Focus on Improving Quantification of Agricultural Greenhouse Gases

Reducing emissions of agricultural greenhouse gases (GHGs), such as methane and nitrous oxide, and sequestering carbon in the soil or in living biomass can help reduce the impact of agriculture on climate change while improving productivity and reducing resource use. There is an increasing demand for improved, low cost quantification of GHGs in agriculture, whether for national reporting to the United Nations Framework Convention on Climate Change (UNFCCC), underpinning and stimulating improved practices, establishing crediting mechanisms, or supporting green products. This ERL focus issue highlights GHG quantification to call attention to our existing knowledge and opportunities for further progress. In this article we synthesize the findings of 21 papers on the current state of global capability for agricultural GHG quantification and visions for its improvement. We conclude that strategic investment in quantification can lead to significant global improvement in agricultural GHG estimation in the near term.

1. Introduction

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.

The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

2. Agriculture and climate change mitigation

The main agricultural GHGs—methane and nitrous oxide—account for 10%–12% of anthropogenic emissions globally (Smith et al 2008), or around 50% and 60% of total anthropogenic methane and nitrous oxide emissions, respectively, in 2005. Net carbon dioxide fluxes between agricultural land and the atmosphere linked to food production are relatively small, although significant carbon emissions are associated with degradation of organic soils for plantations in tropical regions (Smith et al 2007, FAO 2012). Population growth and shifts in dietary patterns toward more meat and dairy consumption will lead to increased emissions unless we improve production efficiencies and management. Developing countries currently account for about three-quarters of direct emissions and are expected to be the most rapidly growing emission sources in the future (FAO 2011).

Reducing agricultural emissions and increasing carbon sequestration in the soil and biomass has the potential to reduce agriculture's contribution to climate change by 5.5–6.0 gigatons (Gt) of carbon dioxide equivalent (CO₂eq)/year. Economic potentials, which take into account costs of implementation, range from 1.5 to 4.3 GT CO₂eq/year, depending on marginal abatement costs assumed and financial resources committed, with most of this potential in developing countries (Smith et al 2007). The opportunity for mitigation in agriculture is thus significant, and, if realized, would contribute to making this sector carbon neutral. Yet it is only through a robust and shared understanding of how much carbon can be stored or how much CO₂ is reduced from mitigation practices that informed decisions can be made about how to identify, implement, and balance a suite of mitigation practices as diverse as enhancing soil organic matter, increasing the digestibility of feed for cattle, and increasing the efficiency of nitrogen fertilizer applications. Only by selecting a portfolio of options adapted to regional characteristics and goals can mitigation needs be best matched to also serve rural development goals, including food security and increased resilience to climate change.

Expansion of agricultural land also remains a major contributor of greenhouse gases, with deforestation, largely linked to clearing of land for cultivation or pasture, generating 80% of emissions from developing countries (Hosonuma et al 2012). There are clear opportunities for these countries to address mitigation strategies from the forest and agriculture sector, recognizing that agriculture plays a large role in economic and development potential. In this context, multiple development goals can be reinforced by specific climate funding granted on the basis of multiple benefits and synergies, for instance through currently negotiated mechanisms such as Nationally Appropriate Mitigation Actions (NAMAs) (REDD+, Kissinger et al 2012).

3. Challenges to quantifying GHG information for the agricultural sector

The quantification of GHG emissions from agriculture is fundamental to identifying mitigation solutions that are consistent with the goals of achieving greater resilience in production systems, food security, and rural welfare. GHG emissions data are already needed for such varied purposes as guiding national planning for low-emissions development, generating and trading carbon credits, certifying sustainable agriculture practices, informing consumers' choices with regard to reducing their carbon footprints, assessing product supply chains, and supporting farmers in adopting less carbon-intensive farming practices. Demonstrating the robustness, feasibility, and cost effectiveness of agricultural GHG inventories and monitoring is a necessary technical foundation for including agriculture in the international negotiations under the United Nations Framework Convention on Climate Change (UNFCCC), and is needed to provide robust data and methodology platforms for global corporate supply-chain initiatives (e.g., SAFA, FAO 2012).

Given such varied drivers for GHG reductions, there are a number of uses for agricultural GHG information, including (1) reporting and accounting at the national or company level, (2) land-use planning and management to achieve specific objectives, (3) monitoring and evaluating impact of management, (4) developing a credible and thus tradable offset credit, and (5) research and capacity development. The information needs for these uses is likely to differ in the required level of certainty, scale of analysis, and need for comparability across systems or repeatability over time, and they may depend on whether descriptive trends are sufficient or an understanding of drivers and causes are needed. While there are certainly similar needs across uses and users, the necessary methods, data, and models for quantifying GHGs may vary. Common challenges for quantification noted in an informal survey of users of GHG information by Olander et al (2013) include the following.

3.1. Need for user-friendly methods that work across scales, regions, and systems

Much of the data gathered and models developed by the research community provide high confidence in data or indicators computed at one place or for one issue, thus they are relevant for only specific uses, not transparent, or not comparable. These research approaches need to be translated to practitioners though the development of farmer friendly, transparent, comparable, and broadly applicable methods. Many users noted the need for quantification data and methods that work and are accurate across region and scales. One of the interviewed users, Charlotte Streck, summed it up nicely: 'A priority would be to produce comparable datasets for agricultural GHG emissions of particular agricultural practices for a broad set of countries ... with a gradual increase in accuracy'.

3.2. Need for lower cost, feasible approaches

Concerns about cost and complexity of existing quantification methods were raised by a number of users interviewed in the survey. In the field it is difficult to measure changes in GHGs from agricultural management due to spatial and temporal variability, and the scale of the management-induced changes relative to background pools and fluxes. Many users noted data gaps and inconsistencies and insufficient technical capacity and infrastructure to generate necessary information, particularly in developing countries. The need for creative approaches for data collection and analysis, such as crowd sourcing and mobile technology, were noted.

3.3. Need for methods that can crosswalk between emission-reduction strategy and inventories or reporting

A few users emphasized the need for information and quantification approaches that cannot only track GHGs but also help with strategic planning on what to grow where and when to maximize mitigation and adaptation benefits. Methods need to incorporate the quantification context, taking into account climate impacts, viability, and cost of management options. Thus, data and methods are needed that integrate climate impacts into models used to assess the potential and costs of GHG mitigation strategies.

3.4. Need for confidence thresholds and rules that are appropriate for use

Users noted that national inventories through the UNFCCC or Intergovernmental Panel on Climate Change (IPCC) require 95% confidence, while some offset market standards leave confidence levels to the discretion of the developer, using discounts in value for greater uncertainty. Nonetheless, these standards tend to have expectations of 20% confidence or better. In fact, both regulatory and voluntary reporting suffer from large uncertainties in the underlying activity data as well as in emission factors. In some circumstances emissions factors may add as much as 50–150% uncertainty to GHG estimates (IPCC 2006). Uncertainty clearly needs to be assessed in implementing projects and programs. In some cases there are uncertainty thresholds, while in others uncertainty is assessed and used as part of the quantification process. What is not always clear is where uncertainty thresholds are necessary to maintain the usefulness of the information and where they are hindering early progress.

3.5. Easily understood and common metrics for policy and market users

Inventories usually track tons of CO₂ equivalents, while supply-chain and corporate reporting are more likely to track efficiency metrics, such as GHG emissions per unit of product; offsets protocols may combine both approaches. As demand for food rises, efficiency of production becomes an increasingly important metric, even if total CO₂ equivalents need to be tracked in parallel to assess climate impacts. For livestock systems it is unclear which metrics are most important to track, GHGs per unit of meat or milk or perhaps per calorie? Different metrics are likely needed for different uses.

3.6. Capacity development in developing countries

There is need to improve on the current lack of capacities to monitor land use and land-use change and their associated GHG emissions and removals for national inventories (UNFCCC 2008, Romijn et al 2012). Since there are ongoing efforts to improve, data, methods and capacities for monitoring forests in the context of REDD+ (Herold and Skutsch 2011), synergies should be sought to use and build upon joint data sources and approaches, such as remote sensing, field inventories, crowd sourcing. and human capacities to estimate and report on GHG balance in both forests and agriculture.

A number of specific objectives to meet these challenges are discussed in this special issue.

• Improve the accuracy of emissions factors across regional differences.

• Improve national inventory data of management activities, crop type and variety, and livestock breeds.

• Use historical data and data collection over time to show trends.

• Test the extent of model applications through field validation (e.g., can they be used in regions with less data?).

• Enhance technical capacity and infrastructure for data acquisition and for application of mitigation strategies in field programs.

