Teagasc, Environment, Soils and Land Use Department, Johnstown Castle, Co. Wexford, Ireland; School of Natural Sciences, Bangor University, Bangor, Wales, UK; Teagasc, Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Athenry, Co. Galway, Ireland.
School of Natural Sciences, Bangor University, Bangor, Wales, UK.
Sci Total Environ. 2022 Jan 10;803:149935. doi: 10.1016/j.scitotenv.2021.149935. Epub 2021 Aug 27.
Excreta deposition onto pasture, range and paddocks (PRP) by grazing ruminant constitute a source of nitrous oxide (NO), a potent greenhouse gas (GHG). These emissions must be reported in national GHG inventories, and their estimation is based on the application of an emission factor, EF (proportion of nitrogen (N) deposited to the soil through ruminant excreta, which is emitted as NO) Depending on local data available, countries use various EFs and approaches to estimate NO emissions from grazing ruminant excreta. Based on ten case study countries, this review aims to highlight the uncertainties around the methods used to account for these emissions in their national GHG inventories, and to discuss the efforts undertaken for considering factors of variation in the calculation of emissions. Without any local experimental data, 2006 the IPCC default (Tier 1) EFs are still widely applied although the default values were revised in 2019. Some countries have developed country-specific (Tier 2) EF based on local field studies. The accuracy of estimation can be improved through the disaggregation of EF or the application of models; two approaches including factors of variation. While a disaggregation of EF by excreta type is already well adopted, a disaggregation by other factors such as season of excreta deposition is more difficult to implement. Empirical models are a potential method of considering factors of variation in the establishment of EF. Disaggregation and modelling requires availability of sufficient experimental and activity data, hence why only few countries have currently adopted such approaches. Replication of field studies under various conditions, combined with meta-analysis of experimental data, can help in the exploration of influencing factors, as long as appropriate metadata is recorded. Overall, despite standard IPCC methodologies for calculating GHG emissions, large uncertainties and differences between individual countries' accounting remain to be addressed.
放牧反刍动物将排泄物排放到牧场、草地和围场（PRP）会产生一氧化二氮（NO），这是一种强效温室气体（GHG）。这些排放物必须在国家温室气体清单中报告，其估算基于排放系数（EF）的应用，EF 是指通过反刍动物排泄物沉积到土壤中的氮（N）中，有多少会作为 NO 排放。根据当地可用数据，各国使用不同的 EF 和方法来估算放牧反刍动物排泄物产生的 NO 排放量。本综述基于十个案例研究国家，旨在强调在国家温室气体清单中核算这些排放物时所使用方法的不确定性，并讨论为考虑计算排放量的变化因素而做出的努力。由于缺乏任何当地实验数据，2006 年，尽管默认值在 2019 年进行了修订，仍广泛应用 2006 年 IPCC 默认（Tier 1）EF。一些国家根据当地实地研究制定了国家特定的（Tier 2）EF。通过对 EF 进行细分或应用模型（包括变化因素），可以提高估算的准确性。虽然已经很好地采用了按排泄物类型对 EF 进行细分的方法，但按排泄物沉积季节等其他因素进行细分则更难实施。经验模型是考虑 EF 变化因素的一种潜在方法。细分和建模需要有足够的实验和活动数据，因此目前只有少数几个国家采用了这种方法。在各种条件下复制实地研究，并对实验数据进行荟萃分析，可以帮助探索影响因素，只要记录了适当的元数据。总体而言，尽管有计算温室气体排放的 IPCC 标准方法，但仍需要解决个别国家核算中存在的大量不确定性和差异。
