Bridging the data gap: engaging developing country farmers in greenhouse gas accounting

Abstract

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-CO 2 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 economy 1 (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 CO 2 ,N 2 O and CH 4 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 scales 2 . 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-est