Search

Your search keyword '"Ferrise, Roberto"' showing total 261 results
Start Over Author "Ferrise, Roberto"
261 results on '"Ferrise, Roberto"'

1. Global needs for nitrogen fertilizer to improve wheat yield under climate change

Author

Martre, Pierre, Dueri, Sibylle, Guarin, Jose Rafael, Ewert, Frank, Webber, Heidi, Calderini, Daniel, Molero, Gemma, Reynolds, Matthew, Miralles, Daniel, Garcia, Guillermo, Brown, Hamish, George, Mike, Craigie, Rob, Cohan, Jean-Pierre, Deswarte, Jean-Charles, Slafer, Gustavo, Giunta, Francesco, Cammarano, Davide, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Padovan, Gloria, Ruane, Alex C., Srivastava, Amit Kumar, Stella, Tommaso, Supit, Iwan, Thorburn, Peter, Wang, Enli, Wolf, Joost, Zhao, Chuang, Zhao, Zhigan, and Asseng, Senthold

Published
2024
Full Text
View/download PDF

2. Proposal and extensive test of a calibration protocol for crop phenology models

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter, Mielenz, Henrike, Buis, Samuel, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cecile, Gayler, Sebastian, Harrison, Matthew, Hiremath, Santosh, Horan, Heidi, Hoogenboom, Gerrit, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt-Christian, Launay, Marie, Lewan, Elisabet, Liu, Ke, Mequanint, Fasil, Moriondo, Marco, Nendel, Claas, Padovan, Gloria, Qian, Budong, Schütze, Niels, Seserman, Diana-Maria, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Trombi, Giacomo, Weber, Tobias K. D., Weihermüller, Lutz, Wöhling, Thomas, and Seidel, Sabine J.

Published
2023
Full Text
View/download PDF

4. Priority for climate adaptation measures in European crop production systems

Author

Zhao, Jin, Bindi, Marco, Eitzinger, Josef, Ferrise, Roberto, Gaile, Zinta, Gobin, Anne, Holzkämper, Annelie, Kersebaum, Kurt-Christian, Kozyra, Jerzy, Kriaučiūnienė, Zita, Loit, Evelin, Nejedlik, Pavol, Nendel, Claas, Niinemets, Ülo, Palosuo, Taru, Peltonen-Sainio, Pirjo, Potopová, Vera, Ruiz-Ramos, Margarita, Reidsma, Pytrik, Rijk, Bert, Trnka, Mirek, van Ittersum, Martin K., and Olesen, Jørgen E.

Published
2022
Full Text
View/download PDF

5. Expected effects of climate change on the production and water use of crop rotation management reproduced by crop model ensemble for Czech Republic sites

Author

Pohanková, Eva, Hlavinka, Petr, Kersebaum, Kurt-Christian, Rodríguez, Alfredo, Jan Balek, Bednařík, Martin, Dubrovský, Martin, Gobin, Anne, Hoogenboom, Gerrit, Moriondo, Marco, Nendel, Claas, Olesen, Jørgen E., Rötter, Reimund Paul, Ruiz-Ramos, Margarita, Shelia, Vakhtang, Stella, Tommaso, Hoffmann, Munir Paul, Takáč, Jozef, Eitzinger, Josef, Dibari, Camilla, Ferrise, Roberto, Bláhová, Monika, and Trnka, Miroslav

Published
2022
Full Text
View/download PDF

6. The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, Dibari, Camilla, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cecile, Gayler, Sebastian, Ghahramani, Afshin, Hiremath, Santosh, Hoek, Steven, Horan, Heidi, Hoogenboom, Gerrit, Huang, Mingxia, Jabloun, Mohamed, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Klosterhalfen, Anne, Launay, Marie, Lewan, Elisabet, Luo, Qunying, Maestrini, Bernardo, Mielenz, Henrike, Moriondo, Marco, Nariman Zadeh, Hasti, Padovan, Gloria, Olesen, Jørgen Eivind, Poyda, Arne, Priesack, Eckart, Pullens, Johannes Wilhelmus Maria, Qian, Budong, Schütze, Niels, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Stella, Tommaso, Streck, Thilo, Trombi, Giacomo, Wallor, Evelyn, Wang, Jing, Weber, Tobias K.D., Weihermüller, Lutz, de Wit, Allard, Wöhling, Thomas, Xiao, Liujun, Zhao, Chuang, Zhu, Yan, and Seidel, Sabine J.

