Methods and guidance to support MRV of livestock emissions: Methods for data collection, analysis and summary results from a pilot baseline survey for the Kenya dairy NAMA - Wilkes, Andreas; Odhong’, Charles; Dijk, Suzanne van; Fraval, Simon; Wassie, Shimels Eshete (2019)
There is increasing interest in mitigation of greenhouse gas (GHG) emissions from the dairy sector in developing countries. However, there is little prior experience with measurement, reporting and verification (MRV) of GHG emissions and emission reductions. A voluntary carbon market methodology, the Smallholder Dairy Methodology, has proposed a methodology for establishing a standardized performance baseline for a region targeted by a GHG mitigation initiative. This working paper reports the first experience of implementing a survey and analyzing survey data to establish a standardized performance baseline using survey data from central Kenya, which is a region targeted by the Kenya dairy NAMA promoted by the Government of Kenya. The publication of this report enables transparent documentation of the baseline setting process for the Kenya dairy NAMA. Data from the survey were also used to characterize dairy production in the intensive production system in Kenya’s Tier 2 GHG inventory for dairy cattle. Publication of the survey data also supports transparency of Kenya’s Tier 2 GHG inventory. The report summarizes the requirements of the Smallholder Dairy Methodology, the methods used for sampling, data collection and data analysis, the main results of data analysis and recommendations for future similar initiatives to quantify standardized baselines for dairy GHG mitigation programs. Appendices present data collection tools, summary statistics, and the data used to estimate parameters in Kenya’s Tier 2 dairy GHG inventory. Analysis of the survey data following the Smallholder Dairy Methodology’s requirements shows that the relationship between GHG intensity (kg CO2e/kg fat and protein corrected milk [FPCM]) and milk yield (kg FPCM per farm per year) can be represented by a power regression: y = 81.868x-0.436. Using this relationship, dairy initiatives in central Kenya need only to measure change in milk yield per farm per year, and can estimate GHG emissions and emission reductions using the relationship published here. The regression has an r2 of 0.43, and an uncertainty of 18.6% as measured by the root mean square error (RMSE) of the regression. The Smallholder Dairy Methodology does not require quantification of uncertainty, but other mitigation initiatives may use estimated uncertainty to discount the GHG emission reductions claimed in order to ensure conservativeness. The baseline survey is representative of 8 counties with a dairy cattle population of about 1.7 million, and data collection and analysis cost about US$ 75,000. The methodology is therefore a cost-effective way to set baselines for an initiative with large numbers of participating farms.