Fertilizer management for global ammonia emission reduction

Peng Xu, Geng Li, Yi Zheng (), Jimmy C. H. Fung (), Anping Chen, Zhenzhong Zeng, Huizhong Shen, Min Hu, Jiafu Mao, Yan Zheng, Xiaoqing Cui, Zhilin Guo, Yilin Chen, Lian Feng, Shaokun He, Xuguo Zhang, Alexis K. H. Lau, Shu Tao and Benjamin Z. Houlton
Additional contact information
Peng Xu: Southern University of Science and Technology
Geng Li: The Hong Kong University of Science and Technology
Yi Zheng: Southern University of Science and Technology
Jimmy C. H. Fung: The Hong Kong University of Science and Technology
Anping Chen: Colorado State University
Zhenzhong Zeng: Southern University of Science and Technology
Huizhong Shen: Southern University of Science and Technology
Min Hu: Peking University
Jiafu Mao: Oak Ridge National Laboratory
Yan Zheng: Southern University of Science and Technology
Xiaoqing Cui: Beijing Forestry University
Zhilin Guo: Southern University of Science and Technology
Yilin Chen: Southern University of Science and Technology
Lian Feng: Southern University of Science and Technology
Shaokun He: Southern University of Science and Technology
Xuguo Zhang: The Hong Kong University of Science and Technology
Alexis K. H. Lau: The Hong Kong University of Science and Technology
Shu Tao: Southern University of Science and Technology
Benjamin Z. Houlton: Cornell University

Nature, 2024, vol. 626, issue 8000, 792-798

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41586-024-07020-z

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