title: 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. 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.
authors.affiliation: International Rice Research Institute, Los Baños, Philippines.
authors.forename: Tao
authors.initials: T
authors.lastname: Li
authors.forename: Toshihiro
authors.initials: T
authors.lastname: Hasegawa
authors.forename: Xinyou
authors.initials: X
authors.lastname: Yin
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhu
authors.forename: Kenneth
authors.initials: K
authors.lastname: Boote
authors.forename: Myriam
authors.initials: M
authors.lastname: Adam
authors.forename: Simone
authors.initials: S
authors.lastname: Bregaglio
authors.forename: Samuel
authors.initials: S
authors.lastname: Buis
authors.forename: Roberto
authors.initials: R
authors.lastname: Confalonieri
authors.forename: Tamon
authors.initials: T
authors.lastname: Fumoto
authors.forename: Donald
authors.initials: D
authors.lastname: Gaydon
authors.forename: Manuel
authors.initials: M
authors.lastname: Marcaida
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Nakagawa
authors.forename: Philippe
authors.initials: P
authors.lastname: Oriol
authors.forename: Alex C
authors.initials: AC
authors.lastname: Ruane
authors.forename: Françoise
authors.initials: F
authors.lastname: Ruget
authors.forename: Balwinder-
authors.initials: B
authors.lastname: Singh
authors.forename: Upendra
authors.initials: U
authors.lastname: Singh
authors.forename: Liang
authors.initials: L
authors.lastname: Tang
authors.forename: Fulu
authors.initials: F
authors.lastname: Tao
authors.forename: Paul
authors.initials: P
authors.lastname: Wilkens
authors.forename: Hiroe
authors.initials: H
authors.lastname: Yoshida
authors.forename: Zhao
authors.initials: Z
authors.lastname: Zhang
authors.forename: Bas
authors.initials: B
authors.lastname: Bouman
mesh_terms: D000383:Agriculture; D001208:Asia; D002980:Climate; D005523:Food Supply; D008962:Models, Theoretical; D012275:Oryza / Q000254:growth & development*; D012680:Sensitivity and Specificity; D035501:Uncertainty
keywords: AgMIP; Oryza sativa; climate change; crop-model ensembles; yield prediction uncertainty