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1. Pour un portail INRAE d’information sur les impacts et l’adaptation au changement climatique de l’agriculture, la forêt et les ressources en eau. Cadrage, état des lieux et propositions

Author: Gascuel, Chantal, Patrick, Bertuzzi, Allard, Denis, Raynal, Helene, and Raynal, Hélène
Subjects: changement climatique, [SDE.MCG] Environmental Sciences/Global Changes, agriculture
Published: 2021

2. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Author: Elizabeth A. Meier, Isaac N. Alou, Eckart Priesack, Bruno Basso, Edward Gérardeaux, Heidi Webber, Eric Justes, Michel Giner, Saseendran S. Anapalli, Delphine Deryng, Marcelo Valadares Galdos, Alex C. Ruane, Bouba Sidi Traoré, Dominique Ripoche, Ward Smith, Babacar Faye, Thomas Gaiser, Patrick Bertuzzi, Folorunso M. Akinseye, Dilys S. MacCarthy, Frédéric Baudron, Alain Ndoli, Brian Grant, Claas Nendel, Kenneth J. Boote, Bernardo Maestrini, Louise Leroux, Christian Baron, Tracy E. Twine, Kokou Adambounou Amouzou, Upendra Singh, Sumit Sinha, Amit Kumar Srivastava, Yi Chen, Michael van der Laan, Gerrit Hoogenboom, Marc Corbeels, Dennis Timlin, M. Elsayed, Anthony M. Whitbread, Fulu Tao, Soo-Hyung Kim, Tesfaye Shiferaw Sida, Bahareh Kamali, Jon I. Lizaso, Myriam Adam, Kurt Christian Kersebaum, Peter J. Thorburn, François Affholder, Esther S. Ibrahim, Andrew J. Challinor, Sebastian Gayler, Lajpat R. Ahuja, Gatien N. Falconnier, Cheryl Porter, Fasil Mequanint, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), University of Florida [Gainesville] (UF), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), University of Ghana, GISS Climate impacts group, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC)-NASA Goddard Space Flight Center (GSFC), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), International Crops Research Institute for the Semi-Arid Tropics [Niger] (ICRISAT), 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), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Département Environnements et Sociétés (Cirad-ES)
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, Mali, 01 natural sciences, exploitant agricole, smallholder farming systems, Leaching (agriculture), uncertainty, General Environmental Science, 2. Zero hunger, Global and Planetary Change, Biomass (ecology), Ecology, U10 - Informatique, mathématiques et statistiques, Rendement des cultures, model intercomparison, Fertilizer, Crop simulation model, crop simulation model, Nitrogen, P40 - Météorologie et climatologie, Climate Change, Climate change, engineering.material, 010603 evolutionary biology, Zea mays, Petite exploitation agricole, ensemble modelling, Environmental Chemistry, Leaf area index, Fertilizers, 0105 earth and related environmental sciences, Changement climatique, Agriculture faible niveau intrants, Nutrient management, Modélisation des cultures, Engrais azoté, Modèle de simulation, 15. Life on land, Agronomy, 13. Climate action, Soil water, engineering, Système d'exploitation agricole, Environmental science, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract: International audience; Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.
Published: 2020
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3. Conceptual basis, formalisations and parameterization of the STICS crop model, second edition

Author: Nicolas Beaudoin, Dominique Ripoche, Strullu, L., Bruno Mary, Marie Launay, Joël Léonard, Patrice Lecharpentier, François Affholder, Patrick Bertuzzi, Samuel Buis, Eric Casellas, Julie Constantin, Dumont, B., Jean-Louis Durand, Inaki Garcia de Cortazar Atauri, Fabien Ferchaud, Anne-Isabelle Graux, Jego, G., Christine Le Bas, Florent Levavasseur, Gaétan Louarn, Alain Mollier, Francoise Ruget, Eric Justes, Transfrontalière BioEcoAgro (Transfrontalière BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Association pour le Suivi Agronomique des Epandages (ASAE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST, 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agriculture and Agri-Food [Ottawa] (AAFC), InfoSol (InfoSol), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, 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), and Agriculture and Agri-Food (AAFC)
Subjects: [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2020

4. AgGlob: Workflow for simulation of agronomic models at a global scale

Author: RAYNAL, Helene, Le Bas, Christine, Patrick, Bertuzzi, Cahuzac, Eric, Eric, Casellas, Constantin, Julie, Thomas, Pomeon, Toutain, Benoit, Raynal, Hélène, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), InfoSol (InfoSol), Agroclim (AGROCLIM), Département Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: [SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy, parallel computing, Global simulation, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Crop modeling, Computational modeling, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2020

5. The PERPHECLIM ACCAF Project – perennial fruit crops and forest phenology evolution facing climatic changes

Author: C. Van Leeuween, I. Garcia de Cortazar-Atauri, Jean-Michel Legave, Helene Raynal, C. Anger, Hendrik Davi, Christian Pichot, Jean-Marc Audergon, Isabelle Chuine, Marc Bonhomme, Eric Duchêne, Patrick Bertuzzi, Sylvain Delzon, Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Ecophysiologie et Génomique Fonctionnelle de la Vigne (UMR EGFV), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Génétique et Amélioration des Fruits et Légumes (GAFL), Génétique et Biomasse Forestières Orléans (GBFOR), Laboratoire de Physique et Physiologie Intégratives de l'Arbre Fruitier et Forestier (PIAF), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Ecologie des Forêts Méditerranéennes (URFM)
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, Perennial plant, Climate change, Context (language use), Information System, Horticulture, 010603 evolutionary biology, 01 natural sciences, Process Based Models, Databases, Documentation, Information system, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Adaptation (computer science), Phenological Stages, 0105 earth and related environmental sciences, 2. Zero hunger, business.industry, Phenology, Environmental resource management, 15. Life on land, Geography, 13. Climate action, Agriculture, business, Protocols
Abstract: International audience; Phenology is a bio-indicator of climate evolution. Measurements of phenological stages on perennial species provide actually significant illustrations and assessments of the impact of climate change. Phenology is also one of the main key characteristics of the capacity of adaptation of perennial species, generating questions about its consequences on plant growth and development or on fruit quality. Predicting phenology evolution and adaptive capacities of perennial species needs to override three main methodological limitations: 1) existing observations and associated databases are scattered and sometimes incomplete, rendering difficult implementation of multi-site study of genotype-environment interaction analyses 2) there are not common protocols to observe phenological stages 3) access to generic phenological models platforms is still very limited. In this context, the PERPHECLIM project, which is funded by the Adapting Agriculture and Forestry to Climate Change Meta-Program ( ACCAF) from INRA ( French National Institute of Agronomic Research), aims to develop the necessary infrastructure at INRA level ( observatories, information system, modeling tools) to enable partners to study the phenology of various perennial species ( grapevine, fruit trees and forest trees). Currently, the PERPHECLIM project involves 28 research units in France, mainly from INRA institutes. Five activities have been developed: define protocols and observation forms to observe phenology for various species of interest for the project : organize observation training, develop generic modeling solutions to simulate phenology ( Phenological Modelling Platform software and modelling platform solutions), support the building of research projects at national and international levels, develop environment/genotype observation networks for fruit-tree species, and develop an information system to manage data and documentation concerning phenology. Finally, the PERPHECLIM project aims to build strong collaborations with public ( Observatoire des Saisons) and private ( technical institutes) sector partners in order to allow a more direct transfer of knowledge.
Published: 2018
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6. Simulation of maize evapotranspiration: An inter-comparison among 29 maize models

