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The uncertainty of crop yield projections is reduced by improved temperature response functions

Nature Plants volume 3, Article number: 17102 (2017) | Download Citation

  • An Erratum to this article was published on 03 August 2017
  • An Author Correction to this article was published on 27 September 2017

This article has been updated


Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

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  • 27 September 2017

    In the original version of this Article, the name of one co-author was omitted. This has now been corrected by the addition of Benjamin Dumont to the author list.


  1. 1.

    & Crop responses to climatic variation. Philos. T. Roy. Soc. B: Biological Sciences 360, 2021–2035 (2005).

  2. 2.

    et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).

  3. 3.

    et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).

  4. 4.

    et al. The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agr. Forest Meteorol. 170, 166–182 (2013).

  5. 5.

    , , & Crop-climate models need an overhaul. Nat. Clim. Change 1, 175–177 (2011).

  6. 6.

    et al. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob. Change Biol. 21, 1328–1341 (2015).

  7. 7.

    et al. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320 (2014).

  8. 8.

    et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).

  9. 9.

    , , & Gas exchange and water relations of spring wheat under full-season infrared warming. Glob. Change Biol. 17, 2113–2133 (2011).

  10. 10.

    , , , & Responses of time of anthesis and maturity to sowing dates and infrared warming in spring wheat. Field Crop. Res. 124, 213–222 (2011).

  11. 11.

    , , , & Physiological and morphological traits associated with spring wheat yield under hot, irrigated conditions. Funct. Plant Biol. 21, 717–730 (1994).

  12. 12.

    et al. The International Heat Stress Genotype Experiment: results from 1990-1992 (CIMMYT, DF, 1994).

  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.

    , , , & Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J. Exp. Bot. 61, 2057–2069 (2010).

  15. 15.

    & Simulation of phenological development of wheat crops. Agric. Syst. 58, 1–24 (1998).

  16. 16.

    & C3 and C4 photosynthesis models: an overview from the perspective of crop modelling. NJAS WAGEN J LIFE SC 57, 27–38 (2009).

  17. 17.

    & Thermal acclimation and the dynamic response of plant respiration to temperature. Trends Plant Sci. 8, 343–351 (2003).

  18. 18.

    et al. High-resolution temperature responses of leaf respiration in snow gum (Eucalyptus pauciflora) reveal high-temperature limits to respiratory function. Plant Cell Environ. 36, 1268–1284 (2013).

  19. 19.

    , , & Modeling grain nitrogen accumulation and protein composition to understand the sink/source regulations of nitrogen remobilization for wheat. Plant Physiol. 133, 1959–1967 (2003).

  20. 20.

    , & Postanthesis temperature effects on duration and rate of grain filling in some winter and spring wheats. Can. J. Plant Sci. 71, 609–617 (1991).

  21. 21.

    , , & Factors influencing the rate and duration of grain filling in wheat. Funct. Plant Biol. 4, 785–797 (1977).

  22. 22.

    & SPASS: a generic process-oriented crop model with versatile windows interfaces. Environ. Modell. Softw. 15, 179–188 (2000).

  23. 23.

    , & Environmentally-induced changes in protein composition in developing grains of wheat are related to changes in total protein content. J. Exp. Bot. 54, 1731–1742 (2003).

  24. 24.

    & Differences between rice and wheat in temperature responses of photosynthesis and plant growth. Plant Cell Physiol. 50, 744–755 (2009).

  25. 25.

    et al. Accuracy of root modelling and its impact on simulated wheat yield and carbon cycling in soil. Field Crop. Res. 165, 99–110 (2014).

  26. 26.

    et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol. 23, 1258–1281 (2017).

  27. 27.

    , , & Wheat growth response to increased temperature from varied planting dates and supplemental infrared heating. Agron. J. 104, 7–16 (2012).

  28. 28.

    , & Registration of ‘Yecora Rojo’ wheat. Crop Sci. 25, 1130 (1985).

  29. 29.

    in Wheat in heat stressed environments: irrigated dry areas and rice-wheat farming systems (eds Saunders, D.A. & Hettel, G.P.) 184–192 (DF, 1993).

  30. 30.

