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Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions
Li T.; Hasegawa, T.; Yin, X. Y.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida, M.; Nakagawa, H.; Oriol, P.; Ruane, A. C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Tao, F. L.; Wilkens, P.; Yoshida, H.; Zhang, Z.; Bouman, B.
Source PublicationGlobal Change Biology
2015
Volume21
Issue3
Pages1328-1341
KeywordAgmip Climate Change Crop-model Ensembles Oryza Sativa Yield Prediction Uncertainty Air Co2 Enrichment High-temperature Stress Elevated Co2 Spikelet Fertility Night Temperature Carbon-dioxide Growth Sterility Face Productivity
AbstractPredicting 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.
Indexed BySCI
Language英语
ISSN1354-1013
DOI10.1111/gcb.12758
Citation statistics
Cited Times:127[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeSCI/SSCI论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/38723
Collection历年回溯文献
Recommended Citation
GB/T 7714
Li T.,Hasegawa, T.,Yin, X. Y.,et al. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. 2015.
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