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The critical role of extreme heat for maize production in the United States

Nature Climate Change volume 3, pages 497501 (2013) | Download Citation


Statistical studies of rainfed maize yields in the United States1 and elsewhere2 have indicated two clear features: a strong negative yield response to accumulation of temperatures above 30 °C (or extreme degree days (EDD)), and a relatively weak response to seasonal rainfall. Here we show that the process-based Agricultural Production Systems Simulator (APSIM) is able to reproduce both of these relationships in the Midwestern United States and provide insight into underlying mechanisms. The predominant effects of EDD in APSIM are associated with increased vapour pressure deficit, which contributes to water stress in two ways: by increasing demand for soil water to sustain a given rate of carbon assimilation, and by reducing future supply of soil water by raising transpiration rates. APSIM computes daily water stress as the ratio of water supply to demand, and during the critical month of July this ratio is three times more responsive to 2 °C warming than to a 20% precipitation reduction. The results suggest a relatively minor role for direct heat stress on reproductive organs at present temperatures in this region. Effects of elevated CO2 on transpiration efficiency should reduce yield sensitivity to EDD in the coming decades, but at most by 25%.

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  1. 1.

    & Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

  2. 2.

    , , & Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Clim. Change 1, 42–45 (2011).

  3. 3.

    FAO Food and Agriculture Organization of the United Nations (FAO), FAO Statistical Databases; available at  (2012).

  4. 4.

    et al. in Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Parry, M. L. et al.) 273–313 (Cambridge Univ. Press, 2007).

  5. 5.

    & Climate and management contributions to recent trends in US agricultural yields. Science 299, 1032 (2003).

  6. 6.

    , , & Climate–crop yield relationships at provincial scales in China and the impacts of recent climate trends. Clim. Res. 38, 83–94 (2008).

  7. 7.

    , & Varying temporal and spatial effects of climate on maize and soybean affect yield prediction. Clim. Res. 49, 143–154 (2012).

  8. 8.

    & Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 014010 (2010).

  9. 9.

    & Global scale climate-crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 004000 (2007).

  10. 10.

    , , & Climatic characteristics of heat waves and their simulation in plant experiments. Glob. Change Biol. 16, 1992–2000 (2010).

  11. 11.

    & The importance of the anthesis-silking interval in breeding for drought tolerance in tropical maize. Field Crops Res. 48, 65–80 (1996).

  12. 12.

    & Sensitivity of photosynthesis in a C4 plant, maize, to heat stress. Plant Physiol. 129, 1773–1780 (2002).

  13. 13.

    & Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytol. 194, 760–774 (2012).

  14. 14.

    , & Water-use efficiency in crop production. BioScience 34, 36–40 (1984).

  15. 15.

    & Some characteristics of reduced leaf photosynthesis at midday in maize growing in the field. Field Crops Res. 62, 53–62 (1999).

  16. 16.

    , , & The effect of vapor pressure deficit on maize transpiration response to a drying soil. Plant Soil 239, 113–121 (2002).

  17. 17.

    Rising atmospheric carbon dioxide concentration and the future of C4 crops for food and fuel. Proc. R. Soc. B 1666, 2333–2343 (2009).

  18. 18.

    , & Agronomic weather measures in econometric models of crop yield with implications for climate change. Am. J. Agricult. Econom. 95, 236–243 (2013).

  19. 19.

    , , & Methodologies for simulating impacts of climate change on crop production. Field Crops Res. 124, 357–368 (2011).

  20. 20.

    et al. Hybrid-maize—a maize simulation model that combines two crop modeling approaches. Field Crops Res. 87, 131–154 (2004).

  21. 21.

    & CERES-Maize: A Simulation Model of Maize Growth and Development (Texas A&M Univ. Press, 1986).

  22. 22.

    et al. Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J. Exp. Bot. 61, 2185–2202 (2010).

  23. 23.

    Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: Has its importance been underestimated? Plant Cell Environ. 14, 729–739 (1991).

  24. 24.

    & Water deficit effects on maize yields modeled under current and greenhouse climates. Agron. J. 83, 1052–1059 (1991).

  25. 25.

    et al. Photosynthesis in a CO2-rich atmosphere. Photosynthesis 34, 733–768 (2012).

  26. 26.

    & An independent method of deriving the carbon dioxide fertilization effect in dry conditions using historical yield data from wet and dry years. Glob. Change Biol. 17, 2689–2696 (2011).

  27. 27.

    , & Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

  28. 28.

    , , & Simulation supplements field studies to determine no-till dryland corn population recommendations for semiarid western Nebraska. Agron. J. 95, 884–891 (2003).

  29. 29.

    et al. Can changes in canopy and/or root system architecture explain historical maize yield trends in the US corn belt? Crop Sci. 49, 299–312 (2009).

  30. 30.

    , , & Water extraction by grain sorghum in a sub-humid environment. I. Analysis of the water extraction pattern. Field Crops Res. 33, 81–97 (1993).

  31. 31.

    & in Limitations to Efficient Water Use in Crop Production (eds Taylor, H.M. et al.) 1–27 (ASA, CSSA and SSSA, 1983).

  32. 32.

    , & A sunflower simulation model: I. Model development. Agron. J. 85, 725–735 (1993).

  33. 33.

    , , & Attribution of observed surface humidity changes to human influence. Nature 449, 710–712 (2007).

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We thank J. Jones and M. Burke for helpful comments. D.B.L., M.J.R. and W.S. were supported by NSF grant SES-0962625, and D.B.L. also by NOAA grant NA11OAR4310095. G.L.H. and G.M. were supported by grant LP100100495 from the Australian Research Council.

Author information


  1. Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, California 94305, USA

    • David B. Lobell
  2. The University of Queensland, Queensland Alliance For Agriculture and Food Innovation, Brisbane, Queensland 4072, Australia

    • Graeme L. Hammer
  3. Queensland Department of Agriculture, Forestry, and Fisheries, Toowoomba, Queensland 4350, Australia

    • Greg McLean
  4. Pioneer Hi-Bred International, Johnston, Iowa 50131, USA

    • Carlos Messina
  5. Department of Economics, University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA

    • Michael J. Roberts
  6. Department of Agricultural and Resource Economics, University of California, Berkeley, California 94720, USA

    • Wolfram Schlenker


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D.B.L. and G.L.H. conceived the study, and all authors contributed to analysis and writing the paper.

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The authors declare no competing financial interests.

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Correspondence to David B. Lobell.

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