Fertilizer management for global ammonia emission reduction

• 成果类型: Article
• 署名作者: 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.
• 署名单位: 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 System; Colorado State University Fort Collins; Colorado State University System; Colorado State University Fort Collins; 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
• 刊物名称: Nature
• ISSN/ISSBN: 0028-4313
• DOI: 10.1038/s41586-024-07020-z
• 发表日期: 2024-02-22
• 关键词: enhanced-efficiency fertilizers; nitrogen; nutrient

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.