气候变化领域集成服务门户(CCPortal): Fertilizer management for global ammonia emission reduction

CCPortal

DOI	10.1038/s41586-024-07020-z
	Fertilizer management for global ammonia emission reduction
	Xu, Peng; Li, Geng; Zheng, Yi; Fung, Jimmy C. H.; Chen, Anping; Zeng, Zhenzhong; Shen, Huizhong; Hu, Min; Mao, Jiafu; Zheng, Yan; Cui, Xiaoqing; Guo, Zhilin; Chen, Yilin; Feng, Lian; He, Shaokun; Zhang, Xuguo; Lau, Alexis K. H.; Tao, Shu; Houlton, Benjamin Z.
发表日期	2024
ISSN	0028-0836
EISSN	1476-4687
起始页码	626
结束页码	8000
卷号	626 期号:8000
英文摘要	Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 +/- 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 +/- 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 +/- 2.7% under SSP1-2.6 and 5.5 +/- 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases. A machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally shows that global NH3 emissions in 2018 were lower than previous estimates that did not fully consider fertilizer management practices.
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001174216600013
来源期刊	NATURE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297807
作者单位	Southern University of Science & Technology; Tianjin University; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; Southern University of Science & Technology; Southern University of Science & Technology; Hong Kong University of Science & Technology; Colorado State University; Colorado State University; Peking University; United States Department of Energy (DOE); Oak Ridge National Laboratory; Beijing Forestry University; Hong Kong University of Science & Technology; Peking University; Cornell University; Cornell University
推荐引用方式 GB/T 7714	Xu, Peng,Li, Geng,Zheng, Yi,et al. Fertilizer management for global ammonia emission reduction[J],2024,626(8000).
APA	Xu, Peng.,Li, Geng.,Zheng, Yi.,Fung, Jimmy C. H..,Chen, Anping.,...&Houlton, Benjamin Z..(2024).Fertilizer management for global ammonia emission reduction.NATURE,626(8000).
MLA	Xu, Peng,et al."Fertilizer management for global ammonia emission reduction".NATURE 626.8000(2024).