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1. Stics Soil Crop Model

Author: Nicolas Beaudoin, Patrice Lecharpentier, Dominique Ripoche-Wachter, Loïc Strullu, Bruno Mary, Joël Léonard, Marie Launay, and Éric Justes
Published: 2022

2. Modelling intercrops functioning to advance the design of innovative agroecological systems

Author: Rémi Vezy, Sebastian Munz, Noémie Gaudio, Marie Launay, Patrice Lecharpentier, Dominique Ripoche, and Eric Justes
Abstract: The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between the plants, their environment and the agricultural practices. Crop models are crucial to understand and predict such interactions, yet few are able to simulate bi-specific intercrops correctly, mainly because they contradict assumptions used to simulate sole crops.In this study, we propose simple and generic formalisms for key interactions in intercropping systems that can be readily included into existing dynamic crop models. We provide an implementation into the STICS soil-crop model with an independent evaluation of the consistency and genericity of the combined formalisms under a wide range of conditions. Simulations were close to observations for all situations (nRMSE = 25% for max. LAI, 22% for shoot biomass at harvest, and 17% for yield), which showed the consistency and accuracy of the proposed formalisms despite their relative simplicity.
Published: 2022

3. Climate change impact and adaptation for wheat protein

Author: Mohamed Jabloun, Pierre Martre, Garry O'Leary, Dominique Ripoche, Bruno Basso, Pramod K. Aggarwal, Daniel Wallach, Matthew P. Reynolds, Marijn van der Velde, John R. Porter, Heidi Webber, Enli Wang, Frank Ewert, Joost Wolf, Christian Klein, Belay T. Kassie, Christian Biernath, Margarita Garcia-Vila, M. Ali Babar, Pierre Stratonovitch, Yujing Gao, Glenn J. Fitzgerald, Davide Cammarano, Bing Liu, Peter J. Thorburn, Fulu Tao, Andrew J. Challinor, Reimund P. Rötter, Christine Girousse, Zhigan Zhao, Christoph Müller, Ann-Kristin Koehler, Jørgen E. Olesen, Elias Fereres, Iwan Supit, Andrea Maiorano, Marco Bindi, Sebastian Gayler, Kurt Christian Kersebaum, Giacomo De Sanctis, Alex C. Ruane, Rosella Motzo, Juraj Balkovic, Manuel Montesino San Martin, Roberto Ferrise, Mikhail A. Semenov, Claudio O. Stöckle, Soora Naresh Kumar, Gerrit Hoogenboom, Benjamin Dumont, Ehsan Eyshi Rezaei, Mukhtar Ahmed, Senthold Asseng, Thilo Streck, Yan Zhu, R. Cesar Izaurralde, Katharina Waha, Ahmed M. S. Kheir, Taru Palosuo, Liujun Xiao, Sara Minoli, Eckart Priesack, Heidi Horan, Curtis D. Jones, Francesco Giunta, Zhao Zhang, Claas Nendel, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, 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), 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), European Food Safety Authority = Autorité européenne de sécurité des aliments, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Department of Economic Drt and Resources, Grains Innovation Park, Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Faculty of Veterinary and Agricultural Science [Melbourne], Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Department of Agricultural Sciences, University of Naples Federico II = Università degli studi di Napoli Federico II, World Food Crops Breeding, Department of Agronomy, IFAS, University of Florida [Gainesville] (UF), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Soils, Water and Environment Research Institute, Agricultural Research Center (ARC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, University of El-Tarf, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Earth and Environmental Sciences [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Institute of Biochemical Plant Pathology, Research Center for Environmental Health, Helmholtz Zentrum München = German Research Center for Environmental Health, Department of Agri‐food Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence (UniFI), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Collaborative Research Program from CGIAR and Future Earth on Climate Change, Agriculture and Food Security (CCAFS), International Center for Tropical Agriculture, GMO Unit, European Food Safety Authority, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Crop Sciences, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Biochemical Plant Pathology [Neuherberg], German Research Center for Environmental Health - Helmholtz Center München (GmbH), National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Member of the Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Centre for Environment Science and Climate Resilient Agriculture [New Delhi], Indian Agricultural Research Institute (IARI), Natural Resources Institute Finland (LUKE), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), 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)-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), University of Lincoln, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Water & Food and Water Systems & Global Change Group, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Joint Research Centre (IPTS), Commission Européenne, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), CSIRO Agriculture and Food (CSIRO), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), National Research Foundation for the Doctoral Program of Higher Education of China, Grant/Award Number: 20120097110042, International Food Policy, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), 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 la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Georg-August-University [Göttingen], Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, University of Naples Federico II, Helmholtz-Zentrum München (HZM), Universtiy of Florence, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Natural Resources Institute Finland, 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), Wageningen University, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Georg-August-Universität Göttingen, University of Goettingen, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Research Program on Climate Change, Agriculture and Food Security, BISA‐CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Comenius University [Bratislava], Helmholtz Zentrum München, Institute of Crop Science and Resource Conservation INRES, University of Bonn, IAS‐CSIC, Universidad de Cordoba, Institute for Sustainable Food Systems, Texas A&M AgriLife Research and Extension Center, Aarhus University, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Wageningen University and Research Center (WUR), Beijing Normal University, and China Agricultural University
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Water en Voedsel, 01 natural sciences, grain protein, adaptation au milieu, climate change adaptation, climate change impact, food security, wheat, Co2 concentration, adaptation to the environment, Triticum, General Environmental Science, 2. Zero hunger, changement climatique, Global and Planetary Change, Food security, Ecology, Temperature, food and beverages, Adaptation, Physiological, Droughts, Nitrogen, Climate Change, Climate change, 010603 evolutionary biology, blé, Crop production, Food Quality, Grain quality, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, Grain Proteins, global change, 0105 earth and related environmental sciences, WIMEK, Water and Food, Global change, Carbon Dioxide, Models, Theoretical, 15. Life on land, Agronomy, 13. Climate action, Grain yield, Environmental science, Water Systems and Global Change, Protein concentration
Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).
Published: 2019

4. 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

5. 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

6. Simulation using the STICS model of C&N dynamics in alfalfa from sowing to crop destruction

Author: Nicolas Beaudoin, Gaëtan Louarn, Lucia Rakotovololona, Françoise Ruget, Dominique Ripoche, Pascal Thiebeau, Bruno Mary, Loïc Strullu, Bernadette Julier, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ASAE, 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), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), Transfrontalière BioEcoAgro (Transfrontalière BioEcoAgro), and 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)
Subjects: 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Soil Science, chemistry.chemical_element, Biomass, Plant Science, 01 natural sciences, Crop, chemistry.chemical_compound, Crop destruction, Nitrate, Forage quality, Crop model, 2. Zero hunger, Abiotic component, Alfalfa, fungi, Sowing, food and beverages, 04 agricultural and veterinary sciences, 15. Life on land, Nitrogen, chemistry, Agronomy, Perennial reserves, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Soil mineral nitrogen, Agronomy and Crop Science, Cropping, 010606 plant biology & botany
Abstract: International audience; We adapted the STICS agro-environmental model to simulate the effects of cultivation practices on the biomass production and nitrogen accumulation of perennial crops undergoing regular defoliation, using alfalfa as an example. A unique set of parameters was used to simulate both establishment and regrowth phases over several years, with the assumption that crop growth is driven by interaction between crop development stage and abiotic stresses. The model accurately simulated the total biomass (stems + leaves + crown + taproot + roots) and aboveground biomass of the crop, with model efficiencies of 0.75 and 0.70, respectively, and relative root mean squared errors (rRMSE) of 42% and 36%, respectively. The evaluation results were also satisfactory with respect to total nitrogen content and the aboveground biomass nitrogen content, with model efficiencies of 0.90 and 0.60, respectively, and rRMSE values of 29% and 31%, respectively. The model thus enabled simulations of both the establishment and regrowth of alfalfa and accurately reproduced its seasonal patterns of growth, even though it tended to underestimate spring biomass production. It also produced accurate simulations of the water and nitrate contents of the soil during cropping and after crop destruction. It could therefore be a useful tool regarding the multi-criteria assessment of cropping systems based on alfalfa with respect to their sustainability.
Published: 2020

7. High-resolution assessment of French grassland dry matter and nitrogen yields

Author: Dominique Ripoche, Eric Casellas, Francoise Ruget, Jean-Louis Peyraud, Françoise Vertès, R. Resmond, Anne-Isabelle Graux, Luc Delaby, Philippe Faverdin, Olivier Therond, C. Le Bas, 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), 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), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Sol Agro et hydrosystème Spatialisation (SAS)
Subjects: 0106 biological sciences, modèle de prédiction, Nitrogen yield, [SDV]Life Sciences [q-bio], matière sèche, Soil Science, Forage, rendement, Plant Science, Dry matter yield, 01 natural sciences, Grassland, Modelling, Crop, Grazing, modélisation, 2. Zero hunger, azote, geography, geography.geographical_feature_category, modèle mécaniste, prairie, STICS, 04 agricultural and veterinary sciences, Vegetation, 15. Life on land, Snow, Agronomy, 13. Climate action, Soil water, Frost, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, France, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Grasslands offer many environmental and economic advantages that put them at the heart of future sustainable ruminant production systems. This study aimed to quantify and map the dry matter yield (DMY) and nitrogen yield (NY) of French grasslands resulting from cutting and grazing practices, based on the existing diversity of grassland vegetation, management, soil and climate conditions, using a research version of the STICS crop model called PâturSTICS. This model simulates daily dry matter (DM), nitrogen (N) and water fluxes involved in the functioning of grasslands and crops in response to management and environmental conditions. It was improved to represent deposition of animal waste on grassland soils during grazing and to simulate DM production and N content of grasses and legumes more accurately. Simulations were performed for locations across France on a highresolution grid composed of pedoclimatic units (PCU) obtained by combining the spatial resolutions of climate and soil. The main grassland types and associated management types were determined for each PCU and then simulated over 30 years (1984-2013). Using the simulated values, predictive metamodels of annual grassland DMY and NY were developed from easily accessible explanatory variables using a random forest approach. Annual model predictions were aggregated and averaged at the PCU scale, then compared to regional observations. Predicted DMY agreed with available observations, except in semi-mountainous and mountainous regions, where PâturSTICS tended to overpredict DMY, probably because it ignores effects of snow, frost and slope, and due to how it represents effects of temperature and water stress on plant growth. According to results, three-quarters of French grasslands produce and export at least 7.6 t DM ha-1 yr-1 and 172 kg N ha-1 yr-1, respectively. One-quarter of French grasslands produce and export at least 10.7 t DM ha-1 yr-1 and 254 kg N ha-1 yr-1, respectively. The latter are located mainly in northwestern France, the north-western Massif Central, the French Alps and the western Pyrénées, all of which have environmental conditions favourable for grass growth. The metamodels developed are interesting proxies for PâturSTICS' predictions of grassland DMY and NY. Our results provided valuable knowledge that promotes better use of the potential forage production of French and European grasslands to improve protein selfsufficiency and N fertilisation management in ruminant livestock systems.
Published: 2020

8. Climate change effects on leaf rust of wheat: Implementing a coupled crop-disease model in a French regional application

