et al. 2014 for an agent-based modelling approach).


 This four-step approach demonstrates the way in which the strengths of different methods of trade-off analysis can be combined, and how such an analysis can move stepwise towards more complex and data-demanding exercises.

 All in all it is not straightforward to give concrete advice that relates the purpose of analysis to the technique and approach to be used. Researchers make personal choices about complexity and analytical approach as part of the 'art' of modelling and tradeoff analyses. This is sometimes diffi cult to reconcile with the 'objectivity' that we pursue in scientifi c research. However, some general indications can be given.

 If the objective of the analysis is to assess the overall potential for system improvement and the room for manoeuvre to increase effi ciencies and profi tability without negative effects on environmental indicators, then optimization approaches are the most logical choice. If the purpose is to analyse the short- and long-term consequences of certain interventions and the trade-offs between different objectives over different time scales, then simulation modelling is an obvious candidate. This may be combined with some sort of multiobjective, non-linear optimization or inverse modelling approach.

 Both optimization and simulation are typically used for scientifi cally oriented studies. In order to have real-life impact, that takes into account the complexities of agricultural systems and the large diversity of drivers and options in agricultural land use, especially in developing countries, a variety of quantitative and qualitative approaches are likely to be needed (e.g. Murungweni et al. 2011 ). The setup of these tools, the identifi cation of indicators, and the presentation of results need to be determined using participatory approaches where key stakeholders are involved and drive decisions from the beginning of the project. This might lead to the study having less value in terms of scientifi c novelty, but will increase its practical relevance on the ground. With the topic of this chapter in mind, it is ironic that in many cases there might be a trade-off between the scientifi c and societal impact that can be achieved by a research project that has its own constraints in terms of time and money.

 **Acknowledgements** This study is an outcome of a workshop entitled 'Analysis of Trade-offs in Agricultural Systems' organised at Wageningen University, February 2013. We thank all participants for their discussions, which contributed strongly to the content of this chapter. The workshop and subsequent work were funded by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Theme 4.2: *Integration for Decision-Making—Data and Tools for Analysis and Planning* . This chapter is a modifi ed and extended version of Klapwijk et al. ( 2014 ).

**Open Access** This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

## **References**


## **Index**

#### **A**

 Activity data , 38 Agriculture, Forestry, and Other Land-Use (AFOLU) Guidelines, 48 Allometric models , 170

#### **B**

 Biomass expansion factors (BEFs) , 129 Bottom-up approach fi eld selection , 29 fi eld typology defi nition , 30–31 fi eld type scores , 29 , 30 food security and poverty , 30 human-induced changes , 30 inherent land quality , 29 land management practices, diversity of , 31 landscape plots , 32 lower Nyando site , 30 replicated fi eld sites selection , 32

#### **C**

 Chamber measurements auxiliary measurements , 91–92 dynamic chambers , 82–83 environmental conditions, changes in , 83–85 GC , 89 spatial variability , 85–86 spectroscopic methods , 90–91 static chamber , 83 , 89

 Climate Change, Agriculture, and Food Security (CCAFS), 20 Climate smart agricultural (CSA) practices , 164 Cool Farm Tool , 5 , 182 Crop cards method , 169 , 172 Crop-cut method , 165 , 171 Crop modelling , 169 , 172 Cumulative mass coordinate approach , 144

#### **D**

 Data (dis)aggregation techniques , 182 Dendrochronology , 130 Diameter at breast height (DBH) , 121 , 123 , 126 , 127

#### **E**

 Elemental combustion technique , 143 Emission factors , 38 Empirical models activity data , 180 advantage , 182 cumulative balance , 180 data collection , 182 emission factors , 180 higher levels organizations and large spatial extent, 181 IPCC Guidelines , 180–181 poorly constrained emission factors , 182 Enteric methane production (EMP) , 98 Equivalent soil mass approach , 144 EX-Ante Carbon Balance Tool (EX-ACT) , 5 , 182

© The Editor(s) (if applicable) and the Author(s) 2016 199 T.S. Rosenstock et al. (eds.), *Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture*, DOI 10.1007/978-3-319-29794-1

#### **F**

 Farmers' estimation method , 172 Fermentation processes , 98 Field typology defi nition , 30–31 food security and poverty , 30 human-induced changes , 30 inherent land quality , 29 land management practices, diversity of , 31 Flame ionization detector (FID) , 90

