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1. Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa

Author: Couëdel, Antoine, Falconnier, Gatien N., Adam, Myriam, Cardinael, Rémi, Boote, Kenneth, Justes, Eric, Smith, Ward N., Whitbread, Anthony M., Affholder, François, Balkovic, Juraj, Basso, Bruno, Bhatia, Arti, Chakrabarti, Bidisha, Chikowo, Regis, Christina, Mathias, Faye, Babacar, Ferchaud, Fabien, Folberth, Christian, Akinseye, Folorunso M., Gaiser, Thomas, Galdos, Marcelo V., Gayler, Sebastian, Gorooei, Aram, Grant, Brian, Guibert, Hervé, Hoogenboom, Gerrit, Kamali, Bahareh, Laub, Moritz, Maureira, Fidel, Mequanint, Fasil, Nendel, Claas, Porter, Cheryl H., Ripoche, Dominique, Ruane, Alex C., Rusinamhodzi, Leonard, Sharma, Shikha, Singh, Upendra, Six, Johan, Srivastava, Amit, Vanlauwe, Bernard, Versini, Antoine, Vianna, Murilo, Webber, Heidi, Weber, Tobias K.D., Zhang, Congmu, and Corbeels, Marc
Published: 2024
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2. Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model

Author: Vezy, Rémi, Munz, Sebastian, Gaudio, Noémie, Launay, Marie, Lecharpentier, Patrice, Ripoche, Dominique, and Justes, Eric
Published: 2023
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3. AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat

Author: Liu, Bing, primary, Martre, Pierre, additional, Ewert, Frank, additional, Webber, Heidi, additional, Waha, Katharina, additional, Thorburn, Peter J., additional, Ruane, Alex C., additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Balkovič, Juraj, additional, Basso, Bruno, additional, Biernath, Christian, additional, Bindi, Marco, additional, Cammarano, Davide, additional, Cao, Weixing, additional, Challinor, Andy J., additional, Sanctis, Giacomo De, additional, Dumont, Benjamin, additional, Espadafor, Mónica, additional, Rezaei, Ehsan Eyshi, additional, Fereres, Elias, additional, Ferrise, Roberto, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Gao, Yujing, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Izaurralde, Roberto C., additional, Jabloun, Mohamed, additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann-Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Martin, Manuel Montesino San, additional, Müller, Christoph, additional, Kumar, Soora Naresh, additional, Nendel, Claas, additional, O’Leary, Garry J., additional, Olesen, Jørgen Eivind, additional, Palosuo, Taru, additional, Porter, John R., additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Velde, Marijn Van der, additional, Wang, Enli, additional, Wolf, Joost, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional
Published: 2023
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4. AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat

Author: Liu, Bing, Martre, Pierre, Ewert, Frank, Webber, Heidi, Waha, Katharina, Thorburn, Peter, Ruane, Alex, Aggarwal, Pramod, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Cao, Weixing, Challinor, Andy, de Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto, Jabloun, Mohamed, Jones, Curtis, Kassie, Belay, Kersebaum, Kurt, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O’leary, Garry, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, van der Velde, Marijn, Wang, Enli, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Liu, Bing, Martre, Pierre, Ewert, Frank, Webber, Heidi, Waha, Katharina, Thorburn, Peter, Ruane, Alex, Aggarwal, Pramod, Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Cao, Weixing, Challinor, Andy, de Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto, Jabloun, Mohamed, Jones, Curtis, Kassie, Belay, Kersebaum, Kurt, Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O’leary, Garry, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, van der Velde, Marijn, Wang, Enli, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Abstract: The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5Â°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.
Published: 2023

5. 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: Coucheney, Elsa, Buis, Samuel, Launay, Marie, Constantin, Julie, Mary, Bruno, García de Cortázar-Atauri, Iñaki, Ripoche, Dominique, Beaudoin, Nicolas, Ruget, Françoise, Andrianarisoa, Kasaina Sitraka, Le Bas, Christine, Justes, Eric, and Léonard, Joël
Published: 2015
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6. Harmonization and translation of crop modeling data to ensure interoperability

Author: Porter, Cheryl H., Villalobos, Chris, Holzworth, Dean, Nelson, Roger, White, Jeffrey W., Athanasiadis, Ioannis N., Janssen, Sander, Ripoche, Dominique, Cufi, Julien, Raes, Dirk, Zhang, Meng, Knapen, Rob, Sahajpal, Ritvik, Boote, Kenneth, and Jones, James W.
Published: 2014
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7. Climate Change Impact and Adaptation for Wheat Protein

Author: Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P, O’Leary, Garry J, Fitzgerald, Glenn J, Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P, Kheir, Ahmed M. S, Thorburn, Peter J, Waha, Katharina, Ruane, Alex C, Aggarwal, Pramod K, Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J, Sanctis, Giacomo De, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. César, Jabloun, Mohamed, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Martin, Manuel Montesino San, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R, Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A, Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Velde, Marijn Van der, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
Subjects: Meteorology And Climatology
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.
Published: 2018
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8. Modelling intercrops functioning to advance the design of innovative agroecological systems

Author: Vezy, Rémi, primary, Munz, Sebastian, additional, Gaudio, Noémie, additional, Launay, Marie, additional, Lecharpentier, Patrice, additional, Ripoche, Dominique, additional, and Justes, Eric, additional
Published: 2022
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9. The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

Author: Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rotter, Reimund P, Kimball, Bruce A, Ottman, Michael J, White, Jeffrey W, Reynolds, Matthew P, Alderman, Phillip D, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kersebaum, Kurt C, Koehler, Ann-Kristin, Liu, Leilei, Muller, Christoph, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry, Oleson, Jorgen E, Palosuo, Tara, Priesack, Eckhart, Eyshi, Rezaei, Ehsan, Ripoche, Dominique, Ruane, Alex C, Semenov, Mikhail A, Scherbak, Lurii, Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold
Subjects: Meteorology And Climatology
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 is greater than 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.
Published: 2017
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10. How Accurately Do Maize Crop Models Simulate the Interactions of Atmospheric CO2 Concentration Levels With Limited Water Supply on Water Use and Yield?

