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Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia

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Abstract

Future climate projections and impact analyses are pivotal to evaluate the potential change in crop yield under climate change. Impact assessment of climate change is also essential to prepare and implement adaptation measures for farmers and policymakers. However, there are uncertainties associated with climate change impact assessment when combining crop models and climate models under different emission scenarios. This study quantifies the various sources of uncertainty associated with future climate change effects on wheat productivity at six representative sites covering dry and wet environments in Australia based on 12 soil types and 12 nitrogen application rates using one crop model driven by 28 global climate models (GCMs) under two representative concentration pathways (RCPs) at near future period 2021–2060 and far future period 2061–2100. We used the analysis of variance (ANOVA) to quantify the sources of uncertainty in wheat yield change. Our results indicated that GCM uncertainty largely dominated over RCPs, nitrogen rates, and soils for the projections of wheat yield at drier locations. However, at wetter sites, the largest share of uncertainty was nitrogen, followed by GCMs, soils, and RCPs. In addition, the soil types at two northern sites in the study area had greater effects on yield change uncertainty probably due to the interaction effect of seasonal rainfall and soil water storage capacity. We concluded that the relative contributions of different uncertainty sources are dependent on climatic location. Understanding the share of uncertainty in climate impact assessment is important for model choice and will provide a basis for producing more reliable impact assessment.

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Acknowledgments

We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S3 of this paper) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank anonymous reviewers for detailed and constructive comments that helped us to improve the manuscript.

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Authors and Affiliations

  1. NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, 2650, Australia

    Bin Wang & De Li Liu

  2. Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052, Australia

    De Li Liu

  3. NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW, 2800, Australia

    Cathy Waters

  4. School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW, 2007, Australia

    Qiang Yu

  5. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, 712100, Shaanxi, China

    Qiang Yu

  6. College of Resources and Environment, University of Chinese Academy of Science, Beijing, 100049, China

    Qiang Yu

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  1. Bin Wang
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  4. Qiang Yu
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Correspondence to Bin Wang.

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Wang, B., Liu, D.L., Waters, C. et al. Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia. Climatic Change 151, 259–273 (2018). https://doi.org/10.1007/s10584-018-2306-z

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