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Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Abstract

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

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Fig. 1: Ensemble end-of-century crop productivity response.
Fig. 2: Comparison of [CO2] and temperature changes between CMIP5 and CMIP6.
Fig. 3: Projections of global crop productivity for the twenty-first century.
Fig. 4: Shift towards earlier and more pronounced climate impact emergence.
Fig. 5: Geographic patterns in TCIE.
Fig. 6: Latitudinal profile of crop yield changes.
Fig. 7: Driver attribution of crop model responses.
Fig. 8: Variance decomposition of ensemble projections.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information. Model inputs are publicly available via https://www.isimip.org/ or from the corresponding author. The GGCMI crop calendar is accessible at https://doi.org/10.5281/zenodo.5062513; fertilizer inputs are available at https://doi.org/10.5281/zenodo.4954582. Crop model simulations will be made publicly available under the CC0 license pending publication.

Code availability

Details and code for each crop model can be requested from the contact persons listed in Supplementary Table 3. Code developed for data analysis and figures is available from the corresponding author upon request.

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Acknowledgements

J.J., A.C.R., C.R. and M.P. were supported by NASA GISS Climate Impacts Group and Indicators for the National Climate Assessment funding from the NASA Earth Sciences Division. J.J. and J.R.G. received support from the Open Philanthropy Project and thank the University of Chicago Research Computing Center for supercomputer allocations to run the pDSSAT model. Ludwig-Maximilians-Universität München thanks the Leibniz Supercomputing Center of the Bavarian Academy of Sciences and Humanities for providing capacity on the Cloud computing infrastructure to run the PROMET model. J.M.S. was supported by the German Federal Ministry of Education and Research (grant number 031B0230A: BioNex—The Future of the Biomass Nexus). O.M. and J.F.S. were supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Earth@lternatives project, grant agreement number 834716). J.A.F. and H.S. were supported by the NSF NRT programme (grant number DGE-1735359). J.A.F was supported by the NSF Graduate Research Fellowship Program (grant number DGE-1746045). RDCEP is funded by NSF through the Decision Making Under Uncertainty programme (grant number SES-1463644). T.I. was partly supported by the Environment Research and Technology Development Fund (2-2005) of the Environmental Restoration and Conservation Agency and Grant-in-Aid for Scientific Research B (18H02317) of the Japan Society for the Promotion of Science. A.K.J and T.-S.L. were supported by the US National Science Foundation (NSF - 831361857). M.O. was supported by the Climate Change Adaptation Research Program of NIES, Japan. S.L. was supported by the German Federal Office for Agriculture and Food (BLE) in the framework of OptAKlim (grant number 281B203316). S.S.R. acknowledges funding from the German Federal Ministry of Education and Research (BMBF) via the ISIpedia project.

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Contributions

J.J. and C.M. conceived the paper and coordinated GGCMI. J.J., C.M. and S.S.R. developed the simulation protocol. A.C.R. and C.R. coordinated AgMIP integration. C.M., J.J., J.B., O.C., B.F., C.F., K.F., G.H., T.I., A.K.J., N.K., T.-S.L., W.L., S.M., M.O., O.M., C.P., S.S.R., J.M.S., J.F.S., R.S., A.S., T.S. and F.Z. conducted crop model simulations. S.L. prepared climate data inputs. J.J. conducted the data analysis, and developed the manuscript and figures. All coauthors supported manuscript writing.

Corresponding author

Correspondence to Jonas Jägermeyr.

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Peer review information Nature Food thanks Bin Peng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Tables S1–S4 and text (‘Winter and spring wheat separation’, ‘Koeppen–Geiger climate class aggregation’, ‘GGCMI crop calendar’).

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Jägermeyr, J., Müller, C., Ruane, A.C. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat Food 2, 873–885 (2021). https://doi.org/10.1038/s43016-021-00400-y

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