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Reducing soil nitrogen losses from fertilizer use in global maize and wheat production

Nature Geoscience volume 17, pages 1008–1015 (2024)Cite this article

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Abstract

Maize and wheat are two major staple foods that collectively contribute two-thirds of the world’s grain supply. The extensive use of nitrogen (N) fertilizers during the cultivation of both crops leads to significant losses of reactive nitrogen (Nr) into the environment. Here, using machine learning algorithms, we generate high-resolution maps of crop-specific soil Nr losses based on global field measurements. We estimate that global annual soil Nr losses from the use of synthetic N fertilizer in 2020, including direct emissions of nitrous oxide (N2O), nitric oxide (NO), ammonia (NH3), N leaching and run-off, amount to 0.18, 1.62, 0.09, 1.47 and 1.10 million tonnes N for maize, and 0.12, 1.33, 0.07, 1.21 and 0.95 million tonnes N for wheat, respectively. The annual indirect N2O emissions induced by synthetic N fertilizer use from these soil Nr losses are estimated to be 45,000 and 37,000 tonnes for maize and wheat, respectively, with hydrologic pathways playing a predominant role. Enhancing N use efficiency up to 60% for regions below this value can achieve a total soil Nr loss mitigation potential of 4.00 million tonnes per year for the two crops, thereby reducing indirect N2O emissions by 49%. Our results contribute to constrain global N budgets from the use of fertilizer in agriculture, which then can help to improve projections of nitrogen cycle–climate feedbacks using modelling approaches.

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Fig. 1: Global soil Nr loss factors for maize and wheat fields at a 5 arcmin resolution in 2020 derived from RF models.
Fig. 2: Spatial patterns of dominant drivers regulating variation in soil Nr loss factors.
Fig. 3: Synthetic N fertilizer-induced soil Nr losses and mitigation potentials of different scenarios for maize.
Fig. 4: Synthetic N fertilizer-induced soil Nr losses and mitigation potentials of different scenarios for wheat.
Fig. 5: Global budgets of synthetic N fertilizer-induced soil Nr losses from maize and wheat cultivation.

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Data availability

The dataset supporting our results and maps generated during this study are available via figshare data at https://doi.org/10.6084/m9.figshare.25546771 (ref. 59). The base maps used in this study were sourced from Natural Earth as provided at http://www.naturalearthdata.com/downloads/.

Code availability

The source code used for this study has been deposited in the figshare data repository at https://doi.org/10.6084/m9.figshare.25546771 (ref. 59).

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (grant no. 2022YFD1901600 to S.L.), the Technology Innovation Special Fund of Jiangsu Province for Carbon Dioxide Emission Peaking and Carbon Neutrality (grant nos. BE2023398 and BE2022423 to S.L. and J.Z.) and the Fundamental Research Funds for the Central Universities (grant nos. KYT2023001, KYTZ2023017 and KJJQ2024018 to S.L. and C.W.).

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Authors and Affiliations

  1. Key Laboratory of Low-Carbon and Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, China

    Chao Wang, Yun Shen, Xiantao Fang, Shuqi Xiao, Genyuan Liu, Jianwen Zou & Shuwei Liu

  2. Jiangsu Key Laboratory of Low Carbon Agriculture and GHGs Mitigation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, China

    Chao Wang, Yun Shen, Xiantao Fang, Shuqi Xiao, Genyuan Liu, Jianwen Zou & Shuwei Liu

  3. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

    Ligang Wang

  4. College of Environmental and Resource Sciences, Zhejiang University, Zhejiang, China

    Baojing Gu

  5. Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Feng Zhou

  6. School of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Parkville, Victoria, Australia

    Deli Chen

  7. Earth and Environmental Sciences Department, Boston College, Boston, MA, USA

    Hanqin Tian

  8. Laboratoire des Sciences du Climat et de l’Environnement, UVSQ, CEA CNRS, Gif-sur-Yvette, France

    Philippe Ciais

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Contributions

S.L., J.Z. and C.W. designed the investigation. C.W., Y.S. and S.X. established the dataset. C.W., G.L., S.X., X.F. and Y.S. performed the statistical analyses, created the figures and wrote the paper. L.W., B.G., F.Z., D.C., H.T. and P.C. participated in relevant scientific discussions and commented on the paper. All authors contributed to improving and finalizing the paper.

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Correspondence to Jianwen Zou or Shuwei Liu.

