IGSNRR OpenIR  > 历年回溯文献
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
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
Citation statistics
Cited Times:148[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeSCI/SSCI论文
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Li-2015-Uncertaintie(680KB) 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li T.]'s Articles
[Hasegawa, T.]'s Articles
[Yin, X. Y.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li T.]'s Articles
[Hasegawa, T.]'s Articles
[Yin, X. Y.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li T.]'s Articles
[Hasegawa, T.]'s Articles
[Yin, X. Y.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Li-2015-Uncertainties in pre.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.