Analysis of spatially distributed enteric methane emissions from cattle across the geo-climatic regions of Mexico and uncertainty assessment

Abstract

The present work aims to calculate a bottom-up IPCC-Tier 2 inventory for enteric CH4 emissions from cattle in Mexico, disaggregate the inventory into different geo-climatic regions to analyze the effect of the contrasting climates of Mexico on the inventory and identify the relevant sources of uncertainty associated with the inventory. Peer-reviewed country-specific emission factors (EF), activity data (AD) on animal characteristics, feeding management, and CH4 conversion factors (Ym) were used in developing the emissions inventory. Monte Carlo simulation (MCS) was used to propagate the uncertainty throughout the Tier 2 model (T2model). Spearman-ranked correlation analysis (SRCA) was used to identify relevant input parameters (IPAs) for which CH4 emissions variables were most sensitive. The estimated inventory for the year 2018 was 2039 Gg CH4 year���� 1 with an uncertainty of ���� 18.3 % to +21.2 %. The geo-climatic regions had an important influence on the inventory because emissions varied among regions, with the dry and tropical sub-humid geo-climatic regions being the highest CH4 emitters due to their larger cattle populations and the effect of climate on cattle diets’ quality, and in turn, the effect of diet on CH4 emission. The IPAs associated with dry matter intake. and groossenergy intake (GEI) of cattle considerably impacted the uncertainty of enteric CH4 emission estimates. This study concludes that implementing a bottom-up Tier 2 approach using disaggregated AD and country-specific EF allows a more accurate inventory estimation and assessment of its uncertainty than existing inventories. Future efforts to improve the quality of CH4 inventories must focus on improving the accuracy of AD, like DMI, GEI, and country-specific EF.