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Get Free AccessLivestock and poultry farming sectors are among the largest anthropogenic methane (CH4) emission sources, mainly from enteric fermentation and manure management. Previous inventories of CH4 emission were generally based on constant emission factor (EF) per head, which had some weaknesses mainly due to the succession of breeding and feeding systems over decades. Here, more reliable long-term changes of CH4 emissions from livestock and poultry farming in Beijing are estimated using the dynamic EFs based on the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method, and high-resolution spatial patterns of CH4 emissions are also estimated with intensive field survey. The results showed that the estimated CH4 emissions derived by dynamic EFs were approximately 13–19% lower than those based on the constant EF before 2010. After 2011, however, the dynamic EFs-derived CH4 emissions were a little higher (3%) than the constant EF method. Temporal CH4 emissions in Beijing had experienced four developing stages (1978–1988: stable; 1989–1998: slow growth; 1999–2004: rapid growth and reached hot moments; 2005–2014: decline) during 1978–2014. Over the first two decades, the contributions of pigs (45%) and cattle (46%) to annual CH4 emission were similar; subsequently, the cattle emitted more CH4 compared to the pigs. At a spatial scale, Shunyi, Daxing, and Tongzhou districts with more cattle and pigs are the hotspots of CH4 emission. In conclusion, the dynamic EFs method obviously improved the spatio-temporal estimates of CH4 emissions compared to the constant EF approach, and the improvements depended on the period and aquaculture structure. Therefore, the dynamic EFs method should be recommended for estimating CH4 emissions from livestock and poultry farming in the future.
Yixuan Guo, Yidong Wang, Shufeng Chen, Shunan Zheng, Chuanyong Guo, Dongmei Xue, Yakov Kuzyakov, Zhong‐Liang Wang (2019). Inventory of Spatio-Temporal Methane Emissions from Livestock and Poultry Farming in Beijing. Sustainability, 11(14), pp. 3858-3858, DOI: 10.3390/su11143858.
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Type
Article
Year
2019
Authors
8
Datasets
0
Total Files
0
Language
English
Journal
Sustainability
DOI
10.3390/su11143858
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