Author: Durand, Jean-Louis, Delusca, Kenel, Boote, Ken, Lizaso, Jon, Manderscheid, Remy, Weigel, Hans Johachim, Ruane, Alexander Clark, Rosenzweig, Cynthia E, Jones, Jim, Ahuja, Laj, Anapalli, Saseendran, Basso, Bruno, Baron, Christian, Bertuzzi, Patrick, Biernath, Christian, Deryng, Delphine, Ewert, Frank, Gaiser, Thomas, Gayler, Sebastian, Heilein, Florian, Kersebaum, Kurt Christian, Kim, Soo-Hyung, Muller, Christoph, Nendel, Claas, Olioso, Albert, Priesack, Eckart, Villegas, Julian Ramirez, Ripoche, Dominique, Rotter, Reimund P, Seidel, Sabine I, Srivastava, Amit, Tao, Fulu, Timlin, Dennis, Twine, Tracy, Wang, Enli, Webber, Heidi, and Zhao, Zhigan
Subjects: Earth Resources And Remote Sensing, Statistics And Probability, Meteorology And Climatology
Abstract: 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 Thunen 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 percent of models within a range of plus/minus 1 Mg ha(exp. -1) around the mean. The bias of the median of the 21 models was less than 1 Mg ha(exp. -1). However under water deficit in one of the two years, the models captured only 30 percent of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.
Published: 2017
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11. The International Heat Stress Genotype Experiment for Modeling Wheat Response to Heat: Field Experiments and AgMIP-Wheat Multi-Model Simulations

Author: Martre, Pierre, Reynolds, Matthew P, Asseng, Senthold, Ewert, Frank, Alderman, Phillip D, Cammarano, Davide, Maiorano, Andrea, Ruane, Alexander C, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hohenheim, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt T, Koehler, Ann-Kristin, Mueller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B, Nendel, Claas, O’Leary, Garry, Olesen, Jørgen E, Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Roetter, Reimund P, Semenov, Mikhail A, Stoeckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, White, Jeffrey W, Wolf, Joost, Zhao, Zhigan, and Zhu, Yan
Subjects: Meteorology And Climatology
Abstract: The 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
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12. Possible Effects of Climate Change on Wheat and Maize Crops in France

Author: Delécolle, Richard, primary, Ruget, Françoise, additional, Ripoche, Dominique, additional, and Gosse, Ghislain, additional
Published: 2015
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13. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Author: Makowski, David, primary, Asseng, Senthold, additional, Ewert, Frank, additional, Bassu, Simona, additional, Durand, Jean-Louis, additional, Martre, Pierre, additional, Adam, Myriam, additional, Aggarwal, Pramod K., additional, Angulo, Carlos, additional, Baron, Christian, additional, Basso, Bruno, additional, Bertuzzi, Patrick, additional, Biernath, Christian, additional, Boogaard, Hendrik, additional, Boote, Kenneth J., additional, Brisson, Nadine, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, Conijn, Sjakk J. G., additional, Corbeels, Marc, additional, Deryng, Delphine, additional, De Sanctis, Giacomo, additional, Doltra, Jordi, additional, Gayler, Sebastian, additional, Goldberg, Richard, additional, Grassini, Patricio, additional, Hatfield, Jerry L., additional, Heng, Lee, additional, Hoek, Steven, additional, Hooker, Josh, additional, Hunt, Tony L. A., additional, Ingwersen, Joachim, additional, Izaurralde, Cesar, additional, Jongschaap, Raymond E. E., additional, Jones, James W., additional, Kemanian, Armen R., additional, Kersebaum, Christian, additional, Kim, Soo-Hyung, additional, Lizaso, Jon, additional, Müller, Christoph, additional, Kumar, Naresh S., additional, Nendel, Claas, additional, O'Leary, Garry J., additional, Olesen, Jorgen E., additional, Osborne, Tom M., additional, Palosuo, Taru, additional, Pravia, Maria V., additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rosenzweig, Cynthia, additional, Ruane, Alexander C., additional, Sau, Fredirico, additional, Semenov, Mickhail A., additional, Shcherbak, Iurii, additional, Steduto, Pasquale, additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Teixeira, Edmar I., additional, Thorburn, Peter, additional, Timlin, Denis, additional, Travasso, Maria, additional, Rötter, Reimund, additional, Waha, Katharina, additional, Wallach, Daniel, additional, White, Jeffrey W., additional, Williams, Jimmy R., additional, and Wolf, Joost, additional
Published: 2015
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14. Recent advances in intercropping modelling: the new version of the STICS soil-crop model simulates consistently a wide range of bi-specific annual intercrops