Author: Amit Kumar Srivastava, Sotirios V. Archontoulis, Qianjing Jiang, Kenneth J. Boote, Delphine Deryng, Sophie Moulin, Jean-Louis Durand, Munir P. Hoffmann, Lajpat R. Ahuja, Sebastian Gayler, Jerry L. Hatfield, Philip Parker, Thomas Gaiser, Dennis Timlin, Tracy E. Twine, Claudio O. Stöckle, Benjamin Dumont, Jon I. Lizaso, Claas Nendel, Soo-Hyung Kim, Kelly R. Thorp, Karina Williams, Christian Baron, Tommaso Stella, Zhiming Qi, Bruno Basso, Heidi Webber, F. Ewert, Taru Palosuo, Fulu Tao, Magali Willaume, Eckart Priesack, Julie Constantin, Patrick Bertuzzi, Bruce A. Kimball, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville] (UF), National laboratory for agriculture and the environment, Agricultural Systems Research Unit, USDA, Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Université de Montpellier (UM), Michigan State University [East Lansing], Michigan State University System, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), 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, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), IRI THESys, Humboldt State University (HSU), Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institute of Crop Science and Resource Conservation [Bonn], Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Department of Bioresource Engineering, McGill University = Université McGill [Montréal, Canada], School of Environmental and Forest Sciences, University of Washington [Seattle], Technical University of Madrid, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Spatial Business Integration, Partenaires INRAE, Natural Resources Institute Finland (LUKE), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Institute of Geographic Sciences and Natural Resources Research, CAS, Crop Systems and Global Change Research Unit, Met Office Hadley Centre, (BMBF, Germany) FKZ031A258B, and German Federal Ministry of Education and Research 01LL1304A
Subjects: 0106 biological sciences, Atmospheric Science, Yield, 010504 meteorology & atmospheric sciences, maïs, F60 - Physiologie et biochimie végétale, Crop water use, Eddy covariance, Zea mays, 01 natural sciences, Maize, Simulation, Evapotranspiration, Water Use, Model, Statistics, Range (statistics), [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Squared deviations, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Global and Planetary Change, U10 - Informatique, mathématiques et statistiques, Phenology, Forestry, Évapotranspiration, 13. Climate action, Besoin en eau, Agronomy and Crop Science, Modèle mathématique, Water use, 010606 plant biology & botany
Abstract: International audience; Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006-2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET.
Published: 2019
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7. How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

Author: Patrick Bertuzzi, Jon I. Lizaso, Jean-Louis Durand, Dennis Timlin, Julián Ramírez Villegas, Fulu Tao, Kurt Christian Kersebaum, Sabine I. Seidel, Lajpat R. Ahuja, Christoph Müller, Delphine Deryng, Amit Kumar Srivastava, Bruno Basso, James W. Jones, Heidi Webber, F. Ewert, Dominique Ripoche, Eckart Priesack, Christian Biernath, Cynthia Rosenzweig, Remy Manderscheid, Alex C. Ruane, Hans Johachim Weigel, Thomas Gaiser, Christian Baron, Claas Nendel, Tracy E. Twine, Enli Wang, Kenneth J. Boote, Saseendran S. Anapalli, Soo-Hyung Kim, Zhigan Zhao, Sebastian Gayler, Florian Heinlein, Albert Olioso, Reimund P. Rötter, Kenel Delusca, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), CEIGRAM, Technical University of Madrid, Johann Heinrich von Thünen Institut, NASA Goddard Space Flight Center (GSFC), CPSRU, USDA-ARS : Agricultural Research Service, Department of Geological Sciences, University of Oregon [Eugene], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agroclim (AGROCLIM), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Computation Institute, Loyola University of Chicago, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, Section Biogeophysics, University of Hohenheim, School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), School of Earth and Environment (UWA), The University of Western Australia (UWA), International Center for Tropical Agriculture [Colombie] (CIAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Natural resources institute Finland, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Laboratory, Department of Soil, Water, & Climate, University of Minnesota System, Land and Water, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), China Agricultural University (CAU), University of Florida [Gainesville], Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), Institute of Crop Science and Resource Conservation (INRES), CGIAR Research Program on Climate Change Colombia International Center for Tropical Agriculture (CIAT), Agriculture and Food Security (CCAFS), Natural Resources Institute Finland, China Agricultural University, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, Water supply, Plant Science, 01 natural sciences, modèle de culture, Atmospheric carbon dioxide concentration, Evapotranspiration, Zea Mays, Atmospheric Carbon Dioxide Concentration, Multi-model Ensemble, Stomata Conductance, Grain Number, Water Use, Photosynthèse, Transpiration, 2. Zero hunger, Multi-model ensemble, U10 - Informatique, mathématiques et statistiques, 04 agricultural and veterinary sciences, Rendement des cultures, Stomatal conductance, Irrigation, Grain number, Soil Science, approvisionnement eau, Zea mays, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Leaf area index, weather data, 0105 earth and related environmental sciences, carbonic anhydride, business.industry, culture de mais, Modèle de simulation, 15. Life on land, Évapotranspiration, donnée météorologique, F61 - Physiologie végétale - Nutrition, Agronomy, 13. Climate action, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, business, estimation de rendement, Agronomy and Crop Science, Water use, concentration atmosphérique, Dioxyde de carbone
Abstract: Conference: International Crop Modelling Symposium on Crop Modelling for Agriculture and Food Security under Global Change (iCropM) - Proceedings Paper Berlin, GERMANY 2016; This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.
Published: 2017
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8. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models

Author: Kimball, Bruce A., Boote, Kenneth J., Hatfield, Jerry L., Ahuja, Laj R., Stöckle, Claudio O., Archontoulis, Sotiris V., Christian Baron, Bruno Basso, Patrick Bertuzzi, Julie Constantin, Delphine Deryng, Benjamin Dumont, Franck Ewert, Thomas Gaiser, Griffis, Timothy J., Hoffmann, Munir P., Qianjing Jiang, Soo-Hyung Kim, Jon Lizaso, Sophie Moulin, Philip Parker, Taru Palusuo, Zhiming Qi Z., Amit Srivastava, Tao, F., Thorp, K., Dennis Timlin, Heidi Webber, Magali Willaume, Williams, K., Ming Chen, Jean-Louis Durand, Sebastian Gayler, Eckart Priesack, Tracy Twine, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville] (UF), Agricultural Systems Research Unit, USDA, Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), 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, Computation Institute, Loyola University of Chicago, Dpt. Agronomy, Bio- Engineering and Chemistry, Crop Science Unit, Université de Liège, Gembloux Agro-Bio Tech [Gembloux], Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Soil, Water, and Climate, University of Minnesota System, Crop Production Systems in the Tropics, Georg-August-University [Göttingen], Department of Bioresource Engineering [Montréal] (BIOENG), McGill University = Université McGill [Montréal, Canada], Center for Urban Horticulture, University of Washington, Dept. Producción Agraria-CEIGRAM, Universidad Politécnica de Madrid (UPM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Natural resources institute Finland, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Research Unit, Climate Adaptation Scientist Meteorological Office, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Soil, Water and Climate, University of Florida [Gainesville], Agricultural Research Service, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Georg-August-Universität Göttingen, McGill University, Natural Resources Institute Finland, and United States Department of Agriculture - Agricultural Research Service
Subjects: consommation en eau, comparaison de modèles, [SDV]Life Sciences [q-bio], evapotranspiration, évapotranspiration, culture de mais, croissance des cultures, analyse de rendement, modèle de croissance, caracteristique variétale
Abstract: An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 29 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved somewhat in later stages when the modelers were provided additional leaf area and growth information that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc.
Published: 2016

