Estimating first-grade rice production due to high temperature after heading date utilizing the statistical data

Several eco-physiological process-based crop models have been used in combination with climate models to predict agricultural yield to assess the impact of climate change. However, the quality degradation of rice caused by the influence of climate change is a prevailing problem. Although there is extensive elucidation of the mechanism of the occurrence of white immature grain because of high temperatures resulting in quality degradation, there are fewer studies that incorporate this into their prediction models. In this study, a statistical model to estimate the first-grade rice ratio was developed for three major rice cultivars in Japan. Parameters for a heat-dose index were estimated by employing the particle swarm optimization method and parameters for the statistical model were estimated with the maximum likelihood method. Parameters of the statistical model varied depending on the cultivar variety. It was observed that the statistical model showed varied prediction accuracy for the first-grade rice ratio based on the temperature that was incorporated into model, that is, daily mean, maximum, or minimum temperatures. Our result can generate more accurate predictions of the impact of climate change on rice production, incorporating the farmers’ choice of adaptation to climate change, including the shift in transplanting day.