Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions
Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions
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DOI：
10.1111/gcb.12758
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发表时间：
2015-03-01
影响因子：
11.6
通讯作者：
Bouman, Bas
中科院分区：
文献类型：
--
作者：
Li, Tao;Hasegawa, Toshihiro;Bouman, Bas
预测未来气候下的水稻生产力对于全球粮食安全非常重要。生态生理作物模型与气候模型输出相结合通常用于产量预测，但与作物模型相关的不确定性在很大程度上仍未量化。我们根据亚洲不同气候条件的四个地点的多年实验产量数据评估了 13 个水稻模型，并检验了主要生理过程的不同建模方法是否会导致田间测量产量预测的不确定性以及对温度和二氧化碳浓度 [CO2] 变化的敏感性的不确定性。我们还研究了使用作物模型集合是否可以减少不确定性。个别模型并不能一致地很好地再现实验和区域产量，并且在最温暖和最冷的地点不确定性更大。作物模型之间产量预测的差异大于 16 个基于全球气候模型的情景造成的差异。然而，所有作物模型的预测平均值均再现了实验数据，其不确定性小于测量产量的 10%。使用仅针对物候学校准的八个模型或详细校准的五个模型的集合导致的不确定性相当于在良好控制的农艺田间实验中测量的产量的不确定性。敏感性分析表明，有必要提高预测生物量和收获指数的准确性，以应对 [CO2] 和温度的增加。
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.