9. Multi-wheat-model ensemble responses to interannual climate variability

Author: Carlos Angulo, Frank Ewert, Tom M. Osborne, Pramod K. Aggarwal, Senthold Asseng, Pasquale Steduto, Kurt Christian Kersebaum, Eckart Priesack, Patrick Bertuzzi, Roberto C. Izaurralde, Dominique Ripoche, Thilo Streck, Joost Wolf, Pierre Stratonovitch, Alex C. Ruane, Richard Goldberg, Robert F. Grant, Taru Palosuo, Iurii Shcherbak, Kenneth J. Boote, Christian Biernath, Garry O'Leary, J. Hooker, Peter J. Thorburn, Joachim Ingwersen, Soora Naresh Kumar, Lee Heng, Maria I. Travasso, Pierre Martre, Katharina Waha, Nicholas I. Hudson, Claas Nendel, Fulu Tao, Christoph Müller, Andrew J. Challinor, Jørgen E. Olesen, Reimund P. Rötter, Davide Camarrano, L. A. Hunt, Sebastian Gayler, Nadine Brisson, Daniel Wallach, Mikhail A. Semenov, Claudio O. Stöckle, Iwan Supit, Jordi Doltra, Jeffrey W. White, Bruno Basso, 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], Agricultural & Biological Engineering Department, University of Florida [Gainesville], The James Hutton Institute, Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), 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), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, International Water Management Institute, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Cantabrian Agricultural Research and Training Centre, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Potsdam Institute for Climate Impact Research (PIK), Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University, National Centre for Atmospheric Science, Department of Meteorology, Environmental Impacts Group, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Institute for Future Environments, Queensland University of Technology, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Earth System Science-Climate Change and Adaptive Land-use and Water Management, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water [Castelnar], Instituto Nacional de Tecnología Agropecuaria (INTA), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, USDA-ARS, Arid-Land Agricultural Research Center, United States Department of Agriculture, Plant Production Systems, Modelling European Agriculture with Climate Change for Food Security (MACSUR), JPI FACCE, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn], 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), Agroclim (AGROCLIM), Aarhus University [Aarhus], Natural resources institute Finland, Georg-August-University [Göttingen], Wageningen University and Research [Wageningen] (WUR), 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, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Biotechnology and Biological Sciences Research Council (BBSRC), Queensland University of Technology [Brisbane] (QUT), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Université de Toulouse (UT)-Université de Toulouse (UT), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Center for Climate Systems Research [New York] ( CCSR ), University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Leibniz Centre for Agricultural Landscape Research (ZALF), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), 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 ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Commonwealth Scientific and Industrial Research Organisation, Department of Geological Sciences and Kellogg Biological Station, Michigan State University, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), University of Maryland, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Potsdam Institute for Climate Impact Research ( PIK ), Washington State University ( WSU ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Institute for Climate and Water, and Instituto Nacional de Tecnología Agropecuaria
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Temperature sensitivity, 010504 meteorology & atmospheric sciences, Precipitation, 01 natural sciences, modèle de croissance, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, climate impacts, 2. Zero hunger, Multi-model ensemble, changement climatique, Ecological Modeling, Temperature, Uncertainty, 04 agricultural and veterinary sciences, PE&RC, Plant Production Systems, Climatology, PRECIPITATION, Climate impacts, Environmental Engineering, interannual variability, Yield (finance), australie, variation interannuelle, Growing season, Climate change, multi-model ensemble, Earth System Science, Interannual variability, blé, température, pays bas, global change, 0105 earth and related environmental sciences, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, précipitation, business.industry, argentine, Simulation modeling, temperature, Global change, 15. Life on land, inde, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, 040103 agronomy & agriculture, AgMIP, 0401 agriculture, forestry, and fisheries, Environmental science, Leerstoelgroep Aardsysteemkunde, Crop modeling, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, Software
Abstract: We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2???0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Compares interannual climate response of 27 wheat models at four locations.Calculates the diminishing return of constructing multi-model ensembles for assessment.Identifies similarities and major differences of model responses.Differentiates between interannual temperature sensitivity and climate change response.
Published: 2016
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10. The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Author: Sonali P. McDermid, Alexander C. Ruane, Cynthia Rosenzweig, Nicholas I. Hudson, Monica D. Morales, Prabodha Agalawatte, Shakeel Ahmad, L. R. Ahuja, Istiqlal Amien, Saseendran S. Anapalli, Jakarat Anothai, Senthold Asseng, Jody Biggs, Federico Bert, Patrick Bertuzzi, Virender S. Bhatia, Marco Bindi, Ian Broad, Davide Cammarano, Ramiro Carretero, Ashfaq Ahmad Chattha, Uran Chung, Stephanie Debats, Paola Deligios, Giacomo De Sanctis, Thanda Dhliwayo, Benjamin Dumont, Lyndon Estes, Frank Ewert, Roberto Ferrise, Thomas Gaiser, Guillermo Garcia, Sika Gbegbelegbe, Vellingiri Geethalakshmi, Edward Gerardeaux, Richard Goldberg, Brian Grant, Edgardo Guevara, Jonathan Hickman, Holger Hoffmann, Huanping Huang, Jamshad Hussain, Flavio Barbosa Justino, Asha S. Karunaratne, Ann-Kristin Koehler, Patrice K. Kouakou, Soora Naresh Kumar, Arunachalam Lakshmanan, Mark Lieffering, Xiaomao Lin, Qunying Luo, Graciela Magrin, Marco Mancini, Fabio Ricardo Marin, Anna Dalla Marta, Yuji Masutomi, Theodoros Mavromatis, Greg McLean, Santiago Meira, Monoranjan Mohanty, Marco Moriondo, Wajid Nasim, Lamyaa Negm, Francesca Orlando, Simone Orlandini, Isik Ozturk, Helena Maria Soares Pinto, Guillermo Podesta, Zhiming Qi, Johanna Ramarohetra, Muhammad Habib ur Rahman, Helene Raynal, Gabriel Rodriguez, Reimund Rötter, Vaishali Sharda, Lu Shuo, Ward Smith, Val Snow, Afshin Soltani, K. Srinivas, Benjamin Sultan, Dillip Kumar Swain, Fulu Tao, Kindie Tesfaye, Maria I. Travasso, Giacomo Trombi, Alex Topaj, Eline Vanuytrecht, Federico E. Viscarra, Syed Aftab Wajid, Enli Wang, Hong Wang, Jing Wang, Erandika Wijekoon, Lee Byun-Woo, Yang Xiaoguang, Ban Ho Young, Jin I. Yun, Zhigan Zhao, and Lareef Zubair
Subjects: P40 - Météorologie et climatologie, U10 - Informatique, mathématiques et statistiques, business.industry, Impact assessment, Crop yield, Environmental resource management, Climate change, Water resources, F01 - Culture des plantes, Agriculture, Soil retrogression and degradation, Environmental science, Climate model, business, Environmental planning, Downscaling
Abstract: Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...
Published: 2015

11. Multimodel ensembles of wheat growth: many models are better than one

Author: Pierre Martre, Bruno Basso, James W. Jones, Jordi Doltra, Garry O'Leary, Pramod K. Aggarwal, Christian Biernath, Jeffrey W. White, Sebastian Gayler, R. Goldberg, Eckart Priesack, Robert F. Grant, Nadine Brisson, Patrick Bertuzzi, Thilo Streck, Daniel Wallach, Joachim Ingwersen, Davide Cammarano, J. Hooker, Fulu Tao, Christoph Müller, Carlos Angulo, Soora Naresh Kumar, Claas Nendel, Jørgen E. Olesen, Lee Heng, Maria I. Travasso, Iurii Shcherbak, Mikhail A. Semenov, Claudio O. Stöckle, Tom M. Osborne, L. A. Hunt, Alex C. Ruane, Frank Ewert, Kenneth J. Boote, Andrew J. Challinor, Reimund P. Rötter, Iwan Supit, Jerry L. Hatfield, Roberto C. Izaurralde, Senthold Asseng, Cynthia Rosenzweig, Pasquale Steduto, Kurt Christian Kersebaum, Dominique Ripoche, Peter J. Thorburn, Pierre Stratonovitch, Joost Wolf, Katharina Waha, Taru Palosuo, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Plant Production Research, Agrifood Research Finland, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), National Laboratory for Agriculture and Environment, Consultative Group on International Agricultural Research, Research Program on ClimateChange, Agriculture and Food Security, International Water Management Institute, Department of Geological Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, German Research Center for Environmental Health, Helmholtz-Zentrum München (HZM), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Cantabrian Agricultural Research and Training Centre, Water & Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, Universität Stuttgart, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Primary Industries, Landscape & Water Sciences, Université Paris Diderot - Paris 7 (UPD7), Department of Agroecology, Aarhus University [Aarhus], National Centre for Atmospheric Science, Department of Meteorology, Institute of Soil Ecology German Research Center for Environmental Health, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization of the United Nations, Washington State University (WSU), University of Hohenheim, Wageningen University and Research Centre [Wageningen] (WUR), Institute Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), CIRN, Institute forClimate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Arid-Land Agricultural Research Center, United States Department of Agriculture, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn], Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Institut National de la Recherche Agronomique ( INRA ), Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Kellogg Biological Station, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, School of Earth and Environment [Leeds] ( SEE ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, and Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: Calibration (statistics), Climate, Statistics, process-based model, grain, uncertainty, Triticum, General Environmental Science, Mathematics, 2. Zero hunger, Global and Planetary Change, Ecology, Mathematical model, Estimator, PE&RC, simulation, ecophysiological model, Europe, [ SDE.MCG ] Environmental Sciences/Global Changes, model intercomparison, Plant Production Systems, Wheat (Triticum aestivum L.), climate-change, wheat (Triticum aestivum L.), Centre for Crop Systems Analysis, impact, Seasons, simulations, europe, ensemble modeling, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, australia, crop production, Environment, Models, Biological, Consistency (statistics), Approximation error, Environmental Chemistry, Alterra - Centrum Bodem, impacts, Hydrology, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Ensemble forecasting, Crop yield, Simulation modeling, Soil Science Centre, Australia, yield, calibration, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Plantaardige Productiesystemen, biodiversity conservation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Environmental Sciences, billion
Abstract: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
Published: 2015
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12. Statistical analysis of large simulated yield datasets for studying climate change effects