Author: Dominique Ripoche, Marie Odile Bancal, Marie Launay, David Gouache, Frédéric Huard, Samuel Buis, Julie Caubel, Laurent Huber, François Brun, Instituts techniques agricoles (ACTA), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), ARVALIS - Institut du végétal [Paris], Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, MICMAC Design project, ANR-09-STRA-06 and the ACCAF-CLIF project (Climate change and fungal diseases), and Agroclim (AGROCLIM)
Subjects: 0106 biological sciences, Canopy, STICS soil-crop model, maladie foliaire, sporulation, [SDE.MCG]Environmental Sciences/Global Changes, Microclimate, Soil Science, Climate change, Context (language use), Plant Science, Biology, 01 natural sciences, Rust, MILA model, high temperature, Crop, Effects of global warming, modèle sol culture, Durum wheat, Overwintering, triticum durum, 2. Zero hunger, rouille jaune du blé, Ecology, food and beverages, Puccinia triticina, puccinia, 04 agricultural and veterinary sciences, 15. Life on land, Foliar diseases, modèle couplé stics - mila, Agronomy, blé dur, 13. Climate action, hard wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Climate change impact, adaptation au changement climatique, haute température, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Leaf rust is responsible for significant wheat yield losses. Its occurrence and severity have increased in recent years, partly because of warmer climate. It is therefore critical to understand and anticipate the effects of climate change on leaf rust. Direct climate effects and indirect effects via host plants that provide a biophysical environment for disease development were both considered. The coupled STICS-MILA model simulates both crop and pathogen dynamics in a mechanistic way and their interaction is managed by two sub-models: one calculating the microclimate within the canopy and the other converting numbers of spores and lesions to affected surfaces. In this study, STICS-MILA was first calibrated and evaluated using leaf rust severity observed at various sites in France for multiple years. STICS-MILA was then run on three contrasting French sites under 2.6, 4.5 and 8.5 RCP future climate scenarios. Results focused firstly on changes in disease earliness and intensity, secondly on disease dynamics, particularly the synchronism between plant and disease developments, and finally on elementary epidemic processes. The calibration and evaluation of STICS-MILA revealed a high sensitivity to the initial amount of primary inoculum (a forcing variable in STICS-MILA) and thus the need to properly simulate the summering and overwintering pathogen survival. The simulations in the context of future climate showed a significant change in host-pathogen synchronism: in the far future, according to RCP 4.5 and 8.5 scenarios, disease onset is expected to occur not only with an advance of around one month but also at an earlier developmental stage of wheat crops. This positive effect results from rising temperatures, nevertheless partly counter-balanced during spring by lower wetness frequency. The crop growth accelerates during juvenile stages, providing a greater support for disease development. The resulting microclimate shortens latency periods and increases infection and sporulation efficiencies, thus causing more infectious cycles. An increase of final disease severity is thus forecasted with climate change.
Published: 2017

9. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author: Mónica Espadafor, Bing Liu, Frank Ewert, Mukhtar Ahmed, Liujun Xiao, Thilo Streck, Senthold Asseng, Soora Naresh Kumar, Gerrit Hoogenboom, John R. Porter, Sara Minoli, Margarita Garcia-Vila, Joost Wolf, Juraj Balkovic, Alex C. Ruane, Giacomo De Sanctis, Pierre Martre, Roberto C. Izaurralde, Marijn van der Velde, Dominique Ripoche, Roberto Ferrise, Davide Cammarano, Fulu Tao, Bruno Basso, Christoph Müller, Heidi Webber, Yujing Gao, Andrea Maiorano, Christian Klein, Ann-Kristin Koehler, Andrew J. Challinor, Reimund P. Rötter, Garry O'Leary, Manuel Montesino San Martin, Eckart Priesack, Peter J. Thorburn, Heidi Horan, Kurt Christian Kersebaum, Iwan Supit, Zhigan Zhao, Taru Palosuo, Belay T. Kassie, Christian Biernath, Pramod K. Aggarwal, Katharina Waha, Sebastian Gayler, Daniel Wallach, Yan Zhu, Marco Bindi, Zhao Zhang, Claas Nendel, Enli Wang, Curtis D. Jones, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), 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 la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), 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)-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), 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), University of Copenhagen = Københavns Universitet (KU), Lincoln University, University of Leeds, CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, Potsdam Institute for Climate Impact Research (PIK), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), 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), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, affiliation inconnue, Comenius University in Bratislava, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Agrifood Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), The James Hutton Institute, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Université de Liège, University of Córdoba, Department of Crop Sciences, Georg-August-University [Göttingen], Institute of Soil Science and Land Evaluation, University of Hohenheim, 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), Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, European Food Safety Authority (EFSA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Agriculture Victoria Research, Institute for Natural Resources, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, Rothamsted Research, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), European Commission - Joint Research Centre [Ispra] (JRC), 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, Plant Production Systems, Wageningen University and Research [Wageningen] (WUR), State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP), and Biotechnology and Biological Sciences Research Council (BBSRC), Grant/Award Number: BB/, P016855/1
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Food prices, Water en Voedsel, Climate change, Atmospheric sciences, 010603 evolutionary biology, 01 natural sciences, Model ensemble, Extreme low yields, model ensemble, 1.5°C warming, Temperate climate, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, WIMEK, Water and Food, Food security, Ecology, business.industry, wheat production, Crop yield, Global warming, food security, 15. Life on land, PE&RC, climate change, Plant Production Systems, 13. Climate action, Agriculture, Plantaardige Productiesystemen, extreme low yields, 8. Economic growth, Environmental science, Wheat production, Water Systems and Global Change, business, Cropping
Abstract: Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming, We thank the Agricultural Model Intercomparison and Improvement Project (AgMIP) for support. B.L., L.X., and Y.Z. were supported by the National Science Foundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (31801260, 51711520319, and 31611130182), the Natural Science Foundation of Jiangsu province (BK20180523), the 111 Project (B16026), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). S.A. and B.K. received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and the CGIAR Research Program on Wheat. P.M, D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493, 31761143006, and 31561143003). R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF) through project “Limpopo Living Landscapes” project (SPACES program; grant number 01LL1304A). Rothamsted Research receives grant‐aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project [BB/P016855/1]. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. acknowledges support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources. P.K.A. was supported by the multiple donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1). J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501‐003). La. T.P. and F.T. received financial support from the Academy of Finland through the project PLUMES (decision nos. 277403 and 292836) and from Natural Resources Institute Finland through the project ClimSmartAgri.
Published: 2019

10. ORCHIDEE-CROP (v0), a new process-based agro-land surface model : Model description and evaluation over Europe

Author: Benjamin Loubet, Nicolas Vuichard, Eric Ceschia, Christian Bernhofer, Tiphaine Tallec, Nicolas Viovy, Jan Elbers, P. Ciais, Luca Vitale, Martin Wattenbach, Xiuchen Wu, Anne De Ligne, Xuhui Wang, Dominique Ripoche, Christine Moureaux, Eddy Moors, Eric Larmanou, Pierre Cellier, Vincenzo Magliulo, Thomas Grünwald, N. de Noblet-Ducoudré, W.W.P. Jans, P. Di Tommasi, Tanguy Manise, Leibniz-Institut für Katalyse (LIKAT Rostock), Universität Rostock-Leibniz Association, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), State Key Laboratory on Electrical Insulation and Power Equipment (SKLEI), Xi'an Jiaotong University (Xjtu), Institute for Agriculture and Forest Systems in the Mediterranean, German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Alterra [Wageningen] (ESS-CC), Centre for Water and Climate [Wageningen], Centre National de la Recherche Scientifique (CNRS), Centre National d'Études Spatiales [Toulouse] (CNES), Institut de Recherche pour le Développement (IRD [France-Ouest]), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Hydrology and Meteorology [Dresden], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Gembloux Agro-Bio Tech [Gembloux], Université de Liège, Environnement et Grandes Cultures (EGC), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Consiglio Nazionale delle Ricerche [Roma] (CNR), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Leibniz Association-Universität Rostock, Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) - Grenoble, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dresden (TUD), AgroParisTech-Institut National de la Recherche Agronomique (INRA), UE Agroclim (UE AGROCLIM), Earth and Climate, and Amsterdam Global Change Institute
Subjects: 010504 meteorology & atmospheric sciences, Energy balance, Eddy covariance, Sensible heat, Atmospheric sciences, 01 natural sciences, Latent heat, Temperate climate, Life Science, Leaf area index, SDG 2 - Zero Hunger, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, 2. Zero hunger, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Phenology, Crop yield, lcsh:QE1-996.5, 04 agricultural and veterinary sciences, 15. Life on land, lcsh:Geology, Climate Resilience, Agronomy, 13. Climate action, Klimaatbestendigheid, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: International audience; The response of crops to changing climate and atmospheric CO 2 concentration ([CO 2 ]) could have large effects on food production, and impact carbon, water, and energy fluxes, causing feedbacks to the climate. To simulate the response of temperate crops to changing climate and [CO 2 ], which accounts for the specific phenology of crops mediated by management practice, we describe here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module, and a very simple pa-rameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate bio-physical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but is tested here using maize and winter wheat, with the phenological parameteriza-tions of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO 2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m 2 m −2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop Published by Copernicus Publications on behalf of the European Geosciences Union. 858 X. Wu et al.: ORCHIDEE-CROP (v0), a new process-based agro-land surface model phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of ∼ 9.0-20.1 and ∼ 9.4-22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was ∼ 8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements , which themselves showed an incomplete energy balance closure within the range 80.6-86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to un-realistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.
Published: 2016

11. How does STICS crop model simulate crop growth and productivity under shade conditions?

Author: F. Ruget, Sidonie Artru, Marie Launay, Benjamin Dumont, Ludivine Lassois, Dominique Ripoche, Sarah Garré, Université de Liège, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), and Institut National de la Recherche Agronomique (INRA)
Subjects: 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Grain number, STICS model, Soil Science, 01 natural sciences, Crop, Shade, Yield (wine), Dry matter, Agroforestry, Grain yield, 2. Zero hunger, Sunlight, Biomass (ecology), 04 agricultural and veterinary sciences, 15. Life on land, Winter wheat, Available light, Agronomy, Productivity (ecology), Shoot, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Most crop models have been developed with crops growing under full sunlight conditions and they commonly use daily cumulated global radiation as part of the climatic input data. This approach neglects the spatio-temporal dimension of the light reduction experienced by the crop in agroforestry systems. In this study, we evaluate the ability of the crop model STICS to predict winter wheat (Triticum aestivum L.) growth and yield under three distinct light conditions using field observations from a two year artificial shade experiment. The shade structure induced a continuous shade (CS) treatment characterized by a reduction of the proportion of light during the entire day and a periodic shade (PS) treatment defined by an intermittent shade varying on the plot throughout the day. These two shade conditions were compared to a no shade treatment (NS) receiving 100% of the available light. The model accurately predicted the timing of the grain maturity stage under the PS treatment by reducing the daily global radiation only. A correct prediction of this growth stage in the CS treatment required a decrease of the daily maximum air temperature in addition to the reduction of global radiation. Overall, the model accurately reproduces the total aboveground dry matter dynamics under the CS and NS treatments, but did not simulate the reduction observed under the PS treatment correctly. Three parameters (nbjgrain, cgraln and cgrainvo) involved in the determination of the number of grains have been calibrated with the NS treatment data and were then used to predict the crop behavior under the shaded treatments. Using this adjusted parameter set, the STICS model gave a good prediction of the grain number under all treatments. Nevertheless, the simulation of final grain yield under the shade treatments was not satisfactory yet, presumably due to an overestimation of the reallocation of the biomass between shoots and grains. Improving the prediction of these reallocation processes is challenging and critical to improve the simulation of crop behavior under fluctuating light environments such as encountered in agroforestry systems.
Published: 2018

12. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

Author: Roberto C. Izaurralde, Jakarat Anothai, Andrew J. Challinor, Reimund P. Rötter, Jørgen E. Olesen, Curtis D. Jones, Bing Liu, T. Palosuo, Peter J. Thorburn, Kurt Christian Kersebaum, Mohamed Jabloun, Iwan Supit, Frank Ewert, Mikhail A. Semenov, Margarita Garcia-Vila, Claudio O. Stöckle, Benjamin Dumont, Belay T. Kassie, Jordi Doltra, Christian Biernath, Dominique Ripoche, Giacomo De Sanctis, Enli Wang, Elias Fereres, Bruce A. Kimball, Thilo Streck, Gerard W. Wall, L. A. Hunt, Pierre Stratonovitch, Fulu Tao, Jeffrey W. White, Christoph Müller, Bruno Basso, Senthold Asseng, Katharina Waha, Ann-Kristin Koehler, Andrea Maiorano, Ehsan Eyshi Rezaei, Eckart Priesack, Soora Naresh Kumar, Claas Nendel, Gerrit Hoogenboom, Davide Cammarano, David B. Lobell, Joost Wolf, Pierre Martre, Pramod K. Aggarwal, Garry O'Leary, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Yan Zhu, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), 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 la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, 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, Consultative Group on International Agricultural Research (CGIAR), AgWeatherNet Program, Washington State University (WSU), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Landscape & Water Sciences, Department of Environment of Victoria, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, PPS and WSG &CALM, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), College of Agronomy and Biotechnology, and Southwest University
Subjects: Simulations, 0106 biological sciences, Irrigation, 010504 meteorology & atmospheric sciences, Water en Voedsel, Wheat, Field experimental data, Heat stress, Crop model simulations, AgMIP, Hot Serial Cereal, 01 natural sciences, donnée expérimentale, Crop, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Cultivar, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Water and Food, Vegetal Biology, Global warming, Sowing, essai en plein champ, food and beverages, série climatique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Agronomy, Plant Production Systems, 13. Climate action, Plantaardige Productiesystemen, Modeling and Simulation, Frost, Weather data, Environmental science, Water Systems and Global Change, stress hydrique, Biologie végétale, modèle de production, 010606 plant biology & botany
Abstract: The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F
Published: 2018

13. Analyzing ecosystem services in apple orchards using the STICS model

Author: Marie Launay, Nicolas Beaudoin, Michel Génard, Christiane Raynal, Daniel Plénet, Constance Demestihas, Sylvaine Simon, Dominique Ripoche, Françoise Lescourret, Iñaki García de Cortázar-Atauri, Marie Charreyron, Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de la Recherche Agronomique (INRA), Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL), Agroclim (AGROCLIM), Agroressources et Impacts environnementaux (AgroImpact), Unité Expérimentale Recherches Intégrées - Gotheron (UERI), Station expérimentale La Pugère, and Centre de Lanxade
Subjects: Denitrification, Soil Science, Plant Science, 010501 environmental sciences, Carbon sequestration, 01 natural sciences, Pedoclimatic condition, Ecosystem services, Soil functions, Apple orchard, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Ecosystem, Leaching (agriculture), Crop model, Nitrogen cycle, 0105 earth and related environmental sciences, 2. Zero hunger, Agroforestry, STICS, 04 agricultural and veterinary sciences, 15. Life on land, Agronomy, 13. Climate action, 040103 agronomy & agriculture, Multiple ecosystem services, 0401 agriculture, forestry, and fisheries, Environmental science, Orchard, Agronomy and Crop Science, Cropping system
Abstract: International audience; Fruit tree production faces the major challenge of ensuring maximal productivity with due consideration for the environment and human health. The increasingly recognized concept of ecosystem service could help to address this duality. In this paper, we propose an analytical framework based on a soil crop model to investigate how agricultural management and pedoclimatic conditions affect the joint production of marketed and non-marketed ecosystem services through underlying ecosystem functions in apple orchards. The ecosystem services considered on an annual scale were soil nitrogen availability, climate regulation, water regulation and fruit production. Ecosystem functions and services were described by specific indicators that were quantified using the STICS soil crop model. This model was parameterized using data collected on two experimental apple orchard sites under conventional and low-input or organic management in southeastern France. The interdependencies between environmental components, cultural operations and ecosystem functions were dynamically integrated by the model and highlighted significant interactions between the indicators of ecosystem services. Thus, the service indicators soil organic nitrogen variation and the prevention of nitrogen denitrification and of leaching were positively correlated and in conflict with soil mean nitrate concentration and mean soil humidity. They were also linked negatively to nitrogen mineralization enhanced by irrigation and positively to soil carbon sequestration impacted by fertilization; these two functions were impacted by soil conditions. Yield and carbon sequestration presented a strong synergy and were positively correlated to nitrogen absorption increased by mineral fertilization. Globally, nitrogen fertilization management and planting density were particularly important for the delivery of multiple ecosystem services, but soil and climate effects were far from negligible, especially for nitrogen and water-related services. The ecosystem service profiles of the studied cropping systems were diversified, with contrasted profiles showing high yield and carbon sequestration but low prevention of nitrogen denitrification and of nitrogen leaching, and more balanced profiles. The STICS crop model made it possible to quantify and analyze profiles of ecosystem services and should be helpful in instrumenting the dialogue between fruit growers and other stakeholders by simulating scenarios to optimize multiple services. However, it has to be improved to address the impact of grass cover on soil functions and the long-term functioning of apple orchards.
Published: 2018

14. Multimodel ensembles improve predictions of crop–environment–management interactions

Author: Giacomo De Sanctis, Taru Palosuo, Davide Cammarano, Frank Ewert, Soora Naresh Kumar, Roberto C. Izaurralde, Gerrit Hoogenboom, Elias Fereres, Bing Liu, Thilo Streck, Mikhail A. Semenov, Ehsan Eyshi Rezaei, Peter J. Thorburn, Claudio O. Stöckle, Benjamin Dumont, Andrea Maiorano, Eckart Priesack, Iwan Supit, Heidi Horan, Kurt Christian Kersebaum, Pierre Martre, Margarita Garcia-Vila, Dominique Ripoche, Pierre Stratonovitch, Yujing Gao, Zhao Zhang, Ann-Kristin Koehler, Curtis D Jones, Fulu Tao, Claas Nendel, Christoph Müller, Andrew J. Challinor, Reimund P. Rötter, Mukhtar Ahmed, Senthold Asseng, Christine Girousse, Bruno Basso, Christian Klein, Pramod K. Aggarwal, Joost Wolf, Glenn J. Fitzgerald, Martin K. van Ittersum, Garry O'Leary, Belay T. Kassie, Christian Biernath, Sebastian Gayler, Daniel Wallach, Sara Minoli, 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, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), 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 la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Nanjing Agricultural University, 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, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Systems Group, Wageningen University and Research [Wageningen] (WUR), Agriculture and Food Security (CCAFS), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Plant Pathology, The James Hutton Institute, University of Leeds, Consultative Group on International Agricultural Research (CGIAR), GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Center for Development Research, University of Córdoba, Agriculture Victoria Research, University of Melbourne, Institute of Soil Science and Land Evaluation, University of Hohenheim, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), University of Florida [Gainesville] (UF), Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Institute of landscape systems analysis, School of Earth and Environment, European Food Safety Authority (EFSA), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), University Medical Center Göttingen (UMG), Computational and Systems Biology Department, Rothamsted Research, Water & Food and Water Systems & Global Change Group, Wageningen University, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Université de Toulouse (UT)-Université de Toulouse (UT), 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), Nanjing Agricultural University (NAU), Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), and Chinese Academy of Sciences [Beijing] (CAS)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Mean squared error, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, Inverse, Water en Voedsel, Monotonic function, multi-model ensemble, Environment, High skill, 01 natural sciences, crop models, ensemble mean, Statistics, Environmental Chemistry, Triticum, 0105 earth and related environmental sciences, General Environmental Science, Mathematics, 2. Zero hunger, ensemble median, Global and Planetary Change, WIMEK, Water and Food, Ecology, Ensemble average, Simulation modeling, Agriculture, 04 agricultural and veterinary sciences, Variance (accounting), prediction, Models, Theoretical, PE&RC, Plant Production Systems, multimodel ensemble, 13. Climate action, Plantaardige Productiesystemen, climate change impact, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Water Systems and Global Change, Environment management
Abstract: International audience; A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2‐6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
Published: 2018

15. Harmonization and translation of crop modeling data to ensure interoperability

Author: Dirk Raes, Dean Holzworth, Ritvik Sahajpal, Rob Knapen, Chris Villalobos, James W. Jones, Julien Cufi, Kenneth J. Boote, R. Nelson, Sander Janssen, Dominique Ripoche, Ioannis N. Athanasiadis, Cheryl H. Porter, Jeffrey W. White, Meng Zhang, 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), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Biological Systems Engineering, Washington State University (WSU), United States Department of Agriculture (USDA), Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTH), Wageningen University and Research Centre (WUR), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Université Catholique de Louvain = Catholic University of Louvain (UCL), Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, iPlant Collaborative, and UK Department for International Development
Subjects: Collaborative software design, Earth Observation and Environmental Informatics, Engineering, Environmental Engineering, Systems simulation, [SDV]Life Sciences [q-bio], seamless, Interoperability, protocols, integration, Harmonization, computer.software_genre, nitrogen, Data modeling, Database, framework, Aardobservatie en omgevingsinformatica, simulate yield response, Schema, Crop model, 2. Zero hunger, systems simulation, business.industry, Ecological Modeling, Experimental data, Data structure, Data science, openmi, 13. Climate action, Data exchange, [SDE]Environmental Sciences, climate-change, Economic model, business, computer, Software
Abstract: The Agricultural Model Intercomparison and Improvement Project (AgMIP) seeks to improve the capability of ecophysiological and economic models to describe the potential impacts of climate change on agricultural systems. AgMIP protocols emphasize the use of multiple models; consequently, data harmonization is essential. This interoperability was achieved by establishing a data exchange mechanism with variables defined in accordance with international standards; implementing a flexibly structured data schema to store experimental data; and designing a method to fill gaps in model-required input data. Researchers and modelers are able to use these tools to run an ensemble of?models on a single, harmonized dataset. This allows them to compare models directly, leading ultimately to model improvements. An important outcome is the development of a platform that facilitates researcher collaboration from many organizations, across many countries. This would have been very difficult to achieve without the AgMIP data interoperability standards described in this paper. Heterogeneous data can be harmonized and translated to multiple model formats.The ICASA data standards provide an extensible data structure and ontology.JSON structures provide a flexible, efficient means of handling heterogeneous data.DOME functions enable a consistent means of providing missing or inadequate data.Data provenance is maintained from data sources through simulated model outputs.
Published: 2014

16. Evolution of the STICS crop model to tackle new environmental issues: New formalisms and integration in the modelling and simulation platform RECORD

Author: Eric Casellas, E. Coucheney, Dominique Ripoche, F. Ruget, Bruno Mary, Eric Justes, Helene Raynal, Julie Caubel, Patrick Chabrier, I. Garcia de Cortazar-Atauri, Jacques-Eric Bergez, Jérôme Dury, Marie Launay, Nicolas Beaudoin, 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, INRA, UR875, MIAT, Mathématiques et Informatique Appliquées de Toulouse (MIAT), Institut National de la Recherche Agronomique (INRA)-Institut National de la Recherche Agronomique (INRA), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), UR 1158 Agroressources et impacts environnementaux, Institut National de la Recherche Agronomique (INRA)-Agroressources et impacts environnementaux (AgroImpact)-Environnement et Agronomie (E.A.)-Biologie et Amélioration des Plantes (BAP), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), Agroressources et Impacts environnementaux (AgroImpact), INRA, ARVALIS, CETIOM, CEFIPRA, and ANR-09-STRA-0006,MicMac Design,Conception et évaluation par expérimentation et modélisation de prototypes de systèmes de culture intégrés à bas niveaux d'intrants(2009)
Subjects: Engineering, Environmental Engineering, 010504 meteorology & atmospheric sciences, Process (engineering), 01 natural sciences, Field (computer science), 0105 earth and related environmental sciences, 2. Zero hunger, Innovative cropping systems, Scope (project management), business.industry, Ecological Modeling, Scale (chemistry), Environmental resource management, 04 agricultural and veterinary sciences, 15. Life on land, Encapsulation (networking), Water management, Work (electrical), In silico experiments, [SDE]Environmental Sciences, Soil processes, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, business, Cropping, Software, Farmer's decision-making modelling
Abstract: To address new environmental and social issues, crop models need to widen their scope and be linked to other tools. The crop model STICS was encapsulated in the modelling platform RECORD and new process-based developments were added to address environmental issues. We present plant and soil processes developed recently in STICS and describe three projects using STICS within RECORD: MICMAC-Design aims to design innovative cropping systems at field scale, integrating economic and epidemiological analysis and using a database to represent agricultural management; CRASH aims to develop and evaluate crop-allocation strategies at farm scale that meet water-shortage regulations, using links with databases, optimisation processes and farmers' representation; AICHA aims to analyse impacts of irrigation on the water table at catchment scale using links to a hydrological model, cluster computation, integrated economic and agronomic optimisation. Automated encapsulation procedures allow STICS and RECORD communities to work independently but to benefit from mutual exchanges. The crop model STICS was encapsulated in the modelling and simulation platform RECORD.This coupling allowed addressing larger environmental and social issues.The two research community can evolve independently thanks to automated encapsulation procedures.Several pluridisciplinary research projects benefit from this coupling.
Published: 2014

17. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Enli Wang, Pierre Martre, Zhigan Zhao, Frank Ewert, Andrea Maiorano, Reimund P. Rötter, Bruce A. Kimball, Michael J. Ottman, Gerard W. Wall, Jeffrey W. White, Matthew P. Reynolds, Phillip D. Alderman, Pramod K. Aggarwal, Jakarat Anothai, Bruno Basso, Christian Biernath, Davide Cammarano, Andrew J. Challinor, Giacomo De Sanctis, Jordi Doltra, Elias Fereres, Margarita Garcia-Vila, Sebastian Gayler, Gerrit Hoogenboom, Leslie A. Hunt, Roberto C. Izaurralde, Mohamed Jabloun, Curtis D. Jones, Kurt C. Kersebaum, Ann-Kristin Koehler, Leilei Liu, Christoph Müller, Soora Naresh Kumar, Claas Nendel, Garry O’Leary, Jørgen E. Olesen, Taru Palosuo, Eckart Priesack, Ehsan Eyshi Rezaei, Dominique Ripoche, Alex C. Ruane, Mikhail A. Semenov, Iurii Shcherbak, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Peter Thorburn, Katharina Waha, Daniel Wallach, Zhimin Wang, Joost Wolf, Yan Zhu, and Senthold Asseng
Subjects: Plant Science
Published: 2017

18. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Alex C. Ruane, Peter J. Thorburn, Mikhail A. Semenov, Joost Wolf, Claudio O. Stöckle, Pramod K. Aggarwal, Gerard W. Wall, Margarita Garcia-Vila, Matthew P. Reynolds, Eckart Priesack, Jørgen E. Olesen, Enli Wang, Bruce A. Kimball, Jordi Doltra, Iurii Shcherbak, Ehsan Eyshi Rezaei, Jeffrey W. White, Leilei Liu, L. A. Hunt, Senthold Asseng, Frank Ewert, Yan Zhu, Fulu Tao, Christoph Müller, Daniel Wallach, Christian Biernath, Davide Cammarano, Mohamed Jabloun, Zhigan Zhao, Michael J. Ottman, Pierre Martre, Sebastian Gayler, Garry O'Leary, Zhimin Wang, Jakarat Anothai, Elias Fereres, Claas Nendel, Bruno Basso, Thilo Streck, Curtis D. Jones, Andrea Maiorano, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Taru Palosuo, Iwan Supit, Katharina Waha, Giacomo De Sanctis, Kurt Christian Kersebaum, Soora Naresh Kumar, Gerrit Hoogenboom, Dominique Ripoche, Pierre Stratonovitch, Ann-Kristin Koehler, Roberto C. Izaurralde, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), CSIRO, 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), College of Agronomy and Biotechnology, Southwest University, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Crop Sciences, University of Goettingen, Centre of Biodiversity and Sustainable Land Use (CBL), United States Department of Agriculture - Agricultural Research Service, The School of Plant Sciences, University of Arizona, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Cantabrian Agricultural Research and Training Centre, Dep. Agronomia, University of Córdoba [Córdoba], Spanish National Research Council (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A&M AgriLife Research and Extension Center, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland, Institute of Crop Science and Resource Conservation (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, University of Wisconsin-Madison, PPS and WSG &CALM, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Agriculture and Food, Universidad de La Rioja (UR), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, China Agricultural University, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), 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 la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), China Agricultural University (CAU), Institute of Crop Science and Resource Conservation [Bonn], Georg-August-University [Göttingen], Arid-Land Agricultural Research Center, Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), 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 Leeds, European Food Safety Authority (EFSA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), AgWeatherNet Program, Texas A and M AgriLife Research, Jiangsu Collaborative Innovation Center for Modern Crop Production, Landscape and Water Sciences, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Agricultural Sciences (CAAS), 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, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), 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 ), Leibniz Centre for Agricultural Landscape Research, International Maize and Wheat Improvement Center ( CIMMYT ), CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Washington State University ( WSU ), Michigan State University, Institute of Biochemical Plant Pathology ( BIOP ), Institute for Climate and Atmospheric Science, School of Earth and Environment, European Food Safety Authority, Spanish National Research Council ( CSIC ), Texas A and M University ( TAMU ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Department of Economic Development, Jobs, Transport and Resources ( DEDJTR ), University of Bonn (Rheinische Friedrich-Wilhelms), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Universidad de La Rioja ( UR ), University of Bonn-Division of Plant Nutrition, 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), USDA-ARS : Agricultural Research Service, Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], Natural resources institute Finland, Georg-August-University = Georg-August-Universität Göttingen, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: Crops, Agricultural, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Yield (finance), Water en Voedsel, Growing season, Climate change, klim, Plant Science, Agricultural engineering, Models, Biological, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, Life Science, Computer Simulation, Productivity, 0105 earth and related environmental sciences, 2. Zero hunger, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Water and Food, Food security, business.industry, Crop yield, Temperature, Agriculture, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Agronomy, Klimaatbestendigheid, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.
Published: 2017

19. Dans quelle mesure l’exportation d’azote par les prairies françaises peut-elle atténuer les pollutions azotées ?

Author: Anne-Isabelle Graux, Rémi Resmond, Eric Casellas, Luc Delaby, Remy Delagarde, Michel Duru, Philippe Faverdin, Christine Le Bas, Anne Meillet, Helene Raynal, Dominique Ripoche, Francoise Ruget, Thomas Poméon, Olivier Therond, Francoise Vertès, Jean-Louis Peyraud, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-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), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), 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, InfoSol (InfoSol), Observatoire des Programmes Communautaires de Développement Rural (US ODR), Agroclim (AGROCLIM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST, 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)-Institut National de la Recherche Agronomique (INRA)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, nitrate, lixiviation du nitrate, gestion de la prairie, directive européenne, ComputingMilieux_MISCELLANEOUS, Sciences agricoles, exportation d'azote, Agricultural sciences, modélisation, fertilisation
Abstract: National audience
Published: 2017

20. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

Author: L. A. Hunt, David B. Lobell, Phillip D. Alderman, Alex C. Ruane, Zhigan Zhao, T. Palosuo, Mohamed Jabloun, Margarita Garcia-Vila, Andrew J. Challinor, Reimund P. Rötter, Jordi Doltra, Dominique Ripoche, Jeffrey W. White, Bing Liu, Jakarat Anothai, Fulu Tao, Katharina Waha, Eckart Priesack, Sebastian Gayler, Pierre Stratonovitch, Andrea Maiorano, Davide Cammarano, Christoph Müller, Bruno Basso, Ehsan Eyshi Rezaei, Senthold Asseng, Claas Nendel, Joost Wolf, Curtis D. Jones, Ann-Kristin Koehler, Matthew P. Reynolds, Enli Wang, Belay T. Kassie, Christian Biernath, Soora Naresh Kumar, Pierre Martre, Frank Ewert, Iwan Supit, Jørgen E. Olesen, Gerrit Hoogenboom, Giacomo De Sanctis, Thilo Streck, Elias Fereres, Yan Zhu, Kurt Christian Kersebaum, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, Roberto C. Izaurralde, Peter J. Thorburn, Garry O'Leary, and Pramod K. Aggarwal
Subjects: Simulations, 0106 biological sciences, Water en Voedsel, klim, 01 natural sciences, Heat stress, Crop, heat stress, Anthesis, Yield (wine), wheat, Life Science, Cultivar, field experimental data, Biomass (ecology), WIMEK, Water and Food, Field experimental data, Sowing, 04 agricultural and veterinary sciences, PE&RC, Productivity (ecology), Agronomy, Plant Production Systems, Plantaardige Productiesystemen, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, simulations, Cropping, 010606 plant biology & botany
Abstract: All data are available via DOI http://doi.org/10.7910/DVN/ECSFZG, he data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
Published: 2017

21. 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

22. Performance of process-based models for simulation of grain N in crop rotations across Europe

Author: Gaëtan Louarn, Kiril Manevski, Isik Öztürk, Dominique Ripoche-Wachter, Iñaki García de Cortázar-Atauri, Xiaogang Yin, Remy Manderscheid, Nicolas Beaudoin, Mirek Trnka, Behzad Sharif, Sanmohan Baby, Lianhai Wu, Chris Kollas, Tobias Conradt, Thomas Gaiser, Holger Hoffmann, Jørgen E. Olesen, Bruno Mary, Taru Palosuo, Kurt Christian Kersebaum, Marie Launay, P. Hlavinka, Luisa Giglio, Claas Nendel, Wilfried Mirschel, Monia Charfeddine, Françoise Ruget, Domenico Ventrella, Reimund P. Rötter, Julie Constantin, Andreas Pacholski, Frank Ewert, Munir P. Hoffmann, Hans Joachim Weigel, Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Department of Agroecology, Aarhus University [Aarhus], Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie]), Rheinische Friedrich-Wilhelms-Universität Bonn, Rothamsted Research, Tropical Plant Prodution and Agricultural Systems Modelling (TROPAGS), Georg-August-University [Göttingen], Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi (CREA-SCA), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), 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, Agroclim (AGROCLIM), Global Change Research Institute, Czech Academy of Sciences [Prague] (CAS), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Mendel University in Brno (MENDELU), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Thünen Institute of Biodiversity, Graduate School/Inkubator, Leuphana University of Lüneburg, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), MACSUR project (2812ERA147) CARBIOCIAL research project (01LL0902 ) MACSUR project (031A103B), NORFASYS (decision nos. 268277 and 292944) MACSUR project (2851ERA01), Program I (NPU I LO1415) National Agency for Agricultural Research project no. QJ1310123., Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Georg-August-Universität Göttingen, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, UE Agroclim (UE AGROCLIM), Czech Academy of Sciences [Prague] (ASCR), and Mendel University in Brno
Subjects: Secale, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Irrigation, model evaluation, 010504 meteorology & atmospheric sciences, Grain N content, klim, 01 natural sciences, Sustainability Science, Crop, Sativum, crop rotation, model initialization, Cover crop, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Crop mode, biology, crop model, 04 agricultural and veterinary sciences, Crop rotation, biology.organism_classification, calibration, Agronomy, Ecosystems Research, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Continuous simulation, Animal Science and Zoology, Hordeum vulgare, Agronomy and Crop Science, grain N content
Abstract: The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, iii) under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena sativa L.), winter rye (Secale cereale L.), pea (Pisum sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism.
Published: 2017

23. Les prairies françaises : production, exportation d'azote et risques de lessivage

Author: Anne-Isabelle Graux, Luc Delaby, Jean-Louis Peyraud, Eric Casellas, Philippe Faverdin, Christine Le Bas, Anne Meillet, Thomas Poméon, Helene Raynal, Rémi Resmond, Dominique Ripoche, Francoise Ruget, Olivier Therond, Francoise Vertes, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-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), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), InfoSol (InfoSol), Observatoire des Programmes Communautaires de Développement Rural (US ODR), Agroclim (AGROCLIM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Sol Agro et hydrosystème Spatialisation (SAS), 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 la Recherche Agronomique (INRA), INRA, Ministère de l’Alimentation, l’Agriculture et de la Forêt, Contrat : 2101748631, Commanditaire : Ministère de l’Alimentation, l’Agriculture et de la Forêt (France), Type de commande : Commande avec contrat/convention/lettre de saisine, Type de commanditaire ou d'auteur de la saisine : Ministères, parlements et les structures qui leur sont directement rattachées, Date de signature : 2016-01-04, AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Unité INFOSOL (ORLEANS INFOSOL), and UE Agroclim (UE AGROCLIM)
Subjects: azote, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, production herbagère, lixiviation du nitrate, prairie, simulation models, manure spreading, modèle de simulation, nitrogen, modelling, herb, épandage, herbe, grassland, modélisation
Abstract: Les prairies françaises : production, exportation d'azote et risques de lessivage
Published: 2017

24. Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach

Author: Lana, M., Kersebaum, K. C., Kollas, C., Xiaogang Yin, Nendel, C., Kiril Manevski, Müller, C., Palosuo, T., Armas-Herrera, Ceclia M., Nicolas Beaudoin, Bindi, M., Monia Charfeddine, Tobias Conradt, Julie Constantin, Eitzinger, J., Ewert, F., Ferrise, R., Thomas Gaiser, Iñaki Garcia de Cortazar-Atauri, Luisa Giglio, Hlavinka, P., Hoffmann, H., Hoffmann, Munir P., Marie Launay, Remy Manderscheid, Bruno Mary, Mirschel, W., Moriondo, M., Olesen, Jørgen E., Isik Öztürk, Pacholski, A., Dominique Ripoche-Wachter, Pier Paolo Roggero, Svenja Roncossek, Rötter, R. P., Ruget, F., Behzad Sharif, Mirek Trnka, Domenico Ventrella, Waha, K., Martin Wegehenkel, Hans-Joachim Weigel, Wu, L., Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Wageningen University and Research Center (WUR), Department of Agroecology, Aarhus University [Aarhus], Molecular Ecology Lab, Department of Biological Sciences, Macquarie University, Natural Resources Institute Finland, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), University of Florence (UNIFI), Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Potsdam Institute for Climate Impact Research (PIK), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institute of Meteorology, Universität Leipzig [Leipzig], Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Global Change Research Institute CAS, Georg-August-Universität Göttingen, Thünen Institute of Biodiversity, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Leuphana University of Lüneburg, Nucleo di Ricerca sulla Desertificazione e Dipartimento di Agraria, University of Sassari, TROPAGS, Department of Crop Sciences, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Landscape Systems Analysis, Rothamsted Research, Wageningen University and Research [Wageningen] (WUR), Natural resources institute Finland, Agroressources et Impacts environnementaux (AgroImpact), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), 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, Agroclim (AGROCLIM), Mendel University in Brno (MENDELU), and Georg-August-University [Göttingen]
Subjects: [SDV]Life Sciences [q-bio]
Abstract: Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"
Published: 2016

25. 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

26. Pesticide fate modeling in soils with the crop model STICS: Feasibility for assessment of agricultural practices

Author: Hélène Blanchoud, Dominique Ripoche, Florence Habets, Marie Launay, Wilfried Queyrel, Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE)-Centre National de la Recherche Scientifique (CNRS), Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Agroclim (AGROCLIM), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols ( METIS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -École pratique des hautes études ( EPHE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté ( UBFC ), UE Agroclim ( UE AGROCLIM ), and Institut National de la Recherche Agronomique ( INRA )
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV]Life Sciences [q-bio], [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Environmental pollution, Agricultural engineering, 010501 environmental sciences, 01 natural sciences, Soil, Soil Pollutants, Crop model, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, Waste Management and Disposal, Netherlands, 2. Zero hunger, Catch crop, food and beverages, Agriculture, 04 agricultural and veterinary sciences, Pollution, Tillage, Agricultural practices, France, Environmental Monitoring, Pesticide leaching, Environmental Engineering, Context (language use), [ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/Agronomy, Environmental Chemistry, Computer Simulation, Model evaluation, 0105 earth and related environmental sciences, [ SDV ] Life Sciences [q-bio], Pesticide residue, business.industry, Herbicides, fungi, Pesticide Residues, Environmental engineering, 15. Life on land, Pesticide, Models, Chemical, 13. Climate action, Nutrient pollution, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Environmental Pollution, business
Abstract: Numerous pesticide fate models are available, but few of them are able to take into account specific agricultural practices, such as catch crop, mixing crops or tillage in their predictions. In order to better integrate crop management and crop growth in the simulation of diffuse agricultural pollutions, and to manage both pesticide and nitrogen pollution, a pesticide fate module was implemented in the crop model STICS. The objectives of the study were: (i) to implement a pesticide fate module in the crop model STICS; (ii) to evaluate the model performance using experimental data from three sites with different pedoclimatic contexts, one in The Netherlands and two in northern France; (iii) to compare the simulations with several pesticide fate models; and (iv) to test the impact of specific agricultural practices on the transfer of the dissolved fraction of pesticides. The evaluations were carried out with three herbicides: bentazone, isoproturon, and atrazine. The strategy applied in this study relies on a noncalibration approach and sensitivity test to assess the operating limits of the model. To this end, the evaluation was performed with default values found in the literature and completed by sensitivity tests. The extended version of the STICS named STICS-Pest, shows similar results with other pesticide fate models widely used in the literature. Moreover, STICS-Pest was able to estimate realistic crop growth and catch crop dynamic, which thus illustrate agricultural practices leading to a reduction of nitrate and a change in pesticide leaching. The dynamic plot-scale model, STICS-Pest is able to simulate nitrogen and pesticide fluxes, when the hydrologic context is in the validity range of the reservoir (or capacity) model. According to these initial results, the model may be a relevant tool for studying the effect of long-term agricultural practices on pesticide residue dynamics in soil and the associated diffuse pollution transfer.
Published: 2016

27. Uncertainty in simulating N uptake and N use efficiency in the crop rotation systems across Europe

Author: Xiaogang Yin, Kc, Kersebaum, Kollas, C., Armas-Herrera, Cecilia M., Sanmohan Baby, Nicolas Beaudoin, Bindi, M., Monica Charfeddine, Tobias Conradt, Iñaki Garcia de Cortazar-Atauri, Ewert, F., Roberto Ferrise, Hlavinka, P., Hoffmann, H., Lana, M., Marie Launay, Remy Manderscheid, Kiril Manevski, Bruno Mary, Mirschel, W., Moriondo, M., Müller, C., Nendel, C., Isik Öztürk, Palosuo, T., Dominique Ripoche-Wachter, Rp, Rötter, Ruget, F., Behzad Sharif, Trnka, M., Domenico Ventrella, Hans-Joachim Weigel, Wu, L., and Olesen, Jørgen E.
Published: 2016

28. Crop rotation modelling:A European model intercomparison

Author: Remy Manderscheid, Behzad Sharif, Svenja Doreen Roncossek, Roberto Ferrise, Mirek Trnka, Holger Hoffmann, Martin Wegehenkel, Pier Paolo Roggero, Marco Moriondo, Monia Charfeddine, Katharina Waha, Josef Eitzinger, Frank Ewert, Taru Palosuo, Reimund P. Rötter, Andreas Pacholski, Munir P. Hoffmann, Julie Constantin, Chris Kollas, Hans Joachim Weigel, Cecilia M. Armas-Herrera, Domenico Ventrella, Marie Launay, Tobias Conradt, Wilfried Mirschel, Luisa Giglio, Françoise Ruget, Thomas Gaiser, Lianhai Wu, Kurt Christian Kersebaum, Dominique Ripoche-Wachter, Jørgen E. Olesen, Nicolas Beaudoin, Isik Öztürk, Marco Bindi, Christoph Müller, Bruno Mary, Kiril Manevski, Petr Hlavinka, Iñaki García de Cortázar-Atauri, Claas Nendel, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Department of Agroecology, Aarhus University [Aarhus], Natural Resources Institute Finland (LUKE), Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), 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, Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Agroclim (AGROCLIM), Global Change Research Centre (CzechGlobe), Institute of Agrosystems and Bioclimatology, Mendel University in Brno (MENDELU), Crop Production Systems in the Tropics, Georg-August-University [Göttingen], Johann Heinrich von Thünen Institut, Institute of landscape systems analysis, Consiglio Nazionale delle Ricerche (CNR), Graduate School/Inkubator, Leuphana University of Lüneburg, Università degli Studi di Sassari, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, and Rothamsted Research
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Irrigation, 010504 meteorology & atmospheric sciences, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Soil Science, Plant Science, catch crop Intermediate, 01 natural sciences, Model ensemble, Sustainability Science, crop Treatment, Crop, model ensemble, Crop simulation models, 0105 earth and related environmental sciences, 2. Zero hunger, multi-year, Catch crop, business.industry, Simulation modeling, 04 agricultural and veterinary sciences, Crop rotation, Intermediate crop, Tillage, Multi-year, Treatment, Agronomy, 13. Climate action, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Crop simulation model, business, crop simulation models, Agronomy and Crop Science
Abstract: Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fifteen crop growth simulation models to predict yields in crop rotations at five sites across Europe under minimal calibration. Crop rotations encompassed 301 seasons of ten crop types common to European agriculture and a diverse set of treatments (irrigation, fertilisation, CO2 concentration, soil types, tillage, residues, intermediate or catch crops).We found that the continuous simulation of multi-year crop rotations yielded results of slightly higher quality compared to the simulation of single years and single crops. Intermediate crops (oilseed radish and grass vegetation) were simulated less accurately than main crops (cereals). The majority of models performed better for the treatments of increased CO2 and nitrogen fertilisation than for irrigation and soil-related treatments. The yield simulation of the multi-model ensemble reduced the error compared to single-model simulations.The low degree of superiority of continuous simulations over single year simulation was caused by (a) insufficiently parameterised crops, which affect the performance of the following crop, and (b) the lack of growth-limiting water and/or nitrogen in the crop rotations under investigation. In order to achieve a sound representation of crop rotations, further research is required to synthesise existing knowledge of the physiology of intermediate crops and of carry-over effects from the preceding to the following crop, and to implement/improve the modelling of processes that condition these effects.
Published: 2015

29. Création de IDE-STICS (base de données intercontinentale pour l'évaluation de STICS)

Author: Nicolas Beaudoin, Cecilia Armas-Herrera, Christine Le Bas, Samuel Buis, Inaki Garcia de Cortazar Atauri, Francoise Ruget, Dominique Ripoche, Marie Launay, Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), InfoSol (InfoSol), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Agroclim (AGROCLIM)
Subjects: [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2015

30. 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

31. 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

32. 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

33. Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France

Author: Elsa Coucheney, Nicolas Beaudoin, Françoise Ruget, Bruno Mary, Eric Justes, Julie Constantin, Kasaina Sitraka Andrianarisoa, Samuel Buis, Dominique Ripoche, Marie Launay, Christine Le Bas, Joël Léonard, Iñaki García de Cortázar-Atauri, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), 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), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Biotechnologie et Gestion des Agents Pathogènes en agriculture (BioGAP), Institut Supérieur d'Agriculture de Lille (ISA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Institut Charles Viollette (ICV) - EA 7394 (ICV), Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille-Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille, InfoSol (InfoSol), UR 1158 Unité de recherche Agronomie Laon-Reims-Mons, Institut National de la Recherche Agronomique (INRA)-Environnement et Agronomie (E.A.)-Unité de recherche Agronomie Laon-Reims-Mons (UA LRM), Department of Soil and Environment, UE Agroclim (UE AGROCLIM), 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, Institut Charles Viollette (ICV) - EA 7394 (ICV), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture-Institut Supérieur d'Agriculture de Lille (Groupe ISA), Unité INFOSOL (ORLEANS INFOSOL), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut Supérieur d'Agriculture de Lille (ISA)-Institut Charles Viollette (ICV) - EA 7394 (ICV), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture, and Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille-Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille-Institut Supérieur d'Agriculture de Lille (Groupe ISA)
Subjects: Biomass (ecology), Environmental Engineering, Mean squared error, Ecological Modeling, [SDV]Life Sciences [q-bio], Environmental engineering, Scale (descriptive set theory), Soil science, 15. Life on land, chemistry.chemical_compound, Nitrate, chemistry, Soil water, Range (statistics), Environmental science, Scenario testing, Robustness (economics), Software
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. STICS v8.2.2 soil-crop model was evaluated over a large and varied dataset using its standard set of parameters.Level of accuracy is 10-50% for plant, soil water and nitrate outputs.Model reproduces well trends arising from contrasted agro-environmental conditions.Errors are weakly dependent on the agro-environmental conditions tested.Model accuracy and robustness is considered good for scenario testing and large scale use within the conditions tested here.
Published: 2015