#### **G**

 Gas chromatography (GC) , 89 Gas pooling technique , 86 Geographic information systems (GIS) , 41 Gestalt-theory , 19 Greenfeed ® , 110 Greenhouse gas (GHG) emission biosphere–atmosphere exchange processes , 72 , 82–86 chamber measurements (*see* Chamber measurements ) micrometeorological measurements , 74–82 CH 4 , 72 CO 2 , soils , 72 empirical models activity data , 180 advantage , 182 cumulative balance , 180 data collection , 182 emission factors , 180 higher levels organizations and large spatial extent, 181 IPCC Guidelines , 180–181 poorly constrained emission factors , 182 environmental conditions changes , 73 fi eld irrigation and water logging , 73 fl ux measurements , 74 intensity , 2 measurement guidelines adaptation and livelihoods, benefi ts of , 7 Cool Farm Tool , 5 current information, uncertainty of , 7 data acquisition , 3 , 9 emissions estimation , 9–10 EX-ACT , 5 fi eld measurement , 2 heterogeneous landscapes , 2–3 list of practices , 7 livelihood and food security improvement , 3 mitigation analysis , 9–10

 mitigation potential , 7 NARS , 5 national and subnational mitigation plans, 5 national GHG inventories, compilers of, 5 PCR , 5 potential practices identifi cation , 6 question defi nition , 8 resource limitations , 3 SAMPLES website , 5 students and instructors , 5 Tier 1 emission factors , 2 microbial processes , 72 N 2 O emissions , 72 process-based models , 182–185 quantifi cation of , 164 recarbonization , 72 rice paddies methane , 86 rice chamber design and general procedure, 87–88 sampling frequency , 89 sampling, timing of , 88 upland systems , 87 scaling methods , 177–179 soil–atmosphere exchange processes , 72 temperature changes , 72 whole-farm and landscape level , 176

## **I**

 IMPACTlite tool , 30 Infrared thermography , 112 Intergovernmental Panel on Climate Change (IPCC) AFOLU Guidelines , 48 approaches , 39–41 carbon pools , 57 Tier 1 , 39 , 180–181 Tier 2 , 39 Tier 3 , 39 Intraruminal telemetry , 112 ISODATA algorithm , 29

#### **L**

 Land-use and land-cover (LULC) classifi cation activity data , 38 baseline scenario , 52–56 boundaries , 25 carbon stock changes calculation individual/combined carbon pools , 57 initial carbon stock estimation , 57–58

 key carbon pools , 57 process-based method , 59 stock-based method , 59 carbon storing land , 38 category defi nition , 47–48 change detection accuracy assessment , 62 activity data , 51 spatially explicit methods , 51–52 classifi cation accuracy assessment , 60–62 data acquisition existing datasets , 42 ground-based fi eld sampling methods , 42–43 remote sensing data , 44–43 spatial considerations , 44–46 temporal considerations , 46–47 DEM , 25 direct measurement methods , 63 emerging technologies , 63 emission factors , 38 feature extraction , 24 GHG emissions/removals calculation , 38 image segmentation , 21 IPCC Guidelines , 38–41 landscape units identifi cation , 19 non-spatially explicit methods , 49 Nyando , 25 object limits , 25 object-based approaches , 20 , 25 reference regions , 56–57 RF , 25 RGB composite , 25 setting project boundaries , 41–42 spatially explicit methods , 49 stratifi cation , 49–51 supervised classifi cation , 50–51 transitions monitoring , 38 uncertainty , 59 , 62–63 unsupervised classifi cation , 50 visual interpretation , 50 Low-Emission Development Strategies (LEDS), 5

#### **M**

 Methane emissions , 86 , 87 blood methane concentration , 111–112 direct measurement open-circuit respiration chambers , 99–101 open-path lasers , 108–110 polytunnels , 101–107

 sulfur hexafl uoride tracer technique , 107–108 ventilated hood system , 101 indirect estimation diet , 99 in vitro incubation , 98–99 infrared thermography , 112 intraruminal telemetry , 112 principles , 98 quantitative molecular biology , 112 short-term measurement CH 4 :CO 2 ratio , 111 Greenfeed ® , 110 PAC , 110–111 spot measurements , 111 Micrometeorological measurements , 74–82 Minimum information unit (MIU) , 44 Minimum mapping unit (MMU) , 44 Mitigation, smallholder agriculture , 29–31 analysis , 16 bottom-up approach (*see* Bottom-up approach ) climate implications , 16 fi eld types , 17 GIS , 17 implementation , 17 initial steps , 17–18 land productivity , 17 landscape unit , 16 RS , 17 systematic selection , 16 top-down approach (*see* Top-down approach ) typology , 17 Monitor, report, and verify (MRV) , 5