Author: Justes, Eric, Vezy, Rémi, Munz, Sebastian, Paff, Kirsten, Bedoussac, Laurent, Gaudio, Noémie, Lecharpentier, Patrice, Ripoche, Dominique, Launay, Marie, Direction Générale Déléguée à la Recherche et à la Stratégie (Cirad-Dgdrs), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Universität Hohenheim, Agrosystèmes Biodiversifiés (UMR ABSys), 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), 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), 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), École Nationale Supérieure de Formation de l'Enseignement Agricole de Toulouse-Auzeville (ENSFEA), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Association of Applied Biologists, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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 de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - 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), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Sticks, Intercropping, Barley, [SDV]Life Sciences [q-bio], Wheat, Pea, Crop model, Bi-specific
Abstract: International audience; STICS is a dynamic soil-crop model capable of simulating diverse crop rotations over short and long-term (Brisson et al., 2003). Intercropping increases the complexity of the system by adding inter-species competition. Crop models are useful tools for analyzing complex systems, as they allow the user far more control over individual variables than is possible in field experiments. An initial version of the STICS intercrop model was created by Brisson et al. (2004) from the standard version (Brisson et al., 2003). Recently Vezy et al. (2020) improved this initial version [STICS-Intercrop_v2020] by adding new formalisms and replacing some equations which were not found relevant during the first step of testing with our database. The aim of paper is to present the STICS-Intercrop_v2020 functions and to show the quality of simulations obtained for a wide range of winter and spring intercrop mixtures of durum wheat - winter pea and barley - spring pea. STICS-Intercrop_v2020 was tested for two sites in France using various plant densities and N-fertilizer rates in order to determine its relevance and validity domain for simulating intercropping. This research work was done in 3 main steps: 1) improving the existing formalisms and introducing new equations in the model algorithm for improving the light sharing and nitrogen competition between the two species, and then creating the new version STICS-Intercrop_v2020; 2) parameterising the model for sole crops only; and 3) independently evaluating the quality of predictions for intercrops without any re-parametrisation. The results of parametrisation were satisfactory with low Root Mean Square Errors and high Model Efficiencies, illustrating the robustness and accuracy for sole crops, as already shown for many crops, pedoclimatioc conditions and agronomic managements (Brisson et al., 2003). STICS-Intercrop_v2020 was reliably and efficient to simulate inter-specific interactions, development and growth variables, and provided coherent results for predicting yield and grain protein content for the two species, in winter and spring bi-specific cereal-legume intercrops, without any specific parameterization for intercropping. This illustrates the relevance of the formalisms to simulate dynamically both intra and inter-specific plant interactions. These results are very encouraging for using the STICS-intercrop model for future work where virtual experiments will help us to analyse the performance (LER, level of production in low inputs systems, etc.) and resilience of sole crops versus intercrops according to management practices, pedoclimatic variability and climate change scenarios. This work was part of the ReMIX project funded by EU H2020 program.
Published: 2021

15. Modeling of climate change impacts on maize yields in low-nitrogen conditions in Africa

Author: Falconnier, Gatien N., Corbeels, Marc, Boote, Kenneth J., Ruane, Alex C., Whitbread, Anthony M., Adam, Myriam, Affholder, François, Ahuja, Lajpat R., Akinseye, Folorunso M., Alou, Isaac N., Amouzou, Kokou A., Anapalli, Saseendran S., Baron, Christian, Basso, Bruno, Baudron, Frédéric, Bertuzzi, Patrick, Challinor, Andrew J., Chen, Yi, Deryng, Delphine, Elsayed, Maha L., Faye, Babacar, Gaiser, Thomas, Galdos, Marcelo, Gayler, Sebastian, Gérardeaux, Edward, Giner, Michel, Grant, Brian, Hoogenboom, Gerrit, Ibrahim, Esther S., Justes, Eric, Kamali, Bahareh, Kersebaum, Kurt Christian, Kim, Soo-Hyung, Van Der Laan, Michael, Leroux, Louise, Lizaso, Jon, MacCarthy, Dilys Sefakor, Maestrini, Bernardo, Meier, Elizabeth A., Mequanint, Fasil, Ndoli, Alain, Nendel, Claas, Porter, Cheryl, Priesack, Eckart, Ripoche, Dominique, Sida, Tesfaye S., Singh, Upendra, Smith, Ward N., Srivastava, Amit Kumar, Sinha, Sumit, Tao, Fulu, Thorburn, Peter J., Timlin, Dennis, Traoré, B., Twine, Tracy E., and Webber, Heidi
Published: 2020

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

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

Author: Yin, Xiaogang, primary, Kersebaum, Kurt-Christian, additional, Beaudoin, Nicolas, additional, Constantin, Julie, additional, Chen, Fu, additional, Louarn, Gaëtan, additional, Manevski, Kiril, additional, Hoffmann, Munir, additional, Kollas, Chris, additional, Armas-Herrera, Cecilia M., additional, Baby, Sanmohan, additional, Bindi, Marco, additional, Dibari, Camilla, additional, Ferchaud, Fabien, additional, Ferrise, Roberto, additional, de Cortazar-Atauri, Inaki Garcia, additional, Launay, Marie, additional, Mary, Bruno, additional, Moriondo, Marco, additional, Öztürk, Isik, additional, Ruget, Françoise, additional, Sharif, Behzad, additional, Wachter-Ripoche, Dominique, additional, and Olesen, Jørgen E., additional
Published: 2020
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18. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Author: Falconnier, Gatien N., primary, Corbeels, Marc, additional, Boote, Kenneth J., additional, Affholder, François, additional, Adam, Myriam, additional, MacCarthy, Dilys S., additional, Ruane, Alex C., additional, Nendel, Claas, additional, Whitbread, Anthony M., additional, Justes, Éric, additional, Ahuja, Lajpat R., additional, Akinseye, Folorunso M., additional, Alou, Isaac N., additional, Amouzou, Kokou A., additional, Anapalli, Saseendran S., additional, Baron, Christian, additional, Basso, Bruno, additional, Baudron, Frédéric, additional, Bertuzzi, Patrick, additional, Challinor, Andrew J., additional, Chen, Yi, additional, Deryng, Delphine, additional, Elsayed, Maha L., additional, Faye, Babacar, additional, Gaiser, Thomas, additional, Galdos, Marcelo, additional, Gayler, Sebastian, additional, Gerardeaux, Edward, additional, Giner, Michel, additional, Grant, Brian, additional, Hoogenboom, Gerrit, additional, Ibrahim, Esther S., additional, Kamali, Bahareh, additional, Kersebaum, Kurt Christian, additional, Kim, Soo‐Hyung, additional, Laan, Michael, additional, Leroux, Louise, additional, Lizaso, Jon I., additional, Maestrini, Bernardo, additional, Meier, Elizabeth A., additional, Mequanint, Fasil, additional, Ndoli, Alain, additional, Porter, Cheryl H., additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Sida, Tesfaye S., additional, Singh, Upendra, additional, Smith, Ward N., additional, Srivastava, Amit, additional, Sinha, Sumit, additional, Tao, Fulu, additional, Thorburn, Peter J., additional, Timlin, Dennis, additional, Traore, Bouba, additional, Twine, Tracy, additional, and Webber, Heidi, additional
Published: 2020
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19. Modélisation du fonctionnement des agroécosystèmes : l'épopée Stics

