National Engineering and Technology Center for Information Agriculture, Engineering Research Center for Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu, Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, PR China.
Agro-Environmental Research Division, Tohoku Agricultural Research Center, National Agricultural and Food Research Organization (NARO), Morioka, Japan.
Glob Chang Biol. 2021 Jan;27(2):402-416. doi: 10.1111/gcb.15393. Epub 2020 Nov 17.
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 (T ). 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.
在未来气候变化下，预计极端气候事件的发生将使作物生产面临巨大挑战。在生殖阶段的关键时期发生的热浪对水稻（Oryza sativa）的籽粒产量形成有不利影响。准确估计这些影响对于评估气候变化对水稻的影响至关重要。然而，作物模型的这些预测的准确性尚未得到广泛测试。在这项研究中，我们评估了 14 个水稻生长模型，这些模型针对四个不同开花后时间点施加四个不同水平热处理的四年温室实验。我们发现，所有模型都大大低估了热对籽粒产量的负面影响，这表明，这些模型的产量预测并不能反映短期极端热应激（SEHS）下可能发生的粮食冲击。因此，作物模型组合并不能帮助准确估计热胁迫下的籽粒产量。我们研究了 8 个模型中用于设定粒重对温度响应函数（TRF_GS）的功能，并表明调整 TRF_GS 的有效时段可以改善模型性能，特别是对于模拟累积日温度效应的模型。对于使用有效时段内平均每日最高温度的 TRF_GS，通过仅在每日最高温度超过基础温度（T）时平均使用最高温度，模型可以提供更好的籽粒产量估计值。另一种基于积热日和阶段依赖性热敏感参数的方法进一步降低了热胁迫下籽粒产量的预测不确定性，其中阶段依赖性热敏感性对于 SEHS 下模型改进比热剂量更为重要。这些结果表明，现有水稻模型在不同气候条件下的适用性有限，迫切需要一种替代的粒重设定功能，以考虑阶段依赖性热敏感性。