#### **N**

 National agricultural research centers (NARS) , 5 National Forest Inventories (NFIs) , 120 Nationally Appropriate Mitigation Actions (NAMAs) , 4 , 5 Normalized Difference Vegetation Index (NDVI), 26 , 44

#### **O**

 Open-path lasers method , 108–110 Out-of-bag (OOB) , 25

#### **P**

 Photoacoustic spectroscopy (PAS) , 90 Polytunnels , 101–107

 Portable accumulation chambers (PAC **)** , 110–111 Process-based models , 59 , 149 , 182–185 Product Category Rules (PCRs) , 5

#### **Q**

Quantitative molecular biology , 112

#### **R**

 Random forest (RF) , 25 Random sampling approach , 43 , 125 Recarbonization , 72 Reducing Emissions from Deforestation and Forest Degradation (REDD+) , 40 Remote sensing , 170

#### **S**

 Scaling methods , 130 , 177–179 Smallholder production system allometric models , 170 crop cards method , 169 , 172 crop-cut method , 165 , 171 crop production estimation , 164–170 crop yield estimation , 166 expert assessment , 168–169 farmers' estimation method , 165–166 , 172 harvest unit, sampling for , 168 remote sensing , 170 whole plot harvest method , 167–168 , 172 Soil bulk density (SBD) , 142 , 144 Soil organic carbon (SOC) stock quantifi cation accuracy , 136–139 biotic factors and management activities , 136 comparative analysis , 136 cumulative mass coordinate approach , 144 equivalent soil mass approach , 144 sample collection , 142 sample preparation and analytical methods, 143 sampling design farm level , 139 geographic/ecological boundary , 139 landscape level , 140–142 SBD , 144 scaling average calculation , 145 fi eld's surface area , 146 standard error , 146 uncertainty , 146 variance , 145 soil density rings , 145

 spatial coordinate approach , 143 stock changes coupling erosion processes , 150 dynamics , 150 frequency monitoring , 151–152 in situ analyses , 147 laboratory-based analyses , 147 monitoring , 147 process-based models , 149 recommendations , 151–152 remote spectroscopy , 149 stable conditions assumption , 150 Soil organic matter (SOM) , 135 Spatial coordinate approach , 143 Spatially stratifi ed systematic sampling approaches, 141 Spectroscopic methods , 90–91 Stock-based method , 59 Sulfur hexafl uoride (SF 6 ) technique , 107–108

#### **T**

 Targeted sampling , 43 Top-down approach carbon credit project , 18–19 fi eld topology , 31 Gestalt-theory , 19 landscape boundaries , 19 landscape characterization and spatial confi guration , 19 landscape plots , 32 landscape stratifi cation , 20 CGIAR Program , 20 landscape classifi cation , 25–29 LULC (*see* Land-use and land-cover (LULC) classifi cation ) visual classifi cation , VHR imagery , 20 mitigation potential , 18 Panoramio/Confl uence Project , 19 replicated fi eld sites selection , 32 Trade-off analysis in agriculture , 190 defi nition , 190 econometric tools , 193 , 194 empirical analyses , 193 , 194 hypothesis , 190 integrated approach , 194 nature of , 191 optimization approaches , 193 participatory methods , 193 simulation models , 193–194 strengths and weaknesses , 194 tiered approach , 194–196

Index

 Tree biomass carbon stocks and fl uxes quantifi cation accuracy , 121 annual changes , 129–130 average time , 128 biomass assessments , 120 climate change , 120 cost , 122–124 direct methods , 123 indirect methods , 123 , 124 NFIs , 120 plots selection , 125 proxies measurements , 125–128 scale , 122 TOF , 120 tree management , 121 typical precision , 119 whole-farms and landscapes , 130 Tree outside forests (TOF) , 120

#### **V**

 Verifi ed Carbon Standard (VCS) , 40 Very high-resolution (VHR) satellite images LULC boundaries , 25 DEM , 25 feature extraction , 24 image segmentation , 21 Nyando , 25 object limits , 25 object-based approaches , 20 , 25 RF , 25 RGB composite , 25 visual classifi cation , 20 Voluntary Carbon Standard (VCS) methodologies, 56

#### **W**

Whole plot harvest method , 167–168 , 172