• Increase understanding of which mitigation practices result in more resilient systems.

• Improve understanding of the GHG tradeoffs of expanding fertilizer use.

4. Current data infrastructure and systems supporting GHG quantification in the agricultural sector

To understand the challenges facing GHG quantification it is helpful to understand the existing supporting infrastructure and systems for quantification. The existing and developing structures for national and local data acquisition and management are the foundation for the empirical and process-based models used by most countries and projects currently quantifying agricultural greenhouse gases. Direct measurement can be used to complement and supplement such models, but this is not yet sufficient by itself given costs, complexities, and uncertainties.

One of the primary purposes of data acquisition and quantification is for national-level inventories and planning. For such efforts countries are conducting national-level collection of activity data (who is doing which agricultural practices where) and some are also developing national or regional-level emissions factors.

Infrastructure that supports these efforts includes intergovernmental panels, global alliances, and data-sharing networks. Multilateral data sharing for applications, such as the FAO Statistical Database (FAOSTAT) (FAO 2012), the IPCC Emission Factor Database (IPCC 2012), and UNFCCC national inventories (UNFCCC 2012), are building greater consistency and standardization by using global standards such as the IPCC's Good Practice Guidance for Land Use, Land-Use Change and Forestry (e.g., IPCC 1996, 2003, 2006). There is also work on common quantification methods and accounting, for example agreed on global warming potentials for different contributing gases and GHG quantification methodologies for projects (e.g., the Verified Carbon Standard Sustainable Agricultural Land Management [SALM] protocol, VCS 2011). Other examples include the Global Research Alliance on Agricultural Greenhouse Gases (2012) and GRACEnet (Greenhouse gas Reduction through Agricultural Carbon Enhancement network) (USDA Agricultural Research Service 2011), which aim to improve consistency of field measurement and data collection for soil carbon sequestration and soil nitrous oxide fluxes.

Often these national-level activity data and emissions factors are the basis for regional and smaller-scale applications. Such data are used for model-based estimates of changes in GHGs at a project or regional level (Olander et al 2011). To complement national data for regional-, landscape-, or field-level applications, new data are often collected through farmer knowledge or records and field sampling. Ideally such data could be collected in a standardized manner, perhaps through some type of crowd sourcing model to improve regional—and national—level data, as well as to improve consistency of locally collected data.

Data can also be collected by companies working with agricultural suppliers and in country networks, within efforts aimed at understanding firm and product (supply-chain) sustainability and risks (FAO 2009). Such data may feed into various certification processes or reporting requirements from buyers. Unfortunately, this data is likely proprietary. A new process is needed to aggregate and share private data in a way that would not be a competitive concern so such data could complement or supplement national data and add value.

A number of papers in this focus issue discuss issues surrounding quantification methods and systems at large scales, global and national levels, while others explore landscape- and field-scale approaches. A few explore the intersection of top-down and bottom-up data measurement and modeling approaches.

5. The agricultural greenhouse gas quantification project and ERL focus issue

Important land management decisions are often made with poor or few data, especially in developing countries. Current systems for quantifying GHG emissions are inadequate in most low-income countries, due to a lack of funding, human resources, and infrastructure. Most non-Annex 1 countries reporting agricultural emissions to the UNFCCC have used only Tier I default emissions factors (Nihart 2012, unpublished data), yet default numbers are based on a very limited number of studies. Furthermore, most non-Annex I countries have reported their National Communications only one or two times in the period 1990–2010. China, for instance, has not submitted agricultural inventory data since 1994.

As we move toward the next IPCC assessment report on climate change and while UNFCCC negotiations give greater attention to the role of agriculture within international agreements, it is valuable to understand our current and potential near-term capacity to quantify and track emissions and assess mitigation potential in the agriculture sector, providing countries—especially least developed countries (LDCs)—with the information they need to promote and implement actions that, while conducive to mitigation, are also consistent with their rural development and food security goals. The purpose of this focus issue is to improve the knowledge and practice of quantifying GHG emissions from agriculture around the globe. The issue discusses methodological, data, and capacity gaps and needs across scales of quantification, from global and national-scale inventories to landscape- and farm-scale measurement. The inherent features of agriculture and especially smallholder farming have made quantification expensive and complicated, as farming systems and farmers' practices are diverse and impermanent and exhibit high temporal and spatial variability. Quantifying the emissions of the complex crop livestock or diverse cropping systems that characterize smallholder systems presents particular challenges. New ideas, methods, and uses of technology are needed to address these challenges. Many papers in this special issue synthesize the state of the art in their respective fields, analyze gaps, identify innovations, and make recommendations for improving quantification. Special attention is given to methods appropriate to low-income countries, where strategies are needed for getting robust data with extremely limited resources in order to support national mitigation planning within widely accepted standards and thus provide access to essential international support, including climate funding.

Managing agricultural emissions needs to occur in tandem with managing for agricultural productivity, resilience to climate change, and ecosystem impacts. Management decisions and priorities will require measures and information that identify GHG efficiencies in production and reduce inputs without reducing yields, while addressing climate resilience and maintaining other essential environmental services, such as water quality and support for pollinators. Another set of papers in this issue considers the critical synergies and tradeoffs possible between these multiple objectives of mitigation, resilience, and production efficiency to help us understand how we need to tackle these in our quantification systems.

Significant capacity to quantify greenhouse gases is already built, and with some near-term strategic investment, could become an increasingly robust and useful tool for planning and development in the agricultural sector around the world.

Acknowledgments

The Climate Change Agriculture and Food Security Program of the Consultative Group on International Agricultural Research, the Technical Working Group on Agricultural Greenhouse Gases (T-AGG) at Duke University's Nicholas Institute for Environmental Policy Solutions, and the United Nations Food and Agriculture Organization (FAO) have come together to guide the development of this focus issue and associated activities and papers, given their common desire to improve our understanding of the state of agricultural greenhouse gas (GHG) quantification and to advance ideas for building data and methods that will help mitigation policy and programs move forward around the world. We thank the David and Lucile Packard Foundation for their support of this initiative. The project has been developed with guidance from an esteemed steering group of experts and users of mitigation information (http://nicholasinstitute.duke.edu/ecosystem/t-agg/international-project). Many of the papers in this issue were commissioned. Authors of each of the commissioned papers met with guest editors at FAO in Rome in April 2012 to further develop their ideas, synthesize state of the art knowledge and generate new ideas (http://nicholasinstitute.duke.edu/ecosystem/t-agg/events-and-presentations). Additional interesting and important research has come forward through the general call for papers and has been incorporated into this issue.

References

CCAFS (Climate Change, Agriculture and Food Security) 2011 Victories for food and farming in Durban climate deals Press Release 13 December 2011 (http://ccafs.cgiar.org/news/press-releases/victories-food-and-farming-durban-climate-deals)

FAO (Food and Agriculture Organization of the United Nations) 2009 Expert consultation on GHG emissions and mitigation potentials in the agricultural, forestry and fisheries sectors (Rome: FAO)

FAO 2011 Linking Sustainability and Climate Financing: Implications for Agriculture (Rome: FAO)

FAO 2012 FAOSTAT online database (http://faostat.fao.org/)

Global Research Alliance on Agricultural Greenhouse Gases 2012 www.globalresearchalliance.org/

Herold M and Skutsch M 2011 Monitoring, reporting and verification for national REDD+ programmes: two proposals Environ. Res. Lett. 6 014002

Hosonuma N, Herold M, De Sy V, De Fries R S, Brockhaus M, Verchot L, Angelsen A and Romijn E 2012 An assessment of deforestation and forest degradation drivers in developing countries Environ. Res. Lett. 7 044009

IPCC (Intergovernmental Panel on Climate Change) 1996 Guidelines for National Greenhouse Gas Inventories (Paris: Organisation for Economic Co-operation and Development)

IPCC 2003 Good Practice Guidance for Land Use, Land-Use Change and Forestry (Hayama: IPCC National Greenhouse Gas Inventories Programme)

IPCC 2006 Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme ed H S Eggleston et al (Hayama: IGES)

IPCC 2012 IPCC Emission Factor Database (EFDB) (www.ipcc-nggip.iges.or.jp/EFDB/main.php)

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Murphy D, McCandless M and Drexhage J 2010 Expanding Agriculture's Role in the International Climate Change Regime: Capturing the Opportunities (Winnipeg: International Institute for Sustainable Development)

Nihart A 2012 unpublished data

Olander L, Wollenberg L and Van de Bogert A 2013 Understanding the users and uses of agricultural greenhouse gas information CCAFS/NI T-AGG Report (in progress)

