Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

Hasegawa Toshihiro, Li Tao, Yin Xinyou, Zhu Yan, Boote Kenneth J., Baker Jeff, Bregaglio Simone, Buis Samuel, Confalonieri Roberto, Fugice Job, Fumoto Tamon, Gaydon Donald, Kumar Soora Naresh, Lafarge Tanguy, Marcaida Manuel, Masutomi Yuji, Nakagawa Hitochi, Oriol Philippe, Ruget Françoise, Singh Upendra, Tang Liang, Tao Fulu, Wakatsuki Hitomi, Wallach Daniel, Wang Yulong, Wilson Lloyd Ted, Yang Lianxin, Yang Yubin, Yoshida Hiroe, Zhang Zhao, Zhu Jinyu. 2017. Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments. Scientific Reports, 7:14858, 13 p.

Journal article ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total
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Hasegawa 2017 Yield response to CO2 of AgMIP rice models - Scientific Reports.pdf

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Abstract : The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Mots-clés Agrovoc : riz, Photosynthèse, Morphologie végétale, Feuille, Surface foliaire, Teneur en azote, Rendement des cultures, Productivité, Méthode statistique, Modèle de simulation, Pratique culturale, Fertilisation, Cycle du carbone

Mots-clés libres : CO2 fertilization, Rice yield simulation, Inter-model comparisons, AgMIP Rice

Classification Agris : F01 - Crops
F60 - Plant physiology and biochemistry
F50 - Plant structure
F61 - Plant physiology - Nutrition

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

Agence(s) de financement européenne(s) : European Commission

Programme de financement européen : FP7

Projet(s) de financement européen(s) : MODelling vegetation response to EXTREMe Events

Auteurs et affiliations

  • Hasegawa Toshihiro, Tohoku Agricultural Research Center (JPN)
  • Li Tao, IRRI (PHL)
  • Yin Xinyou, Centre for Crop Systems Analysis (NLD)
  • Zhu Yan, National Engineering and Technology Center for Information Agriculture (CHN)
  • Boote Kenneth J., University of Florida (USA)
  • Baker Jeff, ARS (USA)
  • Bregaglio Simone, CREA (ITA)
  • Buis Samuel, INRA (FRA)
  • Confalonieri Roberto, University of Milan (ITA)
  • Fugice Job, IFDC (USA)
  • Fumoto Tamon, NIAES (JPN)
  • Gaydon Donald, CSIRO (AUS)
  • Kumar Soora Naresh, IARI (IND)
  • Lafarge Tanguy, CIRAD-BIOS-UMR AGAP (FRA)
  • Marcaida Manuel, IRRI (PHL)
  • Masutomi Yuji, Ibaraki University (JPN)
  • Nakagawa Hitochi, Ibaraki University (JPN)
  • Oriol Philippe
  • Ruget Françoise, INRA (FRA)
  • Singh Upendra, IFDC (USA)
  • Tang Liang, National Engineering and Technology Center for Information Agriculture (CHN)
  • Tao Fulu, Chinese Academy of Sciences (CHN)
  • Wakatsuki Hitomi, NARO (JPN)
  • Wallach Daniel, INRA (FRA)
  • Wang Yulong, Yangzhou University (CHN)
  • Wilson Lloyd Ted, Texas A&M AgriLife Research (USA)
  • Yang Lianxin, Yangzhou University (CHN)
  • Yang Yubin, Texas A&M AgriLife Research (USA)
  • Yoshida Hiroe, NARO (JPN)
  • Zhang Zhao, Beijing Normal University (CHN)
  • Zhu Jinyu, CAAS (CHN)

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