Author: David Makowski, Senthold Asseng, Frank Ewert, Simona Bassu, Jean-Louis Durand, Pierre Martre, Myriam Adam, Pramod K. Aggarwal, Carlos Angulo, Christian Baron, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Hendrik Boogaard, Kenneth J. Boote, Nadine Brisson, Davide Cammarano, Andrew J. Challinor, Sjakk J. G. Conijn, Marc Corbeels, Delphine Deryng, Giacomo De Sanctis, Jordi Doltra, Sebastian Gayler, Richard Goldberg, Patricio Grassini, Jerry L. Hatfield, Lee Heng, Steven Hoek, Josh Hooker, Tony L. A. Hunt, Joachim Ingwersen, Cesar Izaurralde, Raymond E. E. Jongschaap, James W. Jones, Armen R. Kemanian, Christian Kersebaum, Soo-Hyung Kim, Jon Lizaso, Christoph Müller, Naresh S. Kumar, Claas Nendel, Garry J. O'Leary, Jorgen E. Olesen, Tom M. Osborne, Taru Palosuo, Maria V. Pravia, Eckart Priesack, Dominique Ripoche, Cynthia Rosenzweig, Alexander C. Ruane, Fredirico Sau, Mickhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Edmar I. Teixeira, Peter Thorburn, Denis Timlin, Maria Travasso, Reimund Rötter, Katharina Waha, Daniel Wallach, Jeffrey W. White, Jimmy R. Williams, Joost Wolf, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, University of Florida [Gainesville] (UF), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Michigan State University [East Lansing], Michigan State University System, Agroclim (AGROCLIM), German Research Center for Environmental Health, Centre for Geo-Information, University of Leeds, International Center for Tropical Agriculture, Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Sciences [Changchun Branch] (CAS), University of East Anglia, Catabrian Agricultural Research and Training Center (CIFA), University of Tübingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), University of Nebraska [Lincoln], University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), University of Reading (UOR), University of Guelph, University of Hohenheim, Joint Global Change Research Institute, Instituto Nacional de Investigación Agropecuaria (INIA), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), Department of Environment and Primary Industries, Landscape and Water Sciences, Aarhus University [Aarhus], Agrifood Research Finland, Pennsylvania State University (Penn State), Penn State System, Rothamsted Research, FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Washington State University (WSU), Plant & Food Research, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS : Agricultural Research Service, Institute for Climate and Water, 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, Arid-Land Agricultural Research Center, Texas A&M University System, Hillel, D., Rosenzweig, C., Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Clermont Auvergne ( UCA ), Université Blaise Pascal (Clermont Ferrand 2) ( UBP ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Michigan State University, UE Agroclim ( UE AGROCLIM ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Catabrian Agricultural Research and Training Center ( CIFA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska Lincoln ( UNL ), International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Indian Agricultural Research Institute ( IARI ), Aarhus University, PennState University [Pennsylvania] ( PSU ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), New Zealand Institute for Plant and Food Research Limited, Commonwealth Scientific and Industrial Research Organisation, United States Department of Agriculture - Agricultural Research Service, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), AgroParisTech-Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville], Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Blaise Pascal (Clermont Ferrand 2) (UBP), UE Agroclim (UE AGROCLIM), Wageningen University and Research Center (WUR), Food and Agricultural Organization (FAO), Helmholtz Zentrum München = German Research Center for Environmental Health, University of East Anglia [Norwich] (UEA), University of Nebraska–Lincoln, Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: analyse de données, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Yield (finance), data analysis, Climate change, 01 natural sciences, Agro Water- en Biobased Economy, statistical analysis, Effects of global warming, Aardobservatie en omgevingsinformatica, Life Science, Alterra - Centrum Bodem, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, global change, 0105 earth and related environmental sciences, 2. Zero hunger, changement climatique, WIMEK, Mathematical model, analyse statistique, Crop yield, Soil Science Centre, Global change, Statistical model, 04 agricultural and veterinary sciences, 15. Life on land, PE&RC, Climate resilience, Climate Resilience, Plant Production Systems, Klimaatbestendigheid, 13. Climate action, Plantaardige Productiesystemen, Climatology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.
Published: 2015
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13. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Author: Joost Wolf, Xinyou Yin, Pierre Martre, Zhengtao Zhang, H. K. Soo, Manuel Marcaida, Nadine Brisson, Patrick Bertuzzi, Soo-Hyung Kim, Yan Zhu, Roberto C. Izaurralde, L. A. Hunt, Maria I. Travasso, Christian Baron, James W. Jones, R.E.E. Jongschaap, T. Palosuo, Daniel Wallach, Jerry L. Hatfield, Christian Biernath, G. De Sanctis, Senthold Asseng, H. Yoshida, Donald S. Gaydon, Edmar Teixeira, Davide Cammarano, Alex C. Ruane, C. Nendel, T. Hasegawa, Thilo Streck, Garry O'Leary, Upendra Singh, Frank Ewert, Delphine Deryng, R. Goldberg, Bas A. M. Bouman, Peter J. Thorburn, Tao Li, Roberto Confalonieri, Myriam Adam, Jes Olesen, Reimund P. Rötter, Tamon Fumoto, Patricio Grassini, Joachim Ingwersen, Robert F. Grant, Katharina Waha, James Williams, Fulu Tao, Eckart Priesack, Pramod K. Aggarwal, Liang Tang, Sebastian Gayler, Jordi Doltra, L. Heng, Christoph Müller, J.G. Conijn, Iwan Supit, S. Naresh Kumar, Iurii Shcherbak, Jeffrey W. White, Hendrik Boogaard, Kenneth J. Boote, David Makowski, Federico Sau, Jean-Louis Durand, Mikhail A. Semenov, Claudio O. Stöckle, Marc Corbeels, Steven Hoek, Simone Bregaglio, Hiroshi Nakagawa, Philippe Oriol, Anthony Challinor, R. A. Kemanian, Carlos Angulo, Pasquale Steduto, Bruno Basso, Kurt Christian Kersebaum, Cynthia Rosenzweig, Dennis Timlin, J. Hooker, Samuel Buis, Maria Virginia Pravia, Françoise Ruget, Dominique Ripoche, Simona Bassu, Pierre Stratonovitch, Jon I. Lizaso, Balwinder Singh, Tom M. Osborne, Paul W. Wilkens, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Int Rice Res Inst, Los Banos, Philippines, Université Paris Diderot - Paris 7 (UPD7), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), 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), International Water Management Institute, Research Program on Climate Change, Agriculture and Food Security, CGIAR, Institute of Crops Science and Resource Conservation INRES, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Geological Sciences [East Lansing], Agroclim (AGROCLIM), German Research Center for Environmental Health, Institute of Soil Ecololgy, Helmholtz-Zentrum München (HZM), Center for Geo-information, Alterra, Department of Agronomy, University of Florida [Gainesville] (UF), 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), The James Hutton Institute, CGIAR ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Plant Research International, Wageningen University and Research [Wageningen] (WUR), Embrapa Cerrados, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, School of Environmental Science, University of East Anglia [Norwich] (UEA), European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Tsukuba, National Institute of Agro-Environmental Sciences (NIAES), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), WESS Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Departement of Renewable Resources, University of Alberta, Department of Agronomy and Horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), Centre for Geo-Information, Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, 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, Instituto Nacional de Investigación Agropecuaria (INIA), Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), College of the Environment, School of Environmental and Forest Sciences, University of Washington, Department Produccion Vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), National Agriculture and Food Research Organization (NARO), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Department of Economic Development Jobs, Transport and Resources, Grains Innovation Park, Department of Agroecology, Aarhus University [Aarhus], Walker Institute, NCAS Climate, Natural Resources Institute Finland, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, German Research Center for Environmental Health, Institute of Soil Ecology, Department Biologia Vegetal, Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences and W.K. Kellogg Biological Station, International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), International Fertilizer Development Center (IFDC), College of the Environment, School of Environmental and Forest Science, University of Washington [Seattle], FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Biological Systems Engineering, Washington State University (WSU), Plant Production Systems and Earth System Science, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, CIRN, Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agriculture, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Arid-Land Agricultural Research Center, Texas AgriLife Research and Extension, Texas A&M University System, Centre for Crop Systems Analysis, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), Metaprogramme ACCAF, 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), Helmholtz Zentrum München = German Research Center for Environmental Health, Università degli Studi di Milano = University of Milan (UNIMI), University of Nebraska–Lincoln, Université de Toulouse (UT)-Université de Toulouse (UT), Natural Resources Institute Finland (LUKE), Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Nanjing Agricultural University (NAU), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation INRES, International Rice Research Institute, 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), UE Agroclim (UE AGROCLIM), Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Eberhard Karls Universität Tübingen, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), National Agriculture and Food Research Organization, International Maize and Wheat Improvement Centre (CIMMYT), Food and Agricultural Organization (FAO), New Zealand Institute for Plant and Food Research Limited, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Beijing Normal University, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), 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 ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ) -AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Department of Geological Sciences, W.K. Kellogg Biological Station, Michigan State Univ, Dept Geol Sci, E Lansing, MI 48823 USA, UE Agroclim ( UE AGROCLIM ), Helmholtz-Zentrum München ( HZM ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Invergowrie, School of Earth and Environment [Leeds] ( SEE ), Wageningen University and Research Centre [Wageningen] ( WUR ), Agro-ecologyand Sustainable Intensification of Annual Crops, Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), University of East Anglia [Norwich] ( UEA ), European Commission - Joint Research Centre [Ispra] ( JRC ), National Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation, NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska-Lincoln, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), UMR 1248 Agrosystèmes et Développement Territorial (AGIR), Agro-ecology and Sustainable Intensification of Annual Crops, Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), PennState University [Pennsylvania] ( PSU ), W.K. Kellogg Biological Station, Department of Geological Sciences, International Maize and Wheat Improvement Centre ( CIMMYT ), International Fertilizer Development Center ( IFDC ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, and Texas A and M University ( TAMU )
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, F62 - Physiologie végétale - Croissance et développement, 01 natural sciences, Statistics, Aardobservatie en omgevingsinformatica, Climate change, Crop model, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, Triticum, Mathematics, 2. Zero hunger, Global and Planetary Change, Mathematical model, Air, Forestry, Regression analysis, 04 agricultural and veterinary sciences, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, Rendement des cultures, Plant Production Systems, Statistical model, Modèle mathématique, Atmosphère, Earth Observation and Environmental Informatics, Yield, Crop Physiology, P40 - Météorologie et climatologie, [SDE.MCG]Environmental Sciences/Global Changes, Oryza sativa, Zea mays, Earth System Science, Emulator, Agro Water- en Biobased Economy, Alterra - Centrum Bodem, Precipitation, Croissance, 0105 earth and related environmental sciences, Meta-model, Changement climatique, Hydrology, Modélisation des cultures, Crop yield, Simulation modeling, Soil Science Centre, 15. Life on land, Température, Laboratorium voor Phytopathologie, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Yield (chemistry), Plantaardige Productiesystemen, Laboratory of Phytopathology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leerstoelgroep Aardsysteemkunde, Plante de culture, Agronomy and Crop Science, Dioxyde de carbone
Abstract: Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.
Published: 2015
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14. PERPHECLIM ACCAF Project perennial fruit crops and forest phenology evolution facing climatic changes