Olander L P and Haugen-Kozyra K with contributions from Del Grosso S, Izaurralde C, Malin D, Paustian K and Salas W 2011 Using Biogeochemical Process Models to Quantify Greenhouse Gas Mitigation from Agricultural Management Projects (Durham, NC: Nicholas Institute for Environmental Policy Solutions, Duke University) (http://nicholasinstitute.duke.edu/ecosystem/t-agg/using-biogeochemical-process)

Romijn J E, Herold M, Kooistra L, Murdiyarso D and Verchot L 2012 Assessing capacities of non-Annex I countries for national forest monitoring in the context of REDD+ Environ. Sci. Policy 20 33–48

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UNFCCC (United Nations Framework Convention on Climate Change) 2008 Financial support provided by the Global Environment Facility for the preparation of National Communications from Parties not included in Annex I to the Convention FCCC/SBI/2008/INF.10 (http://unfccc.int/resource/docs/2008/sbi/eng/inf10.pdf)

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USDA (US Department of Agriculture) 2011 Agricultural Research Service (www.ars.usda.gov/research/programs/programs.htm?np_code=204&docid=17271)

VCS (Verified Carbon Standard) 2011 New Methodology: VM0017 Sustainable Agricultural Land Management (http://v-c-s.org/SALM_methodology_approved)

^* We dedicate this special issue to the memory of Daniel Martino, a generous leader in greenhouse gas quantification and accounting from agriculture, land-use change, and forestry.

The potential for improving productivity and increasing the resilience of smallholder agriculture, while also contributing to climate change mitigation, has recently received considerable political attention (Beddington et al 2012). Financial support for improving smallholder agriculture could come from performance-based funding including sale of carbon credits or certified commodities, payments for ecosystem services, and nationally appropriate mitigation action (NAMA) budgets, as well as more traditional sources of development and environment finance. Monitoring the greenhouse gas fluxes associated with changes to agricultural practice is needed for performance-based mitigation funding, and efforts are underway to develop tools to quantify mitigation achieved and assess trade-offs and synergies between mitigation and other livelihood and environmental priorities (Olander 2012). High levels of small scale variability in carbon stocks and emissions in smallholder agricultural systems (Ziegler et al 2012) mean that data intensive approaches are needed for precise and unbiased mitigation monitoring. The cost of implementing such monitoring programmes is likely to be high, and this introduces the risk that projects will not be developed in areas where there is the greatest need for agricultural improvements, which are likely to correspond with areas where existing data or research infrastructure are lacking. When improvements to livelihoods and food security are expected as co-benefits of performance-based mitigation finance, the risk of inaction is borne by the rural poor as well as the global climate.

In situ measurement of carbon accumulation in smallholders' soils are not usually feasible because of the costs associated with sampling in a heterogeneous landscape, although technological advances could improve the situation (Milori et al 2012). Alternatives to in situ measurement are to estimate greenhouse gas fluxes by extrapolating information from existing research to other areas with similar land uses and environmental conditions, or to combine information on land use activities with process-based models that describe expected emissions and carbon accumulation under specified conditions.

Unfortunately long-term studies that have measured biomass and soil organic carbon accumulation in smallholder agriculture are scarce, and default values developed for national level emissions assessments (IPCC 2006) fail to capture local variability and may not scale linearly, so cannot be applied at the project scale without introducing considerable uncertainty and the potential for bias. If there is reliable information on the agricultural activities and environmental conditions at a project site, process-based models can provide accurate estimations of agricultural greenhouse gas fluxes that capture temporal and spatial variability (Olander 2012) but collecting the necessary data to parameterize and drive the models can be costly and time consuming. Assessing and monitoring greenhouse gas fluxes in smallholder agriculture therefore involves a balance between the resources required to collect information from thousands of smallholders across large areas, and the accuracy and precision of model predictions.

Accuracy, or the absence of bias, is clearly an important consideration in the quantification of mitigation benefits for performance-based finance since a bias towards over-estimation of mitigation achieved would risk misallocating limited finance to projects that have not achieved mitigation benefits. Such a bias would also lead to a net increase in emissions if credits were used to offset emissions elsewhere. The accuracy of model predictions is related to uncertainty in model input data, which affects the precision of predictions, and errors in the model structure (Olander 2012). To limit the risk that projects receive credit for mitigation benefits that are not real, a precise-or-conservative approach to carbon accounting has emerged that requires projects to report mitigation benefits to a prescribed level of precision—for example with a 90% confidence interval that is less than 20% of the estimated mitigation benefit; and if this level of precision is not reached then the lower confidence limit of the value is encouraged (VCS 2012). This helps to ensure projects that lack precision in their estimates are biased towards an underestimation of mitigation benefits, which helps limit the risk of increasing net greenhouse gas emissions. It can also mean that finance from the sale of emission reduction certificates is insufficient to support smallholder agricultural projects without donor assistance to cover the cost of project establishment (Seebauer et al 2012).

Understanding the mitigation benefits of improving agricultural practice is important for many purposes other than developing carbon offsets however, and with appropriate accounting approaches risks to smallholders can be reduced and scarce resources channelled to improving land use practices. Less precision is tolerable when making payments for a broad range ecosystem services, or assessing the impacts of donor support, than it is for industrial carbon offsets. Approaches that have greater uncertainty in expected emission reductions or removals may therefore be more appropriate if there is an equal emphasis on the livelihood and environmental benefits of projects as there is on mitigation benefits.

One way to balance the risk of inaction against the need for accuracy is to use process-based models in greenhouse gas accounting and decision support tools, which give users control over the precision and cost of their accounting. Such models can be parameterized and driven using readily available information or best estimates for input data, as well as site specific environmental and activity data. The potential for bias in model predictions can be limited by making use of appropriate models that are validated against regionally specific data. Although process-based models have been adopted for quantifying mitigation benefit in smallholder agriculture systems (for example Seebauer et al 2012), their use is currently limited to those with specialist knowledge or access to detailed site specific information. Web-based tools that link existing global, regional, and local environmental data with process-based models (such as RothC (Coleman and Jenkinson 1996), CENTURY (Parton et al 1987), DNDC (Li et al 1994) and DAYCENT (Del Grosso et al 2002)) that have been validated for specific areas allow users to generate initial estimates of the carbon sequestration potential of agricultural systems simply by specifying the location and intervention. This can support assessments of the feasibility of supporting these interventions through various funding sources. The same tools can also generate accurate, site specific assessments and monitoring to varying levels of detail, when required, given the inclusion of new data collected in situ .

When accounting for greenhouse gases in smallholder agriculture systems users should be free to decide whether it is worthwhile to invest in collecting input data to estimate mitigation benefits with sufficient precision to meet the requirements for carbon offsets, or if greater uncertainty is tolerable. By using tools that do not require specialist support and accepting estimates of mitigation benefits that are less precise, and not necessarily conservative, those providing performance-based finance can help ensure that a greater proportion of limited budgets are spent on the activities that directly benefit smallholders and that are likely to benefit the global climate. The Small-Holder Agriculture Monitoring and Baseline Assessment methodology and prototype tool (SHAMBA 2012), which has been trialled with fifteen agroforestry and conservation agriculture projects in Malawi and is currently under review for validation under the Plan Vivo Standard (Plan Vivo 2012), provides a proof of this concept and a platform on which greater functionality and flexibility can be built. We hope that this, and other similar initiatives, will deliver approaches to greenhouse gas accounting that reduce risks and maximize benefits to smallholder farmers.

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Seebauer M et al 2012 Carbon accounting for smallholder agricultural soil carbon projects Climate Change Mitigation and Agriculture ed E Wollenberg, A Nihart, M-L Tapio-Bistrom and M Greig-Gran (Abingdon: Earthscan)

SHAMBA (Small-Holder Agriculture Monitoring and Baseline Assessment) 2012 Project webpage: http://tinyurl.com/shambatool

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Ziegler A D et al 2012 Carbon outcomes of major land-cover transitions in SE Asia: great uncertainties and REDD+ policy implications Glob. Change Biol. 18 3087–99

For many developing countries, the land use sector, particularly agriculture and forestry, represents a large proportion of their greenhouse gas (GHG) emissions, making this sector a priority for GHG mitigation activities. Previous global surveys (e.g., IPCC 2000) as well as the most recent IPCC assessment report clearly indicate that the greatest technical potential for carbon sequestration and reductions of non-CO₂ GHG emissions from the land use sector is in developing countries. Estimates that consider economic feasibility suggest that agriculture and forestry together provide among the greatest opportunities for short-term and low-cost mitigation measures across all sectors of the global economy¹ (IPCC 2007). In addition, it is widely recognized that the ecosystem changes entailed by most mitigation practices, i.e., building soil organic matter, reducing losses and tightening nutrient cycles, more efficient production systems and preserving native vegetation, are well aligned with goals of increasing food security and rural development as well as buffering land use systems against climate change (Lal 2004). Hence, there is growing interest in jump-starting the capacity for broad-based engagement in agriculturally-based GHG mitigation projects in developing countries.