    & Simulation of growth, water and nitrogen uptake of a wheat crop using the SPASS model. Environ. Modell. Softw. 17, 387–402 (2002).

  31. 31.

    , , & Improving predictions of developmental stages in winter wheat: a modified Wang and Engel model. Agr. Forest Meteorol. 115, 139–150 (2003).

  32. 32.

    , & Predicting phenological development in winter wheat. Climate Res. 25, 243–252 (2004).

  33. 33.

    , , & Simulating maize phenology as a function of air temperature with a linear and a nonlinear model. Pesquisa Agropecuária Brasileira 43, 449–455 (2008).

  34. 34.

    , & Simulating leaf appearance in rice. Agron. J. 100, 490–501 (2008).

  35. 35.

    , , & Impact of elevated temperature scenarios on potato leaf development. Eng. Agríc. 32, 689–697 (2012).

  36. 36.

    Visual quantification of wheat development. Agron. J. 65, 116–119 (1973).

  37. 37.

    , , & Cardinal temperatures for wheat leaf appearance as assessed from varied sowing dates and infrared warming. Field Crop. Res. 137, 213–220 (2012).

  38. 38.

    , , & A winter wheat crop simulation model without water or nutrient limitations. J. Agr. Sci. 102, 371–382 (1984).

  39. 39.

    , , & Working with Dynamic Crop Models, 2nd Edition: Methods, Tools and Examples for Agriculture and Environment (Academic Press, 2013).

  40. 40.

    & River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290 (1970).

  41. 41.

    et al. The international heat stress genotype experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations. Harvard Dataverse (2017).

  42. 42.

    et al. The hot serial cereal experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations. Harvard Dataverse (2017).

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The authors thank D. Lobell for useful comments on an earlier version of the paper. E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.

Author information

Author notes

    • Enli Wang
    •  & Pierre Martre

    These authors contributed equally to this work

    • Andrea Maiorano
    • , Phillip D. Alderman
    • , Jakarat Anothai
    • , Davide Cammarano
    • , Gerrit Hoogenboom
    • , Iurii Shcherbak
    •  & Katharina Waha

    Present address: European Commission Joint Research Centre, 21 027 Ispra, Italy (A.M.); Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, Oklahoma 74078-6028, USA (P.D.A.); Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University, Songkhla 90112, Thailand (J.A.); James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK (D.C.); Institute for Sustainable Food Systems, University of Florida, Gainesville, Florida 32611, USA (G.H.); Institute of Future Environment, Queensland University of Technology, Brisbane, Queensland 4001, Australia (I.S.); CSIRO Agriculture and Food, St Lucia, Queensland 4067, Australia (K.W.)

    • Reimund P. Rötter

    Formerly: Natural Ressources Institute Finland (Luke), 00790 Helsinki, Finland

    • Pramod K. Aggarwal
    •  & Yan Zhu

    Authors from P.K.A. to Y.Z. are listed in alphabetical order

    • Giacomo De Sanctis

    The views expressed in this paper are the views of the authors and do not necessarily represent the views of the organization or institution with which they are currently affiliated.


  1. CSIRO Agriculture and Food, Black Mountain, Australian Capital Territory 2601, Australia

    • Enli Wang
    •  & Zhigan Zhao
  2. UMR LEPSE, INRA, Montpellier SupAgro, 2 Place Viala, 34 060 Montpellier, France

    • Pierre Martre
    •  & Andrea Maiorano
  3. College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China

    • Zhigan Zhao
    •  & Zhimin Wang
  4. Institute of Crop Science and Resource Conservation (INRES), University of Bonn, 53115 Bonn, Germany

    • Frank Ewert
    •  & Ehsan Eyshi Rezaei
  5. Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany

    • Frank Ewert
    • , Kurt C. Kersebaum
    •  & Claas Nendel
  6. Department of Crop Sciences, University of Goettingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), 37077 Göttingen, Germany

    • Reimund P. Rötter
  7. Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Büsgenweg 1, 37077 Göttingen, Germany

    • Reimund P. Rötter
  8. USDA, Agricultural Research Service, U.S. Arid-Land Agricultural Research Center, Maricopa, Arizona 85138, USA

    • Bruce A. Kimball
    • , Gerard W. Wall
    •  & Jeffrey W. White
  9. The School of Plant Sciences, University of Arizona, Tucson, Arizona 85721, USA