Author: Beaudoin, Nicolas, Launay, Marie, Ripoche, Dominique, Buis, Samuel, Eric Justes, Ruget, Françoise, Jego, Maël, Garcia Cortazar-Atauri, Inaki, Léonard, Joël, Raynal, Hélène, Noblet-Ducoudré, Nathalie, Seguin, Bernard, Lescourret, Françoise, Aubertot, Jean-Noël, Habets, Florence, Jayet, Pierre-Alain, Gate, Philippe, Mary, Bruno, Laboratoire des Fluides Complexes et leurs Réservoirs (LFCR), TOTAL FINA ELF-Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), 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), Cirad Direction Générale (Cirad-DG), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Agroressources et Impacts environnementaux (AgroImpact), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), 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), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Economie Publique (ECO-PUB), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ARVALIS - Institut du végétal [Paris], New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Richard Guy (ed.), Stengel Pierre (ed.), Lemaire Gilles (ed.), Cellier Pierre (ed.), and Valceschini Egizio (ed.)
Subjects: Recherche agronomique, F08 - Systèmes et modes de culture, agroécologie, agroecosystemes, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Impact sur l'environnement, gestion des ressources naturelles, A50 - Recherche agronomique, Agroécosystème, Stics, P01 - Conservation de la nature et ressources foncières
Abstract: International audience; Cette epopee doit beaucoup a la regrettee Nadine Brisson, premiere capitaine de« l' embarcation Sties ». Nous lui dedions avec emotion ce chapitre, dont les axes reprennent ceux de l'hommage qui lui a ete rendu le 16 octobre 2012 conjointement par le departement Environnement et Agronomie (EA) de l'Inra et !'Association frans;aise d' agronomie 123. Le modele Sties (Simulateur multidisciplinaire pour les cultures standard) a plusde vingt ans. Il fait maintenant partie des cinq modeles de culture les plus cites au monde. Cette success story s' est fondee sur un projet collectif dont le creuset a ete le departement EA naissant. Ce texte tente une analyse reflexive des interactions entre un groupe elargi de chercheurs modelisateurs et la necessite de mobiliser des moyens sur une tres longue duree, marquee par le renouvellement des questions posees face a l'evolution rapide des usages et des contextes d' application.
Published: 2019

20. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

Author: Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J., Müller, Christoph, Ruane, Alex C., Waha, Katharina, Thorburn, Peter J., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O’Leary, Garry J., Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
Subjects: 1.5°C warming, climate change, extreme low yields, food security, model ensemble, wheat production
Published: 2019

21. Climate change impact and adaptation for wheat protein

Author: Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
Published: 2019

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

Author: 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, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andrew J., Müller, Christoph, Ruane, Alexander C., Waha, Katharina, Thorburn, Peter, Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Jurajb, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, 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, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andrew J., Müller, Christoph, Ruane, Alexander C., Waha, Katharina, Thorburn, Peter, Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Jurajb, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold
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 <2°C on wheat production is therefore not
Published: 2019

23. Climate change impact and adaptation for wheat protein

Author: 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, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P., O'Leary, Garry, Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew, Kheir, Ahmed, M .S., Thorburn, Peter, Waha, Katharina, Ruane, Alexander C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, 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, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P., O'Leary, Garry, Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew, Kheir, Ahmed, M .S., Thorburn, Peter, Waha, Katharina, Ruane, Alexander C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank
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.
Published: 2019

24. Production, nitrogen exportation and nitrate leaching from managed grasslands in France

Author: Graux, Anne-Isabelle, Resmond, Rémi, Casellas, Eric, Delaby, Luc, Faverdin, Philippe, Le Bas, Christine, Meillet, Anne, Poméon, Thomas, Raynal, Helene, Ripoche, Dominique, Ruget, Francoise, Therond, Olivier, Vertès, Francoise, Peyraud, Jean-Louis, 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), 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 de la Recherche Agronomique (INRA), and Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST
Subjects: azote, [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, prairie, [INFO]Computer Science [cs], [MATH]Mathematics [math], modèle de simulation, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences, modélisation
Abstract: National audience
Published: 2018

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

Author: Liu, Bing, primary, Martre, Pierre, additional, Ewert, Frank, additional, Porter, John R., additional, Challinor, Andy J., additional, Müller, Christoph, additional, Ruane, Alex C., additional, Waha, Katharina, additional, Thorburn, Peter J., additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Balkovič, Juraj, additional, Basso, Bruno, additional, Biernath, Christian, additional, Bindi, Marco, additional, Cammarano, Davide, additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Espadafor, Mónica, additional, Eyshi Rezaei, Ehsan, additional, Ferrise, Roberto, additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Gao, Yujing, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Montesino San Martin, Manuel, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, O’Leary, Garry J., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Van der Velde, Marijn, additional, Wallach, Daniel, additional, Wang, Enli, additional, Webber, Heidi, additional, Wolf, Joost, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional
Published: 2019
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26. The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations

Author: Martre, Pierre, Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Asseng, Senthold, Ewert, Frank, Cammarano, Davide, Maiorano, Andrea, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Koehler, Ann-Kristin, Müller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B., Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, Wolf, Joost, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Asseng, Senthold, Ewert, Frank, Cammarano, Davide, Maiorano, Andrea, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Koehler, Ann-Kristin, Müller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B., Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, Wolf, Joost, Zhao, Zhigan, and Zhu, Yan
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.
Published: 2018

27. Évaluation et amélioration du module microclimatique de STICS

Author: Ben Othman, Wafa, Gaudio, Noemie, Launay, Marie, Lecharpentier, Patrice, Ripoche, Dominique, Saint-Jean, Sébastien, Saudreau, M., Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire de Physique et Physiologie Intégratives de l’Arbre en environnement Fluctuant (PIAF), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: formalisations, changement climatique, climate change, formalismes, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, température de culture, expérimentation, crop temperature, field experiments, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract: National audience
Published: 2017