Against this favorable background, there are a number of significant challenges—in addition to the fundamental need for comprehensive mandatory reduction policies—to accelerating the involvement of agriculture in GHG mitigation. As detailed by articles in this special issue, quantifying emissions and emission reductions/sequestration of agricultural sources of CO₂,N₂O and CH₄ is difficult. Emissions and C sequestration are distributed across the landscape, with high spatial and temporal variability and with multiple and interacting climate, soil and management factors that affect rates. In most cases, this makes instrument-based measurement of fluxes and C stock changes in agricultural environments difficult, expensive and impractical for routine project-scale deployment. However, there is growing acceptance in the use of models—ranging from simple empirical emission factors to dynamic process-based models—for quantifying emissions and stock changes at project scales². This approach relies on the strategic use of direct instrument-based measurements carried out by university and government researchers (Jawson et al 2005, Skiba et al 2009) to calibrate and validate appropriate models, in which the models represent the relationship between key environmental variables (e.g., precipitation, temperature, soil texture and mineralogy, etc) and land management practices (e.g., fertilizer use, tillage, crop selection, residue management, land cover changes, etc) that determine anthropogenic GHG fluxes. National or regional scale monitoring networks can provide additional, independent measurements to estimate model-based uncertainties and to incrementally improve model performance (van Wesemael et al 2011).

In many developing countries the information infrastructure to support model-based GHG estimates is just beginning to emerge; however initiatives such as the World Digital Soils Map project (Sanchez et al 2009) and growing availability of free or low-cost climate data sets and remote sensing data (e.g. land cover/land use, fire, vegetation condition, etc) suggest that our knowledge of many of the environmental variables controlling GHG emissions and C sequestration will increase greatly in the next few years. However, the other key ingredient to GHG quantification—knowing where and what land management activities are actually occurring on the landscape—will require its own technological breakthrough.

In its most basic form, the emission rate of a greenhouse gas can be expressed as the product of an emission factor and a measure of the activity that is causing the emission. In this simplified depiction, the emission factor embodies the set of research-based measurements, environmental variables, process models and monitoring networks described above. The second part of the equation is generally referred to as activity data, which includes the type and amount of human-activities (i.e., management) responsible for the emissions. In most developed countries, there is a well-developed infrastructure to collect and analyze data on land use and management activities that are used for a variety of purposes, including greenhouse gas inventories. For example, the US Department of Agriculture's (USDA) National Agricultural Statistical Service (NASS) conducts a variety of surveys of farmers to collect information on management practices as well as economic and demographic data; other entities such as USDA National Resource Inventory use remote sensing and field visits to gather agricultural resource use data. These and other data sources are utilized for the national agricultural emissions inventory in the US (EPA 2012). However, even these well-established and resourced (e.g., 2011 NASS budget was $165 million) data collections lack some variables of interest for GHG estimates and more importantly tend to be available only as aggregated averages (e.g., state or county level) that do not fully capture the local interaction of environmental variables (e.g. climate and soil properties) and management practices that determine GHG emissions.

In most developing countries, this type of agricultural activity data is much scarcer and most countries do not have the resources to collect extensive survey data on agricultural practices as in the developed world. Country-level statistics such as compiled in FAOSTAT provide a useful first-order estimate of agricultural activities that can be used in national and global GHG accounting (see Tubiello et al 2013), but are inadequate for finer scale and more accurate emissions estimates. Given financial and resource constraints, there is little expectation of dramatic near-term improvements in the availability of data on agricultural management practices in many developing countries using traditional top-down agency-directed surveys.

So how do we overcome this critical data gap, which I would argue is a prerequisite for broad-based implementation of GHG mitigation policies and projects in the developing world. A potential answer—have the farmers tell us themselves!

The explosive growth in mobile phone accessibility and use in developing countries has been widely noted and has begun to be exploited for a variety of purposes to support rural development (Qiang et al 2011). To date, applications have centered mainly on providing market information to farmers so that they can make more profitable decisions on where and when to market their products. Dissemination of advice, such as weather forecasts and management recommendations is another area of development.

The use of mobile device technology for 'crowd-sourcing' of land management data to support local-scale greenhouse gas accounting is still very much 'on-the-drawing board' (Paustian 2012); however, several factors argue in favor of the viability of this type of approach. First, is the fact that many key variables driving agricultural emissions (e.g., fertilizer applications, manure management) cannot be obtained by means other than asking the farmers themselves—either by traditional survey methods or through self-reporting. Remote sensing can provide data on variables such as land cover and land cover change, as well as some 'within land cover' management variables such as crop species, crop residue coverage, extent and periodicity of flooding (e.g. for rice) (NAS 2010). However, these latter observations are still highly uncertain and particularly challenging in the heterogeneous, fine-grained, land use mosaics that are typical for small-holder agriculture in the tropics. Hence, most of the management information needed as activity data, e.g., land area farmed, amount, type and timing of fertilizer applied, tillage implements used, crops growth, etc, are best known by the land users themselves.

At present, second generation (2G) mobile phones predominate in developing countries (Qiang et al 2011), but with the likely increase in future smart phone usage, the possibility for powerful applications for data collection as well as computation and reporting (e.g., for GHG mitigation project participants) is far-reaching. Capabilities include geo-referencing of locations, uploading photos for image analysis (e.g., crop species present, canopy density, surface coverage by crop residues) and wireless connection to remote sensing imagery, geospatial databases and cloud-computing. Sophisticated web-based applications for GHG accounting are becoming available in the US and Europe (Denef et al 2012, Paustian et al 2012) which opens the opportunity for similar deployments to support GHG mitigation projects in developing countries (Milne et al 2013).

Incentivizing farmers to supply management information and ensuring timely and accurate reporting are two major challenges to a 'crowd-sourcing' system for activity data collection. A logical place to start might be with participants or field coordinators of GHG offset projects or other funded agricultural development projects, as they would have a direct incentive to provide data as a condition of project participation. However, a cost-effective means of collecting land use data might also be of interest to governmental and regulatory agencies, in which case direct financial incentives for reporting could be developed. Compensation such as awarding cell phone minutes would be an alternative that would entail minimal transaction costs. Data quality control would be an important component, requiring careful formulation of the data gathering procedures (i.e., the design of a mobile-app based survey) as well as data screening for outliers and independent resampling of a portion of the responses. However, QA/QC procedures for traditional self-reporting and polling methods (which face the same challenges) are well-developed and could be adapted to a mobile-app system. Finally, opportunities for incorporating graphics/pictures as a substitute or complement to text, as well as increasingly sophisticated voice recognition capabilities, could provide added benefits for working with populations having low literacy and education levels.

Many issues remain to be resolved for improving our capabilities to quantify emissions and emission reductions from agriculture—both in developed and developing parts of the globe—including improvements in emission factors/models, better geospatial databases for soils and climate, and deployment of distributed monitoring networks. Similarly, a crowd-sourcing approach to compile activity data on agricultural management practices faces challenges such as making applications simple and locally relevant, literacy barriers, data quality control, and incentivizing the data providers. However, the growing use by developing country farmers of mobile apps for marketing, financing and extension services, suggests that engaging them directly, as the true experts of what is happening on the landscape, could be a key to bridging the data gap and realizing the potential for agricultural GHG mitigation.

References

Denef K, Paustian K, Archibeque S, Biggar S and Pape D 2012 Report of greenhouse gas accounting tools for agriculture and forestry sectors Interim Report to USDA Under Contract No GS23F8182H (www.usda.gov/oce/climate_change/techguide/Denef_et_al_2012_GHG_Accounting_Tools_v1.pdf (ver. 30/10/2012))

EPA 2012 Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2010 (EPA 430-R-12-001) (Washington, DC: Office of Atmospheric Programs) (www.epa.gov/climatechange/ghgemissions/usinventoryreport/archive.html (ver. 23/03/2013))

IPCC 2000 Land Use, Land Use Change, and Forestry (Intergovernmental Panel on Climate Change Special Report) (Oxford: Oxford University Press) p 377

IPCC 2007 Agriculture Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ed B Metz, O R Davidson, P R Bosch, R Dave and L A Meyer (Cambridge: Cambridge University Press) chapter 8, pp 498–540

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Milne E et al 2013 Methods for the quantification of GHG emissions at the landscape level for developing countries in smallholder contexts Environ. Res. Lett. 8 015019

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¹ About 4.7 Pg CO₂eq yr^-1, at $50 tonne^-1 CO₂eq.

