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Climate-driven global cropland changes and consequent feedbacks

Nature Geoscience volume 18, pages 639–645 (2025)Cite this article

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Abstract

The interdependence of climate change and agricultural land use remains a critical, yet unquantified, area of concern for future food production. Here we determine climate-driven cropland change based on an empirical model of cropland response to changes in agricultural productivity. By estimating counterfactual total factor productivity in a scenario without climate change, we find that 88 million hectares (90% confidence interval (CI) 5–179 Mha), or 6.3% (90% CI 3.6–12.8%) of the cropland currently used in 110 countries, can be attributed to climate change via reduced agricultural productivity growth over 1992–2020. This area exceeds the observed 3.9% net cropland expansion in the studied countries, indicating that total cropland area would have decreased in the absence of climate effects. The release of about 21.8 GtCO2 (lower/upper bound: 4.4–41.4 GtCO2) could have been prevented without climate-driven cropland change, accounting for about 18.9% (3.8–35.9%) of land-use change emissions in these countries. Climate-driven cropland change also triggered noticeably warmer and drier local climate feedback in some regions, with potential repercussions for food security. The substantial emissions will probably impose further long-term negative impacts on agricultural efficiency.

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Fig. 1: Changes in TFP and cropland area during 1992–2020.
Fig. 2: Cumulative changes in cropland area and sources of change.
Fig. 3: Carbon emissions due to climate-driven cropland change during 1992–2020.
Fig. 4: The effects of climate-driven cropland change on daily average LST and annual precipitation during 1992–2020.

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Data availability

All data used in this study are publicly available, as described in Methods. Briefly, TFP growth data are available at https://www.ers.usda.gov/data-products/international-agricultural-productivity; agricultural trade data before 2008 from the GTAP 8 database can be accessed through the free demo version of the GTAPAgg package (https://www.gtap.agecon.purdue.edu/databases/v8/default.asp); balanced OECD trade data from 2007 to 2018 are available at https://www.oecd.org/sdd/its/balanced-trade-statistics.htm; FAO cropland area and gross agricultural production are available at https://www.fao.org/faostat/en/#data; CCI-LC land-cover maps are available at https://maps.elie.ucl.ac.be/CCI/viewer/download.php; the 300-m harmonized global map of above- and below-ground biomass carbon density in the year 2010 is available at https://doi.org/10.3334/ORNLDAAC/1763; the SOC density map is available at https://github.com/whrc/Soil-Carbon-Debt; the dataset of potential biophysical effects of vegetation cover change is available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/ECOCLIM/Biophysical-effects-vgt-change/v2.0/; the ERA5 model level data can be downloaded through the CDS API (https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5); the multi-source satellite-based precipitation dataset is summarized in Supplementary Table 4.

Code availability

The full codes used to analyse the data, reproduce the results and create the main figures are available and reproducible at https://codeocean.com/capsule/9316102/tree. The original code to perform the analysis of cropland response to TFP growth39 is available in the supporting information provided at https://doi.org/10.1093/ajae/aay088. The original code to reproduce the analysis of climate effects on TFP15 is available at https://doi.org/10.6077/pfsd-0v93. The Python interface to LAGRANTO can be found at https://git.iac.ethz.ch/atmosdyn/Lagranto. The pyGAM package for building generalized additive models can be found at https://pygam.readthedocs.io/en/latest/notebooks/tour_of_pygam.html#Regression.

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Acknowledgements

We thank D. Tilman and L. Liu for helpful suggestions and comments for the manuscript development.

Author information

Author notes

  1. These authors contributed equally: Nanshan You, Jessica Till.

Authors and Affiliations

  1. Department of Bioproducts and Biosystems Engineering, University of Minnesota, St Paul, MN, USA

    Nanshan You, Jessica Till & Zhenong Jin

  2. Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA, USA

    David B. Lobell

  3. Department of Geography, The University of Hong Kong, Hong Kong SAR, China

    Peng Zhu

  4. Project Drawdown, Madison, WI, USA

    Paul C. West

  5. Department of Applied Economics, University of Minnesota, St Paul, MN, USA

    Paul C. West

  6. School of Architecture and Civil Engineering, Xiamen University, Xiamen, China

    Hui Kong

  7. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Wei Li

  8. Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland

    Michael Sprenger

  9. Department of Agricultural Economics, Kansas State University, Manhattan, KS, USA

    Nelson B. Villoria

  10. Institute of Medical Technology, Peking University, Beijing, China

    Pengfei Li

  11. Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment, Ministry of Education, Chongqing University, Chongqing, China

    Yi Yang

  12. Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing, China

    Zhenong Jin

  13. State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), Peking University, Beijing, China

    Zhenong Jin

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Contributions

Z.J., N.Y. and J.T. conceived and designed the work. N.Y. and J.T. jointly led the analysis, ran the models, generated figures and tables and drafted the initial paper with input from Z.J., D.B.L., P.Z. and P.C.W. M.S. and N.B.V. contributed to the development of methods and models. H.K., W.L., P.L. and Y.Y. contributed to the interpretation of the results. All authors contributed to writing and revision.

Corresponding author

Correspondence to Zhenong Jin.

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The authors declare no competing interests.

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Nature Geoscience thanks Oz Kira, Karina Winkler 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 The schematic workflow diagram.

The schematic workflow diagram for the analysis. Here, ΔTFP, ΔCropland, ΔLST and ΔP represents climate impacts on TFP, climate-driven cropland change, and associated changes in Land Surface Temperature (LST) and precipitation, respectively. f refers to generalized additive models (GAMs), which are used to model the precipitation response to local and upwind land cover change (Precip-LC linkage). The details of the equations displayed here can be found in the Methods (Eqs. 4, 5, 7, and 10). Basemaps in 3 are from Natural Earth (https://www.naturalearthdata.com/).

Extended Data Fig. 2 The confidence intervals of climate-driven cropland change.

90% confidence intervals (CI) of climate-driven cropland change by (a) country and by (b) decade. Note that CI per country is expressed in percentages because elasticities provide the percentage change in cropland area in response to a certain percentage change in TPF. CI was estimated using bootstrapping with 500 replicates (n = 500). Basemap in a is from Natural Earth (https://www.naturalearthdata.com/).

Supplementary information

Supplementary Information

Supplementary Figs. 1–12, Tables 1–7 and Text 1–4.

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You, N., Till, J., Lobell, D.B. et al. Climate-driven global cropland changes and consequent feedbacks. Nat. Geosci. 18, 639–645 (2025). https://doi.org/10.1038/s41561-025-01724-1

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