Current rice models underestimate yield losses from short‐term heat stresses

• Published in: Global change biology Vol. 27; no. 2; pp. 402 - 416
• Main Authors: Sun, Ting, Hasegawa, Toshihiro, Liu, Bing, Tang, Liang, Liu, Leilei, Cao, Weixing, Zhu, Yan
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.01.2021
• Subjects: Agricultural production; Climate Change; Climate effects; Climatic conditions; Crop production; Crop yield; Crops; Daily; Edible Grain; effective period; Estimates; Extreme heat; Flowering; Grain; grain‐setting rate; Growth models; Heat; Heat stress; Heat treatment; Heat treatments; Heat waves; Heat-Shock Response; Heatwaves; Model accuracy; model ensembles; Oryza; Oryza sativa; Parameter sensitivity; Parameter uncertainty; Rice; stage‐dependent sensitivity; Temperature; Temperature effects; temperature response function; yield; Oryza sativa; heat stress; effective period; temperature response function; grain-setting rate; yield; model ensembles; stage-dependent sensitivity; climate change
• ISSN: 1354-1013; 1365-2486
• DOI: 10.1111/gcb.15393

Crop production will likely face enormous challenges against the occurrences of extreme climatic events projected under future climate change. Heat waves that occur at critical stages of the reproductive phase have detrimental impacts on the grain yield formation of rice (Oryza sativa). Accurate estimates of these impacts are essential to evaluate the effects of climate change on rice. However, the accuracy of these predictions by crop models has not been extensively tested. In this study, we evaluated 14 rice growth models against four year phytotron experiments with four levels of heat treatments imposed at different times after flowering. We found that all models greatly underestimated the negative effects of heat on grain yield, suggesting that yield projections with these models do not reflect food shocks that may occur under short‐term extreme heat stress (SEHS). As a result, crop model ensembles do not help to provide accurate estimates of grain yield under heat stress. We examined the functions of grain‐setting rate response to temperature (TRF_GS) used in eight models and showed that adjusting the effective periods of TRF_GS improved the model performance, especially for models simulating accumulative daily temperature effects. For TRF_GS which uses daily maximum temperature averaged for the effective period, the models provided better grain yield estimates by using maximum temperatures averaged only when daily maximum temperatures exceeded the base temperature (Tbase). An alternative method based on heating‐degree days and stage‐dependent heat sensitivity parameters further decreased the prediction uncertainty of grain yield under heat stress, where stage‐dependent heat sensitivity was more important than heat dose for model improvement under SEHS. These results suggest the limitation of the applicability of existing rice models to variable climatic conditions and the urgent need for an alternative grain‐setting function accounting for the stage‐dependent heat sensitivity. Current rice models greatly underestimated the negative and acute effects of short‐term extreme heat stress on grain yields, while crop model ensembles did not help to provide accurate estimates of grain yield under heat stress. In this study, we adjusted the effective periods and inputs of temperature functions for grain‐setting rate response to temperature, which improved the model performance significantly. An alternative method based on heat dose and stage‐dependent heat sensitivity parameters further decreased the prediction uncertainty of grain yield greatly under heat stress.