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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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.
References
Mbow, C. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) 437–550 (IPCC, 2019).
Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).
Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).
Rosenzweig, C. et al. The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agric. For. Meteorol. 170, 166–182 (2013).
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, 2021); https://www.isimip.org/
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a Global Gridded Crop Model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).
Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).
O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12, 3055–3070 (2019).
Hawkins, E. et al. Observed emergence of the climate change signal: from the familiar to the unknown. Geophys. Res. Lett. 47, e2019GL086259 (2020).
Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).
Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 953–1028 (IPCC, Cambridge Univ. Press, 2013).
Rojas, M., Lambert, F., Ramirez-Villegas, J. & Challinor, A. J. Emergence of robust precipitation changes across crop production areas in the 21st century. Proc. Natl Acad. Sci. USA 116, 6673–6678 (2019).
Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).
Park, C. E. et al. Keeping global warming within 1.5 °C constrains emergence of aridification. Nat. Clim. Change https://doi.org/10.1038/s41558-017-0034-4 (2018).
Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1701762114 (2017).
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2014).
Toreti, A. et al. Narrowing uncertainties in the effects of elevated CO2 on crops. Nat. Food 1, 775–782 (2020).
Ahmed, M. et al. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity. Sci. Rep. 9, 7813 (2019).
Leakey, A. D. B., Bishop, K. A. & Ainsworth, E. A. A multi-biome gap in understanding of crop and ecosystem responses to elevated CO2. Curr. Opin. Plant Biol. https://doi.org/10.1016/j.pbi.2012.01.009 (2012).
Kimball, B. A. Crop responses to elevated CO2 and interactions with H2O, N, and temperature. Curr. Opin. Plant Biol. https://doi.org/10.1016/j.pbi.2016.03.006 (2016).
Zabel, F. et al. Large potential for crop production adaptation depends on available future varieties. Glob. Change Biol. https://doi.org/10.1111/gcb.15649 (2021).
Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
Ahmad, S. et al. Climate warming and management impact on the change of phenology of the rice–wheat cropping system in Punjab, Pakistan. Field Crops Res. 230, 46–61 (2019).
Porter, J. R. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 485–533 (IPCC, Cambridge Univ. Press, 2014).
Levis, S., Badger, A., Drewniak, B., Nevison, C. & Ren, X. CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5. Clim. Change 146, 501–515 (2018).
Falconnier, G. N. et al. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa. Glob. Change Biol. 26, 5942–5964 (2020).
O’Neill, B. C. et al. IPCC reasons for concern regarding climate change risks. Nat. Clim. Change 7, 28–37 (2017).
Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Change Biol. 25, 2325–2337 (2019).
Zhu, P., Zhuang, Q., Archontoulis, S. V., Bernacchi, C. & Müller, C. Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration. Glob. Change Biol. 25, 2470–2484 (2019).
Iizumi, T. et al. Responses of crop yield growth to global temperature and socioeconomic changes. Sci. Rep. 7, 7800 (2017).
Sherwood, S. C. et al. An assessment of Earth’s climate sensitivity using multiple lines of evidence. Rev. Geophys. 58, e2019RG000678 (2020).
Nijsse, F. J. M. M., Cox, P. M. & Williamson, M. S. Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models. Earth Syst. Dyn. 11, 737–750 (2020).
Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).
Tokarska, K. B. et al. Past warming trend constrains future warming in CMIP6 models. Sci. Adv. 6, eaaz9549 (2020).
Fan, X., Miao, C., Duan, Q., Shen, C. & Wu, Y. The performance of CMIP6 versus CMIP5 in simulating temperature extremes over the global land surface. J. Geophys. Res. Atmos. 125, e2020JD033031 (2020).
Xin, X., Wu, T., Zhang, J., Yao, J. & Fang, Y. Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int. J. Climatol. 40, 6423–6440 (2020).
Ridder, N. N., Pitman, A. J. & Ukkola, A. M. Do CMIP6 climate models simulate global or regional compound events skilfully? Geophys. Res. Lett. https://doi.org/10.1029/2020gl091152 (2020).
Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).
Von Bloh, W. et al. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geosci. Model Dev. 11, 2789–2812 (2018).
Jägermeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields. Sci. Adv. 4, eaat4517 (2018).
Müller, C. et al. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environ. Res. Lett. 16, 034040 (2021).
Franke, J. A. et al. The GGCMI Phase 2 emulators: Global Gridded Crop Model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). Geosci. Model Dev. 13, 2315–2336 (2020).
Allen, L. H. et al. Fluctuations of CO2 in free-air CO2 enrichment (FACE) depress plant photosynthesis, growth, and yield. Agric. For. Meteorol. 284, 107899 (2020).
Durand, J. L. et al. How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? Eur. J. Agron. https://doi.org/10.1016/j.eja.2017.01.002 (2018).
Myers, S. S. et al. Increasing CO2 threatens human nutrition. Nature 510, 139–142 (2014).
Zhu, C. et al. Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Sci. Adv. 4, eaaq1012 (2018).
Rising, J. & Devineni, N. Crop switching reduces agricultural losses from climate change in the United States by half under RCP 8.5. Nat. Commun. 11, 4991 (2020).
Asseng, S. et al. Climate Change impact and adaptation for wheat protein. Glob. Change Biol. 25, 155–173 (2019).
Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).
Giorgi, F. & Bi, X. Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys. Res. Lett. 36, L06709 (2009).
Lange, S. WFDE5 Over Land Merged with ERA5 Over the Ocean (W5E5). V. 1.0 (GFZ Data Services, 2019); https://doi.org/10.5880/pik.2019.023
Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12, 2097–2120 (2020).
Ruane, A. C. et al. Strong regional influence of climatic forcing datasets on global crop model ensembles. Agric. For. Meteorol. 300, 108313 (2021).
FAOSTAT (United Nation’s Food and Agricultural Organization, 2019); http://www.fao.org/faostat/
Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochem. Cycles 24, GB1011 (2010).
Siebert, S. et al. A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci. 19, 1521–1545 (2015).
Heinke, J., Müller, C., Mueller, N. D. & Jägermeyr, J. N application rates from mineral fertiliser and manure Zenodo https://doi.org/10.5281/zenodo.4954582 (2021).
Zhang, B. et al. Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling. Earth Syst. Sci. Data 9, 667–678 (2017).
Tian, H. et al. The global N2O model intercomparison project. Bull. Am. Meteorol. Soc. 99, 1231–1251 (2018).
Nachtergaele, F. et al. Harmonized World Soil Database (version 1.2) (FAO and IIASA, 2012).
Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for Earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).
Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS ONE 9, e114788 (2014).
Müller, C. et al. Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).
Franke, J. A. et al. The GGCMI Phase 2 experiment: Global Gridded Crop Model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0). Geosci. Model Dev. 13, 2315–2336 (2020).
Elliott, J. et al. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).
Ruane, A. C. et al. Multi-wheat-model ensemble responses to interannual climate variability. Environ. Model. Softw. 81, 86–101 (2016).
Wang, R., Bowling, L. C. & Cherkauer, K. A. Estimation of the effects of climate variability on crop yield in the Midwest USA. Agric. For. Meteorol. 216, 141–156 (2016).
Folberth, C., Gaiser, T., Abbaspour, K. C., Schulin, R. & Yang, H. Regionalization of a large-scale crop growth model for sub-Saharan Africa: model setup, evaluation, and estimation of maize yields. Agric. Ecosyst. Environ. 151, 21–33 (2012).
Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.0. Harvard Dataverse, V1 (International Food Policy Research Institute, 2019); https://doi.org/10.7910/DVN/PRFF8V
Jägermeyr, J. et al. A regional nuclear conflict would compromise global food security. Proc. Natl Acad. Sci. USA 117, 7071–7081 (2020).
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).
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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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.
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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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DOI: https://doi.org/10.1038/s43016-021-00400-y
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