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

Author: Therond, Olivier, Tichit, Muriel, Tibi, Anaïs, Accatino, Francesco, Biju-Duval, Luc, Bockstaller, Christian, Bohan, David, Bonaudo, Thierry, Boval, Maryline, Cahuzac, Eric, Casellas, Eric, Chauvel, Bruno, Choler, Philippe, Constantin, Julie, Cousin, Isabelle, Daroussin, Joël, David, Maia, Delacote, Philippe, Derocles, Stéphane, De Sousa, Laetitia, DOMINGUES, joao pedro, dross, Camille, Duru, Michel, Eugène, Maguy, Fontaine, C., Garcia, B., Geijzendorffer, Ilse R., Girardin, Annette, Graux, Anne-Isabelle, Jouven, Magali, Langlois, Barbara, Le Bas, Christine, Le Bissonnais, Yves, Lelievre, Virginie, Lifran, Robert, MAIGNE, Elise, Martin, Guillaume, Märtin, R., Martin-Laurent, Fabrice, Martinet, Vincent, McLaughlin, Orla, Meillet, Anne, Mignolet, Catherine, Mouchet, M., NOZIERES-PETIT, Marie-Odile, Ostermann, O.P., Paracchini, Maria Luisa, Pellerin, Sylvain, Peyraud, Jean-Louis, Petit, Sandrine, Picaud, Calypso, Plantureux, Sylvain, Poméon, Thomas, Porcher, Emmanuelle, Puech, Thomas, Puillet, Laurence, Rambonilaza, Tina, Raynal, Helene, Resmond, Rémi, Ripoche, Dominique, Ruget, Francoise, Rulleau, Bénédicte, Rusch, Adrien, Salles, Jean-Michel, Sauvant, Daniel, Schott, Céline, Tardieu, Léa, 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), AgroParisTech-Institut National de la Recherche Agronomique (INRA), 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), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, 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), 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), 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), 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, and Type de commanditaire ou d'auteur de la saisine : Ministères, parlements et les structures qui leur sont directement rattachées
Subjects: territoire, [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, 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.
Published: 2017

29. SimTraces a numerical simulator for predicting the accumulation of trace elements by crops

Author: Nguyen, Christophe, Bruchou, Claude, Cornu, Jean-Yves, Launay, Marie, Laporte, Marie-Aline, LIN, Zhongbing, Maron, Ronan, Liñero, Olaia, Pages, Loic, Ripoche, Dominique, Schneider, André, Sirguey, Catherine, Sterckeman, Thibault, 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), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Laboratoire Sols et Environnement (LSE), and Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)
Subjects: [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences
Abstract: International audience; General presentation of the Simtraces model to predict the soil to plant transfer of trace elements
Published: 2017

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

Author: Casellas, Eric, Faverdin, Philippe, Le Bas, Christine, Meillet, Anne, Poméon, Thomas, Raynal, Helene, Resmond, Rémi, Ripoche, Dominique, Ruget, Francoise, Therond, Olivier, Vertes, Francoise, Graux, Anne-Isabelle, Delaby, Luc, and Peyraud, Jean-Louis
Subjects: azote, production herbagère, lixiviation du nitrate, prairie, herbe, épandage, modèle de simulation, Sciences agricoles, Agricultural sciences, modélisation
Published: 2017

31. Atmospheric CO2 concentration impacts on maize yield performance under dry conditions: do crop model simulate it right ?

Author: Durand, Jean-Louis, Delusca, Kenel, Boote, Ken, Lizaso, Jon, Manderscheid, Remy, Weigel, Hans Johachim, Ruane, Alex C, Rosenzweig, Cynthia, Jones, Jim, Ahuja, Laj, Anapalli, Saseendran, Basso, Bruno, Baron, Christian, Bertuzzi, Patrick, Biernath, Christian, Deryng, Delphine, Ewert, Franck, Gaiser, Thomas, Gayler, Sebastian, Heinlein, Florian, Kersebaum, Kurt Christian, Kim, Soo-Hyung, Müller, Christoph, Nendel, Claas, Olioso, Albert, Priesack, Eckart, Villegas, Julian Ramirez, Ripoche, Dominique, Rötter, Reimund P., Seidel, Sabine I, Srivastava, Amit, Tao, Fulu, Timlin, Dennis, Twine, Tracy, Wang, Enli, Webber, Heidi, Zhao, Zhigan, and olioso, albert
Subjects: Multi-model ensemble, Grain number, évapotranspiration, approvisionnement eau, culture de mais, Stomatal conductance, Zea mays, donnée météorologique, modèle de culture, Atmospheric carbon dioxide concentration, dioxyde de carbone, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, estimation de rendement, weather data, concentration atmosphérique, carbonic anhydride, Water use
Published: 2017

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

Author: Durand, Jean-Louis, primary, Delusca, Kenel, additional, Boote, Ken, additional, Lizaso, Jon, additional, Manderscheid, Remy, additional, Weigel, Hans Johachim, additional, Ruane, Alex C, additional, Rosenzweig, Cynthia, additional, Jones, Jim, additional, Ahuja, Laj, additional, Anapalli, Saseendran, additional, Basso, Bruno, additional, Baron, Christian, additional, Bertuzzi, Patrick, additional, Biernath, Christian, additional, Deryng, Delphine, additional, Ewert, Frank, additional, Gaiser, Thomas, additional, Gayler, Sebastian, additional, Heinlein, Florian, additional, Kersebaum, Kurt Christian, additional, Kim, Soo-Hyung, additional, Müller, Christoph, additional, Nendel, Claas, additional, Olioso, Albert, additional, Priesack, Eckart, additional, Villegas, Julian Ramirez, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Seidel, Sabine I, additional, Srivastava, Amit, additional, Tao, Fulu, additional, Timlin, Dennis, additional, Twine, Tracy, additional, Wang, Enli, additional, Webber, Heidi, additional, and Zhao, Zhigan, additional
Published: 2018
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33. Multimodel ensembles improve predictions of crop–environment–management interactions

