1. Uncertainties in predicting rice yield by current crop models under contrasting climatic environments, 2014
- Author
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Li, Tao, Hasegawa, Toshihiro, Yin, Xinyou, Zhu, Yan, Boote, Kenneth, Adam, Myriam, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fumoto, Tamon, Gaydon, Donald, Marcaida, Manuel, Nakawaga, Hiroshi, Oriol, Philippe, Ruane, Alex C., Ruget, Francoise, Singh, Balwinder, Singh, Upendra, Tang, Liang, Tao, Fulu, Wilkens, Paul, Yoshida, Hiroe, Zhang, Zhao, Bouman, Bas, International Rice Research Institute, National Institute of Agro-Environmental Sciences (NIAES), National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, University of Florida [Gainesville], Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy( DISAA ), University of Milan, 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 [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], International Maize and Wheat Improvement Centre (CIMMYT), International Fertilizer Development Center (IFDC), Natural Resources Institute Finland, National Agriculture and Food Research Organization, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], and Beijing Normal University (BNU)
- Subjects
climate change ,yield prediction uncertainty ,[SDU]Sciences of the Universe [physics] ,[SDE]Environmental Sciences ,AgMIP ,Oryza sativa ,crop-model ensembles - Abstract
International audience; Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions Abstract Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiologi-cal 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 CO 2 concentration [CO 2 ]. 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 [CO 2 ] and temperature.
- Published
- 2015
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