    • Michael J. Ottman
  10. Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT) Apdo, 06600 Mexico, D.F, Mexico

    • Matthew P. Reynolds
    •  & Phillip D. Alderman
  11. CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, International Maize and Wheat Improvement Center (CIMMYT), New Delhi 110012, India

    • Pramod K. Aggarwal
  12. AgWeatherNet Program, Washington State University, Prosser, Washington 99350-8694, USA

    • Jakarat Anothai
    •  & Gerrit Hoogenboom
  13. Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University East Lansing, Michigan 48823, USA

    • Bruno Basso
    • , Benjamin Dumont
    •  & Iurii Shcherbak
  14. Helmholtz Zentrum München – German Research Center for Environmental Health, Institute of Biochemical Plant Pathology, Neuherberg, 85764, Germany

    • Christian Biernath
    •  & Eckart Priesack
  15. Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida 32611, USA

    • Davide Cammarano
    •  & Senthold Asseng
  16. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, UK

    • Andrew J. Challinor
    •  & Ann-Kristin Koehler
  17. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Km 17, Recta Cali-Palmira Apartado Aéreo 6713, Cali, Colombia

    • Andrew J. Challinor
  18. GMO Unit, European Food Safety Authority (EFSA), Via Carlo Magno, 1A, 43126 Parma, Italy

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

    • Jordi Doltra
  20. Dep. Agronomia, University of Cordoba, Apartado 3048, 14080 Cordoba, Spain

    • Elias Fereres
    •  & Margarita Garcia-Vila
  21. IAS-CSIC, Cordoba 14080, Spain

    • Elias Fereres
    •  & Margarita Garcia-Vila
  22. Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart, Germany

    • Sebastian Gayler
    •  & Thilo Streck
  23. Department of Plant Agriculture, University of Guelph, Guelph, Ontario N1G 2W1, Canada

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

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

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

    • Mohamed Jabloun
    •  & Jørgen E. Olesen
  27. National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China

    • Leilei Liu
    •  & Yan Zhu
  28. Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany

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

    • Soora Naresh Kumar
  30. Department of Economic Development, Landscape & Water Sciences, Jobs, Transport and Resources, Horsham 3400, Australia

    • Garry O'Leary
  31. Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland

    • Taru Palosuo
    •  & Fulu Tao
  32. INRA, US1116 AgroClim, 84 914 Avignon, France

    • Dominique Ripoche
  33. NASA Goddard Institute for Space Studies, New York, New York 10025, USA

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

    • Mikhail A. Semenov
    •  & Pierre Stratonovitch
  35. Biological Systems Engineering, Washington State University, Pullman, Washington 99164-6120, USA

    • Claudio Stöckle
  36. PPS and WSG & CALM, Wageningen University, 6700AA Wageningen, The Netherlands

    • Iwan Supit
    •  & Joost Wolf
  37. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China

    • Fulu Tao
  38. CSIRO Agriculture and Food, St Lucia, Queensland 4067, Australia

    • Peter Thorburn
  39. INRA, UMR 1248 Agrosystèmes et développement territorial (AGIR), 31 326 Castanet-Tolosan, France

    • Daniel Wallach


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E.W., P.M., S.A. and F.E. motivated the study; E.W. and P.M. designed and coordinated the study, and analysed the data; E.W., P.M., Z.Z., A.M., L.L. and B.B. conducted model improvement simulations; E.W., P.M., S.A., F.E., Z.Z., A.M., R.P.R.,.K.A., P.D.A., J.A., C.B., D.C., 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., L.L., S.N.K., C.N., G.O'L., J.E.O., T.P., E.P., M.P.R., E.E.R., D.R., A.C.R., M.A.S., I.S., C.S., P.S., T.S., I.S., F.T., P.T., K.W., D.W., J.W. 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. and Z.W. provided experimental data; E.W. and P.M. analysed the results and wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Enli Wang or Pierre Martre.

Supplementary information

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    Supplementary Information

    Supplementary Tables 1–4, Supplementary Figures 1–4, Supplementary References

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    Supplementary Data 1

    Extracted data describing the key temperature response functions in each of the 29 wheat models.

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