34. [Untitled]

Author: Dominique Ripoche, Nadine Brisson, Jorge Sierra, and C. Noël
Subjects: 2. Zero hunger, Chemistry, Soil Science, Soil science, 04 agricultural and veterinary sciences, Plant Science, 15. Life on land, 010501 environmental sciences, 01 natural sciences, Agronomy, Oxisol, Nitrate transport, Soil pH, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Nitrification, Leaching (agriculture), Water content, Nitrogen cycle, 0105 earth and related environmental sciences
Abstract: Oxisols have a high likelihood of NO3 − leaching which may strongly reduce N availability for tropical crops. The aim of this work was to evaluate the N and the water submodels of the STICS crop model for its ability to estimate N availability in N-fertilised field maize crops on two oxisols in Guadeloupe (French West Indies) with and without Al toxicity: a non-limed plot (NLI, pHKCl 3.9, 2.1 cmol Al3+ kg−1), and a limed plot (LI, pHKCl 4.5, 0 cmol Al3+ kg−1). An uncropped plot (UC, pHKCl 4.5, 0 cmol Al3+ kg−1) was used in order to fit some model parameters for soil evaporation, nitrification and NO3 − transport. The model was modified in order to describe nitrification as a partially inhibited process in acid soils, and to take into account NO3 − retention in oxisols. Nitrification was described as the result of the multiplicative effects of soil acidity, temperature and soil water content. Soil moisture and NO3 − and NH4 + content up to 0.8 m soil depth, above-ground biomass and N uptake by crops, and their leaf area index (LAI), were measured from sowing to the beginning of grain filling. The model described correctly the changes in soil water content during the moist and the dry periods of the experiment, and there was some evidence that capillary rise occurred in the dry period. Nitrogen mineralization, nitrification in UC, NO3 − transport and plant uptake were satisfactorily simulated by the model. Because of the effect of Al toxicity on plant growth, LAI at flowering was three times higher in LI than in NLI. Some discrepancies between observed and simulated data were found for the distribution of NO3 − and NH4 + in the cropped plots. This was probably due to the change of the ionic N form absorbed by the crops as a function of soil acidity and available P in the soil. No leaching was observed below 0.8 m depth and this was associated with NO3 − retention in the soil. The results showed that partial inhibition of nitrification and NO3 − retention should be taken into account by crop models to obtain realistic estimates of N availability for plants in tropical acid soils.
Published: 2003

35. STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize

Author: Dominique Ripoche, Marie-Hélène Jeuffroy, Françoise Ruget, Bernard Nicoullaud, Xavier Tayot, Daniel Plénet, Nadine Brisson, Alain Bouthier, Eric Justes, Josiane Lorgeou, Bruno Mary, and Philippe Gate
Subjects: Model study, Forestry, Agronomy and Crop Science, Zea mays, Model validation, Mathematics
Abstract: Une evaluation du modele generique de culture STICS, decrit en details dans Brisson et al. [11], est presentee. Elle repose sur une base de donnees agronomiques qui reunit des situations variees de culture de ble et de mais en France. L'accent est mis sur la necessite d'utiliser des references standards pour le parametrage des varietes, qui concerne surtout les stades de developpement. La validation est realisee sur les variables de sortie du modele, definie comme etant les variables finales d'interet agronomique (rendement et composantes, biomasse aerienne, dates de floraison et de maturite, teneurs en azote dans la plante et dans le grain, quantite d'eau et d'azote dans le sol) au moyen de plusieurs criteres mathematiques (erreurs quadratiques, ecarts moyens, efficacite). Il ressort que le comportement des deux cultures sont assez proches avec des erreurs quadratiques de 1,6 t.ha -1 pour le rendement du ble et de 2,4 t.ha -1 pour le rendement du mais. La simulation des deux composantes du rendement: nombre de grains et poids du grain est plus mauvaise, de meme que les simulations concernant l'azote aussi bien dans la plante que dans le sol qui apparaissent avec un biais systematique. En revanche l'eau dans le sol est correctement simulee. L'analyse de cinetiques d'evolution de variables d'etat majeures du systeme, telles que l'indice foliaire ou l'indice de nutrition azotee, sur quelques cas extraits de la base de donnee permet de mettre en evidence les disfonctionnements du modele et de proposer des modifications pour les corriger. On retiendra essentiellement l'introduction d'une relation entre le nombre de grains et le poids maximal du grain, ce qui rend la variable « nombre de grains » dependante de la variete, la prise en compte de la senescence des feuilles liee aux stress environnementaux, l'arret de l'absorption azotee au debut du remplissage du grain. Ces modifications permettent d'ameliorer les resultats de modelisation concernant les composantes du rendement et le bilan azote. Elles ont peu d'effet sur la biomasse et le rendement qui restent a des niveaux d'erreur de l'ordre de 15 %; cette incompressibilite de l'erreur sur la biomasse et par consequent le rendement est une illustration de la robustesse du modele.
Published: 2002

36. Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Margarita Garcia-Vila, Jordi Doltra, Jeffrey W. White, Fulu Tao, Leilei Liu, Christoph Müller, Davide Cammarano, Zhigan Zhao, Michael J. Ottman, Mikhail A. Semenov, Claudio O. Stöckle, Phillip D. Alderman, Benjamin Dumont, Joost Wolf, Sebastian Gayler, Alex C. Ruane, Daniel Wallach, Yan Zhu, Taru Palosuo, Andrew J. Challinor, Reimund P. Rötter, Katharina Waha, Thilo Streck, Pierre Martre, Pramod K. Aggarwal, Christian Biernath, Frank Ewert, Gerard W. Wall, Jakarat Anothai, Elias Fereres, Andrea Maiorano, Zhimin Wang, Iwan Supit, Giacomo De Sanctis, Senthold Asseng, Ehsan Eyshi Rezaei, Garry O'Leary, Eckart Priesack, Iurii Shcherbak, Claas Nendel, Curtis D. Jones, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, L. A. Hunt, Roberto C. Izaurralde, Peter J. Thorburn, Soora Naresh Kumar, Bruno Basso, Mohamed Jabloun, Gerrit Hoogenboom, Jørgen E. Olesen, Kurt Christian Kersebaum, Dominique Ripoche, Pierre Stratonovitch, and Ann-Kristin Koehler
Subjects: 0301 basic medicine, 2. Zero hunger, WIMEK, Water and Food, Crop yield, Published Erratum, Water en Voedsel, Plant Science, 03 medical and health sciences, 030104 developmental biology, Statistics, Centre for Crop Systems Analysis, Life Science, Water Systems and Global Change, Temperature response, Mathematics
Abstract: Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.
Published: 2017

37. A new integrated approach to assess the impacts of climate change on grapevine fungal diseases : the coupled MILA-STICS model

Author: Julie Caubel, Marie Launay, Inaki Garcia de Cortazar Atauri, Dominique Ripoche, Frederic Huard, Samuel Buis, Nadine Brisson, ProdInra, Migration, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agronomie, and Institut National de la Recherche Agronomique (INRA)-AgroParisTech
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, [SDE.MCG] Environmental Sciences/Global Changes, climate change, integrative integrated approach, [SDE.MCG]Environmental Sciences/Global Changes, process based model, plant pathogen interactions, fungal crop diseases
Abstract: International audience; Climate change is expected to influence the development and occurrence of fungal crop diseases. We therefore need to understand and predict the impacts of climate change on crop biotic stresses. A clearer understanding of these impacts requires consideration of how plants respond to climate change, as host plants provide a microclimate and physical and trophic support for disease development. Models have been developed to predict disease pressure on grapevine, but climate change is expected to generate complex responses that require a more integrated view of plant-pathogen interactions. We present here a new, integrated approach using the process-based MILA model coupled with the STICS crop model in order to understand and predict the potential impacts of climate change on downy mildew epidemics affecting grapevine (Plasmopara viticola). We first describe MILA and its calibration to downy mildew. The MILA-STICS combination has then been applied to future climatic data. Analysis of the general trend for future disease pressure, on the one hand, and the effects of the host plant on the course of certain processes, on the other hand, have demonstrated the value of applying MILA to the context of climate change. As a model that attempts to integrate the different mechanisms thatwhich explain involved in disease development, MILA is an appropriate tool to understand and assess the contribution of different effects on disease pressure. Finally, we describe some of the limitations of applying process-based models to the context of climate change. It is necessary to overcome these obstacles to ensure their effective use.
Published: 2014

38. Assessment of the phenology impact on SVAT modelling through a crop growth model under climate change conditions and consequences on the water balance

Author: Moulin, S., Sébastien Garrigues, Francoise Ruget, Albert Olioso, Dominique Ripoche, Frederic Huard, Samuel Buis, Lecharpentier, P., Desfonds, V., Bertrand, N., Nadine Brisson, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Déposants HAL-Avignon, bibliothèque Universitaire
Subjects: [SDV] Life Sciences [q-bio], changement climatique, modèle svat, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, croissance des cultures, phénologie, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2013

39. 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

40. French contribution to the international AgMIP project: leading maize pilot and STICS simulations for wheat, maize and rice

Author: Bassu, F., Durand, J. L., Sanctis, G., Dominique Ripoche, Francoise Ruget, Samuel Buis, Patrick Bertuzzi, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), and Déposants HAL-Avignon, bibliothèque Universitaire
Subjects: [SDU] Sciences of the Universe [physics], [SDU]Sciences of the Universe [physics], ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2012

41. Proposition d’une évaluation globale du modèle Stics

Author: Coucheney, E., Marie Launay, Buis, S., Dominique Ripoche, Mary, B., Francoise Ruget, Iñaki Garcia de Cortazar-Atauri, Justes, E., Constantin, J., Leonard, J., Nicolas Beaudoin, Biogéochimie et écologie des milieux continentaux (Bioemco), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Paris (ENS Paris), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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, Agrosystèmes et impacts environnementaux carbone-azote (Agro-Impact), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), 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: [SDU]Sciences of the Universe [physics], ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2012

42. Modelling the impact of thermal adaptation of soil microorganisms and crop system on the dynamics of organic matter in a tropical soil under a climate change scenario

