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Climate change unequally affects nitrogen use and losses in global croplands

Nature Food volume 4, pages 294–304 (2023)Cite this article

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Abstract

Maintaining food production while reducing agricultural nitrogen pollution is a grand challenge under global climate change. Yet, the response of global agricultural nitrogen uses and losses to climate change on the temporal and spatial scales has not been fully characterized. Here, using historical data for 1961–2018 from over 150 countries, we show that global warming leads to small temporal but substantial spatial impacts on cropland nitrogen use and losses. Yield and nitrogen use efficiency increase in 29% and 56% of countries, respectively, whereas they reduce in the remaining countries compared with the situation without global warming in 2018. Precipitation and farm size changes would further intensify the spatial variations of nitrogen use and losses globally, but managing farm size could increase the global cropland nitrogen use efficiency to over 70% by 2100. Our results reveal the importance of reducing global inequalities of agricultural nitrogen use and losses to sustain global agriculture production and reduce agricultural pollution.

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Fig. 1: Temperature and precipitation changes across global regions from 1961 to 2018.
Fig. 2: Global impacts of temperature, precipitation and farm size changes on yield, fertilization, NUE and N surplus from 1961 to 2018.
Fig. 3: Spatial variability of impacts of temperature, precipitation and farm size changes on yield, fertilization, NUE and N surplus in 2018.
Fig. 4: Interaction effects of climate and farm size with regard to cropland N use and losses.
Fig. 5: Yield, fertilization, NUE and N surplus changes in 2100 under the SSP1–RCP2.6 scenario.

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Data availability

Data supporting the findings of this study are available within the article and its Supplementary Information files. All data are publicly available and open access. Source data are provided with this paper.

Code availability

All analyses were performed using Stata version 16.0. Codes that allow the estimates to be reproduced are available in the Supplementary Information.

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Acknowledgements

This study was supported by the National Key Research and Development Project of China (2022YFD1700700, 2022YFE0138200), National Natural Science Foundation of China (42261144001 and 42061124001), and Pioneer and Leading Goose R&D Program of Zhejiang (2022C02008).

Author information

Authors and Affiliations

  1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Chenchen Ren, Xiuming Zhang, Sitong Wang, Jianming Xu & Baojing Gu

  2. Department of Land Management, Zhejiang University, Hangzhou, China

    Chenchen Ren

  3. Policy Simulation Laboratory, Zhejiang University, Hangzhou, China

    Chenchen Ren, Sitong Wang & Baojing Gu

  4. School of Agriculture and Food, The University of Melbourne, Melbourne, Victoria, Australia

    Xiuming Zhang

  5. UK Centre for Ecology and Hydrology, Penicuik, UK

    Stefan Reis

  6. University of Exeter Medical School, Knowledge Spa, Truro, UK

    Stefan Reis

  7. The University of Edinburgh, School of Chemistry, Edinburgh, UK

    Stefan Reis

  8. College of Hydrology and Water Resources, Hohai University, Nanjing, China

    Jiaxin Jin

  9. Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China

    Jiaxin Jin

  10. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou, China

    Jianming Xu

  11. Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou, China

    Baojing Gu

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Contributions

B.G. designed the study. C.R. conducted the research. B.G. and C.R. wrote the first draft of the paper. X.Z. and S.R. revised the paper. X.Z., J.J. and S.W. processed the raw data. All authors contributed to the discussion and revision of the paper.

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Correspondence to Baojing Gu.

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Nature Food thanks Luis Lassaletta, Huizhong Shen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Physical impacts of temperature, precipitation and farm size changes on yield, fertilization, NUE and N surplus in 2018.

(a), (d), (g), and (j) present the impacts of temperature change on yield, fertilization, NUE, and N surplus, respectively. The impacts of temperature and precipitation changes are shown in panels (b), (e), (h), and (k). Combined impacts of temperature, precipitation and farm size are depicted in panels (c), (f), (i), and (l). The impact on each country refers to the physical change compared to the actual observed value in 2018. The geographic coordinates of maps can be found in Fig. 1a. The base map is applied without endorsement from GADM data (https://gadm.org/).

Source data

Extended Data Fig. 2 Fertilization changes with yield under temperature, precipitation and farm size changes in 2018.