Published
2021
Full Text
View/download PDF

7. Yield Response of an Ensemble of Potato Crop Models to Elevated CO2 in Continental Europe

Author

Fleisher, David H., Condori, Bruno, Barreda, Carolina, Berguijs, Herman, Bindi, Marco, Boote, Ken, Craigon, Jim, Evert, Frits van, Fangmeier, Andreas, Ferrise, Roberto, Gayler, Sebastian, Hoogenboom, Gerrit, Merante, Paolo, Nendel, Claas, Ninanya, Johan, Pleijel, Håkan, Raes, Dirk, Ramírez, David A., Raymundo, Rubi, Reidsma, Pytrik, Silva, João Vasco, Stöckle, Claudio O., Supit, Iwan, Stella, Tommaso, Vandermeiren, Karine, van Oort, Pepijn, Vanuytrecht, Eline, Vorne, Virpi, and Wolf, Joost

Published
2021
Full Text
View/download PDF

8. Multi-model evaluation of phenology prediction for wheat in Australia

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter, Hochman, Zvi, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Gayler, Sebastian, Hiremath, Santosh, Hoek, Steven, Horan, Heidi, Hoogenboom, Gerrit, Huang, Mingxia, Jabloun, Mohamed, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Launay, Marie, Lewan, Elisabet, Luo, Qunying, Maestrini, Bernardo, Moriondo, Marco, Olesen, Jørgen Eivind, Padovan, Gloria, Poyda, Arne, Priesack, Eckart, Pullens, Johannes Wilhelmus Maria, Qian, Budong, Schütze, Niels, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Kumar Srivastava, Amit, Stella, Tommaso, Streck, Thilo, Trombi, Giacomo, Wallor, Evelyn, Wang, Jing, Weber, Tobias K.D., Weihermüller, Lutz, de Wit, Allard, Wöhling, Thomas, Xiao, Liujun, Zhao, Chuang, Zhu, Yan, and Seidel, Sabine J

Published
2021
Full Text
View/download PDF

9. How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter, Gourdain, Emmanuelle, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, Dibari, Camilla, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cécile, Gayler, Sebastian, Ghahramani, Afshin, Hochman, Zvi, Hoek, Steven, Hoogenboom, Gerrit, Horan, Heidi, Huang, Mingxia, Jabloun, Mohamed, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Klosterhalfen, Anne, Launay, Marie, Luo, Qunying, Maestrini, Bernardo, Mielenz, Henrike, Moriondo, Marco, Nariman Zadeh, Hasti, Olesen, Jørgen Eivind, Poyda, Arne, Priesack, Eckart, Pullens, Johannes Wilhelmus Maria, Qian, Budong, Schütze, Niels, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Stella, Tommaso, Streck, Thilo, Trombi, Giacomo, Wallor, Evelyn, Wang, Jing, Weber, Tobias K.D., Weihermüller, Lutz, de Wit, Allard, Wöhling, Thomas, Xiao, Liujun, Zhao, Chuang, Zhu, Yan, and Seidel, Sabine J.

Published
2021
Full Text
View/download PDF

11. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models

Author

Tao, Fulu, Palosuo, Taru, Rötter, Reimund P., Díaz-Ambrona, Carlos Gregorio Hernández, Inés Mínguez, M., Semenov, Mikhail A., Kersebaum, Kurt Christian, Cammarano, Davide, Specka, Xenia, Nendel, Claas, Srivastava, Amit Kumar, Ewert, Frank, Padovan, Gloria, Ferrise, Roberto, Martre, Pierre, Rodríguez, Lucía, Ruiz-Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka G., Salo, Tapio, Dibari, Camilla, and Schulman, Alan H.

Published
2020
Full Text
View/download PDF

13. Decline in climate resilience of European wheat

Author

Kahiluoto, Helena, Kaseva, Janne, Balek, Jan, Olesen, Jørgen E., Ruiz-Ramos, Margarita, Gobin, Anne, Kersebaum, Kurt Christian, Takáč, Jozef, Ruget, Francoise, Ferrise, Roberto, Bezak, Pavol, Capellades, Gemma, Dibari, Camilla, Mäkinen, Hanna, Nendel, Claas, Ventrella, Domenico, Rodríguez, Alfredo, Bindi, Marco, and Trnka, Mirek

