Publication Abstracts

Li et al. 2015

Li, T., T. Hasegawa, X. Yin, Y. Zhu, K. Boote, M. Adam, S. Bregaglio, S. Buis, R. Confalonieri, T. Fumoto, D. Gaydon, M. Marcaida, III, H. Nakagawa, P. Oriol, A.C. Ruane, F. Ruget, B. Singh, U. Singh, L. Tang, F. Tao, P. Wilkens, H. Yoshida, Z. Zhang, and B. Bouman, 2015: Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob. Change Biol., 21, no. 3, 1328-1341, doi:10.1111/gcb.12758.

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 modelling 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 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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BibTeX Citation

  author={Li, T. and Hasegawa, T. and Yin, X. and Zhu, Y. and Boote, K. and Adam, M. and Bregaglio, S. and Buis, S. and Confalonieri, R. and Fumoto, T. and Gaydon, D. and Marcaida, III, M. and Nakagawa, H. and Oriol, P. and Ruane, A. C. and Ruget, F. and Singh, B. and Singh, U. and Tang, L. and Tao, F. and Wilkens, P. and Yoshida, H. and Zhang, Z. and Bouman, B.},
  title={Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions},
  journal={Glob. Change Biol.},

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RIS Citation

ID  - li00700s
AU  - Li, T.
AU  - Hasegawa, T.
AU  - Yin, X.
AU  - Zhu, Y.
AU  - Boote, K.
AU  - Adam, M.
AU  - Bregaglio, S.
AU  - Buis, S.
AU  - Confalonieri, R.
AU  - Fumoto, T.
AU  - Gaydon, D.
AU  - Marcaida, M., III
AU  - Nakagawa, H.
AU  - Oriol, P.
AU  - Ruane, A. C.
AU  - Ruget, F.
AU  - Singh, B.
AU  - Singh, U.
AU  - Tang, L.
AU  - Tao, F.
AU  - Wilkens, P.
AU  - Yoshida, H.
AU  - Zhang, Z.
AU  - Bouman, B.
PY  - 2015
TI  - Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions
JA  - Glob. Change Biol.
VL  - 21
IS  - 3
SP  - 1328
EP  - 1341
DO  - 10.1111/gcb.12758
ER  -

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