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Sources of uncertainty for wheat yield projections under future climate are site-specific


Understanding sources of uncertainty in climate–crop modelling is critical for informing adaptation strategies for cropping systems. An understanding of the major sources of uncertainty in yield change is needed to develop strategies to reduce the total uncertainty. Here, we simulated rain-fed wheat cropping at four representative locations in China and Australia using eight crop models, 32 global climate models (GCMs) and two climate downscaling methods, to investigate sources of uncertainty in yield response to climate change. We partitioned the total uncertainty into sources caused by GCMs, crop models, climate scenarios and the interactions between these three. Generally, the contributions to uncertainty were broadly similar in the two downscaling methods. The dominant source of uncertainty is GCMs in Australia, whereas in China it is crop models. This difference is largely due to uncertainty in GCM-projected future rainfall change across locations. Our findings highlight the site-specific sources of uncertainty, which should be one step towards understanding uncertainties for more robust climate–crop modelling.

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Fig. 1: Projected climate change in wheat-growing season from 32 GCMs based on two downscaling methods at four study sites.
Fig. 2: Projected wheat yield change and crop model ensemble means in response to climate change at four study sites.
Fig. 3: The relative importance of variables influencing future yield change.
Fig. 4: Proportion of uncertainty in simulated wheat yield changes.
Fig. 5: The contribution to crop model uncertainty made by individual crop models.

Data availability

The wheat data and parameters for each crop model used in this study are available in Supplementary Tables 5 and 916. The detailed downscaling climate data and yield data simulated by each crop model that support the findings of this study are available from the corresponding author upon request.

Code availability

The detailed R code for data processing and illustration is available from the corresponding author upon reasonable request. The executable source code and pseudo-code of the crop models used in this study are available from their respective owners, as listed in Supplementary Table 2.


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This work was part of a study investigating the impacts of and adaptation to our changing climate in Australia and China. Funding support for G.J.O. was provided by the Victorian Department of Jobs, Precincts and Regions and the Grains Research and Development Corporation through the Australian Grains FACE project and Modelling Grain Quality project (CMI 105498). We acknowledge the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP5 multi-model dataset. We thank R. Lines-Kelly for editing to improve an earlier version of this manuscript.

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Authors and Affiliations



B.W. and D.L.L. designed the research. B.W., T.J. and D.X. collected crop data. P.F. and D.L.L. generated change factor and statistical downscaling climate data, respectively. P.F., B.W., D.L.L., G.J.O. and T.J. ran crop models. B.W. and P.F. drew the figures. B.W. wrote the draft manuscript. P.F., D.L.L., I.M., G.J.O., S.A., C.W., A.C., H.R., J.H. and Q.Y. contributed to writing the manuscript.

Corresponding authors

Correspondence to Bin Wang, De Li Liu or Qiang Yu.

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Supplementary Figs. 1–4, Tables 1–17, Methods and References.

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Wang, B., Feng, P., Liu, D.L. et al. Sources of uncertainty for wheat yield projections under future climate are site-specific. Nat Food 1, 720–728 (2020).

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