Published
2019

14. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Author

Brilli, Lorenzo, Bechini, Luca, Bindi, Marco, Carozzi, Marco, Cavalli, Daniele, Conant, Richard, Dorich, Cristopher D., Doro, Luca, Ehrhardt, Fiona, Farina, Roberta, Ferrise, Roberto, Fitton, Nuala, Francaviglia, Rosa, Grace, Peter, Iocola, Ileana, Klumpp, Katja, Léonard, Joël, Martin, Raphaël, Massad, Raia Silvia, Recous, Sylvie, Seddaiu, Giovanna, Sharp, Joanna, Smith, Pete, Smith, Ward N., Soussana, Jean-Francois, and Bellocchi, Gianni

Published
2017
Full Text
View/download PDF

15. Can conservation tillage mitigate climate change impacts in Mediterranean cereal systems? A soil organic carbon assessment using long term experiments

Author

Iocola, Ileana, Bassu, Simona, Farina, Roberta, Antichi, Daniele, Basso, Bruno, Bindi, Marco, Dalla Marta, Anna, Danuso, Francesco, Doro, Luca, Ferrise, Roberto, Giglio, Luisa, Ginaldi, Fabrizio, Mazzoncini, Marco, Mula, Laura, Orsini, Roberto, Corti, Giuseppe, Pasqui, Massimiliano, Seddaiu, Giovanna, Tomozeiu, Rodica, Ventrella, Domenico, Villani, Giulia, and Roggero, Pier Paolo

Published
2017
Full Text
View/download PDF

16. Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

Author

Yin, Xiaogang, Kersebaum, Kurt Christian, Kollas, Chris, Baby, Sanmohan, Beaudoin, Nicolas, Manevski, Kiril, Palosuo, Taru, Nendel, Claas, Wu, Lianhai, Hoffmann, Munir, Hoffmann, Holger, Sharif, Behzad, Armas-Herrera, Cecilia M., Bindi, Marco, Charfeddine, Monia, Conradt, Tobias, Constantin, Julie, Ewert, Frank, Ferrise, Roberto, Gaiser, Thomas, de Cortazar-Atauri, Iñaki Garcia, Giglio, Luisa, Hlavinka, Petr, Lana, Marcos, Launay, Marie, Louarn, Gaëtan, Manderscheid, Remy, Mary, Bruno, Mirschel, Wilfried, Moriondo, Marco, Öztürk, Isik, Pacholski, Andreas, Ripoche-Wachter, Dominique, Rötter, Reimund P., Ruget, Françoise, Trnka, Mirek, Ventrella, Domenico, Weigel, Hans-Joachim, and Olesen, Jørgen E.

Published
2017
Full Text
View/download PDF

17. Designing future barley ideotypes using a crop model ensemble

Author

Tao, Fulu, Rötter, Reimund P., Palosuo, Taru, Díaz-Ambrona, C.G.H., Mínguez, M. Inés, Semenov, Mikhail A., Kersebaum, Kurt Christian, Nendel, Claas, Cammarano, Davide, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez, Lucía, Ruiz-Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka G., Salo, Tapio, Ferrise, Roberto, Bindi, Marco, and Schulman, Alan H.

Published
2017
Full Text
View/download PDF

20. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Author

Guarin, Jose, primary, Martre, Pierre, additional, Ewert, Frank, additional, Webber, Heidi, additional, Dueri, Sibylle, additional, Calderini, Daniel, additional, Reynolds, Matthew, additional, Molero, Gemma, additional, Miralles, Daniel, additional, Garcia, Guillermo, additional, Slafer, Gustavo, additional, Giunta, Francesco, additional, Pequeno, Diego, additional, Stella, Tommaso, additional, Ahmed, Mukhtar, additional, Alderman, Phillip, additional, Basso, Bruno, additional, Berger, Andres, additional, Bindi, Marco, additional, Bracho-Mujica, Gennady, additional, Cammarano, Davide, additional, Chen, Yi, additional, Dumont, Benjamin, additional, Eyshi Rezaei, Ehsan, additional, Fereres, Elias, additional, Ferrise, Roberto, additional, Gaiser, Thomas, additional, Gao, Yujing, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Hochman, Zvi, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie, additional, Kersebaum, Kurt, additional, Nendel, Claas, additional, Olesen, Jorgen, additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Pullens, Johannes, additional, Rodriguez, Alfredo, additional, Rotter, Reimund, additional, Ruiz Ramos, Margarita, additional, Semenov, Mikhail, additional, Senapati, Nimai, additional, Siebert, Stefan, additional, Srivastava, Amit, additional, Stockle, Claudio, additional, Supit, Iwan, additional, Tao, Fulu, additional, Thorburn, Peter, additional, Wang, Enli, additional, Weber, Tobias, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Chuang, additional, Zhao, Jin, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional

Published
2023
Full Text
View/download PDF

21. 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
Full Text
View/download PDF

24. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Author

Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Rebetzke, Greg, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, 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), The New Zealand Institute for Plant & Food Research Limited [Auckland] (Plant & Food Research), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Brandenburg University of Technology [Cottbus – Senftenberg] (BTU), Foundation for Arable Research (FAR), University of Florida [Gainesville] (UF), Earth Institute at Columbia University, Columbia University [New York], International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Swedish University of Agricultural Sciences (SLU), Pir Mehr Ali Shah Arid Agriculture University = PMAS-Arid Agriculture University Rawalpindi (AAUR), Oklahoma State University [Stillwater] (OSU), Michigan State University [East Lansing], Michigan State University System, Instituto Nacional de Investigación Agropecuaria (INIA), Georg-August-University = Georg-August-Universität Göttingen, Aarhus University [Aarhus], Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Gembloux Agro-Bio Tech [Gembloux], Université de Liège, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Universidad de Córdoba = University of Córdoba [Córdoba], Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze = University of Florence (UniFI), Institute of Crop Science and Resource Conservation [Bonn] (INRES), University of Hohenheim, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Global Change Research Centre (CzechGlobe), University of Potsdam = Universität Potsdam, Natural Resources Institute Finland (LUKE), Helmholtz Zentrum München = German Research Center for Environmental Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Institute of Biochemical Plant Pathology (BIOP), Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM), Universidad Politécnica de Madrid (UPM), Universidad de Castilla-La Mancha = University of Castilla-La Mancha (UCLM), Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Washington State University (WSU), Wageningen University and Research [Wageningen] (WUR), Zhejiang University, Nanjing Agricultural University (NAU), China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 and was supported by the French National Research Institute for Agriculture, Food (INRAE) and the International Maize and Wheat Improvement Center (CIMMYT) through the International Wheat Yield Partnership (IWYP, grant IWYP115)., metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE, grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat [BB/P016855/1] and Achieving Sustainable Agricultural Systems [NE/N018125/1] jointly funded with NERC, DivCSA project funded by the Academy of Finland (decision no. 316215)., National Natural Science Foundation of China (No. 31761143006), financial support from BARISTA project (031B0811A) through ERA-NET SusCrop under EU-FACCE JPI, German Federal Ministry of Education and Research (BMBF) through the BonaRes project ’’I4S’’ (031B0513I), German Federal Ministry of Education and Research (BMBF) through the BonaRes Project 'Soil3' (FKZ 031B0026A), Ministry of Education, Youth and Sports of Czech Republic through SustES—Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/000797), Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324', German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), European Project: 618105,EC:FP7:KBBE,FP7-ERANET-2013-RTD,FACCE ERA NET PLUS(2013), Institut National de la Recherche Agronomique (France), International Maize and Wheat Improvement Center, International Wheat Yield Partnership, National Natural Science Foundation of China, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Education, Youth and Sports (Czech Republic), German Research Foundation, Biotechnology and Biological Sciences Research Council (UK), Natural Environment Research Council (UK), and Academy of Finland

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Physiology, Climate Change, sowing date, Plant Science, CHINA, Multi-model Ensemble, New Zealand, Sowing Date, Sowing Density, Tiller Mortality, Tillering, Wheat, Yield Potential, tillering, wheat, USE EFFICIENCY, sowing density, Life Science, Biomass, ADAPTATION, PLANT-DENSITY, Triticum, METAANALYSIS, Multi-model ensemble, WIMEK, CLIMATE-CHANGE, tiller mortality, PRODUCTIVITY, Temperature, CROP MODELS, yield potential, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ROTATION, GROWTH, Water Systems and Global Change, Seasons
Abstract

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures., This study was a part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 and was supported by the French National Research Institute for Agriculture, Food (INRAE) and the International Maize and Wheat Improvement Center (CIMMYT) through the International Wheat Yield Partnership (IWYP, grant IWYP115). SD and PM acknowledge support from the metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE. YC and FT acknowledge support from the National Natural Science Foundation of China (No. 31761143006). RPR and GBM acknowledge financial support from BARISTA project (031B0811A) through ERA-NET SusCrop under EU-FACCE JPI. KCK was funded by the German Federal Ministry of Education and Research (BMBF) through the BonaRes project ’’I4S’’ (031B0513I). AS and TG acknowledge funding by the German Federal Ministry of Education and Research (BMBF) through the BonaRes Project “Soil3” (FKZ 031B0026A). KCK and JEO were supported by the Ministry of Education, Youth and Sports of Czech Republic through SustES—Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/000797). FE acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324”. TKDW was funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017). MAS and NS at Rothamsted Research received grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat [BB/P016855/1] and Achieving Sustainable Agricultural Systems [NE/N018125/1] jointly funded with NERC. TP and FT are supported by the DivCSA project funded by the Academy of Finland (decision no. 316215).

