Search

Your search keyword '"Dominique Ripoche"' showing total 14 results
Start Over Author "Dominique Ripoche" Language undetermined
14 results on '"Dominique Ripoche"'

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 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

4. 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

5. 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

6. 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

7. 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

8. [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

9. 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

10. 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

11. 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

12. 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

13. 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.

14. 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.

Catalog

Books, media, physical & digital resources
See catalog results