Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments

Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders. Nevertheless, there exists inherent uncertainty in such cases. General Circulation Models (GCMs) and future climate change scenarios (different Representative Concentration Pathways (RCPs) in different future time periods) are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield. This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments (Shiraz, Hamedan, Sanandaj, Kermanshah and Khorramabad) in eastern and southern Iran. These five representative locations can be categorized into three climate classes: arid cold (Shiraz), semi-arid cold (Hamedan and Sanandaj) and semi-arid cool (Kermanshah and Khorramabad). Accordingly, the downscaled daily outputs of 29 GCMs under two RCPs (RCP4.5 and RCP8.5) in the near future (2030s), middle future (2050s) and far future (2080s) were used as inputs for the Agricultural Production Systems sIMulator (APSIM)-wheat model. Analysis of variance (ANOVA) was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield. Years from 1980 to 2009 were regarded as the baseline period. The projection results indicated that wheat grain yield was expected to increase by 12.30%, 17.10%, and 17.70% in the near future (2030s), middle future (2050s) and far future (2080s), respectively. The increases differed under different RCPs in different future time periods, ranging from 11.70% (under RCP4.5 in the 2030s) to 20.20% (under RCP8.5 in the 2080s) by averaging all GCMs and locations, implying that future wheat grain yield depended largely upon the rising CO2 concentrations. ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations, followed by scenarios, GCMs, and their interactions. Specifically, at the semi-arid climate locations (Hamedan, Sanandaj, Kermanshah and Khorramabad), most of the variations arose from the scenarios (77.25%), while at the arid climate location (Shiraz), GCMs (54.00%) accounted for the greatest variation. Overall, the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions, particularly in dryland environments characterized by large fluctuations in rainfall and temperature. Moreover, the current research suggested some GCMs (e.g., the IPSL-CM5B-LR, CCSM4, and BNU-ESM) that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.