Published
2022
Full Text
View/download PDF

25. 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 1.5°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

26. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Author

Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres, Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie, Kersebaum, Kurt, Nendel, Claas, Olesen, Jorgen, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes, Rodriguez, Alfredo, Rotter, Reimund, Ruiz Ramos, Margarita, Semenov, Mikhail, Senapati, Nimai, Siebert, Stefan, Srivastava, Amit, Stockle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres, Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie, Kersebaum, Kurt, Nendel, Claas, Olesen, Jorgen, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes, Rodriguez, Alfredo, Rotter, Reimund, Ruiz Ramos, Margarita, Semenov, Mikhail, Senapati, Nimai, Siebert, Stefan, Srivastava, Amit, Stockle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.

Published
2023

27. The nitrogen price of improved wheat yield under climate change

Author

Martre, Pierre, primary, Dueri, Sibylle, additional, Guarin, Jose, additional, Ewert, F, additional, Webber, Heidi, additional, Calderini, Daniel, additional, Molero, Gemma, additional, Reynolds, Matthew, additional, Miralles, Daniel, additional, Garcia, Guillermo, additional, Brown, Hamish, additional, George, Mike, additional, Craigie, Rob, additional, Cohan, Jean-Pierre, additional, Deswarte, Jean-Charles, additional, Slafer, Gustavo, additional, Giunta, F, additional, Cammarano, Davide, additional, Ferrise, Roberto, additional, GAISER, Thomas, additional, Gao, Yujing, additional, Hochman, Zvi, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie A, additional, Kersebaum, Kurt, additional, Nendel, Claas, additional, Padovan, Gloria, additional, Ruane, Alex, additional, Stella, Tommaso, additional, Supit, Iwan, additional, Srivast, Amit, additional, Thorburn, Peter, additional, Wang, Enli, additional, Wolf, Joost, additional, Zhao, Chuang, additional, Zhao, Zhigan, additional, and Asseng, Senthold, additional

Published
2023
Full Text
View/download PDF

29. Crop rotation modelling—A European model intercomparison

Author

Kollas, Chris, Kersebaum, Kurt Christian, Nendel, Claas, Manevski, Kiril, Müller, Christoph, Palosuo, Taru, Armas-Herrera, Cecilia M., Beaudoin, Nicolas, Bindi, Marco, Charfeddine, Monia, Conradt, Tobias, Constantin, Julie, Eitzinger, Josef, Ewert, Frank, Ferrise, Roberto, Gaiser, Thomas, Cortazar-Atauri, Iñaki Garcia de, Giglio, Luisa, Hlavinka, Petr, Hoffmann, Holger, Hoffmann, Munir P., Launay, Marie, Manderscheid, Remy, Mary, Bruno, Mirschel, Wilfried, Moriondo, Marco, Olesen, Jørgen E., Öztürk, Isik, Pacholski, Andreas, Ripoche-Wachter, Dominique, Roggero, Pier Paolo, Roncossek, Svenja, Rötter, Reimund P., Ruget, Françoise, Sharif, Behzad, Trnka, Mirek, Ventrella, Domenico, Waha, Katharina, Wegehenkel, Martin, Weigel, Hans-Joachim, and Wu, Lianhai

Published
2015
Full Text
View/download PDF

32. Stakeholders

Author

Agnew, Maureen D., Goodess, Clare M., Hemming, Debbie, Giannakopoulos, Christos, Bindi, Marco, Dibari, Camilla, El-Askary, Hesham, El-Hattab, Mamdouh, El-Raey, Mohamed, Ferrise, Roberto, Harzallah, Ali, Hatzaki, Maria, Kostopoulou, Effie, Lionello, Piero, Abed, Salah Sahabi, Sánchez-Arcilla, Agustín, Senouci, Mohamed, Sommer, Rolf, Taleb, Mohamed Zoheir, Tanzarella, Annalisa, Navarra, Antonio, editor, and Tubiana, Laurence, editor

