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

Nature Food volume 1, pages 720–728 (2020)Cite this article

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Abstract

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 9–16. 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.

References

  1. Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).

    ADS  Google Scholar 

  2. Trnka, M. et al. Adverse weather conditions for European wheat production will become more frequent with climate change. Nature Clim. Change 4, 637–643 (2014).

    ADS  Google Scholar 

  3. Wang, B., Liu, D. L., Asseng, S., Macadam, I. & Yu, Q. Modelling wheat yield change under CO2 increase, heat and water stress in relation to plant available water capacity in eastern Australia. Eur. J. Agron. 90, 152–161 (2017).

    CAS  Google Scholar 

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

    ADS  CAS  Google Scholar 

  5. Wang, B. et al. Australian wheat production expected to decrease by the late 21st century. Glob. Change Biol. 24, 2403–2415 (2018).

    ADS  Google Scholar 

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

    ADS  Google Scholar 

  7. Sun, S., Yang, X., Lin, X., Sassenrath, G. F. & Li, K. Climate-smart management can further improve winter wheat yield in China. Agric. Syst. 162, 10–18 (2018).

    CAS  Google Scholar 

  8. Wang, B. et al. Designing wheat ideotypes to cope with future changing climate in South-Eastern Australia. Agric. Syst. 170, 9–18 (2019).

    Google Scholar 

  9. Chen, J., Brissette, F. P., Chaumont, D. & Braun, M. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J. Hydrol. 479, 200–214 (2013).

    ADS  Google Scholar 

  10. Liu, D. L. & Zuo, H. Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia. Clim. Change 115, 629–666 (2012).

    ADS  Google Scholar 

  11. Lehmann, J. & Rillig, M. Distinguishing variability from uncertainty. Nat. Clim. Change 4, 153 (2014).

    ADS  Google Scholar 

  12. Tao, F. et al. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Change Biol. 24, 1291–1307 (2018).

    ADS  Google Scholar 

  13. Hernandez-Ochoa, I. M. et al. Climate change impact on Mexico wheat production. Agric. Forest Meteorol. 263, 373–387 (2018).

    ADS  Google Scholar 

  14. Ashraf Vaghefi, S. et al. Regionalization and parameterization of a hydrologic model significantly affect the cascade of uncertainty in climate-impact projections. Clim. Dyn. 53, 2861–2886 (2019).

    Google Scholar 

  15. Chen, J., Brissette, F. P., Poulin, A. & Leconte, R. Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour. Res. 47, W12509 (2011).

    ADS  Google Scholar 

  16. Gao, J. et al. Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources. J. Hydrol. 568, 551–561 (2019).

    ADS  Google Scholar 

  17. Liu, D. L. et al. Effects of different climate downscaling methods on the assessment of climate change impacts on wheat cropping systems. Clim. Change 144, 687–701 (2017).

    ADS  Google Scholar 

  18. Macadam, I., Argüeso, D., Evans, J. P., Liu, D. L. & Pitman, A. J. The effect of bias correction and climate model resolution on wheat simulations forced with a regional climate model ensemble. Int. J. Climatol. 36, 4577–4591 (2016).

    Google Scholar 

  19. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    MATH  Google Scholar 

  20. Climate Change in Australia Information for Australia’s Natural Resource Management Regions Technical Report (CSIRO and Bureau of Meteorology, 2015).

  21. Wu, S.-Y., Wu, Y. & Wen, J. Future changes in precipitation characteristics in China. Int. J. Climatol. 39, 3558–3573 (2019).

    Google Scholar 

  22. Ruan, H. et al. Future climate change projects positive impacts on sugarcane productivity in southern China. Eur. J. Agron. 96, 108–119 (2018).

    Google Scholar 

  23. Zhang, H. et al. Climate-associated rice yield change in the Northeast China Plain: a simulation analysis based on CMIP5 multi-model ensemble projection. Sci. Total Environ. 666, 126–138 (2019).

    ADS  CAS  PubMed  Google Scholar 

  24. Tao, F. et al. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agric. Forest Meteorol. 281, 107851 (2020).

    ADS  Google Scholar 

  25. Webber, H. et al. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: a multi-model comparison. Field Crops Res. 202, 21–35 (2017).

