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2. A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

Author

Confalonieri, Roberto, Bregaglio, Simone, Adam, Myriam, Ruget, Françoise, Li, Tao, Hasegawa, Toshihiro, Yin, Xinyou, Zhu, Yan, Boote, Kenneth, Buis, Samuel, Fumoto, Tamon, Gaydon, Donald, Lafarge, Tanguy, Marcaida, Manuel, Nakagawa, Hiroshi, Ruane, Alex C., Singh, Balwinder, Singh, Upendra, Tang, Liang, Tao, Fulu, Fugice, Job, Yoshida, Hiroe, Zhang, Zhao, Wilson, Lloyd T., Baker, Jeff, Yang, Yubin, Masutomi, Yuji, Wallach, Daniel, Acutis, Marco, and Bouman, Bas

Published
2016
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4. Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

Author

Li, Tao, Hasegawa, Toshihiro, Yin, Xinyou, Zhu, Yan, Boote, Kenneth, Adam, Myriam, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fumoto, Tamon, Gaydon, Donald, Marcaida, Manuel, III, Nakagawa, Hiroshi, Oriol, Philippe, Ruane, Alex C, Ruget, Francoise, Singh, Balwinder, Singh, Upendra, Tang, Liang, Tao, Fulu, Wilkens, Paul, Yoshida, Hiroe, Zhang, Zhao, and Bouman, Bas

Subjects
Earth Resources And Remote Sensing, Meteorology And Climatology
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 percent 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.

Published
2014
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5. Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

Author

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, 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, and Zhu, Jinyu

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

6. Uncertainties in predicting rice yield by current crop models under contrasting climatic environments, 2014

Author

Li, Tao, Hasegawa, Toshihiro, Yin, Xinyou, Zhu, Yan, Boote, Kenneth, Adam, Myriam, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fumoto, Tamon, Gaydon, Donald, Marcaida, Manuel, Nakawaga, Hiroshi, Oriol, Philippe, Ruane, Alex C., Ruget, Francoise, Singh, Balwinder, Singh, Upendra, Tang, Liang, Tao, Fulu, Wilkens, Paul, Yoshida, Hiroe, Zhang, Zhao, Bouman, Bas, International Rice Research Institute, National Institute of Agro-Environmental Sciences (NIAES), National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, University of Florida [Gainesville], Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy( DISAA ), University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agr Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], International Maize and Wheat Improvement Centre (CIMMYT), International Fertilizer Development Center (IFDC), Natural Resources Institute Finland, National Agriculture and Food Research Organization, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-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), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], and Beijing Normal University (BNU)

Subjects
climate change, yield prediction uncertainty, [SDU]Sciences of the Universe [physics], [SDE]Environmental Sciences, AgMIP, Oryza sativa, crop-model ensembles
Abstract

International audience; 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. Ecophysiologi-cal 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 CO 2 concentration [CO 2 ]. 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 [CO 2 ] and temperature.

Published
2015
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7. Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation

Author

Li, Tao, Yin, Xinyou, Hasegawa, Toshihiro, Boote, Ken, Zhu, Yan, Adam, Myriam, Baker, Jeff, Bouman, Bas, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fugice, Job, Fumoto, Tamon, Gaydon, Donald, Kumar, S.N., Lafarge, Tanguy, Marcaida, Manuel, Masutomi, Y., Nakagawa, Hitochi, Pequeno, D.N.L., Ruane, Alex C., Ruget, Françoise, Singh, Upendra, Tang, Liang, Tao, Fulu, Wallach, Daniel, Wilson, Lloyd Ted, Yang, Yubin, Yoshida, Hiroe, Zhang, Zhao, Zhu, Jinyu, Li, Tao, Yin, Xinyou, Hasegawa, Toshihiro, Boote, Ken, Zhu, Yan, Adam, Myriam, Baker, Jeff, Bouman, Bas, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fugice, Job, Fumoto, Tamon, Gaydon, Donald, Kumar, S.N., Lafarge, Tanguy, Marcaida, Manuel, Masutomi, Y., Nakagawa, Hitochi, Pequeno, D.N.L., Ruane, Alex C., Ruget, Françoise, Singh, Upendra, Tang, Liang, Tao, Fulu, Wallach, Daniel, Wilson, Lloyd Ted, Yang, Yubin, Yoshida, Hiroe, Zhang, Zhao, and Zhu, Jinyu

Published
2016

13. Creencias y ritos funerarios en Meñaka

Author

Marcaida, Manuel de and Marcaida, Manuel de

Abstract

Artículo que expone como se ha expresado y sentido la muerte en Meñaka, es decir, que costumbres y ritos se han seguido frente al hecho de la muerte., Meñakan heriotza nola adierazi eta sentitu den azaltzen duen artikulua, hau da, heriotzaren aurrean jarraitutako ohiturak eta errituak.