Published
2013
Full Text
View/download PDF

33. Physical and Socio-economic Indicators

Author

Agnew, Maureen D., Goodess, Clare M., Hemming, Debbie, Giannakopoulos, Christos, Salem, Skander Ben, Bindi, Marco, Bradai, Mohamed Nejmeddine, Dibari, Camilla, El-Askary, Hesham, El-Fadel, Mutasem, El-Raey, Mohamed, Ferrise, Roberto, Grünzweig, José M., Harzallah, Ali, Hattour, Abdallah, Hatzaki, Maria, Kanas, Dina, Kostopoulou, Effie, Lionello, Piero, Oweis, Theib, Pino, Cosimo, Psiloglou, Basil, Abed, Salah Sahabi, Sánchez-Arcilla, Agustín, Senouci, Mohamed, Taleb, Mohamed Zoheir, Tanzarella, Annalisa, Navarra, Antonio, editor, and Tubiana, Laurence, editor

Published
2013
Full Text
View/download PDF

35. Synthesis and the Assessment of Adaptation Measures

Author

Goodess, Clare M., Agnew, Maureen D., Hemming, Debbie, Giannakopoulos, Christos, Bindi, Marco, Dibari, Camilla, El-Askary, Hesham, El-Fadel, Mutasem, El-Hattab, Mamdouh, El-Raey, Mohamed, Ferrise, Roberto, Grünzweig, José M., Harzallah, Ali, Kanas, Dina, Lionello, Piero, Aranda, César Mösso, Oweis, Theib, Sierra, Joan Pau, Reale, Marco, Sánchez-Arcilla, Agustín, Senouci, Mohamed, Sommer, Rolf, Tanzarella, Annalisa, Navarra, Antonio, editor, and Tubiana, Laurence, editor

Published
2013
Full Text
View/download PDF

36. Integration of the Climate Impact Assessments with Future Projections

Author

Goodess, Clare M., Agnew, Maureen D., Giannakopoulos, Christos, Hemming, Debbie, Salem, Skander Ben, Bindi, Marco, Bradai, Mohamed Nejmeddine, Congedi, Letizia, Dibari, Camilla, El-Askary, Hesham, El-Fadel, Mutasem, El-Raey, Mohamed, Ferrise, Roberto, Founda, Dimitra, Grünzweig, José M., Harzallah, Ali, Hatzaki, Maria, Kay, Gillian, Lionello, Piero, Aranda, César Mösso, Oweis, Theib, Sierra, Joan Pau, Psiloglou, Basil, Reale, Marco, Sánchez-Arcilla, Agustín, Senouci, Mohamed, Tanzarella, Annalisa, Varotsos, Konstantinos V., Navarra, Antonio, editor, and Tubiana, Laurence, editor

Published
2013
Full Text
View/download PDF

37. Climate Impact Assessments

Author

Hemming, Debbie, Agnew, Maureen D., Goodess, Clare M., Giannakopoulos, Christos, Salem, Skander Ben, Bindi, Marco, Bradai, Mohamed Nejmeddine, Congedi, Letizia, Dibari, Camilla, El-Askary, Hesham, El-Fadel, Mutasem, El-Raey, Mohamed, Ferrise, Roberto, Grünzweig, José M., Harzallah, Ali, Hattour, Abdallah, Hatzaki, Maria, Kanas, Dina, Lionello, Piero, McCarthy, Mark, Aranda, César Mösso, Oweis, Theib, Sierra, Joan Pau, Psiloglou, Basil, Reale, Marco, Sánchez-Arcilla, Agustín, Senouci, Mohamed, Tanzarella, Annalisa, Navarra, Antonio, editor, and Tubiana, Laurence, editor

Published
2013
Full Text
View/download PDF

38. Introduction

Author

Agnew, Maureen D., Goodess, Clare M., Hemming, Debbie, Giannakopoulos, Christos, Salem, Skander Ben, Bindi, Marco, Bradai, Mohamed Nejmeddine, Congedi, Letizia, Dibari, Camilla, El-Askary, Hesham, El-Fadel, Mutasem, Ferrise, Roberto, Grünzweig, José M., Harzallah, Ali, Hattour, Abdallah, Hatzaki, Maria, Kanas, Dina, Kostopoulou, Effie, Lionello, Piero, Oweis, Theib, Pino, Cosimo, Reale, Marco, Sánchez-Arcilla, Agustín, Senouci, Mohamed, Navarra, Antonio, editor, and Tubiana, Laurence, editor

