Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Li Tao, Hasegawa Toshihiro, Yin Xinyou, Zhu Yan, Boote Kenneth J., Adam Myriam, Bregaglio Simone, Buis Samuel, Confalonieri Roberto, Fumoto Tamon, Gaydon Donald, Marcaida Manuel, Nakagawa Hitochi, Oriol Philippe, Ruane Alex C., Ruget Françoise, Singh Balwinder, Singh Upendra, Tang Liang, Tao Fulu, Wilkens Paul, Yoshida Hiroe, Zhang Zhao, Bouman Bas. 2015. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Global Change Biology, 21 (3) : pp. 1328-1341.

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Quartile : Outlier, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Outlier, Sujet : BIODIVERSITY CONSERVATION / Quartile : Outlier, Sujet : ECOLOGY

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. (Résumé d'auteur)

Mots-clés Agrovoc : Oryza sativa, Rendement des cultures, Modélisation des cultures, Facteur climatique, Changement climatique, Température, Dioxyde de carbone

Mots-clés géographiques Agrovoc : Chine, Philippines, Inde, Japon

Classification Agris : F01 - Crops
P40 - Meteorology and climatology
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

  • Li Tao, IRRI (PHL)
  • Hasegawa Toshihiro, NIAES (JPN)
  • Yin Xinyou, Wageningen University (NLD)
  • Zhu Yan, National Engineering and Technology Center for Information Agriculture (CHN)
  • Boote Kenneth J., University of Florida (USA)
  • Adam Myriam, CIRAD-BIOS-UMR AGAP (BFA) ORCID: 0000-0002-8873-6762
  • Bregaglio Simone, University of Milan (ITA)
  • Buis Samuel, INRA (FRA)
  • Confalonieri Roberto, University of Milan (ITA)
  • Fumoto Tamon, NIAES (JPN)
  • Gaydon Donald, CSIRO (AUS)
  • Marcaida Manuel, IRRI (PHL)
  • Nakagawa Hitochi, NARO (JPN)
  • Oriol Philippe, CIRAD-BIOS-UMR AGAP (FRA)
  • Ruane Alex C., NASA (USA)
  • Ruget Françoise, INRA (FRA)
  • Singh Balwinder, CIMMYT (IND)
  • Singh Upendra, IFDC (USA)
  • Tang Liang, National Engineering and Technology Center for Information Agriculture (CHN)
  • Tao Fulu, Chinese Academy of Sciences (CHN)
  • Wilkens Paul, IFDC (USA)
  • Yoshida Hiroe, NARO (JPN)
  • Zhang Zhao, Beijing Normal University (CHN)
  • Bouman Bas, IRRI (PHL)

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