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

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


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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Figure 1: Temperature response functions in 29 wheat simulation models.
Figure 2: Comparison of multi-model simulations against observations and average growing season temperature.
Figure 3: Uncertainty in simulated wheat responses due to variations in the temperature response functions of phenological development and biomass growth (RUE).
Figure 4: Derived temperature responses of various physiological processes.
Figure 5: Comparison of Q10 for respiration derived from the temperature response function in Fig. 4c to the temperature dependence of the Q10 of foliar respiration rates17.

Change history

  • 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.


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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

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.

Correspondence to Enli Wang or Pierre Martre.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Tables 1–4, Supplementary Figures 1–4, Supplementary References (PDF 2031 kb)

Supplementary Data 1

Extracted data describing the key temperature response functions in each of the 29 wheat models. (XLSX 12620 kb)

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