Author: Iñaki Garcia de Cortazar-Atauri, Jean Marc Audergon, Patrick Bertuzzi, Christel Anger, Marc Bonhomme, Isabelle Chuine, Hendrik Davi, Sylvain Delzon, Eric Duchêne, Jean-Michel Legave, Helene Raynal, Christian Pichot, Cornelis van Leeuwen, Perpheclim Team, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Génétique et Amélioration des Fruits et Légumes (GAFL), Génétique et Biomasse Forestières Orléans (GBFOR), Laboratoire de Physique et Physiologie Intégratives de l'Arbre Fruitier et Forestier (PIAF), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Ecologie des Forêts Méditerranéennes (URFM), Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Santé de la vigne et qualité du vin (SVQV), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Ecophysiologie et Génomique Fonctionnelle de la Vigne (UMR EGFV), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Métaprogramme ACCAF, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-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)-Institut de Recherche pour le Développement (IRD [France-Sud]), 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), and ProdInra, Migration
Subjects: modelling, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, changement climatique, phenologie forestière, blooming period, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, adaptation, fruit, phenology, ComputingMilieux_MISCELLANEOUS, abiotic stresses
Abstract: International audience; Phenology is a bio-indicator of climate evolutions. Measurements of phenological stages on perennial species provide actually significant illustrations and assessments of the impact of climate change. Phenology is also one of the main key characteristics of the capacity of adaptation of perennial species, generating questions about their consequences on plant growth and development or on fruit quality. Predicting phenology evolution and adaptative capacities of perennial species needs to override three main methodological limitations: 1) existing observations and associated databases are scattered and sometimes incomplete, rendering difficult the implementation of multi-site study of genotype-environment interaction analyses; 2) there are no common protocols to observe phenological stages; 3) access to generic phenological models platforms is still very limited. In this context, the PERPHECLIM project, which is funded by the Adapting Agriculture and Forestry to Climate Change Meta-Program (ACCAF) from INRA, has the objective to develop the necessary infrastructure at INRA level (observatories, information system, modeling tools) to enable partners to study the phenology of various perennial species (grapevine, fruit trees and forest trees). Currently the PERPHECLIM project involves 27 research units in France. The main activities currently developed are: defining protocols and observation forms to observe phenology for various species of interest for the project; organizing observation training; developing generic modeling solutions to simulate phenology; supporting the building of research projects at national and international level; developing environment/genotype observation networks for fruit trees species; developing an information system managing data and documentation concerning phenology. Finally, PERPHECLIM aims to build strong collaborations with public (Observatoire des Saisons) and private sector partners (technical institutes) in order to allow a more direct transfer of knowledge.
Published: 2015

15. Understanding dormancy release in apricot flower buds ([i]Prunus armeniaca[/i] L.) using several process-based phenological models

Author: Jean-Marc Audergon, Raffaella Viti, L. Andreini, Jean Michel Legave, Patrick Bertuzzi, Isabelle Chuine, José Antonio Campoy, David Ruiz, Iñaki García de Cortázar-Atauri, Susanna Bartolini, Unité de recherche Génétique et amélioration des fruits et légumes (GALF), Institut National de la Recherche Agronomique (INRA), UE Agroclim (UE AGROCLIM), Department of Agriculture, Food and Environment, Sant’Anna School of Advanced Studies, Spanish National Research Council (CSIC), Biologie du fruit et pathologie (BFP), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, 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), 'MAP C1-2009-Abricotier/CEP Innovation Project' (France), Génétique et Amélioration des Fruits et Légumes (GAFL), Agroclim (AGROCLIM), Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP), 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), Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), 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 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 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), and 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)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, Atmospheric Science, dormancy, 010504 meteorology & atmospheric sciences, Prunus armeniaca, Dormancy Modelling Phenology Prunus armeniaca Genetic variability, phenology, 01 natural sciences, modelling, Species level, Botany, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Cultivar, [SDV.BDD]Life Sciences [q-bio]/Development Biology, 0105 earth and related environmental sciences, Global and Planetary Change, biology, Bud, Phenology, food and beverages, Forestry, penetic variability, biology.organism_classification, Horticulture, Release date, Dormancy, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières; The present study was an initial attempt to calibrate a phenological process-based model of dormancy release with experimental data for apricot flower buds. A large experimental database (88 data points) on dormancy release, concerning several cultivars grown at different geographical sites, was used for the model parameterization. We compared five phenological models. None of them provided accurate prediction of the date of dormancy release at the species level. This inaccuracy appeared to be due to the high variance in dormancy release dates among cultivars. Models fitted for different dormancy release precocity groups provided much more accurate predictions. Parameter estimate analysis of the best model for each cultivar group showed very marked differences in apricot flower bud response to temperature within the species. While in early cultivars dormancy release seemed to be driven by the daily minimum temperature, the daily mean temperature appeared to be the controlling factor in intermediate and late cultivars. Our results show that the apricot dormancy release date cannot be predicted accurately at the species level and that different models should be used for different precocity groups.
Published: 2014
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16. Estimating the root-zone soil moisture from the combined use of time series of surface soil moisture and SVAT modelling