FV, Fruits and Vegetables; Other, Other Crops. Circles in each panel are country-level data in 2018. Circles with different colors represent seven types of countries with different dominated crop categories including wheat, rice, maize, pulses, vegetables and fruits, other crops, and mixed cropping, respectively. Panel (a) and (b) present the relative and physical changes related to temperature rising, respectively. The relative and physical impacts of temperature and precipitation changes are shown in panels (c) and (d), respectively. Combined impacts of temperature, precipitation and farm size are depicted in panels (e) and (f).

Source data

Extended Data Fig. 3 Farm size change from 1961 to 2018.

Panel (a) depicts the global average change in farm size from 1961 to 2018. Panel (b) shows the spatial relative change (%) in 2018 compared to that in 1961. The geographic coordinates of maps can be found in Fig. 1a. The base map is applied without endorsement from GADM data (https://gadm.org/).

Source data

Extended Data Fig. 4 Yield, fertilization, NUE and N surplus trends under SSP-RCP scenarios.

Panel (a), (d), (g) and (j) shows yield, fertilization, NUE and N surplus trends only considering temperature, respectively. And panel (b), (e), (h) and (k) represent yield, fertilization, NUE and N surplus trends under both temperature and precipitation changes. The right panels ((c), (f), (i), (l)) represent trends further considering the role of farm size. All global means in this figure are weighted and taken as three-year moving averages.

Source data

Extended Data Fig. 5 Spatial patterns of yield, fertilization, NUE and N surplus in 2018.

(a) Yield; (b) Fertilization; (c) NUE; (d) N surplus. The geographic coordinates of maps can be found in Fig. 1a. The base map is applied without endorsement from GADM data (https://gadm.org/).

Source data

Extended Data Fig. 6 Yield, fertilization, NUE and N surplus changes in 2100 under SSP5-RCP8.5 scenario.

Value changes refer to the relative change (%) in this figure. It is calculated by the value difference simulated between 2015 and 2100 under the SSP5-RCP8.5 scenario divided by the actual observed value in 2015 and the result is carried out in percentage terms. The left panels ((a), (d), (g), (j)) show changes under the SSP5-RCP8.5 scenario only considering the temperature. Panels in the middle ((b), (e), (h), (k)) present the combined effects of temperature and precipitation. The right panels ((c), (f), (i), (l)) represent changes further considering the role of farm size. The geographic coordinates of maps can be found in Fig. 1a. The base map is applied without endorsement from GADM data (https://gadm.org/).

Source data

Extended Data Fig. 7 Global temperature, precipitation and farm size changes under SSP-RCP scenarios.

SSP126, SSP1-RCP2.6; SSP245, SSP1-RCP4.5; SSP585, SSP5-RCP8.5. (a) Temperature changes of croplands compared to 2015; (b) Precipitation changes of croplands compared to 2015; (c) Farm size from 2020 to 2100.

Source data

Extended Data Fig. 8 Relative effects of temperature, precipitation and farm size changes on NUE, fertilization and yield across different regions.

Panel (a)-(g) shows the relative effects in seven types of countries with crop categories of wheat, rice, maize, pulses, vegetables and fruits, other crops, and mixed cropping, respectively. In each panel, the three bars on the left refer to the effects in the small farm size group (less than 1 ha). Accordingly, the three on the right represent the effects in the large farm size group (more than 1 ha). The effects were derived from the ratio of the standardization coefficient of the explanatory variable to the standard deviation of the explained variable according to equation (5). The effects from temperature and precipitation have summed the effects from itself and its quadratic items in each farm size group. Note that for countries with crop category of rice, the average farm size on the country level is all less than 1 ha.

Source data

Extended Data Table 1 The turning points of temperature and precipitation to yield, fertilization and NUE in seven types of countries
Full size table
Extended Data Table 2 Effects of temperature, precipitation and farm size on yield, fertilization and NUE changes
Full size table

Supplementary information

Supplementary Information

Supplementary Methods, Tables 1–12, Figs. 1–7 and Code availability.

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Ren, C., Zhang, X., Reis, S. et al. Climate change unequally affects nitrogen use and losses in global croplands. Nat Food 4, 294–304 (2023). https://doi.org/10.1038/s43016-023-00730-z

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