² In practice, virtually all emission estimates in national GHG inventories rely fully or partially on model-based methods. At project scales, one of the few examples of direct instrument-based measurement approaches in agriculture is that of methane abatement from manure management, in which enclosed storage facilities allow gases to be collected and measured as a point source.

Globally, agriculture is directly responsible for 14% of annual greenhouse gas (GHG) emissions and induces an additional 17% through land use change, mostly in developing countries (Vermeulen et al 2012). Agricultural intensification and expansion in these regions is expected to catalyze the most significant relative increases in agricultural GHG emissions over the next decade (Smith et al 2008, Tilman et al 2011). Farms in the developing countries of sub-Saharan Africa and Asia are predominately managed by smallholders, with 80% of land holdings smaller than ten hectares (FAO 2012). One can therefore posit that smallholder farming significantly impacts the GHG balance of these regions today and will continue to do so in the near future.

However, our understanding of the effect smallholder farming has on the Earth's climate system is remarkably limited. Data quantifying existing and reduced GHG emissions and removals of smallholder production systems are available for only a handful of crops, livestock, and agroecosystems (Herrero et al 2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteen studies of nitrous oxide emissions from soils have taken place in sub-Saharan Africa, leaving the rate of emissions virtually undocumented. Due to a scarcity of data on GHG sources and sinks, most developing countries currently quantify agricultural emissions and reductions using IPCC Tier 1 emissions factors. However, current Tier 1 emissions factors are either calibrated to data primarily derived from developed countries, where agricultural production conditions are dissimilar to that in which the majority of smallholders operate, or from data that are sparse or of mixed quality in developing countries (IPCC 2006). For the most part, there are insufficient emissions data characterizing smallholder agriculture to evaluate the level of accuracy or inaccuracy of current emissions estimates. Consequentially, there is no reliable information on the agricultural GHG budgets for developing economies. This dearth of information constrains the capacity to transition to low-carbon agricultural development, opportunities for smallholders to capitalize on carbon markets, and the negotiating position of developing countries in global climate policy discourse.

Concerns over the poor state of information, in terms of data availability and representation, have fueled appeals for new approaches to quantifying GHG emissions and removals from smallholder agriculture, for both existing conditions and mitigation interventions (Berry and Ryan 2013, Olander et al 2013). Considering the dependence of quantification approaches on data and the current data deficit for smallholder systems, it is clear that in situ measurements must be a core part of initial and future strategies to improve GHG inventories and develop mitigation measures for smallholder agriculture. Once more data are available, especially for farming systems of high priority (e.g., those identified through global and regional rankings of emission hotspots or mitigation leverage points), better cumulative estimates and targeted actions will become possible.

Greenhouse gas measurements in agriculture are expensive, time consuming, and error prone. These challenges are exacerbated by the heterogeneity of smallholder systems and landscapes and the diversity of methods used. Concerns over methodological rigor, measurement costs, and the diversity of approaches, coupled with the demand for robust information suggest it is germane for the scientific community to establish standards of measurements—'a protocol'—for quantifying GHG emissions from smallholder agriculture. A standard protocol for use by scientists and development organizations will help generate consistent, comparable, and reliable data on emissions baselines and allow rigorous comparisons of mitigation options. Besides enhancing data utility, a protocol serves as a benchmark for non-experts to easily assess data quality. Obviously many such protocols already exist (e.g., GraceNet, Parkin and Venterea 2010). None, however, account for the diversity and complexity of smallholder agriculture, quantify emissions and removals from crops, livestock, and biomass together to calculate the net balance, or are adapted for the research environment of developing countries; conditions that warrant developing specific methods. Here we summarize an approach being developed by the Consultative Group on International Agricultural Research's (CGIAR) Climate Change, Agriculture, and Food Security Program (CCAFS) and partners.

The CGIAR-CCAFS smallholder GHG quantification protocol aims to improve quantification of baseline emission levels and support mitigation decisions. The protocol introduces five novel quantification elements relevant for smallholder agriculture (figure 1). First, it stresses the systematic collection of 'activity data' to describe the type, distribution, and extent of land management activities in landscapes cultivated by smallholder. Second, it advocates an informed sampling approach that concentrates measurement activities on emission hotspots and leverage points to capture heterogeneity and account for the diversity and complexity of farming activities. Third, it quantifies emissions at multiple spatial scales, whole-farm and landscape, to provide information targeted to household and communities decisions. Fourth, it encourages GHG research to document farm productivity and economics in addition to emissions, in recognition of the importance of agriculture to livelihoods. Fifth, it develops cost-differentiated measurement solutions that optimize the relationships among scale, cost, and accuracy. Each of the five innovations is further described below.

Figure 1. The quantification approach. The protocol includes comparative evaluation of various methodologies for each element (e.g., biophysical context, profitability evaluation, etc), recommend methods specific for end users objectives and constraints, and field manuals for implementation of recommended methods. Items with an asterisk indicate novel aspects of this protocol by comparison to others.

Systematizing collection of activity data . Data describing smallholder farming systems, their relative distribution in space and time, and typical management practices are largely unavailable for smallholder agriculture in developing counties. That is significant because empirical or process based models rely on information on the nature and extent of production systems, so called 'activity data'. Without it, it is not possible to run models, scale flux data to larger spatial extents, or target measurements with any certainty. In some cases, uncertainty in the extent and management for farming activities may be equivalent or greater to the uncertainty associated with the GHG fluxes themselves. The CGIAR-CCAFS protocol therefore provides guidelines for using remote sensing, targeted social and soil surveys, and proxies that correlate with socio-ecological condition and farm management to improve the quantity and quality of activity data available.

Informed sampling . Smallholder agriculture typically involves multiple farming activities taking place in a field, nested within higher levels of organization (e.g., farm or landscape), each having interactive impacts on the cumulative GHG balance. To understand the net effect, attention must be paid to the full range of sources and sinks. Yet it is generally too resource intensive to measure them all. The CGIAR-CCAFS protocol deconstructs what is already known about nutrient stock changes and GHG fluxes to guide measurements toward emission hotspots or leverage points (e.g., methane emissions from cows in crop–livestock systems) within complex agroecosystems and landscapes. The premise underlying this approach is that information from other systems can be used to match the intensity of quantification effort with the predicted intensity of the source or sink. By reducing the uncertainty of the largest fluxes, using an informed sampling approach will hypothetically yield a more accurate and more precise estimate of the total systems' GHG balance.

Multi-scale . Farming activities take place at the field level, but climate impacts and decision-making of smallholders extend to larger spatial scales. Households frequently manage farming activities across several fields, while institutions at the village or higher levels can determine land use practices across entire landscapes, as is the case of communal grazing lands or woodlands. Decisions by households and social organizations unite climate impacts across space. It is therefore important to consider spatial scales greater than the farming activity or field to understand GHG impacts and mitigation opportunities. Therefore, the CGIAR-CCAFS protocol targets quantification and mitigation efforts at the whole-farm and landscape levels to align data describing emissions and removals with the decision units of households and communities.

Linking productivity and emissions . Smallholder farmers depend on farm production for food and income, and farm productivity is inextricably linked to food security. The importance of productivity must be taken into account in mitigation decision-making and the GHG research agenda supporting those decisions. So far, livelihood benefits and farmers' own priorities or other social benefits have been mostly ignored in GHG research. Quantification of GHG reductions from mitigation options is arguably irrelevant if the livelihood effects of those mitigation options are ignored, and scaling GHG emissions per unit area is agronomically meaningless if yields are not considered (Linquist et al 2012). Therefore, the CGIAR-CCAFS protocol recommends that future GHG quantification efforts for assessing mitigation options adopt a multi-criteria approach to include data on indicators of household benefits (e.g. productivity and nutrition). In that way, the research captures the balance of benefits between the private landholder and the global public good. Joint assessment of food production and emissions may produce optimal management strategies that balance the competing demands of food production and climate stabilization. For example, nitrous oxide emissions per unit of product are lowest at intermediate (not the lowest) fertilization rates (Van Groenigen et al 2010) which differs from the optimal strategy for reducing emissions per unit area. Costs associated with collecting the additional data are likely to be small relative to the operational budget for GHG field research and could viably become standard practice.

