Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change
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Challinor AJ, Wheeler T, Hemming D, Upadhyaya HD. 2009. Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change. Climate Research 38(2): 117-127.
Permanent link to this item: http://hdl.handle.net/10568/25165
Estimates of the response of crops to climate change rarely quantify the uncertainty inherent in the simulation of both climate and crops. We present a crop simulation ensemble for a location in India, perturbing the response of both crop and climate under both baseline (12 720 simulations) and doubled-CO2 (171 720 simulations) climates. Some simulations used parameter values representing genotypic adaptation to mean temperature change. Firstly, observed and simulated yields in the baseline climate were compared. Secondly, the response of yield to changes in mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. Thirdly, the relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes was examined. In simulations without genotypic adaptation, most of the uncertainty came from the climate model parameters. Comparison with the simulations with genotypic adaptation and with a previous study suggested that the relatively low crop parameter uncertainty derives from the observational constraints on the crop parameters used in this study. Fourthly, the simulations were used, together with an observed dataset and a simple analysis of crop cardinal temperatures and thermal time, to estimate the potential for adaptation using existing cultivars. The results suggest that the germplasm for complete adaptation of groundnut cultivation in western India to a doubled-CO2 environment may not exist. In conjunction with analyses of germplasm and local management practices, results such as this can identify the genetic resources needed to adapt to climate change.
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