    Google Scholar 

  26. Ahmed, M. et al. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity. Sci. Rep. 9, 7813 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  27. O’Leary, G. J. et al. Response of wheat growth, grain yield and water use to elevated CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment. Global Change Biol. 21, 2670–2686 (2015).

    ADS  Google Scholar 

  28. Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang, B., Liu, D. L., Waters, C. & Yu, Q. Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia. Clim. Change 151, 259–273 (2018).

    ADS  Google Scholar 

  30. Wallach, D. et al. How well do crop models predict phenology, with emphasis on the effect of calibration? Preprint at bioRxiv https://doi.org/10.1101/708578 (2019).

  31. Xiong, W. et al. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. Nat. Food 1, 63–69 (2020).

    Google Scholar 

  32. Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).

    ADS  Google Scholar 

  33. Pennell, C. & Reichler, T. On the effective number of climate models. J. Clim. 24, 2358–2367 (2011).

    ADS  Google Scholar 

  34. Angstrom, A. Solar and terrestrial radiation. Report to the International Commission for Solar Research on actinometric investigations of solar and atmospheric radiation. Q. J. R. Meteorol. Soc. 50, 121–126 (1924).

    ADS  Google Scholar 

  35. Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16, 309–330 (2001).

    Google Scholar 

  36. Wijngaard, J. B., Klein Tank, A. M. G. & Können, G. P. Homogeneity of 20th century European daily temperature and precipitation series. Int. J. Climatol. 23, 679–692 (2003).

    Google Scholar 

  37. Wang, B. et al. Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. Int. J. Climatol. 38, 4891–4902 (2018).

    Google Scholar 

  38. Richardson, C. W. & Wright, D. A. WGEN: A Model for Generating Daily Weather Variables (United States Agricultural Research Service, 1984).

  39. He, L. et al. Multi-model ensemble projections of future extreme heat stress on rice across southern China. Theor. Appl. Climatol. 133, 1107–1118 (2017).

    ADS  Google Scholar 

  40. Feng, P. et al. Projected changes in drought across the wheat belt of southeastern Australia using a downscaled climate ensemble. Int. J. Climatol. 39, 1041–1053 (2019).

    Google Scholar 

  41. Liu, D. L. et al. Crop residue incorporation can mitigate negative climate change impacts on crop yield and improve water use efficiency in a semiarid environment. Eur. J. Agron. 85, 51–68 (2017).

    Google Scholar 

  42. Wang, B. et al. Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia. Clim. Change 138, 85–98 (2016).

    ADS  Google Scholar 

  43. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    ADS  CAS  PubMed  Google Scholar 

  44. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    ADS  Google Scholar 

  45. Morim, J. et al. Robustness and uncertainties in global multivariate wind–wave climate projections. Nat. Clim. Change 9, 711–718 (2019).

    ADS  Google Scholar 

Download references

Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: Bin Wang, Puyu Feng.

Authors and Affiliations

  1. New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia

    Bin Wang, Puyu Feng & De Li Liu

  2. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi, China

    Bin Wang, Tengcong Jiang, Jianqiang He & Qiang Yu

  3. College of Land Science and Technology, China Agricultural University, Beijing, China

    Puyu Feng

  4. Climate Change Research Centre, UNSW Sydney, Sydney, New South Wales, Australia

    De Li Liu

  5. Agriculture Victoria, Department of Jobs, Precincts and Regions, Horsham, Victoria, Australia

    Garry J. O’Leary

  6. ARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, UNSW Sydney, Sydney, New South Wales, Australia

    Ian Macadam

  7. New South Wales Department of Primary Industries, Dubbo, New South Wales, Australia

    Cathy Waters

  8. Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, USA

    Senthold Asseng

  9. New South Wales Department of Primary Industries, Armidale, New South Wales, Australia

    Annette Cowie

  10. School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia

    Annette Cowie

  11. Engineering Technology Research Centre, Geographic Information Development and Application of Hebei, Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang, Hebei, China

    Dengpan Xiao

  12. Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology and Key Laboratory of Beibu Gulf Environment Change and Resources Use Utilization of Ministry of Education, Nanning Normal University, Nanning, Guangxi, China

    Hongyan Ruan

  13. College of Resources and Environment, University of Chinese Academy of Science, Beijing, China

    Qiang Yu

  14. School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, New South Wales, Australia

    Qiang Yu

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  1. Bin Wang
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Contributions

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 Information

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). https://doi.org/10.1038/s43016-020-00181-w

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