Cost-differentiated measurements . Potential end users of the protocol are diverse in their purpose, resources available, and capacity to carry out research. For example, development organizations may want to determine the relative difference in emission impacts between mitigation options while governments may be interested in quantification of impacts across landscapes to develop Nationally Appropriate Mitigation Actions. The most useful approach to quantification therefore lies at the nexus among key constraints: objectives, resources, and capacity. The protocol develops a system of 'tiered' entry points for greenhouse gas accounting, with explicit attention directed toward the uncertainty induced from the various measurement selections. The protocol will include decision pathways to help users quickly determine the quantification options suitable for their goals and constraints to optimize the relationship among accuracy, costs, and scale.

The CCAFS-CGIAR protocol is being developed and field-tested in mixed crop–livestock systems of Kenya and intensive rice production in the Philippines, with plans to expand to other sites and agroecosystems in the next year. These initial pilot projects provide a trial of the approach and methods, highlighting technical gaps and promising directions, while generating valuable emissions data.

The role smallholder farming plays in Earth's climate system is uncertain due to lack of data. Better information is needed to calibrate the research, policy, and development communities' thinking on the importance of this issue. Generating the high value information that policy makers, development organizations, and farmers demand however pivots on creating accurate, useful, consistent, and meaningful data. The CCAFS-CGIAR protocol will help advance the scientific community's ability to provide such information by using standard methods of measurement in ways that recognize the data needs and the priorities of smallholder farmers.

Acknowledgments

We thank participants of the October 2012 Protocol Development workshop in Garmisch-Partenkirchen, Germany for their previous and ongoing contributions. We also thank CCAFS, Environment Canada, and the Mitigation of Climate Change in Agriculture (MICCA) Program of the United Nations Food and Agriculture Organization for their support of this initiative.

References

Berry N J and Ryan C M 2013 Overcoming the risk of inaction from emissions uncertainty in smallholder agriculture Environ. Res. Lett. 8 011003

FAO 2012 Smallholders and Family Farmers (Rome: FAO) (www.fao.org/fileadmin/templates/nr/sustainability_pathways/docs/Factsheet_SMALLHOLDERS.pdf, accessed 19 March 2013)

Herrero M, Thornton P K, Kruska R and Reid R S 2008 Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030 Agric. Ecosyst. Environ. 126 122–37

IPCC 2006 2006 IPCC Guidelines for National Greenhouse Gas Inventories ed H S Eggleston, L Buendia, K Miwa, T Ngara and K Tanabe (Hayama: IGES)

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Greenhouse gas (GHG) emissions from agriculture, including crop and livestock production, forestry and associated land use changes, are responsible for a significant fraction of anthropogenic emissions, up to 30% according to the Intergovernmental Panel on Climate Change (IPCC). Yet while emissions from fossil fuels are updated yearly and by multiple sources—including national-level statistics from the International Energy Agency (IEA)—no comparable efforts for reporting global statistics for agriculture, forestry and other land use (AFOLU) emissions exist: the latest complete assessment was the 2007 IPCC report, based on 2005 emission data. This gap is critical for several reasons. First, potentially large climate funding could be linked in coming decades to more precise estimates of emissions and mitigation potentials. For many developing countries, and especially the least developed ones, this requires improved assessments of AFOLU emissions. Second, growth in global emissions from fossil fuels has outpaced that from AFOLU during every decade of the period 1961–2010, so the relative contribution of the latter to total climate forcing has diminished over time, with a need for regular updates. We present results from a new GHG database developed at FAO, providing a complete and coherent time series of emission statistics over a reference period 1961–2010, at country level, based on FAOSTAT activity data and IPCC Tier 1 methodology. We discuss results at global and regional level, focusing on trends in the agriculture sector and net deforestation. Our results complement those available from the IPCC, extending trend analysis to a longer historical period and, critically, beyond 2005 to more recent years. In particular, from 2000 to 2010, we find that agricultural emissions increased by 1.1% annually, reaching 4.6 Gt CO₂ yr⁻¹ in 2010 (up to 5.4–5.8 Gt CO₂ yr⁻¹ with emissions from biomass burning and organic soils included). Over the same decade 2000–2010, the ratio of agriculture to fossil fuel emissions has decreased, from 17.2% to 13.7%, and the decrease is even greater for the ratio of net deforestation to fossil fuel emissions: from 19.1% to 10.1%. In fact, in the year 2000, emissions from agriculture have been consistently larger—about 1.2 Gt CO₂ yr⁻¹ in 2010—than those from net deforestation.

Developing countries face many challenges when constructing national inventories of greenhouse gas (GHG) emissions, such as lack of activity data, insufficient measurements for deriving country-specific emission factors, and a limited basis for assessing GHG mitigation options. Emissions from agricultural production are often significant sources in developing countries, particularly soil nitrous oxide, and livestock enteric and manure methane, in addition to wetland rice methane. Consequently, estimating GHG emissions from agriculture is an important part of constructing developing country inventories. While the challenges may seem insurmountable, there are ways forward such as: (a) efficiently using resources to compile activity data by combining censuses and surveys; (b) using a tiered approach to measure emissions at appropriately selected sites, coupled with modeling to derive country-specific emission factors; and (c) using advanced software systems to guide compilers through the inventory process. With a concerted effort by compilers and assistance through capacity-building efforts, developing country compilers could produce transparent, accurate, complete, consistent and comparable inventories, as recommended by the IPCC (Intergovernmental Panel on Climate Change). In turn, the resulting inventories would provide the foundation for robust GHG mitigation analyses and allow for the development of nationally appropriate mitigation actions and low emission development strategies.

The conversion of wetlands to agriculture through drainage and flooding, and the burning of wetland areas for agriculture have important implications for greenhouse gas (GHG) production and changing carbon stocks. However, the estimation of net GHG changes from mitigation practices in agricultural wetlands is complex compared to dryland crops. Agricultural wetlands have more complicated carbon and nitrogen cycles with both above- and below-ground processes and export of carbon via vertical and horizontal movement of water through the wetland.

This letter reviews current research methodologies in estimating greenhouse gas production and provides guidance on the provision of robust estimates of carbon sequestration and greenhouse gas emissions in agricultural wetlands through the use of low cost reliable and sustainable measurement, modelling and remote sensing applications. The guidance is highly applicable to, and aimed at, wetlands such as those in the tropics and sub-tropics, where complex research infrastructure may not exist, or agricultural wetlands located in remote regions, where frequent visits by monitoring scientists prove difficult.

In conclusion, the proposed measurement-modelling approach provides guidance on an affordable solution for mitigation and for investigating the consequences of wetland agricultural practice on GHG production, ecological resilience and possible changes to agricultural yields, variety choice and farming practice.

Current methods for assessing soil organic carbon (SOC) stocks are generally not well suited for understanding variations in SOC stocks in landscapes. This is due to the tedious and time-consuming nature of the sampling methods most commonly used to collect bulk density cores, which limits repeatability across large areas, particularly where information is needed on the spatial dynamics of SOC stocks at scales relevant to management and for spatially explicit targeting of climate change mitigation options. In the current study, approaches were explored for (i) field-based estimates of SOC stocks and (ii) mapping of SOC stocks at moderate to high resolution on the basis of data from four widely contrasting ecosystems in East Africa. Estimated SOC stocks for 0–30 cm depth varied both within and between sites, with site averages ranging from 2 to 8 kg m⁻². The differences in SOC stocks were determined in part by rainfall, but more importantly by sand content. Results also indicate that managing soil erosion is a key strategy for reducing SOC loss and hence in mitigation of climate change in these landscapes. Further, maps were developed on the basis of satellite image reflectance data with multiple R-squared values of 0.65 for the independent validation data set, showing variations in SOC stocks across these landscapes. These maps allow for spatially explicit targeting of potential climate change mitigation efforts through soil carbon sequestration, which is one option for climate change mitigation and adaptation. Further, the maps can be used to monitor the impacts of such mitigation efforts over time.

Landscape scale quantification enables farmers to pool resources and expertise. However, the problem remains of how to quantify these gains. This article considers current greenhouse gas (GHG) quantification methods that can be used in a landscape scale analysis in terms of relevance to areas dominated by smallholders in developing countries. In landscape scale carbon accounting frameworks, measurements are an essential element. Sampling strategies need careful design to account for all pools/fluxes and to ensure judicious use of resources. Models can be used to scale-up measurements and fill data gaps. In recent years a number of accessible models and calculators have been developed which can be used at the landscape scale in developing country areas. Some are based on the Intergovernmental Panel on Climate Change (IPCC) method and others on dynamic ecosystem models. They have been developed for a range of different purposes and therefore vary in terms of accuracy and usability. Landscape scale assessments of GHGs require a combination of ground sampling, use of data from census, remote sensing (RS) or other sources and modelling. Fitting of all of these aspects together needs to be performed carefully to minimize uncertainties and maximize the use of scarce resources. This is especially true in heterogeneous landscapes dominated by smallholders in developing countries.