Published
2013
Full Text
View/download PDF

39. Uncertainty in crop phenology simulations is driven primarily by parameter variability

Author

Wallach, Daniel, primary, Palosuo, Taru, additional, Mielenz, Henrike, additional, Buis, Samuel, additional, Thorburn, Peter, additional, Asseng, Senthold, additional, Dumont, Benjamin, additional, Ferrise, Roberto, additional, Gayler, Sebastian, additional, Ghahramani, Afshin, additional, Harrison, Matthew Tom, additional, Hochman, Zvi, additional, Hoogenboom, Gerrit, additional, Huang, Mingxia, additional, Jing, Qi, additional, Justes, Eric, additional, Kersebaum, Kurt Christian, additional, Launay, Marie, additional, Lewan, Elisabet, additional, Liu, Ke, additional, Luo, Qunying, additional, Mequanint, Fasil, additional, Nendel, Claas, additional, Padovan, Gloria, additional, Olesen, Jorgen Eivind, additional, Pullens, Johannes Wilhelmus Maria, additional, Qian, Budong, additional, Seserman, Diana-Maria, additional, Shelia, Vakhtang, additional, Souissi, Amir, additional, Specka, Xenia, additional, Wang, Jing, additional, Weber, Tobias K.D., additional, Weihermuller, Lutz, additional, and Seidel, Sabine, additional

Published
2023
Full Text
View/download PDF

40. The ability of crop models to predict soil organic carbon changes in a maize cropping system under contrasting fertilization and residues management: Evidence from a long-term experiment

Author

Pulina, Antonio, Ferrise, Roberto, Mula, Laura, Brilli, Lorenzo, Giglio, Luisa, Iocola, Ileana, Ventrella, Domenico, Zavattaro, Laura, Grignani, Carlo, and Roggero, Pier Paolo

Subjects
soil organic carbon, organic fertilization, Model ensemble, maize, long-term experiment, climate change mitigation, Agronomy and Crop Science
Abstract

This study assesses the ability of an ensemble of crop models (MME) to predict the impacts of fertilization and crop residue management on soil organic carbon (SOC) and aboveground biomass (AGB) in a long-term experiment (LTE) based on continuous maize cropping systems. Data from a LTE in Northern Italy were used. Treatments included continuous grain (MG) or silage (MS) maize, fertilized with mineral, cattle slurry, and farmyard manure. The MME median resulted the best predictor of the observed values. Models performance was better when simulating MG than MS, and for crops treated with mineral compared to organic fertilizers. The ability to predict the dynamics of SOC was affected by the model used and by the year × residues management and year × fertilizer interactions. The model and the residue × fertilizer interaction affected the ability to simulate AGB dynamics. Results showed that a MME can effectively predict the long-term dynamics of SOC and maize crop production under contrasting fertilization and crop residue management, and thus their potential for climate change mitigation. The uncertainty in the simulation of SOC is related to the model routines simulating SOC partitioning and to the complexity of the interactions between management factors over time. Highlights - A crop model ensemble was compiled to simulate soil organic carbon and maize aboveground biomass dynamics in a long-term experiment. - The performances of stand-alone models and their ensemble were assessed under contrasting fertilization and crop residue management. - The multi-model ensemble using the median value of simulation was the best predictor of the variables observed in the long-term experiment. - Improved performances in simulations were observed when crop residues were incorporated into the soil, regardless of the fertilization management. - The uncertainty in SOC simulation increased over time for cropping systems with silage maize and organic fertilization.