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Nature Geoscience thanks Alex Huddell, Phillip Agredazywczuk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Spatial patterns of synthetic N fertilizer-induced soil Nr losses for global maize and wheat fields in 2020.

a-e, Global maps of direct soil N2O emissions, NH3 volatilization, NO emissions, N leaching and runoff per hectare of maize, respectively. f-j, Mapping of direct N2O emissions, NH3 volatilization, NO emissions, N leaching and runoff per hectare of wheat, respectively.

Extended Data Fig. 2 Global synthetic N fertilizer-induced soil Nr losses for maize and wheat fields by country in 2020.

Global maps of direct soil N2O emissions (a, g), NH3 volatilization (b, h), NO emissions (c, i), N leaching (d, j), N runoff (e, k) and total Nr losses (f, l) by country for maize (a-f) and wheat (g-l), respectively.

Extended Data Fig. 3 Synthetic N fertilizer-induced total N2O emissions and mitigation potentials of different scenarios for maize.

The central map shows the synthetic N fertilizer-induced total N2O emissions from maize field in 2020. Each stacked bar charts (a-g) shows the mean value of total N2O emissions under different scenarios for maize globally and across 6 continents. Error bars indicate 95% confidence intervals (CIs) estimated by the Monte Carlo approach with 10,000 simulations. The definitions of different scenarios can be found in Methods section.

Extended Data Fig. 4 Synthetic N fertilizer-induced total N2O emissions and mitigation potentials of different scenarios for wheat.

The central map shows the synthetic N fertilizer-induced total N2O emissions from wheat field in 2020. Each stacked bar charts (a-g) shows the mean value of total N2O emissions under different scenarios for wheat globally and across 6 continents. Error bars indicate 95% confidence intervals (CIs) estimated by the Monte Carlo approach with 10,000 simulations. The definitions of different scenarios can be found in Methods section.

Extended Data Fig. 5 Spatial patterns of N use efficiency (NUE) and their coefficient of variation (c.v.) for global maize and wheat fields.

The mean values (a, b) and bootstrapped (100 iterations) coefficient of variation (standard deviation divided by the mean predicted value) (c, d) of NUE are calculated across the 100 model runs (see Methods) for each grid cell.

Extended Data Fig. 6 Spatial patterns of hotspots for soil Nr loss mitigation from maize and wheat cultivation.

Bivariate maps comparing the N input of grid cells (that is, N input rate multiply by harvest area) with N use efficiency (NUE) for maize (a) and wheat (b). The status groups were allocated using equal distributions. Regions with high N input but low NUE are regarded as hotspots for soil Nr loss mitigation.

Extended Data Fig. 7 Spatial patterns of hotspots for soil Nr loss mitigation by enhanced-efficiency N fertilizers (EEFs) substitution.

Bivariate maps comparing the N input of grid cells (that is, N input rate multiply by harvest area) with NUE enhancement by EEFs substitution for maize (a) and wheat (b). The status groups were allocated using equal distributions. Regions with high N input and high NUE improving potential are regarded as hotspots for soil Nr loss mitigation by EEFs substitution.

Extended Data Fig. 8 Spatial patterns of dominant drivers regulating variation in NUE.

Maize (a) and wheat (b). The dominant driver is defined as the factor with the maximum absolute value of the partial correlation coefficient in each grid after applying a moving 3.75° by 3.75° window. Only significant correlations (P < 0.05) are shown. The inset donut charts indicate the percentage of NUE variations regulated by the dominant drivers. MAP, mean annual precipitation; MAT, mean annual air temperature; SOC, soil organic carbon content.

Extended Data Fig. 9 The coefficient of variation (c.v.) of fertilizer-induced soil Nr losses for maize and wheat fields in 2020 derived from random forest models.

The coefficient of variation of soil Nr loss factors of direct N2O emissions (a, f), NH3 volatilization (b, g), NO emissions (c, h), N leaching (d, i) and runoff (e, j) for maize (a-e) and wheat (f-j) cultivation, respectively. The bootstrapped (100 iterations) coefficient of variation (standard deviation divided by the mean predicted value) are calculated across the 100 model runs for each grid cell (see Methods for details).

Extended Data Fig. 10 Geographical distribution of study sites of soil Nr losses.

Geographical distribution of study sites for maize (a) and wheat (b). Different colors of dots indicate different soil Nr loss pathways. The point size indicates the number of observations from that site. (c) Distribution and the number of observations (the numbers in parentheses) for soil Nr losses from maize and wheat fields.

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Wang, C., Shen, Y., Fang, X. et al. Reducing soil nitrogen losses from fertilizer use in global maize and wheat production. Nat. Geosci. 17, 1008–1015 (2024). https://doi.org/10.1038/s41561-024-01542-x

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