This letter is intended to help potential users select the most appropriate calculator for a landscape-scale greenhouse gas (GHG) assessment of activities for agriculture and forestry. Eighteen calculators were assessed. These calculators were designed for different aims and to be used in different geographical areas and they use slightly different accounting methodologies. The classification proposed is based on the main aim of the assessment: raising awareness, reporting, project evaluation or product assessment. When the aims have been clearly formulated, the most suitable calculator can be selected from the comparison tables, taking account of the geographical area and the scope of the calculation as well as the time and skills required for the calculation. The main issues for interpreting GHG assessments are discussed, highlighting the difficulty of comparing the results obtained from different calculators, mainly owing to differences in scope, calculation methods and reporting units. A major problem is the poor accounting for land use change; the calculators are usually able to account satisfactorily for other emission sources. One of the main challenges at landscape-scale level is to produce a realistic assessment of the various production systems as the uncertainty levels are very high. The results should always give some indication of the link between GHG emissions and the productivity of the area, although no single indicator is able to encompass all the services produced by agriculture and forestry (e.g. food, goods, landscape value and revenue).

Agriculture emits a range of greenhouse gases. Greenhouse gas metrics allow emissions of different gases to be reported in a common unit called CO₂-equivalent. This enables comparisons of the efficiency of different farms and production systems and of alternative mitigation strategies across all gases. The standard metric is the 100 year global warming potential (GWP), but alternative metrics have been proposed and could result in very different CO₂-equivalent emissions, particularly for CH₄. While significant effort has been made to reduce uncertainties in emissions estimates of individual gases, little effort has been spent on evaluating the implications of alternative metrics on overall agricultural emissions profiles and mitigation strategies. Here we assess, for a selection of New Zealand dairy farms, the effect of two alternative metrics (100 yr GWP and global temperature change potentials, GTP) on farm-scale emissions and apparent efficiency and cost effectiveness of alternative mitigation strategies. We find that alternative metrics significantly change the balance between CH₄ and N₂O; in some cases, alternative metrics even determine whether a specific management option would reduce or increase net farm-level emissions or emissions intensity. However, the relative ranking of different farms by profitability or emissions intensity, and the ranking of the most cost-effective mitigation options for each farm, are relatively unaffected by the metric. We conclude that alternative metrics would change the perceived significance of individual gases from agriculture and the overall cost to farmers if a price were applied to agricultural emissions, but the economically most effective response strategies are unaffected by the choice of metric.

Carbon footprint is a key indicator of the contribution of food production to climate change and its importance is increasing worldwide. Although it has been used as a sustainability index for assessing production systems, it does not take into account many other biophysical environmental dimensions more relevant at the local scale, such as soil erosion, nutrient imbalance, and pesticide contamination. We estimated carbon footprint, fossil fuel energy use, soil erosion, nutrient imbalance, and risk of pesticide contamination for five real beef background-finishing systems with increasing levels of intensification in Uruguay, which were combinations of grazing rangelands (RL), seeded pastures (SP), and confined in feedlot (FL). Carbon footprint decreased from 16.7 (RL–RL) to 6.9 kg (SP–FL) CO₂ eq kg body weight⁻¹ (BW; 'eq': equivalent). Energy use was zero for RL–RL and increased up to 17.3 MJ kg BW⁻¹ for SP–FL. Soil erosion values varied from 7.7 (RL–RL) to 14.8 kg of soil kg BW⁻¹ (SP–FL). Nitrogen and phosphorus nutrient balances showed surpluses for systems with seeded pastures and feedlots while RL–RL was deficient. Pesticide contamination risk was zero for RL–RL, and increased up to 21.2 for SP–FL. For the range of systems studied with increasing use of inputs, trade-offs were observed between global and local environmental problems. These results demonstrate that several indicators are needed to evaluate the sustainability of livestock production systems.

The IPCC has compiled the best available scientific methods into published guidelines for estimating greenhouse gas emissions and emission removals from the land-use sector. In order to evaluate existing GHG quantification tools to comprehensively quantify GHG emissions and removals in smallholder conditions, farm scale quantification was tested with farm data from Western Kenya. After conducting a cluster analysis to identify different farm typologies GHG quantification was exercised using the VCS SALM methodology complemented with IPCC livestock emission factors and the cool farm tool. The emission profiles of four farm clusters representing the baseline conditions in the year 2009 are compared with 2011 where farmers adopted sustainable land management practices (SALM). The results demonstrate the variation in both the magnitude of the estimated GHG emissions per ha between different smallholder farm typologies and the emissions estimated by applying two different accounting tools. The farm scale quantification further shows that the adoption of SALM has a significant impact on emission reduction and removals and the mitigation benefits range between 4 and 6.5 tCO₂ ha⁻¹ yr⁻¹ with significantly different mitigation benefits depending on typologies of the crop–livestock systems, their different agricultural practices, as well as adoption rates of improved practices. However, the inherent uncertainty related to the emission factors applied by accounting tools has substantial implications for reported agricultural emissions. With regard to uncertainty related to activity data, the assessment confirms the high variability within different farm types as well as between different parameters surveyed to comprehensively quantify GHG emissions within smallholder farms.

Successful adaptation of agriculture to ongoing climate changes would help to maintain productivity growth and thereby reduce pressure to bring new lands into agriculture. In this paper we investigate the potential co-benefits of adaptation in terms of the avoided emissions from land use change. A model of global agricultural trade and land use, called SIMPLE, is utilized to link adaptation investments, yield growth rates, land conversion rates, and land use emissions. A scenario of global adaptation to offset negative yield impacts of temperature and precipitation changes to 2050, which requires a cumulative 225 billion USD of additional investment, results in 61 Mha less conversion of cropland and 15 Gt carbon dioxide equivalent (CO₂e) fewer emissions by 2050. Thus our estimates imply an annual mitigation co-benefit of 0.35 GtCO₂e yr⁻¹ while spending $15 per tonne CO₂e of avoided emissions. Uncertainty analysis is used to estimate a 5–95% confidence interval around these numbers of 0.25–0.43 Gt and $11–$22 per tonne CO₂e. A scenario of adaptation focused only on Sub-Saharan Africa and Latin America, while less costly in aggregate, results in much smaller mitigation potentials and higher per tonne costs. These results indicate that although investing in the least developed areas may be most desirable for the main objectives of adaptation, it has little net effect on mitigation because production gains are offset by greater rates of land clearing in the benefited regions, which are relatively low yielding and land abundant. Adaptation investments in high yielding, land scarce regions such as Asia and North America are more effective for mitigation.

To identify data needs, we conduct a sensitivity analysis using the Morris method (Morris 1991 Technometrics 33 161–74). The three most critical parameters for improving estimates of mitigation potential are (in descending order) the emissions factors for converting land to agriculture, the price elasticity of land supply with respect to land rents, and the elasticity of substitution between land and non-land inputs. For assessing the mitigation costs, the elasticity of productivity with respect to investments in research and development is also very important. Overall, this study finds that broad-based efforts to adapt agriculture to climate change have mitigation co-benefits that, even when forced to shoulder the entire expense of adaptation, are inexpensive relative to many activities whose main purpose is mitigation. These results therefore challenge the current approach of most climate financing portfolios, which support adaptation from funds completely separate from—and often much smaller than—mitigation ones.

Mitigation of the potential impacts of climate change is one of the leading policy concerns of the 21st century. However, there continues to be heated debate about the nature, the content and, most importantly, the impact of the policy actions needed to limit greenhouse gas emissions. One contributing factor is the lack of systematic evidence on the impact of mitigation policy on the welfare of the poor in developing countries. In this letter we consider two alternative policy scenarios, one in which only the Annex I countries take action, and the second in which the first policy is accompanied by a forest carbon sequestration policy in the non-Annex regions. Using an economic climate policy analysis framework, we assess the poverty impacts of the above policy scenarios on seven socio-economic groups in 14 developing countries. We find that the Annex-I-only policy is poverty friendly, since it enhances the competitiveness of non-Annex countries—particularly in agricultural production. However, once forest carbon sequestration incentives in the non-Annex regions are added to the policy package, the overall effect is to raise poverty in the majority of our sample countries. The reason for this outcome is that the dominant impacts of this policy are to raise returns to land, reduce agricultural output and raise food prices. Since poor households rely primarily on their own labor for income, and generally own little land, and since they also spend a large share of their income on food, they are generally hurt on both the earning and the spending fronts. This result is troubling, since forest carbon sequestration—particularly through avoided deforestation—is a promising, low cost option for climate change mitigation.