Author: Dominique Ripoche, Jorge Sierra, M. Déqué, Nadine Brisson, Agrosystèmes tropicaux (ASTRO), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), Centre national de recherches météorologiques (CNRM), Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), and French National Research Agency (project Climator ANR06-VULN014)
Subjects: 010504 meteorology & atmospheric sciences, CLIMATE CHANGE, Climate change, CLIMATE WARMING, 01 natural sciences, SOIL MICROORGANISMS, Soil respiration, CARBON MINERALISATION, Climate change scenario, Vegetation type, CROP SYSTEM, TROPICAL MAIZE, Water content, 0105 earth and related environmental sciences, 2. Zero hunger, Ecological Modeling, Soil organic matter, BANANIER, Global warming, Soil classification, 04 agricultural and veterinary sciences, 15. Life on land, Agronomy, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, SOIL CARBON INPUT
Abstract: Publication Inra prise en compte dans l'analyse bibliométrique des publications scientifiques mondiales sur les Fruits, les Légumes et la Pomme de terre. Période 2000-2012. http://prodinra.inra.fr/record/256699; International audience; No consensus currently exists about how climate change should affect the status of soil organic matter (SOM) in the tropics. In this study, we analyse the impact of climate change on the underlying mechanisms controlling SOM dynamics in a ferralsol under two contrasting tropical crops: maize (C4 plant) and banana (C3 plant). We model the effect of microbial thermal adaptation on carbon (C) mineralisation at the crop system scale and introduce it in the model STICS, which was previously calibrated for the soil-crop systems tested in this study. Microbial thermal adaptation modelling is based on a reported theory for thermal acclimation of plant and soil respiration. The climate is simulated from 1950 to 2099 for the tropical humid conditions of Guadeloupe (French Antilles), using the ARPEGE model and the IPCC emission scenario A1B. The model predicts increases of 3.4 °C for air temperature and 1100 mm yr−1 for rainfall as a response to an increase of 375 ppm for atmospheric carbon dioxide concentration in the 2090–2099 decade compared with the 1950–1959 decade. The results of the STICS model indicate that the crop affects the response of SOM to climate change by controlling the change in several variables involved in C dynamics: C input, soil temperature and soil moisture. SOM content varies little until 2020, and then it decreases faster for maize than for banana. The decrease is weakened under the hypothesis of thermal adaptation, and this effect is greater for maize (−180 kg C ha−1 yr−1 without adaptation and −140 kg C ha−1 yr−1 with adaptation) than for banana (−60 kg C ha−1 yr−1 and −40 kg C ha−1 yr−1, respectively). The greater SOM loss in maize is mainly due to the negative effect of warming on maize growth decreasing C input from residues. Climate change has a small effect on banana growth, and SOM loss is linked to its effect on C mineralisation. For both crops, annual C mineralisation increases until 2040, and then it decreases continuously. Thermal adaptation reduces the initial increase in mineralisation, but its effect is lower on the final decrease, which is mainly controlled by substrate limitation. No stabilisation in SOM status is attained at the end of the analysed period because C mineralisation is always greater than C input. Model predictions indicate that microbial thermal adaptation modifies, but does not fundamentally change the temporal pattern of SOM dynamics. The vegetation type (C3 or C4) plays a major role in SOM dynamics in this tropical soil because of the different impact of climate change on crop growth and then on C inputs
Published: 2010

43. A crop model for land suitability evaluation a case study of the maize crop in France

Author: D. King, Françoise Ruget, Dominique Ripoche, Bernard Nicoullaud, R. Darthout, Nadine Brisson, ProdInra, Migration, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de recherche Science du Sol (USS), and Unité de recherches en bioclimatologie
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, 0106 biological sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, Land suitability, Water stress, Soil Science, Climatic variables, 04 agricultural and veterinary sciences, Plant Science, 15. Life on land, 01 natural sciences, Crop, Water balance, Agronomy, 040103 agronomy & agriculture, Information system, 0401 agriculture, forestry, and fisheries, Environmental science, AGROMETEOROLOGIE, Spatial variability, Agronomy and Crop Science, Productivity, ComputingMilieux_MISCELLANEOUS, 010606 plant biology & botany
Abstract: A crop model to evaluate land suitability is described. It has been devised to study spatial variation and uses readily available input data. The case study described is for the maize crop and uses a simple growth model for this crop. The model is incorporated within procedures that allow the descrip tion of crop environment variability both in space and time and the model is run under a Geographical Information System. Input data are stored in soil, climate and crop management data bases, for 20 × 20 km areas and constitute the basic information for crop growth simulation. From the network of synoptic meteorological stations, climatic variables are spatially interpolated to give predicted values for each elementary area. The model computes every ten days : i) potential crop productivity in the absence of any stress ; ii) productivity in limited-water situation. The modelling principles for the soilplant-atmosphere system are simple : development depends on thermal time, growth depends on energy use efficiency and the calculated water balance uses a reservoir model. Because of the ten-day time step, particular attention was given to the way in which water stress affects the growth-development functions. A study proved the model to be reliable for estimating maize productivity in various locations although some discrepancies between measurements and simulations can occur for intermediate variables in extreme environmental conditions. As illustrations of the model performance, map outputs of land suitabilities over France for maize growing are presented.
Published: 1992

44. An overview of the crop model STICS

Author: Jean-Pierre Gaudillère, Daniel Zimmer, Nadine Brisson, Philippe Burger, Bruno Mary, Jorge Sierra, Bernard Seguin, Pierre Cellier, Philippe Debaeke, Hervé Sinoquet, Catherine Hénault, Romain Roche, Florent Maraux, Yves-Marie Cabidoche, Christian Gary, Eric Justes, Patrick Bertuzzi, Dominique Ripoche, François Bussière, 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 recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de la Recherche Agronomique (INRA), 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, Unité d'agronomie de Laon-Péronne, Unité de Recherche AgroPédoClimatique de la zone caraïbe (APC), Département Environnement et Agronomie (DPT_EA), Unité d'agronomie, Microbiologie du Sol et de l'Environnement (MSE), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), 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), 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), Unité d'Agronomie de Laon-Péronne ( LILL LAON AGRO), Département Environnement et Agronomie (DEPT EA), M.K.van Ittersum, M.Donatelli, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Unité de recherche Plantes et Systèmes de Culture Horticoles ( PSH ), 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, Unité de Recherche AgroPédoClimatique de la zone caraïbe ( APC ), Département Environnement et Agronomie ( DPT_EA ), Microbiologie du Sol et de l'Environnement ( MSE ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ), 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 ), Ouvrages pour le drainage et l'étanchéité (UR DEAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Tige, F62 - Physiologie végétale - Croissance et développement, Plant Science, Agricultural engineering, DONNEES D'ENTREE, 01 natural sciences, Water balance, Range (statistics), [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, media_common, Mathematics, Generality, Ecology, U10 - Informatique, mathématiques et statistiques, Bilan hydrique du sol, 04 agricultural and veterinary sciences, Rendement des cultures, [SDE]Environmental Sciences, Phénologie, MODELE STICS, CEMAGREF, F40 - Écologie végétale, media_common.quotation_subject, Azote, DEAN, Soil Science, Adaptability, Crop, Croissance, 0105 earth and related environmental sciences, business.industry, Modèle de simulation, BILAN AZOTE, 15. Life on land, Agronomy, Agriculture, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Plante de culture, business, Agronomy and Crop Science, Cropping, Racine
Abstract: International audience; STICS is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, STICS relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of STICS is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because STICS is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil-crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the STICS model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.
Published: 2003

45. The STICS interface : computer environment of a multicrop model

Author: Dominique Ripoche, claude baron, Delecolle, R., Unité de bioclimatologie, and Institut National de la Recherche Agronomique (INRA)
Subjects: U10 - Méthodes mathématiques et statistiques, [SDV]Life Sciences [q-bio], Modèle de simulation, Logiciel, F50 - Anatomie et morphologie des plantes, F62 - Physiologie végétale : croissance et développement, [SDE]Environmental Sciences, Plante de culture, Croissance, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 1999

46. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn

Author: Sylvie Recous, Dominique Ripoche, Nicolas Beaudoin, Bruno Mary, Jean-Marie Machet, Xavier Tayot, F. Devienne-Barret, Marie-Hélène Jeuffroy, Françoise Ruget, Jean-Marc Meynard, Guy Richard, Daniel Plénet, Richard Delécolle, Pierre Cellier, Philippe Gate, Bernard Nicoullaud, Nadine Brisson, Carolyne Dürr, Rodrigo Antonioletti, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité d'Agronomie de Laon-Péronne ( LILL LAON AGRO), Unité associée INRA/INA-PG d'Agronomie, Institut National de la Recherche Agronomique (INRA)-Institut National Agronomique Paris-Grignon (INA P-G), Unité de recherche Science du Sol (USS), Institut Technique des Céréales et des Fourrages, Unité d'agronomie, and Unité de recherches en bioclimatologie
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Nitrogen balance, sol, 010504 meteorology & atmospheric sciences, plante céréaliere, maïs, rendement, BILAN AZOTE, RELATION PLANTE SOL, Agricultural engineering, 01 natural sciences, zea mays, modèle de culture, Crop, Water balance, blé, Production (economics), bilan hydrique, Cropping system, système de culture, Robustness (economics), ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, climat, triticum, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, croissance, Agricultural sciences, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science, Cropping, Sciences agricoles
Abstract: STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) is a crop model constructed as a simulation tool capable of working under agricultural conditions. Outputs comprise the production (amount and quality) and the environment. Inputs take into account the climate, the soil and the cropping system. STICS is presented as a model exhibiting the following qualities: robustness, an easy access to inputs and an uncomplicated future evolution thanks to a modular (easy adaptation to various types of plant) nature and generic. However, STICS is not an entirely new model since most parts use classic formalisms or stem from existing models. The main simulated processes are the growth, the development of the crop and the water and nitrogenous balance of the soil-crop system. The seven modules of STICS - development, shoot growth, yield components, root growth, water balance, thermal environment and nitrogen balance - are presented in turn with a discussion about the theoretical choices in comparison to other models. These choices should render the model capable of exhibiting the announced qualities in classic environmental contexts. However, because some processes (e.g. ammoniac volatilization, drought resistance, etc.) are not taken into account, the use of STICS is presently limited to several cropping systems. (© Inra/Elsevier, Paris.), STICS est un modèle de culture conçu comme un outil de simulation opérationnel en conditions agricoles. Ses variables de sortie sont relatives à la production, à la fois en quantité et en qualité, et à l’environnement. Ses entrées sont relatives au climat, au sol et au système de culture. STICS est présenté comme un modèle montrant les qualités suivantes : robustesse, facilité d’accès aux données d’entrée, souplesse d’évolution par une présentation modulaire et généricité (facilité d’adaptation à divers types de plantes). Pourtant, il ne s’agit pas d’un modèle entièrement nouveau dans les formalismes utilisés. Ils sont, pour la plupart, issus de modèles existants. Les grands processus simulés sont la croissance et le développement de la culture ainsi que les bilans hydrique et azoté du système sol-culture. Les sept modules de STICS sont décrits successivement avec une discussion sur les choix théoriques comparés à ceux d’autres modèles : développement, croissance aérienne, composantes du rendement, croissance racinaire, bilan hydrique, environnement thermique de la culture, bilan azoté. Il ressort que ces choix confèrent à priori au modèle les qualités annoncées dans un contexte environnemental classique. Cependant, l’absence de prise en compte de certains processus (exemples : volatilisation de l’ammoniac, résistance à la sécheresse, ...) restreint pour l’instant son utilisation à certains systèmes de culture. (© Inra/Elsevier, Paris.)
Published: 1998

47. Possible effects of climate change on wheat and maize crops in France

Author: Françoise Ruget, Dominique Ripoche, Ghislain Gosse, Richard Delécolle, Unité de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de recherches en bioclimatologie, ASA, American Society of Agronomy - Madison (USA), and ProdInra, Migration
Subjects: 2. Zero hunger, Mediterranean climate, Irrigation, Winter cereal, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, 04 agricultural and veterinary sciences, 15. Life on land, 01 natural sciences, [SDV] Life Sciences [q-bio], Agronomy, 13. Climate action, Effects of global warming, Soil water, 040103 agronomy & agriculture, Temperate climate, 0401 agriculture, forestry, and fisheries, Environmental science, Water use, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract: This study evaluates the possible effects of climate modifications induced by increasing trace gas concentrations in the atmosphere, on wheat (Triticum aestivum L.) and maize (Zea mays L.) yield and water demand in France. CERES-wheat and CERES-maize models are used with two French weather series and soil conditions. Weather variables were varied from present conditions, as simulated by various global climate models (GCMs). This chapter emphasizes the process of model calibration and the consequent uncertainties in final simulated results. Under the simulation conditions: (i) season lengths are shortened under climate change scenarios; (ii) yield decreases under climate change alone, but the decrease can be somewhat counteracted by direct CO{sub 2} effects on the crop, up to a 5 C temperature increase; and (iii) water use decreases under climate changes. Even if the large diversity of French climates and soils prohibits generalization of these results to the entire country, the main conclusions are: (i) under both temperate and Mediterranean climates, winter cereal yields will not be decreased by future conditions, provided that irrigation supply is not limiting under dry conditions and (ii) under temperate climate, maize could take advantage of development phase shrinkage and improve its radiation use efficiency. Changing sowing more » date produces varying results according to weather scenario, plant, and location. A more precise knowledge of initial soil water or temperature under changing conditions is necessary before optimal agronomic adaptation to future climate can be suggested. « less
Published: 1995