Published
2022
Full Text
View/download PDF

42. Evidence for increasing global wheat yield potential

Author

Guarin, Jose Rafael, primary, Martre, Pierre, additional, Ewert, Frank, additional, Webber, Heidi, additional, Dueri, Sibylle, additional, Calderini, Daniel, additional, Reynolds, Matthew, additional, Molero, Gemma, additional, Miralles, Daniel, additional, Garcia, Guillermo, additional, Slafer, Gustavo, additional, Giunta, Francesco, additional, Pequeno, Diego N L, additional, Stella, Tommaso, additional, Ahmed, Mukhtar, additional, Alderman, Phillip D, additional, Basso, Bruno, additional, Berger, Andres G, additional, Bindi, Marco, additional, Bracho-Mujica, Gennady, additional, Cammarano, Davide, additional, Chen, Yi, additional, Dumont, Benjamin, additional, Rezaei, Ehsan Eyshi, additional, Fereres, Elias, additional, Ferrise, Roberto, additional, Gaiser, Thomas, additional, Gao, Yujing, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Hochman, Zvi, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie A, additional, Kersebaum, Kurt C, additional, Nendel, Claas, additional, Olesen, Jørgen E, additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Pullens, Johannes W M, additional, Rodríguez, Alfredo, additional, Rötter, Reimund P, additional, Ramos, Margarita Ruiz, additional, Semenov, Mikhail A, additional, Senapati, Nimai, additional, Siebert, Stefan, additional, Srivastava, Amit Kumar, additional, Stöckle, Claudio, additional, Supit, Iwan, additional, Tao, Fulu, additional, Thorburn, Peter, additional, Wang, Enli, additional, Weber, Tobias Karl David, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Chuang, additional, Zhao, Jin, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional

Published
2022
Full Text
View/download PDF

43. 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
Full Text
View/download PDF

44. Diverging Importance of Drought Stress for Maize and Winter Wheat in Europe

Author

Webber, Heidi, Ewert, Frank, Olesen, Jørgen E, Müller, Christoph, Fronzek, Stefan, Ruane, Alex C, Bourgault, Maryse, Martre, Pierre, Ababaei, Behnam, Bindi, Marco, Ferrise, Roberto, Finger, Robert, Fodor, Nándor, Gabaldón-Leal, Clara, Gaiser, Thomas, Jabloun, Mohamed, Kersebaum, Kurt-Christian, Lizaso, Jon I, Lorite, Ignacio J, Manceau, Loic, Moriondo, Marco, Nendel, Claas, Rodríguez, Alfredo, Ruiz-Ramos, Margarita, Semenov, Mikhail A, Siebert, Stefan, Stella, Tommaso, Stratonovitch, Pierre, Trombi, Giacomo, and Wallach, Daniel

Subjects
Meteorology And Climatology
Abstract

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

Published
2018
Full Text
View/download PDF

47. Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

Author

Fronzek, Stefan, Pirttioja, Nina, Carter, Timothy R, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz-Ramos, Margarita, Tao, Fulu, Trnka, Miroslav, Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, Francois, Louis, Gaiser, Thomas, Hlavinka, Petr, Jacquemin, Ingrid, Kersebaum, Kurt Christian, Kollas, Chris, Krzyszczak, Jaromir, Lorite, Ignacio J, Minet, Julien, Ines Minguez, M, Montesino, Manuel, Moriondo, Marco, Muller, Christoph, Nendel, Claas, Ozturk, Isik, Perego, Alessia, Rodriguez, Alfredo, Ruane, Alex C, Ruget, Francoise, Sanna, Mattia, Semenov, Mikhail A, Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, Enli, Wu, Lianhai, Zhao, Zhigan, and Rotter, Reimund P

Subjects
Meteorology And Climatology, Earth Resources And Remote Sensing
Abstract

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Published
2017
Full Text
View/download PDF

48. Proposal and extensive test of a calibration protocol for crop phenology models

Author

Wallach, Daniel, primary, Palosuo, Taru, additional, Thorburn, Peter, additional, Mielenz, Henrike, additional, Buis, Samuel, additional, Hochman, Zvi, additional, Gourdain, Emmanuelle, additional, Andrianasolo, Fety, additional, Dumont, Benjamin, additional, Ferrise, Roberto, additional, Gaiser, Thomas, additional, Garcia, Cecile, additional, Gayler, Sebastian, additional, Harrison, Matthew, additional, Hiremath, Santosh, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Jansson, Per-Erik, additional, Jing, Qi, additional, Justes, Eric, additional, Kersebaum, Kurt-Christian, additional, Launay, Marie, additional, Lewan, Elisabet, additional, Liu, Ke, additional, Mequanint, Fasil, additional, Moriondo, Marco, additional, Nendel, Claas, additional, Padovan, Gloria, additional, Qian, Budong, additional, Schütze, Niels, additional, Seserman, Diana-Maria, additional, Shelia, Vakhtang, additional, Souissi, Amir, additional, Specka, Xenia, additional, Srivastava, Amit Kumar, additional, Trombi, Giacomo, additional, Weber, Tobias K.D., additional, Weihermüller, Lutz, additional, Wöhling, Thomas, additional, and Seidel, Sabine J., additional

Published
2022
Full Text
View/download PDF

50. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

Author

Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results