Shi Yanying, Ma Haoyu, Li Tao, Guo Erjing, Zhang Tianyi, Zhang Xijuan, Yang Xianli, Wang Lizhi, Jiang Shukun, Deng Yuhan, Guan Kaixin, Li Mingzhe, Liu Zhijuan, Yang Xiaoguang
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
International Rice Research Institute, Los Baños, Philippines.
J Exp Bot. 2025 Feb 25;76(4):1230-1243. doi: 10.1093/jxb/erae452.
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.
温带和寒带水稻种植区低温事件发生频率和强度的增加，对气候变化下的水稻产量构成威胁。虽然基于过程的作物模型可以预测气候对水稻产量的影响，但其在低温条件下的准确性尚未得到充分评估。我们为期6年的室内试验表明，低温会降低从幼穗分化到开花期的小穗育性、穗发育期间的每穗粒数以及灌浆期的粒重。我们研究了作物模型中用于小穗育性对温度响应的算法。结果表明，如果在孕穗期以外的不同生长阶段使用相同的函数，作物产量的模拟性能较差。事实上，我们替换了ORYZA模型中小穗育性参数的算法，并开发了每穗粒数和粒重的估算函数。之后，将改进方程的算法应用于10个水稻生长模型。新函数考虑了低温在不同阶段对水稻产量的有害影响。此外，还为不同水稻品种设定了耐冷阈值温度。改进后的算法提高了模型在气候变化下模拟水稻产量的能力，为使水稻生产适应未来气候挑战提供了更可靠的工具。
