Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change

James A. Franke, Christoph Müller, Sara Minoli, Joshua Elliott, Christian Folberth, Charles Gardner, Tobias Hank, Roberto Cesar Izaurralde, Jonas Jägermeyr, Curtis D. Jones, Wenfeng Liu, Stefan Olin, Thomas A.M. Pugh, Alex C. Ruane, Haynes Stephens, Florian Zabel, Elisabeth J. Moyer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Modern food production is spatially concentrated in global “breadbaskets.” A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

Original languageEnglish
Pages (from-to)167-181
Number of pages15
JournalGlobal Change Biology
Issue number1
Early online date3 Sept 2021
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
NSF grant SES-1463644, NSF NRT program (grant no. DGE-1735359), NSF Graduate Research Fellowship Program (grant no. DGE-1746045). NASA NNX16AK38G (INCA). European Research Council Synergy (grant no. ERC-530 2013-SynG-610028 Imbalance-P). The authors thank the rest of AgMIP and GGCMI research teams and Frances Moore for helpful comments. Computing resources were provided by the University of Chicago Research Computing Center. This work was supported in part by the U. Chicago Center for Robust Decision-making on Climate and Energy Policy (RDCEP), funded by NSF grant SES-1463644 through the Decision Making Under Uncertainty program. James Franke was supported by the NSF NRT program (grant DGE-1735359) and the NSF Graduate Research Fellowship Program (grant DGE-1746045). Sara Minoli was supported by the AXIS project MAPPY (01LS1903A) funded through the German Federal Ministry of Education and Research (BMBF). Haynes Stephens was supported by the NSF NRT program (grant DGE-1735359). Alex Ruane was supported by NASA NNX16AK38G (INCA) and the NASA Earth Sciences Directorate/GISS Climate Impacts Group. Christian Folberth was supported by the European Research Council Synergy (grant no. ERC-530 2013-SynG-610028 Imbalance-P). Stefan Olin acknowledges support from the Swedish strong research areas BECC and MERGE, together with support from LUCCI (Lund University Centre for studies of Carbon Cycle and Climate Interactions).

Publisher Copyright:
© 2021 John Wiley & Sons Ltd


  • adaptation
  • AgMIP
  • climate change
  • crop modeling

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • Ecology
  • General Environmental Science


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