Rising temperatures reduce global wheat production

Journal name:
Nature Climate Change
Year published:
Published online

Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.

At a glance


  1. Observations and multi-model simulations of wheat phenology and grain yields at different mean seasonal temperatures.
    Figure 1: Observations and multi-model simulations of wheat phenology and grain yields at different mean seasonal temperatures.

    af, Observed values ± 1 standard deviation (s.d.) are shown by red symbols. Multi-model ensemble medians (green lines) and intervals between the 25th and 75th percentiles (shaded grey) based on 30 simulation models are shown. ac, ‘Hot Serial Cereal’ experiment on Triticum aestivum L. cultivar Yecora Rojo with time-of-sowing and infrared heat treatments. DAS, days-after-sowing. df, CIMMYT multi-environment temperature experiments on T. aestivum L. cultivar Bacanora with time-of-sowing treatments. Note, no anthesis and maturity date measurements were available >28 °C in a and b owing to premature death of crops. For details of field experiments and calibration steps, see Supplementary Methods. Error bars are not shown when smaller than the symbol.

  2. Simulated global wheat grain yield change in the past and with higher temperatures.
    Figure 2: Simulated global wheat grain yield change in the past and with higher temperatures.

    a, Grain yield trends for 1981–2010 based on the median yield of a 30-model ensemble. b,c, Relative median grain yield for +2 °C (b) and +4 °C (c) temperature increases imposed on the 1981–2010 period for the 30-model ensemble using region-specific cultivars. Simulation model uncertainty was calculated as the coefficient of variation (CV%) across 30 models and plotted as circle size. The larger the circle, the less the uncertainty.

  3. Variability, uncertainty and causes of simulated wheat grain yield decline with increasing temperature.
    Figure 3: Variability, uncertainty and causes of simulated wheat grain yield decline with increasing temperature.

    a, Coefficient of variation (%) for simulated grain yields according to location and year variability and model uncertainty. In each box plot, horizontal lines represent, from top to bottom, the 10th percentile, 25th percentile, median, 75th percentile and 90th percentile of 900 simulations for present climate (grey), +2 °C (green) and +4 °C (red). b, Box plots of simulated multi-model ensemble medians (of 30 models) of 30-year averages for each location of relative change in grain yield, grain number, grain size and harvest index per °C increase. Red lines indicate the simulated mean for 30 locations (not weighted for cropping area). Zero is indicated as a dotted line.


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Author information

  1. Present addresses: James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK. (D.C.); Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University, Songkhla 90112, Thailand. (J.A.); European Commission Joint Research Center, via Enrico Fermi, 2749 Ispra, 21027, Italy. (G.D.S.)

    • D. Cammarano,
    • J. Anothai &
    • G. De Sanctis


  1. Agricultural & Biological Engineering Department, University of Florida, Gainesville, Florida 32611, USA

    • S. Asseng &
    • D. Cammarano
  2. Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn 53115, Germany

    • F. Ewert &
    • E. Eyshi Rezaei
  3. INRA, UMR 1095 Génétique, Diversité and Ecophysiologie des Céréales (GDEC), F-63 100 Clermont-Ferrand, France

    • P. Martre
  4. Blaise Pascal University, UMR1095 GDEC, F-63 170 Aubière, France

    • P. Martre
  5. Plant Production Research, MTT Agrifood Research Finland, FI-50100 Mikkeli, Finland

    • R. P. Rötter,
    • T. Palosuo &
    • F. Tao
  6. Department of Environmental Earth System Science, Stanford University, Stanford, California 94305, USA

    • D. B. Lobell
  7. Center on Food Security and the Environment, Stanford University, Stanford, California 94305, USA

    • D. B. Lobell
  8. USDA, Agricultural Research Service, US Arid-Land Agricultural Research Center, Maricopa, Arizona 85138, USA

    • B. A. Kimball,
    • G. W. Wall &
    • J. W. White
  9. The School of Plant Sciences, University of Arizona, Tucson, Arizona 85721, USA

    • M. J. Ottman
  10. CIMMYT Int. Adpo, D.F. Mexico 06600, Mexico

    • M. P. Reynolds &
    • P. D. Alderman
  11. Department of Agronomy, Kansas State University, Manhattan, Kansas 66506, USA

    • P. V. V. Prasad
  12. CGIAR Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, New Delhi-110012, India

    • P. K. Aggarwal
  13. Biological Systems Engineering, Washington State University, Prosser, Washington 99350-8694, USA