Author: Wallach, Daniel, primary, Martre, Pierre, additional, Liu, Bing, additional, Asseng, Senthold, additional, Ewert, Frank, additional, Thorburn, Peter J., additional, van Ittersum, Martin, additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Basso, Bruno, additional, Biernath, Christian, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Eyshi Rezaei, Ehsan, additional, Fereres, Elias, additional, Fitzgerald, Glenn J., additional, Gao, Y., additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Girousse, Christine, additional, Hoogenboom, Gerrit, additional, Horan, Heidi, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Müller, Christoph, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, O'Leary, Garry J., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Wolf, Joost, additional, and Zhang, Zhao, additional
Published: 2018
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34. Analyzing ecosystem services in apple orchards using the STICS model

Author: Demestihas, Constance, primary, Plénet, Daniel, additional, Génard, Michel, additional, Garcia de Cortazar-Atauri, Iñaki, additional, Launay, Marie, additional, Ripoche, Dominique, additional, Beaudoin, Nicolas, additional, Simon, Sylvaine, additional, Charreyron, Marie, additional, Raynal, Christiane, additional, and Lescourret, Françoise, additional
Published: 2018
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35. The uncertainty of crop yield projections is reduced by improved temperature response functions

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

36. Evaluation of the STICS soil-crop model for modelling arable intercrops

Author: Gaudio, Noémie, Ripoche, Dominique, Launay, Marie, TRIBOUILLOIS, Hélène, Justes, Eric, 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), and Institut National de la Recherche Agronomique (INRA)
Subjects: [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract: International audience
Published: 2016

37. Crop yields, soil organic carbon and soil nitrogen content change under climate change

Author: Dumont, B., Basso, B., Shcherbak, I., Asseng, S., Bassu, Simona, Boote, K., Cammarano, D., De Sanctis, Giovanni, Durand, Jean-Louis, Ewert, F., Gayler, S., Grace, P., Grant, R., Kent, J., Martre, Pierre, Nendel, C., Paustian, K., Priesack, E., Ripoche, Dominique, Ruane, A., Thorburn, P., Hatfield, J., Jones, J., Rosenzweig, C., Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State 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), Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of agronomy, University of Florida [Gainesville] (UF), The James Hutton Institute, Joint Research center, European Commission, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Institute for Future Environments, Queensland University of Technology, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), 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), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Agroclim (AGROCLIM), National Aeronautics and Space Administration, Partenaires INRAE, Ecosystem sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), United States Department of Agriculture (USDA), and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: blé, maïs, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, température, conduite de la culture, modèle continu, interaction sol plante climat, Milieux et Changements globaux, co2 atmosphérique, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2016

38. Multicriteria evaluation of the stics soil-crop model and implementation of an automated evaluation system

Author: Buis, Samuel, Coucheney, Elsa, Launay, Marie, Lecharpentier, Patrice, Mary, Bruno, Ripoche, Dominique, Beaudoin, Nicolas, Ruget, Francoise, Garcia De Cortazar Atauri, Inaki, Justes, Eric, Constantin, Julie, Andrianarisoa, Kasaina-Sitraka, Le Bas, Christine, Léonard, Joël, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut Supérieur d'Agriculture de Lille (ISA), InfoSol (InfoSol), Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institut Supérieur d'Agriculture de Lille (Groupe ISA), and Unité INFOSOL (ORLEANS INFOSOL)
Subjects: [SDV]Life Sciences [q-bio]
Abstract: Multicriteria evaluation of the stics soil-crop model and implementation of an automated evaluation system. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"
Published: 2016

39. Regional variability of the climate change effect on grassland production

Author: Ruget, Francoise, Ripoche, Dominique, Graux, Anne-Isabelle, Durand, Jean-Louis, 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), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-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)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, changement climatique, variabilité géographique, production fourragère, Sciences agricoles, global change, Agricultural sciences
Abstract: Regional variability of the climate change effect on grassland production. iCROPM2016 International Crop Modelling Symposium
Published: 2016

40. Inter-comparison of wheat models to identify knowledge gaps and improve process modeling

Author: Wang, E., Martre, Pierre, Asseng, S., Ewert, F., Zhao, Z., Maiorano, Andrea, Rotter, R. P., Kimball, B. A., Ottman, Michael J., Wall, G. W., White, J. W., Aggarwal, P. K., Alderman, P. D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A. J., De Sanctis, Giacomo, Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C., Jabloun, M., Jones, C. D., Kersebaum, K.C., Koehler, A. K., Müller, C., Liu, L., Kumar Naresh, S., Nendel, C., O'Leary, G., Olesen, J. E., Palosuo, T., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Ripoche, Dominique, Ruane, A. C., Semenov, M. A., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wallach, Daniel, Wolf, J., Zhu, Y., Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (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), 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 Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Natural Resources Institute Finland (LUKE), ARS/ALARC, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), AgWeatherNet Program, Washington State University (WSU), Michigan State University [East Lansing], Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, International Center for Tropical Agriculture, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], IAS, Princeton University, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, 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-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Nanjing Agricultural University, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape & Water Sciences, Department of Environment of Victoria, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Institute of Soil Science and Land Evaluation, University of Hohenheim, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), 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, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: blé, Modeling and Simulation, comparaison de modèles, température, modèle phénologique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS, modèle de production, incertitude
Abstract: International audience
Published: 2016

41. La modélisation du devenir des pesticides : Recueil de données et utilisation du modèle agronomique STICS pour simuler les transferts sur le long terme

