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

Bin Wang (), Liu De Li, Cathy Waters and Qiang Yu
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Bin Wang: Wagga Wagga Agricultural Institute
Liu De Li: Wagga Wagga Agricultural Institute
Cathy Waters: Orange Agricultural Institute
Qiang Yu: University of Technology Sydney

Climatic Change, 2018, vol. 151, issue 2, No 12, 259-273

Abstract: 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.

Date: 2018
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DOI: 10.1007/s10584-018-2306-z

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