Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Christoph Müller*, James Franke, Jonas Jägermeyr, Alex C. Ruane, Joshua Elliott, Elisabeth Moyer, Jens Heinke, Pete D. Falloon, Christian Folberth, Louis Francois, Tobias Hank, R. César Izaurralde, Ingrid Jacquemin, Wenfeng Liu, Stefan Olin, Thomas A.M. Pugh, Karina Williams, Florian Zabel

*Corresponding author for this work

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Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

Original languageEnglish
Article number034040
JournalEnvironmental Research Letters
Issue number3
Early online date26 Feb 2021
Publication statusPublished - Mar 2021

Bibliographical note

Funding Information:
CMIP5 model output data provided by the WHOI CMIP5 Community Storage Server, Woods Hole Oceanographic Institution, Woods Hole, MA, USA from their website at http://cmip5.whoi.edu/. J A F was supported by the NSF NRT program (Grant No. DGE-1735359) and the NSF Graduate Research Fellowship Program (Grant No. DGE-1746045). RDCEP is funded by NSF through the Decision Making Under Uncertainty program (Grant No. SES-1463644). JJ received support from the Open Philanthropy Project and the NASA Co-op. ACR was supported by NASA GISS Climate Impacts Group funding from the NASA Earth Sciences Directorate. PF and KW were supported by the Newton Fund through the Met Office program Climate Science for Service Partnership Brazil (CSSP Brazil). The publication of this article was partially funded by the Open Access Fund of the Leibniz Association.


  • AgMIP
  • Agriculture
  • Climate change
  • CMIP
  • Crop modeling
  • Uncertainty

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Public Health, Environmental and Occupational Health


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