Author: Albert Olioso, Jean-Christophe Calvet, Andre Chanzy, Jean-Pierre Wigneron, Patrick Bertuzzi, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol
Subjects: Surface (mathematics), 010504 meteorology & atmospheric sciences, télédétection, [SDV]Life Sciences [q-bio], 0207 environmental engineering, MICROONDES, Soil science, réserve en eau de la plante, 02 engineering and technology, 01 natural sciences, Thermal, humidité du sol, 020701 environmental engineering, Water content, ComputingMilieux_MISCELLANEOUS, modélisation, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, système racinaire, Series (mathematics), Vegetation, 15. Life on land, transfert sol végétation atmosphère, Root zone soil moisture, Remote sensing (archaeology), [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Microwave
Abstract: This work investigates the possibility to retrieve the root zone soil moisture from the combined use of time series of surface soil moisture, that can be estimated from microwave remote sensing instruments, and Soil-Vegetation-Atmosphere-Transfer (SVAT) modeling. The analysis is based on the ISBA surface scheme and two data sets acquired over' soybean crops, in 1989 and 1990. They include very detailed measurements of soil and vegetation characteristics, mass and energy transfers in the soil-plant-atmosphere system and crop remote sensing signatures in the thermal and microwave domains during a 2- or 3-month period. The 3-month experiment in 1989, made it possible to investigate the accuracy of the soil reservoir retrievals, as a function of the time period and repetitivity of the surface measurements included in the retrieval process. This work contributes to a better definition of the requirements for the use of remotely-sensed microwave observations of surface soil moisture. This is a crucial problem, if we consider the increasing potential of microwave remote sensing technology and the fundamental needs in atmospheric and hydrological models for water transfer characterization in soil.
Published: 1999
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17. Uncertainty in simulating wheat yields under climate change

Author: J. Hooker, Pramod K. Aggarwal, Joost Wolf, Pierre Martre, Iurii Shcherbak, L. A. Hunt, Kenneth J. Boote, L. Heng, James W. Jones, Jerry L. Hatfield, Katharina Waha, Christian Biernath, Iwan Supit, Eckart Priesack, Pasquale Steduto, S. Naresh Kumar, Davide Cammarano, Joachim Ingwersen, Kurt Christian Kersebaum, Fulu Tao, Christoph Müller, Jordi Doltra, Thilo Streck, Senthold Asseng, Alex C. Ruane, Jeffrey W. White, Roberto C. Izaurralde, Tom M. Osborne, Patrick Bertuzzi, Sebastian Gayler, Andrew J. Challinor, Taru Palosuo, Reimund P. Rötter, Jørgen E. Olesen, Peter J. Thorburn, Nadine Brisson, Mikhail A. Semenov, Claudio O. Stöckle, Maria I. Travasso, Daniel Wallach, James Williams, Garry O'Leary, Cynthia Rosenzweig, Carlos Angulo, Bruno Basso, R. Goldberg, Robert F. Grant, Frank Ewert, Dominique Ripoche, Pierre Stratonovitch, Claas Nendel, Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National Laboratory for Agriculture and Environment, Department of Agronomy, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), CCAFS, IWMI, NASC Complex, DPS Marg, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Centro de Investigaciòn y Formenta Agrario (CIFA), WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, nstitute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape and Water Sciences, Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Institute of Soil Science and Land Evaluation, Plant Production Systems and Earth System Science-Climate Change, Wageningen University and Research Centre [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Florida [Gainesville] (UF), Agroclim (AGROCLIM), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut für Nutzpflanzenwissenschaften und Ressourcenschutz ( INRES ), University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Centro de Investigaciòn y Formenta Agrario ( CIFA ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, projection, crop production, adaptation, Environmental Science (miscellaneous), 01 natural sciences, Greenhouse effect, Uncertainty analysis, 0105 earth and related environmental sciences, 2. Zero hunger, model, [ SDV ] Life Sciences [q-bio], food, Simulation modeling, ensemble, temperature, 04 agricultural and veterinary sciences, 15. Life on land, Transient climate simulation, scenario, 13. Climate action, Greenhouse gas, Climatology, 040103 agronomy & agriculture, impact, 0401 agriculture, forestry, and fisheries, Environmental science, co2, Climate model, Crop simulation model, Social Sciences (miscellaneous)
Abstract: Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking. Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
Published: 2013
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18. Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling

Author: Jean-Christophe Calvet, Jean-Pierre Wigneron, Patrick Bertuzzi, Albert Olioso, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol
Subjects: Surface (mathematics), réserve en eau du sol, 010504 meteorology & atmospheric sciences, télédétection, Energy transfer, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil science, 02 engineering and technology, TSVA, SYSTEME SOL PLANTE ATMOSPHERE, 01 natural sciences, Atmosphere, Soil thermal properties, Pedotransfer function, humidité du sol, 020701 environmental engineering, Water content, ComputingMilieux_MISCELLANEOUS, modélisation, 0105 earth and related environmental sciences, Water Science and Technology, système racinaire, Vegetation, 15. Life on land, transfert sol végétation atmosphère, microonde passive, Root zone soil moisture, [SDE]Environmental Sciences, Environmental science
Abstract: We studied the possibility of estimating root zone soil moisture through the combined use of a time series of observed surface soil moisture data and soil-vegetation-atmosphere transfer modeling. The analysis was based on the interactions between soil- biosphere-atmosphere surface scheme and two data sets obtained from soybean crops in 1989 and 1990. These data sets included detailed measurements of soil and vegetation characteristics and mass and energy transfer in the soil-plant-atmosphere system. The data measured during the 3-month experiment in 1989 are used to investigate the accuracy of soil reservoir retrievals, as a function of the time period and frequency of measurements of surface soil moisture involved in the retrieval process. This study contributes to better defining the requirements for the use of remotely sensed microwave measurements of surface soil moisture.
Published: 1999

19. Estimation of energy fluxes and photosynthesis from thermal infrared, spectral reflectances, microwave data and SVAT modeling

Author: Habiba Chauki, Patrick Bertuzzi, Jean-Pierre Wigneron, Albert Olioso, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), and Unité de Science du Sol
Subjects: Canopy, transfert de masse, échange d'énergie, 010504 meteorology & atmospheric sciences, télédétection, [SDV]Life Sciences [q-bio], RELATION SOL PLANTE ATMOSPHERE, COUVERTURE VEGETALE, 0211 other engineering and technologies, évapotranspiration, glycine max, 02 engineering and technology, Atmospheric model, modèle, 01 natural sciences, Evapotranspiration, Radiative transfer, humidité du sol, photosynthèse, soja, Water content, ComputingMilieux_MISCELLANEOUS, infrarouge thermique, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Transpiration, Moisture, microonde, Vegetation, 15. Life on land, oléoprotéagineux, 6. Clean water, reflectance spectrale, plante légumière, [SDE]Environmental Sciences, Environmental science, radar
Abstract: Soil Vegetation Atmosphere Transfer (SVAT) models have been implemented to simulate energy and mass fluxes between soil, vegetation and atmosphere of various ecosystems. Usually, these models are simple, but they use realistic descriptions of radiative, turbulent and water transfers. These include description of stomatal control of transpiration and CO/sub 2/ fluxes. They can be used for assimilating remote sensing data and derive vegetation canopy evapotranspiration or photosynthesis. Various remote sensing data may provide useful information to drive SVAT models. Surface temperature may be used through inversion procedures to retrieve parameters related to stomatal conductance or root zone soil moisture. Parameters related to vegetation structure (LAI, vegetation height) may be retrieved from reflectance measurements in the solar domain, either through direct relationships with some vegetation index or by inverting radiative transfer formulation against spectral reflectance measurements. The microwave data contribution has not been studied very often in the case of vegetation canopies, but they were proposed for estimating surface soil moisture. In this paper, inversions of the ALiBi model were performed to retrieve canopy evapotranspiration from thermal infrared, spectral reflectances and microwave data on two water stressed soybean crops. In a previous study, thermal infrared data alone were used to invert the model on plant water status parameters, while other parameters, related to canopy structure and soil surface water status, were prescribed from in situ measurements. In the present study, spectral reflectance and radar measurements were used to retrieve canopy structure parameters (LAI and vegetation height) and surface soil moisture by inverting radiative transfer models.
Published: 1998