In this letter, we investigate the effects of crop yield and livestock feed efficiency scenarios on greenhouse gas (GHG) emissions from agriculture and land use change in developing countries. We analyze mitigation associated with different productivity pathways using the global partial equilibrium model GLOBIOM. Our results confirm that yield increase could mitigate some agriculture-related emissions growth over the next decades. Closing yield gaps by 50% for crops and 25% for livestock by 2050 would decrease agriculture and land use change emissions by 8% overall, and by 12% per calorie produced. However, the outcome is sensitive to the technological path and which factor benefits from productivity gains: sustainable land intensification would increase GHG savings by one-third when compared with a fertilizer intensive pathway. Reaching higher yield through total factor productivity gains would be more efficient on the food supply side but halve emissions savings due to a strong rebound effect on the demand side. Improvement in the crop or livestock sector would have different implications: crop yield increase would bring the largest food provision benefits, whereas livestock productivity gains would allow the greatest reductions in GHG emission. Combining productivity increases in the two sectors appears to be the most efficient way to exploit mitigation and food security co-benefits.

Among the main greenhouse gases (CO₂, CH₄ and N₂O), N₂O has the highest global warming potential. N₂O emission is mainly connected to agricultural activities, increasing as nitrogen concentrations increase in the soil with nitrogen fertilizer application. We evaluated N₂O emissions due to application of increasing doses of ammonium nitrate and urea in two sugarcane fields in the mid-southern region of Brazil: Piracicaba (São Paulo state) and Goianésia (Goiás state). In Piracicaba, N₂O emissions exponentially increased with increasing N doses and were similar for urea and ammonium nitrate up to a dose of 107.9 kg ha⁻¹ of N. From there on, emissions exponentially increased for ammonium nitrate, whereas for urea they stabilized. In Goianésia, N₂O emissions were lower, although the behavior was similar to that at the Piracicaba site. Ammonium nitrate emissions increased linearly with N dose and urea emissions were adjusted to a quadratic equation with a maximum amount of 113.9 kg N ha⁻¹. This first effort to measure fertilizer induced emissions in Brazilian sugarcane production not only helps to elucidate the behavior of N₂O emissions promoted by different N sources frequently used in Brazilian sugarcane fields but also can be useful for future Brazilian ethanol carbon footprint studies.

Soil tillage and other methods of soil management may influence CO₂ emissions because they accelerate the mineralization of organic carbon in the soil. This study aimed to quantify the CO₂ emissions under conventional tillage (CT), minimum tillage (MT) and reduced tillage (RT) during the renovation of sugarcane fields in southern Brazil. The experiment was performed on an Oxisol in the sugarcane-planting area with mechanical harvesting. An undisturbed or no-till (NT) plot was left as a control treatment. The CO₂ emissions results indicated a significant interaction (p < 0.001) between tillage method and time after tillage. By quantifying the accumulated emissions over the 44 days after soil tillage, we observed that tillage-induced emissions were higher after the CT system than the RT and MT systems, reaching 350.09 g m⁻² of CO₂ in CT, and 51.7 and 5.5 g m⁻² of CO₂ in RT and MT respectively. The amount of C lost in the form of CO₂ due to soil tillage practices was significant and comparable to the estimated value of potential annual C accumulation resulting from changes in the harvesting system in Brazil from burning of plant residues to the adoption of green cane harvesting. The CO₂ emissions in the CT system could respond to a loss of 80% of the potential soil C accumulated over one year as result of the adoption of mechanized sugarcane harvesting. Meanwhile, soil tillage during the renewal of the sugar plantation using RT and MT methods would result in low impact, with losses of 12% and 2% of the C that could potentially be accumulated during a one year period.

This letter provides an overview of the available measurement techniques for nitrous oxide (N₂O) flux measurement. It is presented to aid the choice of the most appropriate methods for different situations. Nitrous oxide is a very potent greenhouse gas; the effect of 1 kg of N₂O is estimated to be equivalent to 300 kg of CO₂. Emissions of N₂O from the soil have a larger uncertainty compared to other greenhouse gases. Important reasons for this are low atmospheric concentration levels and enormous spatial and temporal variability. Traditionally such small increases are measured by chambers and analyzed by gas chromatography. Spatial and temporal resolution is poor, but costs are low. To detect emissions at the field scale and high temporal resolution, differences at tens of ppt levels need to be resolved. Reliable instruments are now available to measure N₂O by a range of micrometeorological methods, but at high financial cost. Although chambers are effective in identifying processes and treatment effects and mitigation, the future lies with the more versatile high frequency and high sensitivity sensors.

Methane emissions from manure management represent an important mitigation opportunity, yet emission quantification methods remain crude and do not contain adequate detail to capture changes in agricultural practices that may influence emissions. Using the Canadian emission inventory methodology as an example, this letter explores three key aspects for improving emission quantification: (i) obtaining emission measurements to improve and validate emission model estimates, (ii) obtaining more useful activity data, and (iii) developing a methane emission model that uses the available farm management activity data. In Canada, national surveys to collect manure management data have been inconsistent and not designed to provide quantitative data. Thus, the inventory has not been able to accurately capture changes in management systems even between manure stored as solid versus liquid. To address this, we re-analyzed four farm management surveys from the past decade and quantified the significant change in manure management which can be linked to the annual agricultural survey to create a continuous time series. In the dairy industry of one province, for example, the percentage of manure stored as liquid increased by 300% between 1991 and 2006, which greatly affects the methane emission estimates. Methane emissions are greatest from liquid manure, but vary by an order of magnitude depending on how the liquid manure is managed. Even if more complete activity data are collected on manure storage systems, default Intergovernmental Panel on Climate Change (IPCC) guidance does not adequately capture the impacts of management decisions to reflect variation among farms and regions in inventory calculations. We propose a model that stays within the IPCC framework but would be more responsive to farm management by generating a matrix of methane conversion factors (MCFs) that account for key factors known to affect methane emissions: temperature, retention time and inoculum. This MCF matrix would be populated using a mechanistic emission model verified with on-farm emission measurements. Implementation of these MCF values will require re-analysis of farm surveys to quantify liquid manure emptying frequency and timing, and will rely on the continued collection of this activity data in the future. For model development and validation, emission measurement campaigns will be needed on representative farms over at least one full year, or manure management cycle (whichever is longer). The proposed approach described in this letter is long-term, but is required to establish baseline data for emissions from manure management systems. With these improvements, the manure management emission inventory will become more responsive to the changing practices on Canadian livestock farms.

Nitrification inhibitors have the potential to reduce N₂O emissions from maize fields, but optimal results may depend on deployment of integrated N fertilizer management systems that increase yields achieved per unit of N₂O lost. A new micro-encapsulated formulation of nitrapyrin for liquid N fertilizers became available to US farmers in 2010. Our research objectives were to (i) assess the impacts of urea–ammonium nitrate (UAN) management practices (timing, rate and nitrification inhibitor) and environmental variables on growing-season N₂O fluxes and (ii) identify UAN treatment combinations that both reduce N₂O emissions and optimize maize productivity. Field experiments near West Lafayette, Indiana in 2010 and 2011 examined three N rates (0, 90 and 180 kg N ha⁻¹), two timings (pre-emergence and side-dress) and presence or absence of nitrapyrin. Mean cumulative N₂O–N emissions (Q₁₀ corrected) were 0.81, 1.83 and 3.52 kg N₂O–N ha⁻¹ for the rates of 0, 90 and 180 kg N ha⁻¹, respectively; 1.80 and 2.31 kg N₂O–N ha⁻¹ for pre-emergence and side-dress timings, respectively; and 1.77 versus 2.34 kg N₂O–N ha⁻¹ for with and without nitrapyrin, respectively. Yield-scaled N₂O–N emissions increased with N rates as anticipated (averaging 167, 204 and 328 g N₂O–N Mg grain⁻¹ for the 0, 90 and 180 kg N ha⁻¹ rates), but were 22% greater with the side-dress timing than the pre-emergence timing (when averaged across N rates and inhibitor treatments) because of environmental conditions following later applications. Overall yield-scaled N₂O–N emissions were 22% lower with nitrapyrin than without the inhibitor, but these did not interact with N rate or timing.