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Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic ConditionsPredicting 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 percent 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.
Document ID
20160000368
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Li, Tao
(International Rice Research Inst. Manila, Philippines)
Hasegawa, Toshihiro
(National Inst. for Agro-Environmental Sciences (NIAES) Tsukuba, Japan)
Yin, Xinyou
(Wageningen Univ. Wageningen, Netherlands)
Zhu, Yan
(Nanjing Agricultural Univ. China)
Boote, Kenneth
(Florida Univ. Gainesville, FL, United States)
Adam, Myriam
(Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement Montpellier, France)
Bregaglio, Simone
(Milan Univ. Italy)
Buis, Samuel
(Institut National de la Recherche Agronomique Avignon, France)
Confalonieri, Roberto
(Milan Univ. Italy)
Fumoto, Tamon
(National Inst. for Agro-Environmental Sciences (NIAES) Tsukuba, Japan)
Gaydon, Donald
(Commonwealth Scientific and Industrial Research Organization Saint Lucia, Australia)
Marcaida, Manuel, III
(International Rice Research Inst. Manila, Philippines)
Nakagawa, Hiroshi
(National Agriculture and Food Research Organization Tsukuba, Japan)
Oriol, Philippe
(Amelioration genetique et adapatation des plantes mediterraneennes et tropicales (AGAP) Montpellier, France)
Ruane, Alex C.
(NASA Goddard Inst. for Space Studies New York, NY United States)
Ruget, Francoise
(Institut National de la Recherche Agronomique Avignon, France)
Singh, Balwinder
(Indian Council of Agricultural Research New Delhi, India)
Singh, Upendra
(International Fertilizer Development Center Muscle Shoals, AL, United States)
Tang, Liang
(Nanjing Agricultural Univ. China)
Tao, Fulu
(Institute of Geographic Sciences and Natural Resources Research (IGSNRR) Beijing, China)
Wilkens, Paul
(International Fertilizer Development Center Muscle Shoals, AL, United States)
Yoshida, Hiroe
(National Agriculture and Food Research Organization Tsukuba, Japan)
Zhang, Zhao
(Beijing Normal Univ. China)
Bouman, Bas
(International Rice Research Inst. Manila, Philippines)
Date Acquired
January 6, 2016
Publication Date
December 17, 2014
Publication Information
Publication: Global Change Biology
Publisher: Wiley
Volume: 21
Issue: 3 Sprig
Subject Category
Earth Resources And Remote Sensing
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN28876
Funding Number(s)
WBS: WBS 144598.04.01.01.17
Distribution Limits
Public
Copyright
Other
Keywords
crop-model ensembles
Oryza sativa
climate change

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