Innovative modeling on the effects of low-temperature stress on rice yields.

The increasing frequency and intensity of low-temperature events in temperate and cold rice production regions threatens rice yields under climate change. While process-based crop models can project climate impacts on rice yield, their accuracy under low-temperature conditions has not been well evaluated. Our 6 year chamber experiments revealed that low temperatures reduce spikelet fertility from panicle initiation to flowering, grain number per spike during panicle development, and grain weight during grain filling. We examined the algorithms of spikelet fertility response to temperature used in crop models. The results showed that simulation performance is poor for crop yields if the same function was used at different growth stages outside the booting stage. Indeed, we replaced the algorithm for the spikelet fertility parameter of the ORYZA model and developed the function of estimated grain number per spike and grain weight. After that, the algorithm with improved equations was applied to 10 rice growth models. New functions considered the harmful effects of low temperatures on rice yield at different stages. In addition, the threshold temperatures of cold tolerance were set for different rice varieties. The improved algorithm enhances the ability of the models to simulate rice yields under climate change, providing a more reliable tool for adapting rice production to future climatic challenges.