Ongoing climate change is likely to enhance the deterioration of rice quality that has been observed in western Japan, especially in Kyushu, since the 1990s. Therefore, it is important to examine the response of rice quality to environmental variation over a wide geographical domain. To that end, the aims of this study were (i)to propose a statistical model to predict rice quality based on temperature, total radiation during the ripening period, and their multiple effects; and (ii)to evaluate the model validity and uncertainty in prediction. A Bayesian calibration was adopted to account for uncertainty in the parameter values associated with non-climatic factors. The validation results showed that the model performed well in capturing the temporal trend and interannual variation in observed rice quality in all prefectures of Kyushu. We then performed the prediction experiment for rice quality in the extremely hot summer of the year 2010, which was omitted from the model calibration data. The results showed that the predictive capability of the statistical model is somewhat dependent on the calibration data, but this dependency does not necessarily mean that useful predictions for climates not in the calibration data are impossible.
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