48. Actes du XIe séminaire des utilisateurs de Stics

Author: Nicolas Beaudoin, Samuel Buis, Eric Justes, Dominique Ripoche, Patrick Bertuzzi, Eric Casellas, Julie Constantin, Benjamin Dumont, Jean-Louis Durand, Iñaki Garcia de Cortazar-Atauri, Guillaume Jégo, Marie Launay, Christine Le Bas, Patrice Lecharpentier, Joël Léonard, Bruno Mary, Loic Strullu, Francoise Ruget, Gaëtan Louarn, Anne-Isabelle Graux, François Affholder, Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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, Agroclim (AGROCLIM), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Faculté Gembloux Agro Bio-Tech, Université de Liège, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Agriculture and Agri-Food [Ottawa] (AAFC), InfoSol (InfoSol), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-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), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut National de Recherche Agronomique (INRA). UR Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (0004).
Subjects: [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract: National audience

49. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison

Author: Michael J. Ottman, Jørgen E. Olesen, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Giacomo De Sanctis, Bruce A. Kimball, Frank Ewert, Pierre Martre, Gerard W. Wall, Jordi Doltra, Jeffrey W. White, Heidi Webber, Belay T. Kassie, Senthold Asseng, Andrea Maiorano, Dominique Ripoche, Pierre Stratonovitch, Robert F. Grant, Rheinische Friedrich-Wilhelms-Universität Bonn, 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), 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), 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), Arid-Land Agricultural Research Center, School of Plant Sciences, University of Arizona, JRC Institute for Energy and Transport (IET), European Commission - Joint Research Centre [Petten], Cantabrian Agricultural Research and Training Centre, University of Alberta, Department of Agroecology, Aarhus University [Aarhus], Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, German Science Foundation EW 119/5-1 /FACCE JPI MACSUR 031A103B, European Project: 267196, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Agricultural & Biological Engineering Department, University of Florida [Gainesville], and UE Agroclim (UE AGROCLIM)
Subjects: Canopy, stress thermique, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Yield (engineering), ble tendre, 010504 meteorology & atmospheric sciences, comparaison de modèles, Energy balance, Soil Science, Grain filling, Canopy temperature, Atmospheric sciences, 01 natural sciences, heat stress, high temperature, Crop model comparison, crop model comparison, Atmospheric instability, climat semi aride, condition environnementale, 0105 earth and related environmental sciences, semi arid climate, 2. Zero hunger, 04 agricultural and veterinary sciences, Arid, canopy temperature, Heat stress, Agronomy, soft wheat, 13. Climate action, Semi-arid climate, Wheat, 040103 agronomy & agriculture, rendement agricole, 0401 agriculture, forestry, and fisheries, Environmental science, haute température, Agronomy and Crop Science, modèle multifactoriel
Abstract: Even brief periods of high temperatures occurring around flowering and during grain filling can severely reduce grain yield in cereals. Recently, ecophysiological and crop models have begun to represent such phenomena. Most models use air temperature (T-air) in their heat stress responses despite evidence that crop canopy temperature (T-c) better explains grain yield losses. T-c can deviate significantly from T-air based on climatic factors and the crop water status. The broad objective of this study was to evaluate whether simulation of T-c improves the ability of crop models to simulate heat stress impacts on wheat under irrigated conditions. Nine process-based models, each using one of three broad approaches (empirical, EMP; energy balance assuming neutral atmospheric stability, EBN; and energy balance correcting for the atmospheric stability conditions, EBSC) to simulate To simulated grain yield under a range of temperature conditions. The models varied widely in their ability to reproduce the measured T-c with the commonly used EBN models performing much worse than either EMP or EBSC. Use of T-c to account for heat stress effects did improve simulations compared to using only T-air to a relatively minor extent, but the models that additionally use T-c on various other processes as well did not have better yield simulations. Models that simulated yield well under heat stress had varying skill in simulating T-c For example, the EBN models had very poor simulations of T-c but performed very well in simulating grain yield. These results highlight the need to more systematically understand and model heat stress events in wheat.

50. Volet 'écosystèmes agricoles' de l’Evaluation Française des Ecosystèmes et des Services Ecosystémiques

Author: Olivier Therond, Muriel Tichit, Anaïs Tibi, Francesco Accatino, Luc Biju-Duval, Christian Bockstaller, David Bohan, Thierry Bonaudo, Maryline Boval, Eric Cahuzac, Eric Casellas, Bruno Chauvel, Philippe Choler, Julie Constantin, Isabelle Cousin, Joël Daroussin, Maia David, Philippe Delacote, Stéphane Derocles, Laetitia de Sousa, Joao Pedro Domingues, Camille Dross, Michel Duru, Maguy Eugène, Fontaine, C., Garcia B, Geijzendorffer, Ilse R., Annette Girardin, Anne-Isabelle Graux, Magali Jouven, Barbara Langlois, Christine Le Bas, Yves Le Bissonnais, Virginie Lelievre, Robert Lifran, Elise Maigne, Guillaume Martin, Märtin, R., Fabrice Martin-Laurent, Vincent Martinet, Orla Mclaughlin, Anne Meillet, Catherine Mignolet, Mouchet, M., Marie-Odile Nozieres-Petit, Ostermann, O. P., Maria Luisa Paracchini, Sylvain Pellerin, Jean-Louis Peyraud, Sandrine Petit Michaut, Calypso Picaud, Sylvain Plantureux, Thomas Poméon, Emmanuelle Porcher, Thomas Puech, Laurence Puillet, Tina Rambonilaza, Helene Raynal, Rémi Resmond, Dominique Ripoche, Francoise Ruget, Bénédicte Rulleau, Rush, A., Jean-Michel Salles, Daniel Sauvant, Céline Schott, Léa Tardieu, Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Délégation à l'Expertise scientifique collective, à la Prospective et aux Etudes (UAR), Institut National de la Recherche Agronomique (INRA), Agroécologie [Dijon], Université de Bourgogne (UB)-Institut National de la Recherche Agronomique (INRA)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, AgroParisTech, Modélisation Systémique Appliquée aux Ruminants (MoSAR), Observatoire des Programmes Communautaires de Développement Rural (US ODR), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Université Grenoble Alpes (COMUE) (UGA), Centre National de la Recherche Scientifique (CNRS), Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft, 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, Unité de recherche Science du Sol (USS), Economie Publique (ECO-PUB), Bureau d'Économie Théorique et Appliquée (BETA), Université de Lorraine (UL)-Université de Strasbourg (UNISTRA)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Universidade de São Paulo (USP), Ecole Nationale du Génie Rural, des Eaux et des Forêts (ENGREF), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Muséum national d'Histoire naturelle (MNHN), Centre d'Ecologie et des Sciences de la COnservation (CESCO), Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN), Tour du Valat, Research Institute for the conservation of Mediterranean Wetlands, 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 la Recherche Agronomique (INRA), Systèmes d'élevage méditerranéens et tropicaux (UMR SELMET), 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), InfoSol (InfoSol), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-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), Département Environnement et Agronomie (DEPT EA), 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), Université Paul-Valéry - Montpellier 3 (UPVM), Agro-Systèmes Territoires Ressources Mirecourt (ASTER Mirecourt), Muséum national d'Histoire naturelle (MNHN)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Agroclim (AGROCLIM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Santé et agroécologie du vignoble (UMR SAVE), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Montpelliérain d'Économie Théorique et Appliquée (LAMETA), Université Montpellier 1 (UM1)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Services rendus par les écosystèmes, INRA, Commanditaire : Ministère de l'Environnement (France), Type de commande : Commande avec contrat/convention/lettre de saisine, Type de commanditaire ou d'auteur de la saisine : Ministères, parlements et les structures qui leur sont directement rattachées, Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université de Toulouse (UT)-Université de Toulouse (UT), Unité de Science du Sol (Orléans) (URSols), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Universidade de São Paulo = University of São Paulo (USP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Auteur indépendant, Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, European Commission - Joint Research Centre [Seville] (JRC), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and 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)
Subjects: territoire, [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, Écosystème agricole, écosystème agricole, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], [MATH]Mathematics [math], élevage, services écosystémique, [SHS]Humanities and Social Sciences
Abstract: L’ambition de l’étude Inra "EFESE-EA" est de décrire les mécanismes et déterminants de la fourniture des services écosystémiques par les écosystèmes agricoles sur la base d'une revue des connaissances existantes, et de procéder à leur évaluation à l’échelle nationale sur la base d’indicateurs définis dans le cadre de l’étude. L’organisation du travail, telle que prévue en début d’étude, se voulait séquentielle : (1) identification et spécification biophysiques d’une liste de biens agricoles et services écosystémiques ; (2) évaluation biophysique : quantification du niveau de fourniture des biens et services identifiés à l’étape (1) (3) évaluation économique : quantification de la valeur économique des services (le plus souvent dans une unité monétaire) Dans le temps imparti à l’étude, le collectif d’experts a donné la priorité aux volets biophysiques (1) et (2) afin : - d’instruire de façon robuste la conceptualisation des biens et services (volet 1) : ce travail constitue un front de recherche actuel, associé à une littérature académique abondante mais parfois non stabilisée, que le collectif d’experts s’est attaché à analyser de façon à proposer des choix de conceptualisation argumentés ; - de pousser au maximum l’exercice d’évaluation biophysique (volet 2) dans le cadre de la demande initiale formulée par le MEEM : cartographier la production d’un large panel de biens agricoles et les SE rendus par les écosystèmes agricoles à la résolution spatiale la plus fine possible, et à l’échelle France entière. A noter que le présent exercice ne constituant pas un projet de recherche mais bien une étude institutionnelle Inra (au sens des procédures DEPE), l’ensemble des évaluations développées dans le présent rapport est réalisée à partir de données existantes, aucun travail d’expérimentation visant à acquérir de nouvelles données de terrain n’ayant été conduit. Il résulte de ce choix de priorisation que : - le volet d’évaluation économique (3) est initié pour quelques SE mais peu développé en comparaison des volets (1) et (2) ; - tout en veillant à élaborer des méthodologies d’évaluation biophysiques traçables et robustes, les experts ont pris le parti de proposer des méthodologies plus exploratoires pour quelques SE pour lesquels les données actuelles ne permettent pas d’évaluer directement le niveau de fourniture à l’échelle France entière : dans ces cas particuliers (signalés explicitement dans les sections du rapport dont ils font l’objet), les méthodologies ont été mises en œuvre jusqu’à la réalisation des cartographies dans le but de donner à voir le potentiel qu’offrent ces méthodologies et la nature des résultats qu’elles peuvent produire sous condition de leur validation France entière, plutôt que dans le but d’interpréter pour eux-mêmes les résultats obtenus. Les experts se sont alors particulièrement attachés à relativiser les résultats quantitatifs ainsi produits, et à accompagner les cartographies d’un descriptif détaillé des protocoles de validation qu’il faudrait mettre en œuvre dans les suites de l’étude pour stabiliser et valider ces méthodologies exploratoires. Ce parti pris du groupe de travail EFESE-écosystèmes agricoles est compatible avec l’objectif poursuivi dans le programme EFESE, qui se donne pour objectif de produire un guide méthodologique pour l’évaluation des biens et SE en en pointant les limites, difficultés, précautions et améliorations possibles associées à chacune des pistes avancées.

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