The present work aims to calculate a bottom-up IPCC-Tier 2 inventory for enteric CH 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 CH conversion factors () 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 CH emissions variables were most sensitive. The estimated inventory for the year 2018 was 2039 Gg CH year 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 CH emitters due to their larger cattle populations and the effect of climate on cattle diets’ quality, and in turn, the effect of diet on CH emission. The IPAs associated with dry matter intake (DMI) and gross energy intake (GEI) of cattle considerably impacted the uncertainty of enteric CH 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 CH inventories must focus on improving the accuracy of AD, like DMI, GEI, and country-specific EF.

中文翻译：

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墨西哥地理气候区牛肠道甲烷排放空间分布分析及不确定性评估

目前的工作旨在计算墨西哥牛肠道 CH 排放的自下而上的 IPCC-Tier 2 清单，将清单分解到不同的地理气候区域，以分析墨西哥对比气候对清单的影响，并确定相关的与库存相关的不确定性来源。在制定排放清单时使用了经过同行评审的国家特定排放因子 (EF)、有关动物特征的活动数据 (AD)、饲养管理和 CH 转换因子 ()。蒙特卡洛模拟 (MCS) 用于在整个第 2 层模型 (T2model) 中传播不确定性。Spearman 排序相关分析 (SRCA) 用于确定 CH 排放变量最敏感的相关输入参数 (IPA)。2018 年的估计库存为 2039 Gg CH 年，不确定性为 -18.3% 至 +21.2%。地理气候区域对清单具有重要影响，因为各区域的排放量存在差异，干燥和热带半湿润地理气候区域由于其牛群数量较多以及气候对牛饮食的影响而成为 CH 排放量最高的区域。质量，进而影响饮食对 CH 排放的影响。与牛的干物质摄入量 (DMI) 和总能量摄入量 (GEI) 相关的 IPA 在很大程度上影响了肠道 CH 排放估算的不确定性。本研究的结论是，使用分类 AD 和特定国家 EF 实施自下而上的第 2 层方法，可以比现有清单更准确地估计清单并对其不确定性进行评估。未来提高 CH 清单质量的努力必须集中于提高 AD 的准确性，例如 DMI、GEI 和特定国家的 EF。