1. Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments
- Author
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Yulong Wang, Hiroe Yoshida, Liang Tang, Manuel Marcaida, Yuji Masutomi, Donald S. Gaydon, Roberto Confalonieri, Kenneth J. Boote, Hiroshi Nakagawa, Fulu Tao, Philippe Oriol, Lloyd T. Wilson, Yan Zhu, Samuel Buis, Simone Bregaglio, Xinyou Yin, Jeffrey T. Baker, Soora Naresh Kumar, Françoise Ruget, Lianxin Yang, Jianguo Zhu, Job Fugice, Yubin Yang, Upendra Singh, Tao Li, Toshihiro Hasegawa, Zhao Zhang, Tanguy Lafarge, Hitomi Wakatsuki, Daniel Wallach, Tamon Fumoto, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization (NARO), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Centre for Crop Systems Analysis, Wageningen University and Research [Wageningen] (WUR), National Engineering and Technology Center for Information Agriculture, China Agricultural University (CAU), Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, University of Florida [Gainesville] (UF), USDA-ARS : Agricultural Research Service, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cassandra laboratory, University of Milan, International Fertilizer Development Center (IFDC), Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Indian Agricultural Research Institute (IARI), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut National de la Recherche Agronomique (INRA), 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), College of Agriculture, Northeast Agricultural University [Harbin], Muscle Shoals, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Natural resources institute Finland, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Yangzhou University, Texas A&M University System, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences, National Agriculture and Food Research Organization, International Rice Research Institute, Wageningen University and Research Centre [Wageningen] (WUR), Chinese Agricultural University, University of Florida [Gainesville], United States Department of Agriculture - Agricultural Research Service, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), 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), Natural Resources Institute Finland, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Beijing Normal University, Wageningen University and Research Centre [Wageningen] ( WUR ), University of Florida, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria ( CREA ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Avignon et des Pays de Vaucluse ( UAPV ), International Fertilizer Development Center ( IFDC ), Commonwealth Scientific and Industrial Research Organisation, Indian Agricultural Research Institute ( IARI ), UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, AGAP, Université de Montpellier ( UM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de Recherche en Informatique et en Automatique ( Inria ), Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Chinese Academy of Sciences ( CAS ), and Texas A and M AgriLIFE Research Center at Beaumont
- Subjects
010504 meteorology & atmospheric sciences ,lcsh:Medicine ,variabilité du rendement ,diffusion de co2 ,01 natural sciences ,F50 - Anatomie et morphologie des plantes ,Productivité ,modèle de culture ,F01 - Culture des plantes ,Photosynthèse ,lcsh:Science ,Milieux et Changements globaux ,riz ,2. Zero hunger ,Multidisciplinary ,élément fertilisant ,Ecology ,food and beverages ,Feuille ,Surface foliaire ,04 agricultural and veterinary sciences ,PE&RC ,[ SDE.MCG ] Environmental Sciences/Global Changes ,Pratique culturale ,Variation (linguistics) ,Rendement des cultures ,Crops, Agricultural ,Crop Physiology ,Nitrogen ,Climate Change ,Yield (finance) ,comparaison de modèles ,[SDE.MCG]Environmental Sciences/Global Changes ,F60 - Physiologie et biochimie végétale ,Climate change ,Soil science ,Teneur en azote ,Models, Biological ,Article ,Fertilisation ,growth chambers ,Life Science ,chambre de croissance ,Management practices ,0105 earth and related environmental sciences ,atmospheric carbon-dioxide ,climate change ,elevated CO2 ,environmental variation ,leaf-area ,oryza-sativa l ,simulation model ,seasonal change ,crop production ,biomass growth ,Morphologie végétale ,Méthode statistique ,lcsh:R ,Oryza ,Modèle de simulation ,Carbon Dioxide ,Plant Leaves ,F61 - Physiologie végétale - Nutrition ,13. Climate action ,émission d'azote ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Environmental science ,lcsh:Q ,adaptation au changement climatique ,Cycle du carbone - 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.
- Published
- 2017
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