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Fertilizer management for global ammonia emission reduction

Nature volume 626, pages 792–798 (2024)Cite this article

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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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Fig. 1: EFs conditioned on management practices and importance analysis.
Fig. 2: Global cropland NH3 EFs and emissions at a 5-arcmin resolution in 2018 derived by machine learning.
Fig. 3: Country-level differences for mitigation targets.
Fig. 4: Projected changes in NH3 emissions for 2030–2100 relative to the reference year (2018).

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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.

Author information

Author notes

  1. These authors contributed equally: Peng Xu, Geng Li

Authors and Affiliations

  1. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Peng Xu, Yi Zheng, Zhenzhong Zeng, Huizhong Shen, Yan Zheng, Zhilin Guo, Yilin Chen, Lian Feng, Shaokun He & Shu Tao

  2. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China

    Peng Xu

  3. Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China

    Geng Li, Jimmy C. H. Fung & Alexis K. H. Lau

  4. Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Hong Kong, China

    Geng Li

  5. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yi Zheng

  6. Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, Southern University of Science and Technology, Shenzhen, China

    Yi Zheng

  7. Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China

    Jimmy C. H. Fung & Xuguo Zhang

  8. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

  9. State Joint Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Min Hu

  10. Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Jiafu Mao

  11. School of Grassland Science, Beijing Forestry University, Beijing, China

    Xiaoqing Cui

  12. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Alexis K. H. Lau

  13. Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shu Tao

  14. Department of Ecology and Evolutionary Biology and Department of Global Development, Cornell University, Ithaca, NY, USA

    Benjamin Z. Houlton

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  1. Peng Xu
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Contributions

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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Correspondence to Yi Zheng or Jimmy C. H. Fung.

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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.

Source Data

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.

Source Data

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.

Source Data

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.

Source Data

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.

Source Data

Supplementary information

Source data

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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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