Published
2011

14. La religiosidad del pueblo : Meñaka

Author

Marcaida, Manuel de and Marcaida, Manuel de

Abstract

Recopilación de datos sobre costumbres y organización eclesiástica de Meñaka y que han sido recopilados en este trabajo de campo., Meñakako eliz-ohituren eta eliza antolakuntzaren datu-bilduma. Landa lan honetan bildu dira.

Published
2011

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

Author

Li, Tao, Hasegawa, Toshihiro, Yin, Xinyou, Zhu, Yan, Boote, Kenneth, Adam, Myriam, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fumoto, Tamon, Gaydon, Donald, Marcaida, Manuel, Nakagawa, Hiroshi, Oriol, Philippe, Ruane, Alex C., Ruget, Françoise, Singh, Balwinder‐, Singh, Upendra, Tang, Liang, and Tao, Fulu

Subjects
RICE yields, PLANT ecophysiology, PREDICTION models, HARVESTING, RICE, AGRICULTURAL climatology, SPATIO-temporal variation, SENSITIVITY analysis
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. [ABSTRACT FROM AUTHOR]

Published
2015
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17. Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation

Author

Li, Tao, Yin, Xinyou, Hasegawa, Toshihiro, Boote, Ken, Zhu, Yan, Myriam ADAM, Baker, Jeff, Bouman, Bas, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fugice, Job, Fumoto, Tamon, Gaydon, Donald, Kumar, S. N., Lafarge, Tanguy, Marcaida, Manuel, Masutomi, Y., Nakagawa, Hitochi, Pequeno, D. N. L., Ruane, Alex C., Ruget, Françoise, Singh, Upendra, Tang, Liang, Tao, Fulu, Wallach, Daniel, Wilson, Lloyd Ted, Yang, Yubin, Yoshida, Hiroe, Zhang, Zhao, Zhu, Jinyu, International Rice Research Institute, Centre for Crop Systems Analysis, Wageningen University and Research Centre [Wageningen] (WUR), National Institute of Agro-Environmental Sciences (NIAES), University of Florida [Gainesville], National Engineering and Technology Center for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), WCA-Resilient Dryland Systems, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Agricultural Research Service, United States Department of Agriculture, Cassandra lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Muscle Shoals, International Fertilizer Development Center (IFDC), CSIRO, Indian Agricultural Research Institute (IARI), College of Agriculture, Northeast Agricultural University [Harbin], National Agriculture and Food Research Organization, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A&M University System, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences [Changchun Branch] (CAS), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Wageningen University and Research [Wageningen] (WUR), University of Florida [Gainesville] (UF), 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), International Crops Research Institute for the Semi-Arid Tropics [Inde] (ICRISAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), USDA-ARS : Agricultural Research Service, National Agriculture and Food Research Organization (NARO), 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, Beijing Normal University (BNU), ProdInra, Archive Ouverte, Wageningen University and Research Centre [Wageningen] ( WUR ), National Institute for Agro-Environmental Sciences, University of Florida, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), International Crops Research Institute for the Semi-Arid Tropics ( ICRISAT ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), International Fertilizer Development Center ( IFDC ), Indian Agricultural Research Institute ( IARI ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Chinese Academy of Sciences [Beijing] ( CAS ), Texas A and M AgriLIFE Research Center at Beaumont, and Chinese Academy of Sciences [Changchun Branch] ( CAS )

Subjects
production rizicole, [ SDV ] Life Sciences [q-bio], U10 - Informatique, mathématiques et statistiques, P40 - Météorologie et climatologie, F60 - Physiologie et biochimie végétale, crop model, rice, flux de co2, [SDV]Life Sciences [q-bio], education, food and beverages, F62 - Physiologie végétale - Croissance et développement, humanities, fertilisation, [SDV] Life Sciences [q-bio], high temperature, croissance des graines, calibrage du modèle, F01 - Culture des plantes, fertilization, haute température, mesure par chambre, biomasse aérienne, reproductive and urinary physiology, health care economics and organizations
Abstract

Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation . International Crop Modelling Symposium

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