Evaluating the 2019 IPCC refinement for estimating methane conversion factors in Canada

Methane emissions from liquid manure management in Canada are an important greenhouse gas source. A wide range of seasonal temperatures, distribution of livestock farms, and various management practices in Canada means that regional methane conversion factors (MCF) that account for spatially discrete climate and management should be used. This study explores the impacts of using the 2019 IPCC Refinement methodology on estimates of MCFs across Canada. MCFs were calculated for 3403 locations across Canada using historical weather data, with varying management parameters at each location (emptying efficiency, timing, and frequency of manure removal). Sensitivity to two model parameters was also evaluated (minimum manure temperature, damping factor). Results showed the influence of climate, as average MCF in each ecozones ranged from 0.27 in the Mixedwood Plains to 0.15 in the Taiga. Further climate variation within ecozones was evident. For example, the MCF range within Mixedwood Plains was 0.17 to 0.33. The MCF reduction by improving management was evident as the average MCF in Canada was 0.15 for triannual removal, 0.21 for biannual removal, 0.28 for one-time removal in spring, and 0.32 for one-time removal in fall. Emptying efficiency was found to be critical; for example, the average MCF for triannual removal with 100% efficiency was 0.14 but increased to 0.15 at 95%, 0.17 at 85%, and 0.30 at 50% efficiency. The damping factor had higher sensitivity in terms of model parameters because it influences peak manure temperature in summer before manure removal. Our results suggest that the average MCF in Canada will be similar to the 2006 IPCC value, but that using the 2019 IPCC Refinement provides a greater ability to represent the variations in climate and management regionally across the country. This will improve accuracy and enable inventory practitioners to reflect regional farm management changes in national methane emission estimates.