Author: Schott, Céline, Blanchoud, Hélène, Queyrel, Wilfried, Habets, Florence, Ripoche, Dominique, Tallec, Gaëlle, Launay, Marie, and ProdInra, Migration
Subjects: [SDV] Life Sciences [q-bio], [SDE] Environmental Sciences, pratique phytosanitaire, module PeStics, acquisition de données, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, [SHS] Humanities and Social Sciences, bassin versant, orgeval, modélisation
Published: 2016

42. Evaluation of the STICS soil-crop model for modelling arable intercrops

Author: Ripoche, Dominique, Launay, Marie, Tribouillois, Hélène, Justes, Eric, and Gaudio, Noémie
Subjects: interculture, culture arable, modèle stics, modélisation
Published: 2016

43. Do maize crop models catch the impact of future [CO2] on maize yield and water use?

Author: Delusca, Kenel, Durand, Jean-Louis, Boote, K., Lizaso, J.I., Manderscheid, R., Weigel, H.J., Ruane, A., Rosenzweig, C., Jones, J., Ahuja, L., Anapalli, S., Basso, B., Baron, C., Bertuzzi, Patrick, Biernath, C., Derynge, D., Ewert, F., Gaiser, T., Gayler, S., Heinlein, F., Kersebaum, Kurt-Christian, Kim, S.H., Müller, C., Nendel, C., Priesack, E., Ramirez, J., Ripoche, Dominique, Rötter, R., Seidel, S., Srivastava, A., Tao, F., Timlin, D., Twine, T., Waha, K., Wang, E., Webber, H., Zhao, Z., ProdInra, Archive Ouverte, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), Technical University of Madrid, Johann Heinrich von Thünen Institute, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), ASRU, USDA-ARS : Agricultural Research Service, CPSRU, Department of Geological Science, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Agroclim (AGROCLIM), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), School of Environmental Sciences [Norwich], Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, University of Hohenheim, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), International Center for Tropical Agriculture, School of Earth and Environment (UWA), The University of Western Australia (UWA), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Natural resources institute Finland, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Laboratory, Department of Soil, Water and Climate, University of Minnesota System, CSIRO, China Agricultural University (CAU), Métaprogramme ACCAF, University of Florida [Gainesville], UE Agroclim (UE AGROCLIM), Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Natural Resources Institute Finland, Technische Universität Dresden (TUD), China Agricultural University, and 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)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, consommation en eau, U10 - Informatique, mathématiques et statistiques, P40 - Météorologie et climatologie, maïs, F62 - Physiologie végétale - Croissance et développement, rendement, maize, Agricultural sciences, modèle de culture, yields catches, dioxyde de carbone, F01 - Culture des plantes, Sciences agricoles, carbonic anhydride
Abstract: Do maize crop models catch the impact of future [CO2] on maize yield and water use?. iCROPM2016 International Crop Modelling Symposium
Published: 2016

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

Author: Caubel, Julie, primary, Launay, Marie, additional, Ripoche, Dominique, additional, Gouache, David, additional, Buis, Samuel, additional, Huard, Frédéric, additional, Huber, Laurent, additional, Brun, François, additional, and Bancal, Marie Odile, additional
Published: 2017
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45. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

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

Author: Webber, Heidi, primary, Martre, Pierre, additional, Asseng, Senthold, additional, Kimball, Bruce, additional, White, Jeffrey, additional, Ottman, Michael, additional, Wall, Gerard W., additional, De Sanctis, Giacomo, additional, Doltra, Jordi, additional, Grant, Robert, additional, Kassie, Belay, additional, Maiorano, Andrea, additional, Olesen, Jørgen E., additional, Ripoche, Dominique, additional, Rezaei, Ehsan Eyshi, additional, Semenov, Mikhail A., additional, Stratonovitch, Pierre, additional, and Ewert, Frank, additional
Published: 2017
Full Text: View/download PDF

47. How accurately do crop models simulate the impact of CO2 atmospheric concentration on maize yield and water use?

Author: Durand, Jean-Louis, Delusca, Kénel, Boote, K., Lizaso, J., Manderscheid, R., Rosenzweig, C., Jones, J., Weigel, H.J., Ruane, A., Anapalli, S., Ahuja, L., Basso, B., Baron, C., Bertuzzi, Patrick, Ripoche, Dominique, Biernath, C., Priesak, E., Derynge, D., Ewert, F., Gaiser, T., Gayler, S., Heilein, F., Kersebaum, K.C., Kim, S.H., Müller, C., Nendel, C., Ramirez, J., Tao, F., Timlin, D., Waha, K., Twine, T., Wang, E., Webber, H., Zhao, Z., Rötter, R., Srivastava, A., Seidel, S., Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), ETSIA, Johann Heinrich von Thünen Institut, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Colorado State University [Fort Collins] (CSU), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Michigan State University System, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Agroclim (AGROCLIM), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), School of Earth and Environment, University of Leeds, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), United States Department of Agriculture (USDA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Soil, Water and Climate, University of Minnesota System, Land and Water, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Rheinische Friedrich-Wilhelms-Universität Bonn, China Agricultural University (CAU), Natural Resources Institute Finland (LUKE), Institute of Crop Science and Resource Conservation [Bonn], Technische Universität Dresden = Dresden University of Technology (TU Dresden), and ProdInra, Migration
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2015