    • J. Anothai &
    • G. Hoogenboom
  14. Department of Geological Sciences, Michigan State University East Lansing, Michigan 48823, USA

    • B. Basso &
    • I. Shcherbak
  15. W.K. Kellogg Biological Station, Michigan State University East Lansing, Michigan 48823, USA

    • B. Basso &
    • I. Shcherbak
  16. Institute of Soil Ecology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, D-85764, Germany

    • C. Biernath &
    • E. Priesack
  17. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK

    • A. J. Challinor &
    • A-K. Koehler
  18. CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture (CIAT), A.A. 6713, Cali, Colombia

    • A. J. Challinor
  19. INRA, US1116 AgroClim, F-84 914 Avignon, France

    • G. De Sanctis
  20. Cantabrian Agricultural Research and Training Centre (CIFA), 39600 Muriedas, Spain

    • J. Doltra
  21. IAS-CSIC and University of Cordoba, Apartado 3048, 14080 Cordoba, Spain

    • E. Fereres &
    • M. Garcia-Vila
  22. WESS-Water & Earth System Science Competence Cluster, c/o University of Tübingen, 72074 Tübingen, Germany

    • S. Gayler
  23. Department of Plant Agriculture, University of Guelph, Guelph, Ontario N1G 2W1, Canada

    • L. A. Hunt
  24. Department of Geographical Sciences, University of Maryland, College Park Maryland 20742, USA

    • R. C. Izaurralde &
    • C. D. Jones
  25. Texas A&M AgriLife Research and Extension Center, Texas A&M University, Temple, Texas 76502, USA

    • R. C. Izaurralde
  26. Department of Agroecology, Aarhus University, 8830 Tjele, Denmark

    • M. Jabloun &
    • J. E. Olesen
  27. Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany

    • K. C. Kersebaum &
    • C. Nendel
  28. Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany

    • C. Müller &
    • K. Waha
  29. Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, IARI PUSA, New Delhi 110 012, India

    • S. Naresh Kumar
  30. Landscape & Water Sciences, Department of Environment and Primary Industries, Horsham, Victoria 3400, Australia

    • G. O’Leary
  31. NASA Goddard Institute for Space Studies, New York, New York 10025, USA

    • A. C. Ruane
  32. Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK

    • M. A. Semenov &
    • P. Stratonovitch
  33. Biological Systems Engineering, Washington State University, Pullman, Washington 99164-6120, USA

    • C. Stöckle
  34. Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart, Germany

    • T. Streck
  35. Plant Production Systems & Earth System Science, Wageningen University, 6700AA Wageningen, The Netherlands

    • I. Supit &
    • J. Wolf
  36. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China

    • F. Tao
  37. CSIRO Agriculture Flagship, Dutton Park, Queensland 4102, Australia

    • P. J. Thorburn
  38. CSIRO Agriculture Flagship, Black Mountain, ACT 2601, Australia

    • E. Wang &
    • Z. Zhao
  39. INRA, UMR 1248 Agrosystèmes et développement territorial (AGIR), 31326 Castanet-Tolosan Cedex, France

    • D. Wallach
  40. Department of Agronomy and Biotechnology, China Agricultural University, Yuanmingyuan West Road 2 Beijing 100193, China

    • Z. Zhao
  41. College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China

    • Y. Zhu


S.A., F.E., P.M., R.P.R. and D.B.L. motivated the study, S.A. and F.E. coordinated the study, S.A., F.E., P.M., D.C., D.W. and P.D.A. analysed data, D.C., J.W.W., P.K.A., J.A., B.B., C.B., A.J.C., G.D.S., J.D., E.F., M.G-V., S.G., G.H., L.A.H., R.C.I., M.J., C.D.J., K.C.K., A-K.K., C.M., S.N.K., C.N., G.O’L., J.E.O., T.P., E.P., E.E.R., M.A.S., I.Shcherbak, C.S., P.S., T.S., I.Supit, F.T., P.J.T., K.W., E.W., J.W., Z.Z. and Y.Z. carried out crop model simulations and discussed the results, B.A.K., M.J.O., G.W.W., J.W.W., M.P.R., P.D.A., P.V.V.P. and A.C.R. provided experimental data, S.A., F.E., P.M., R.P.R., D.B.L., B.A.K., A.J.C., J.W.W., M.P.R., C.M., A.C.R., M.A.S. and D.W. wrote the paper.

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