20. Mesure de l'humidite des sols par une methode capacitive : analyse des facteurs influencant la mesure

Author: P. Stengel, Patrick Bertuzzi, Andre Chanzy, R. Guennelon, Laurent Bruckler, J. C. Gaudu, JM Mathieu, JC Fumanal, Revues Inra, Import, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), Institut Universitaire de Technologie - Aix-Marseille (IUT AMU), and Aix Marseille Université (AMU)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, sol nu, méthode de mesure, couvert végétal, permittivité diélectrique, structure du sol, teneur en eau, capteur capacitif, Agricultural sciences, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, variabilité spatiale, température, humidité du sol, Agronomy and Crop Science, texture, ComputingMilieux_MISCELLANEOUS, Sciences agricoles, appareillage, salinité
Abstract: On teste en laboratoire et in situ les performances d’un capteur capacitif destiné à la mesure de l’humidité du sol. En laboratoire, on montre que les dérives thermiques propres à l’appareillage sont négligeables, que la mesure de permittivité diélectrique relative est dépendante de la qualité du contact sol-électrodes, et que le volume de mesure a des dimensions de l’ordre de quelques cm3. Les relations linéaires permittivité relative-teneur en eau volumique sont dépendantes de la texture, de la structure (agrégats de 2-3 mm et 4 mm disposés autour des électrodes ou structure continue), de la température (la variation de la permittivité relative en fonction de la température peut atteindre 0,20 par °C dans la gamme de 0 à 45 °C), de la salinité (la mesure de permittivité diélectrique relative est peu sensible à la concentration saline jusqu’à des valeurs de conductance électrique de l’ordre de 2 à 3 mS). In situ, deux expérimentations sont réalisées, l’une sur un sol nu présentant un état structural homogène, l’autre sur un sol couvert (soja) présentant un état structural plus hétérogène. Pour chaque expérimentation, on réalise pendant plusieurs jours (21 et 41 j respectivement) des mesures simultanées de permittivité diélectrique, de température, de conductance électrique, et des contrôles de teneur en eau dans les couches superficielles du sol (0 à 20 cm). Les relations d’étalonnage permittivité-humidité sont généralement linéaires et variables d’un capteur à l’autre. Cette variabilité est probablement due à la variabilité spatiale de la teneur en eau du sol, de la structure à proximité immédiate du capteur, et/ou du contact sol-électrodes. Par ailleurs, les effets thermiques sur la permittivité mesurée sont comparables à ceux obtenus en laboratoire, et la gamme de conductances électriques rencontrées in situ semble compatible avec celle ne présentant pas d’influence notable sur la mesure de permittivité et estimée en laboratoire., The performance of a capacitive probe for soil moisture measurement was studied under laboratory and field conditions. The probe (21-mm ED cylinder) was composed of 2 electrodes connected to an electronic oscillator (38 MHz) located in the probe. Four soils were used to analyse the ’dielectric permittivity-volumetric water content’ relationships under laboratory or field conditions. The sensitivity of the probe including its electronic components in relation to the variations of the temperature (0-45 °C) was very low (< 0.5 permittivity unit). Permittivity measurements were dependent on the quality of the soil-electrodes contact, and on the soil structure near the electrodes. The order of magnitude of the volume of influence was a few cubic centimeters, and the area of the soil most affected by the permissibility measurements was located in the vicinity of the electrodes. The ’dielectric permittivity-volumetric water content’ relationships were linear (r > 0.99), and dependent on soil texture and structure. Moreover, soil temperature influenced the measurements since increases of the order of magnitude of 0.20 permittivity unit per °C could be observed. Permittivity measurements were not sensitive to the concentration of the solution when the electric conductance was < 2-3 mS. Two field experiments were performed using 2 different soil textures. The first soil (C = 10.5%, L = 50.6%, S = 38.8%) was a bare soil, and showed a quite homogeneous soil structure, the second (C = 27.2%, L = 61.7%, S = 11.1%) was cultivated with soybean, and showed a more heterogeneous soil structure. For each experiment field, simultaneous measurements (1 or 2 times per d) of relative dielectric permittivity, soil temperature and gravimetric soil water content were made in the vicinity of the probes (over a 21 and 41 d period, for the 1st and 2nd experiment, respectively). The variation of the relative dielectric permittivity versus time was very similar to the variation of the measurement soil water content. The calibration relationships between the volumetric water content and the relative dielectric permittivity were linear for each capacitive probe. The regression lines for the different capacitive probes exhibited variations between probes, probably due to the spatial variability of the water content, soil structure and/or soil-electrode contact for each sensor. The effects of the soil temperature on the relative dielectric permittivity measurements were similar to the previous results obtained under laboratory conditions, and effects of electrical conductance appeared negligible under our conditions.
Published: 1993

21. Régression linéaire avec erreur sur les variables : application à l'étalonnage d'un gammadensimètre à transmission et d'un humidimètre à neutrons

Author: Laurent Bruckler, Christian Gros, Patrick Bertuzzi, Revues Inra, Import, Unité de Science du Sol, Institut National de la Recherche Agronomique (INRA), and Avignon Université (AU)
Subjects: humidimètre à neutrons, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, régression linéaire, Forestry, Agricultural sciences, erreur de mesure, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, modèle linéaire, gammadensimétrie, Linear regression, étalonnage, Agronomy and Crop Science, ComputingMilieux_MISCELLANEOUS, Sciences agricoles, modélisation, Mathematics
Abstract: On se propose de calculer des estimateurs non biaisés de la régression linéaire dans le cas où les variables (x) et (y) de la régression, sont entachées d’erreurs. Dans une première partie, on présente les bases théoriques de la réduction du biais des estimateurs. Ces bases permettent d’aboutir à la construction d’estimateurs sans biais à l’ordre 1. De plus, on décrit la méthodologie permettant de quantifier rigoureusement l’ensemble des sources d’erreur à prendre en compte successivement pour les variables (x) et (y). Dans une seconde partie, ces résultats théoriques sont appliqués à deux instruments de mesure nécessitant un étalonnage préliminaire, un gamma densimètre à transmission (étalonné en laboratoire) et une sonde à neutrons (étalonnée in situ). Les résultats numériques montrent que les erreurs de mesure sont négligeables pour les variables (x) (taux de comptage) et (y) (masse volumique sèche) dans le cas du gamma densimètre à transmission (≤ 0,4 p. 100). Dans le cas de la sonde à neutrons, il en est de même pour la variable (x) (taux de comptage), mais les erreurs deviennent non négligeables (9 à 16 p. 100) pour la variable (y) (teneur en eau volumique). Lorsque les erreurs de mesure restent faibles (cas du gamma densimètre à transmission), il y a peu de différences entre les estimateurs de la droite d’étalonnage avec ou sans prise en compte des erreurs de mesure. Par contre, les différences deviennent plus sensibles lorsque les erreurs deviennent plus importantes (cas de l’humidimètre à neutrons), notamment en ce qui concerne la précision de l’estimation de valeurs prédites à partir de l’étalonnage., This paper proposes unbiased estimators of linear regression when the variables (x, y) present measurement errors. In the first part, theoretical calculations are described to obtain first-order unbiased estimators, and an error analysis is developed to take into account all the error components involved in each variable (x or y). In the second part, these theoretical results were applied to the calibration of a gamma-ray attenuation probe (laboratory calibration) and a neutron probe (field calibration). Numerical results showed that the measurement errors were negligible in the case of the gamma-ray attenuation equipement (≤ 0.4 %), but were greater (9 to 16 %) for the neutron probe, particularly for variable (y). When the measurement errors are small (gamma-ray attenuation probe), there is no difference between the parameters of the calibration estimated according to the classical approach or the proposed model. But when the measurement errors are greater (neutron probe), the differences between these two calibrations become higher especially for the precision of new predicted values.
Published: 1987

22. Calibration, field-testing, and error analysis of a gamma-ray probe for in situ measurement of dry bulk density

Author: Patrick Bertuzzi, Y. Gabilly, Laurent Bruckler, J. C. Gaudu, Unité de Science du Sol, and Institut National de la Recherche Agronomique (INRA)
Subjects: In situ, Field (physics), Instrumentation, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil Science, Mineralogy, 02 engineering and technology, Optics, Calibration, 020701 environmental engineering, ComputingMilieux_MISCELLANEOUS, Observational error, business.industry, Gamma ray, analyse de l'erreur, 04 agricultural and veterinary sciences, densité du sol, gammadensimètre, Bulk density, 040103 agronomy & agriculture, Measuring instrument, 0401 agriculture, forestry, and fisheries, Environmental science, étalonnage, business
Abstract: International audience
Published: 1987

23. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 31 Maize Models

Author: Bruce Kimball, Boote, K., Hatfield, J., Ahuja, L., Stockle, C., Archontoulis, S., Baron, C., Bruno Basso, Patrick Bertuzzi, Chen, M., Julie Constantin, Derying, D., Dumont, B., Jean-Louis Durand, Ewert, F., Gaiser, T., Gayler, S., Griffis, T., Hoffmann, M., Jiang, Q., Soo-Hyung Kim, Lizaso, J., Sophie Moulin, Nendel, C., Parker, P., Palosuo, T., Priesack, E., Zhiming Qi Z., Srivastava, A., Stella, T., Tao, F., Thorp, K., Timlin, D., Twine, T., Heidi Webber, Magali Willaume, Williams, K., ProdInra, Migration, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville], National laboratory for agriculture and the environment, Agricultural Systems Research Unit, USDA, Agricultural Research Service, United States Department of Agriculture - Agricultural Research Service, Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Department of Soil, Water, and Climate, University of Minnesota System, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Computation Institute, Loyola University of Chicago, ULg-GxABT, Gembloux Agro-Bio Tech Faculty, Dpt. Agronomy, Bio-Engineering and Chemistry, Crop Science Unit, Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Crop Production Systems in the Tropics, Georg-August-Universität Göttingen, Department of Bioresource Engineering, Macdonald Campus, Université McGill, Center for Urban Horticulture, University of Washington, Dept. Producción Agraria-CEIGRAM, Universidad Politécnica de Madrid (UPM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland, Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Research Unit, Climate Adaptation Scientist Meteorological Office, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), University of Florida [Gainesville] (UF), Agroclim (AGROCLIM), 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, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Georg-August-University [Göttingen], McGill University = Université McGill [Montréal, Canada], Natural resources institute Finland, Washington State University (WSU), Michigan State University System, Department of Soil, Water and Climate, Institute of Crop Science and Resource Conservation [Bonn], Natural Resources Institute Finland (LUKE), Institute of Biochemical Plant Pathology (BIOP), and Agricultural Model Intercomparison and Improvement Project (AgMIP). USA.
Subjects: [SDE] Environmental Sciences, Evapotranspiration, maïs, [SDV]Life Sciences [q-bio], évapotranspiration, analyse de covariance, modèle de croissance, [SHS]Humanities and Social Sciences, caracteristique variétale, Maize, covariance analysis, [SDV] Life Sciences [q-bio], densité de surface foliaire, potentiel de croissance, [SDE]Environmental Sciences, rendement agricole, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, [SHS] Humanities and Social Sciences, Crop model
Abstract: International audience; An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 31 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved in later stages when the modelers were provided additional leaf area, growth, and the actual ET observations that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc. Bruce

24. Les effets du changement climatique sur l’agriculture et la forêt en Provence-Alpes-Côte d’Azur

Author: Azur, Groupe Régional D. Experts Sur Le Climat En Provence-Alpes-Côte D., Association Pour L’innovation Et La Recherche Au Service Du Climat, Jean Marc Barbier, Claude Baury, Patrick Bertuzzi, Alberte Bondeau, Vincent Couderc, Francois Courbet, Curt, T., Laurence Dalstein-Richier, Hendrik Davi, Sylvestre Delmotte, Laurent Dobremez, Jean-Luc Dupuy, Marianela Fader, Anne-Marie Farnet da Silva, Olivier Ferreira, Thomas Fouant, Iñaki Garcia de Cortazar-Atauri, Laurent Garde, Thierry Gauquelin, David Gouache, Raphaël Gros, Frédéric Guibal, Roy Hammond, Laure Hossard, Stéphane Jézéquel, Jean Ladier, Francois Lefevre, Jean-Michel Legave, Jean-Claude Mouret, Claude Napoleone, François Pimont, Bernard Prévosto, Eric Rigolot, Philippe Rossello, Pierre Sicard, Michel Vennetier, Benoît Vial, Simon Vieux, Groupe Régional d'Experts sur le Climat en Provence-Alpes-Côte d'Azur, Partenaires INRAE, Association pour l’innovation et la recherche au service du climat, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), 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), Chambre d'Agriculture des Bouches du Rhône (CA 13), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), Ecologie des Forêts Méditerranéennes (URFM), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Groupe International d'Etude des Forets Subalpines, Développement des Territoires Montagnards (DTM), German Federal Institute for Geosciences and Natural Resources, Office National des Forêts (ONF), Centre d'Etudes et de Réalisations Pastorales Alpes Méditerranée (CERPAM), ARVALIS - Institut du végétal [Paris], 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), Unité de recherche d'Écodéveloppement (ECODEVELOPPEMENT), Le Centre Régional de l'Information Géographique de Provence-Alpes-Côte d'Azur (CRIGE), ACRI-HE, Observatoire de la Forêt Méditerranéenne, Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UMR237-Aix Marseille Université (AMU)-Avignon Université (AU), Office national des forêts (ONF), and Développement des territoires montagnards (UR DTGR)
Subjects: alpage, agriculture régionale, production rizicole, [SDE.MCG]Environmental Sciences/Global Changes, forêt méditerranéenne, Agriculture, durabilité de l'activité agricole, sylviculture, ozone, phénologie des peuplements, santé des forêts, Climate change, adaptation au changement climatique, Mediterranean forest, Milieux et Changements globaux
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVIN [ADD1_IRSTEA]Adaptation des territoires au changement globalCahier thématique; National audience; Ce deuxième cahier diffuse des connaissances scientifiques (non exhaustives) sur les effets du changement climatique sur l’agriculture et la forêt en Provence-Alpes-Côte d’Azur. Les cultures (vergers, céréales, riz, maraîchage…) et les forêts (feuillus, conifères) bénéficient des bienfaits du climat méditerranéen qui offre des conditions favorables au développement des plantes sous certaines conditions, mais souffrent aussi des événements extrêmes qui le ponctuent à intervalles irréguliers. Le climat méditerranéen qui sévit en région PACA pourrait se résumer par Toulourenc (« tout ou rien » en provençal), du nom du cours d’eau à caractère torrentiel qui coule dans la vallée étroite située au pied du versant nord du mont Ventoux. Avec le changement climatique actuel, les aspects négatifs de notre climat sont appelés à se renforcer et font déjà peser sur les terroirs agricoles et les forêts emblématiques de la région de nouvelles contraintes auxquelles il est nécessaire de faire face pour éviter des conséquences trop néfastes. Mais la pérennité et le développement des systèmes agricoles et forestiers ne dépendent pas seulement de l’évolution du climat. L’urbanisation, l’occupation des sols, les pollutions locales (sol, air, eau), les incendies, mais aussi les pratiques culturales et la gestion forestière, jouent un rôle fondamental. Il convient donc de privilégier une approche transversale. Cette publication souligne les conséquences du changement climatique sur l’agriculture et la forêt en prenant soin d’identifier les enjeux environnementaux, économiques et sociaux, les risques à l’échelle régionale et locale, mais aussi les solutions susceptibles de réduire les impacts (atténuation, adaptation) et les éventuelles opportunités à saisir. Comme dans le précédent cahier, la contribution des chercheurs et experts, exerçant leur métier en région PACA et dans les territoires limitrophes4, sous forme d’articles et de zooms, apporte des éléments de compréhension afin de mieux cerner les problématiques liées au changement climatique.

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24 results on '"Patrick Bertuzzi"'

1. Pour un portail INRAE d’information sur les impacts et l’adaptation au changement climatique de l’agriculture, la forêt et les ressources en eau. Cadrage, état des lieux et propositions

2. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

3. Conceptual basis, formalisations and parameterization of the STICS crop model, second edition

4. AgGlob: Workflow for simulation of agronomic models at a global scale

5. The PERPHECLIM ACCAF Project – perennial fruit crops and forest phenology evolution facing climatic changes

6. Simulation of maize evapotranspiration: An inter-comparison among 29 maize models

7. How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

8. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models

9. Multi-wheat-model ensemble responses to interannual climate variability

10. The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

11. Multimodel ensembles of wheat growth: many models are better than one

12. Statistical analysis of large simulated yield datasets for studying climate change effects

13. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

14. PERPHECLIM ACCAF Project perennial fruit crops and forest phenology evolution facing climatic changes

15. Understanding dormancy release in apricot flower buds ([i]Prunus armeniaca[/i] L.) using several process-based phenological models

16. Estimating the root-zone soil moisture from the combined use of time series of surface soil moisture and SVAT modelling

17. Uncertainty in simulating wheat yields under climate change

18. Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling

19. Estimation of energy fluxes and photosynthesis from thermal infrared, spectral reflectances, microwave data and SVAT modeling

20. Mesure de l'humidite des sols par une methode capacitive : analyse des facteurs influencant la mesure

21. Régression linéaire avec erreur sur les variables : application à l'étalonnage d'un gammadensimètre à transmission et d'un humidimètre à neutrons

22. Calibration, field-testing, and error analysis of a gamma-ray probe for in situ measurement of dry bulk density

23. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 31 Maize Models

24. Les effets du changement climatique sur l’agriculture et la forêt en Provence-Alpes-Côte d’Azur

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24 results on '"Patrick Bertuzzi"'

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