Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions
Li, Tao | Hasegawa, Toshihiro | Yin, Xinyo | Zhu, Yan | Boote, Kenneth | Adam, Myriam | Bregaglio, Simone | Buis, Samuel | Confalonieri, Roberto | Fumoto, Tamon | Gaydon, Donald | Marcaida, Manuel | Nakagawa, Hiroshi | Oriol, Philippe | Ruane, Alex | Ruget, Francoise | Singh, Balwinder | Singh, Upendra | Tang, Liang | Tao, Fulu | Wilkens, Paul | Yoshida, Hiroe | Zhang, Zhao | Bouman, Bas | International Rice Research Institute [Philippines] (IRRI) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | National Institute of Agro-Environmental Sciences (NIAES) | Centre for Crop Systems Analysis ; Wageningen University and Research [Wageningen] (WUR) | National Engineering and Technology Center for Information Agriculture ; Nanjing Agricutural University | University of Florida [Gainesville] (UF) | Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy( DISAA ) ; Università degli Studi di Milano = University of Milan (UNIMI) | Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH) ; Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Agr Flagship ; Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | National Agriculture and Food Research Organization (NARO) | NASA Goddard Institute for Space Studies (GISS) ; NASA Goddard Space Flight Center (GSFC) | International Maize and Wheat Improvement Centre [Inde] (CIMMYT) ; International Maize and Wheat Improvement Center (CIMMYT) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | International Fertilizer Development Center (IFDC) | Institute of Geographic Sciences and Natural Resources Research (IGSNRR) ; Chinese Academy of Sciences [Changchun Branch] (CAS) | State Key Laboratory of Earth Surface Processes and Resource Ecology ; Beijing Normal University (BNU) | This study was partly financed by the International Rice Research Institute (IRRI) through the Global Rice Science Partnership (GRiSP)
Predicting rice ([i]Oryza sativa[/i]) 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
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