Abstract
Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1,2,3,4,5. 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 practices6,7,8,9. 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.
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Data availability
The full dataset and list of references for publications used in our machine learning model (Supplementary Table 3) and the global cropland NH3 EFs and emissions at a 5-arcmin resolution in 2018 generated using machine learning (Fig. 2) are available in the Zenodo repository (https://doi.org/10.5281/zenodo.10302502). Data processing and visualization were conducted using Microsoft Excel and Python. Source data are provided with this paper.
Code availability
The source code and results of this research are available under the GNU General Public License v3.0 at GitHub (https://github.com/Rickon566/Fertilizer-Management-for-Global-Ammonia-Emission-Reduction). The model card is available in the Supplementary Information. The spatial analysis was run in ArcGIS v.10.2.
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (grant nos. 42325702 to Yi Zheng, 42277086 to P.X. and 42321004 to Yan Zheng), the Natural Science Foundation of Guangdong Province (grant no. 2023A1515012280 to P.X.) and the Research Grants Council of the Hong Kong Special Administrative Region, China (grant no. 16302220 to J.C.H.F.), the Earth and Environmental Systems Sciences Division of the Biological and Environmental Research Office in the Office of Science of the US Department of Energy (DOE) (the Terrestrial Ecosystem Science Scientific Focus Area project and the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing Scientific Focus Area project to J.M.). Oak Ridge National Laboratory is supported by the Office of Science of the DOE under contract DE-AC05-00OR22725. We thank C. P. Ti of the Institute of Soil Science, Chinese Academy of Sciences; S. W. Liu of the Nanjing Agricultural University; and X. Y. Zhan of the Chinese Academy of Agricultural Sciences for providing us with data.
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P.X., G.L., Yi Zheng and J.F. designed the study. P.X. and G.L. performed the research. P.X., G.L. and Yi Zheng analysed the data. P.X., G.L., Yi Zheng, S.H. and J.C.H.F. wrote the initial draft of the paper. B.Z.H., Z.Z., H.S., M.H., J.M., Yan Zheng, X.C., Z.G., L.F., Y.C., X.Z., A.K.H.L., A.C. and S.T. reviewed and revised the paper. All authors contributed to the discussion and interpretation of the results.
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Extended data figures and tables
Extended Data Fig. 1 Global cropland NH3 emissions mitigation potentials.
Emissions maps of rice (a), wheat (b), and maize (c) at the country scale; the proportions of mitigation potentials to NH3 emissions under the best management scenario for rice (d), wheat (e), and maize (f). ���No data��� represents no planting of rice, wheat or maize. Map created using Python 3.8.
Extended Data Fig. 2 The mitigation potentials for global cropland NH3 emissions under the best management scenario for crops.
All crops (a), rice (b), wheat (c), and maize (d). Map created using Python 3.8.
Extended Data Fig. 3 Projected changes in NH3 emissions for 2030–2100 relative to the reference year (2018) when only future temperature changes are accounted for.
SSP-12.6 (a, b) and SSP5-8.5 (c, d) for mid- (2030–2060) and long-term (2061–2100) horizons. Map created using Python 3.8.
Extended Data Fig. 4 Projected changes in NH3 emissions for 2030–2100 relative to the reference year (2018) when only future precipitation changes are accounted for.
SSP-12.6 (a, b) and SSP5-8.5 (c, d) for mid- (2030–2060) and long-term (2061–2100) horizons. Map created using Python 3.8.
Extended Data Fig. 5 The differences between the global NH3 mitigation potential induced under the best EF-related management scenario and the NH3 emission increases for 2030–2100.
SSP-12.6 (a, b) and SSP5-8.5 (c, d) for mid- (2030–2060) and long-term (2061–2100) horizons. Map created using Python 3.8.
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Xu, P., Li, G., Zheng, Y. et al. Fertilizer management for global ammonia emission reduction. Nature 626, 792–798 (2024). https://doi.org/10.1038/s41586-024-07020-z
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DOI: https://doi.org/10.1038/s41586-024-07020-z
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