48. De la parcelle au bassin versant : quelles données pour la modélisation du transfert des pesticides ?

Author: Queyrel, Wilfried, Schott, Céline, Habets, Florence, Ripoche, Dominique, Launay, Marie, Nicola, Laurine, Tallec, Gaëlle, Ansart, P., Blanchoud, Hélène, 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), Agro-Systèmes Territoires Ressources Mirecourt (ASTER Mirecourt), Institut National de la Recherche Agronomique (INRA), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École pratique des hautes études (EPHE)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), Unité de recherche SAD ASTER - Station de Mirecourt (INRA SAD), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), UPMC, Université Pierre et Marie Curie (Paris 6), 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, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Agroclim (AGROCLIM), Programme PIREN-Seine, and Dijon (Kevin Oudard), Institut Agro
Subjects: [SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy, Pratique culturale, Sol, Bassin versant, Modélisation, Climat, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, STICS, Pratique phytosanitaire, Qualité de l'eau
Abstract: Colloque de fin de Phase 6 du PIREN-Seine - Session "Agriculture et qualité de l'eau"; International audience; La Directive Cadre sur l'Eau suppose la mise en œuvre par les pays membres de l'union européenne de la préservation ou la restauration d'une eau de "bonne qualité" pour les grandes masses d'eau. Cependant, les études réalisées sur le transfert des pesticides restent encore insuffisantes pour évaluer la vulnérabilité des eaux de surface et souterraines. La difficulté réside dans la diversité des molécules utilisées et de leurs propriétés physico-chimiques qui imposent que chaque substance active soit considérée de façon indépendante. Ainsi, le suivi de la qualité chimique des masses d'eau comprend un grand nombre de pesticides pour lesquels les techniques analytiques couteuses ne permettent pas toujours de mesurer des concentrations au-dessus du seuil de quantification. Pour les pesticides trop souvent détectés, les décisions prises jusqu'à présent pour la protection de l'environnement sont leur limitation voire leur interdiction d'usage, mais ils sont très vite remplacés par d'autres substances. D'autres alternatives sont étudiées comme l'amélioration des pratiques culturales pour la réduction des usages, mais il est difficile encore à l'heure actuelle d'identifier des pratiques qui permettraient de réduire le transfert des pesticides. Le fonctionnement particulier d'un bassin et la difficulté d'appréhender la contamination sur le moyen ou le long terme nous oblige à considérer l'effet des pratiques agricoles actuelles ou passées. La persistance de quelques molécules ou de leurs produits de dégradation met en évidence que les études préliminaires de contamination du milieu ont sous-estimé la participation du stock dans la restitution des pesticides au cours d'eau. Le bruit de fond encore détectable de l'atrazine dans le bassin de l'Orgeval (Seine et Marne), 10 ans après son interdiction, provient de l'accumulation sur plusieurs années de cette molécule dans le sole, la zone non saturée ou dans la nappe. Ce constat nous oblige à réfléchir sur l'impact que les pratiques actuelles auront dans l'avenir, en considérant un territoire plus large que la parcelle agricole. La modélisation est une approche intéressante qui a l'avantage de tester et comparer les situations les unes par rapport aux autres. Dans le cas particulier des pesticides, certains modèles permettent de représenter finement les processus mis en jeu à la parcelle. Pour chaque simulation, un grand nombre de paramètres doivent être alors renseignés, tant pour la caractérisation des pratiques phytosanitaires, des sols ou du climat. Cependant, ils ne prennent pas suffisamment en compte les pratiques culturales et les jeux de données nécessaires à la parcelle ne peuvent être renseignés à l’échelle du bassin versant.Dans la phase 6 du programme PIREN Seine, nous avons fait le choix de partir du modèle agronomiques STICS déjà largement utilisé pour simuler le transfert des nitrates à l'échelle du bassin de la Seine, et d'y ajouter un module PeStics afin d'intégrer des processus propres aux pesticides : la sorption et la dégradation. Après avoir réalisé des tests de sensibilité à partir de jeux de données utilisés pour la calibration d'autre modèles de transfert des pesticides, PeStics a été testé sur le bassin de l'Orgeval. Ce bassin est suivi depuis plus de 50 ans pour les données hydrologiques et quelques études spécifiques sur le transfert des pesticides y ont été menées. Des études complémentaires ont été engagées en 2008 : l'historique des traitements phytosanitaires par les agriculteurs ainsi que le suivi en continu de la contamination par les pesticides. Les simulations de l'accumulation des pesticides dans le sol ont été réalisées sur un temps long pour l'atrazine et l'isoproturon et un premier bilan annuel de flux simulé de pesticide a été comparé aux mesures à l'exutoire du bassin. Cette application longue durée a notamment permis d'étudier les interactions entre processus de dégradation et d'adsorption/désorption qui conduisent à retrouver des molécules dans les hydrosystèmes 10 ans après leur interdiction. L'enjeu est maintenant d'utiliser le modèle Pestics avec la même approche que pour les nitrates. Une première étape a été menée en ce sens en s'intéressant, sur une parcelle, à l'effet des CIPANS sur la lixiviation des pesticides et des nitrates. Des applications supplémentaires doivent encore être réalisées afin de valider la démarche adoptée.
Published: 2015

49. Stics et exercices internationaux d'inter-comparaison de modeles de culture

Author: Bassu, Simona, Beaudoin, Nicolas, Brisson, Nadine, Bertuzzi, Patrick, Constantin, Julie, Cufi, Julien, Durand, Jean-Louis, Armas-Herrera, Cecilia, Raynal, Helene, Ripoche, Dominique, Ruget, Francoise, De Sanctis, Giacomo, Buis, Samuel, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT 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), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroressources et Impacts environnementaux (AgroImpact), Agroclim (AGROCLIM), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Subjects: [SDV]Life Sciences [q-bio]
Abstract: Session 2; Stics et exercices internationaux d'inter-comparaison de modeles de culture. 10. Colloque Modèle de culture STICS
Published: 2015

50. Utilisation de STICS pour l'analyse de services écosystémiques multiples en verger de pommiers

Author: Demestihas, Constance, Plénet, Daniel, Garcia De Cortazar Atauri, Inaki, Launay, Marie, Ripoche, Dominique, Beaudoin, Nicolas, Lescourret, Francoise, Génard, Michel, Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de la Recherche Agronomique (INRA), Agroclim (AGROCLIM), and Agroressources et Impacts environnementaux (AgroImpact)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, mesure expérimentale, verger de pommes, services écosystémiques, modèle STICS, Sciences agricoles, production fruitière, service écosystémique, ComputingMilieux_MISCELLANEOUS, Agricultural sciences, cycle biogéochimique
Abstract: National audience
Published: 2015

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