Towards a protocol for quantifying greenhouse gas emissions and evaluating mitigation options in smallholder farming systems
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Rosenstock, T.S., Rufino, M.C., Butterbach-Bahl, K. and Wollenberg, E. 2013. Towards a protocol for quantifying greenhouse gas emissions and evaluating mitigation options in smallholder farming systems. Environmental Research Letters 8(2)
Permanent link to cite or share this item: http://hdl.handle.net/10568/33540
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 in the main article.