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2. Simulation of soil temperature under maize: An inter-comparison among 33 maize models

Author

Kimball, Bruce A., Thorp, Kelly R., Boote, Kenneth J., Stockle, Claudio, Suyker, Andrew E., Evett, Steven R., Brauer, David K., Coyle, Gwen G., Copeland, Karen S., Marek, Gary W., Colaizzi, Paul D., Acutis, Marco, Archontoulis, Sotirios, Babacar, Faye, Barcza, Zoltán, Basso, Bruno, Bertuzzi, Patrick, De Antoni Migliorati, Massimiliano, Dumont, Benjamin, Durand, Jean-Louis, Fodor, Nándor, Gaiser, Thomas, Gayler, Sebastian, Grant, Robert, Guan, Kaiyu, Hoogenboom, Gerrit, Jiang, Qianjing, Kim, Soo-Hyung, Kisekka, Isaya, Lizaso, Jon, Perego, Alessia, Peng, Bin, Priesack, Eckart, Qi, Zhiming, Shelia, Vakhtang, Srivastava, Amit Kumar, Timlin, Dennis, Webber, Heidi, Weber, Tobias, Williams, Karina, Viswanathan, Michelle, and Zhou, Wang

Published
2024
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5. 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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7. 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
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11. Unveiling Wheat's Future Amidst Climate Change in the Central Ethiopia Region.

Author

Senbeta, Abate Feyissa, Worku, Walelign, Gayler, Sebastian, and Naimi, Babak

Subjects
COMMODITY futures, MACHINE learning, SEASONAL temperature variations, CLIMATE change, SOLAR radiation
Abstract

Quantifying how climatic change affects wheat production, and accurately predicting its potential distributions in the face of future climate, are highly important for ensuring food security in Ethiopia. This study leverages advanced machine learning algorithms including Random Forest, Maxent, Boosted Regression Tree, and Generalised Linear Model alongside an ensemble approach to accurately predict shifts in wheat habitat suitability in the Central Ethiopia Region over the upcoming decades. An extensive dataset consisting of 19 bioclimatic variables (Bio1–Bio19), elevation, solar radiation, and topographic positioning index was refined by excluding collinear predictors to increase model accuracy. The analysis revealed that the precipitation of the wettest month, minimum temperature of the coldest month, temperature seasonality, and precipitation of the coldest quarter are the most influential factors, which collectively account for a significant proportion of habitat suitability changes. The future projections revealed that up to 100% of the regions currently classified as moderately or highly suitable for wheat could become unsuitable by 2050, 2070, and 2090, illustrating a dramatic potential decline in wheat production. Generally, the future of wheat cultivation will depend heavily on developing varieties that can thrive under altered conditions; thus, immediate and informed action is needed to safeguard the food security of the region. [ABSTRACT FROM AUTHOR]

Published
2024
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12. 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
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13. 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
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14. Simulation of maize evapotranspiration: An inter-comparison among 29 maize models

Author

Kimball, Bruce A., Boote, Kenneth J., Hatfield, Jerry L., Ahuja, Laj R., Stockle, Claudio, Archontoulis, Sotirios, Baron, Christian, Basso, Bruno, Bertuzzi, Patrick, Constantin, Julie, Deryng, Delphine, Dumont, Benjamin, Durand, Jean-Louis, Ewert, Frank, Gaiser, Thomas, Gayler, Sebastian, Hoffmann, Munir P., Jiang, Qianjing, Kim, Soo-Hyung, Lizaso, Jon, Moulin, Sophie, Nendel, Claas, Parker, Philip, Palosuo, Taru, Priesack, Eckart, Qi, Zhiming, Srivastava, Amit, Stella, Tommaso, Tao, Fulu, Thorp, Kelly R., Timlin, Dennis, Twine, Tracy E., Webber, Heidi, Willaume, Magali, and Williams, Karina

Published
2019
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16. 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
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17. 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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19. 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

21. Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models

Author

Kimball, Bruce A., primary, Thorp, Kelly R., additional, Boote, Kenneth J., additional, Stockle, Claudio, additional, Suyker, Andrew E., additional, Evett, Steven R., additional, Brauer, David K., additional, Coyle, Gwen G., additional, Copeland, Karen S., additional, Marek, Gary W., additional, Colaizzi, Paul D., additional, Acutis, Marco, additional, Alimagham, Seyyedmajid, additional, Archontoulis, Sotirios, additional, Babacar, Faye, additional, Barcza, Zoltán, additional, Basso, Bruno, additional, Bertuzzi, Patrick, additional, Constantin, Julie, additional, De Antoni Migliorati, Massimiliano, additional, Dumont, Benjamin, additional, Durand, Jean-Louis, additional, Fodor, Nándor, additional, Gaiser, Thomas, additional, Garofalo, Pasquale, additional, Gayler, Sebastian, additional, Giglio, Luisa, additional, Grant, Robert, additional, Guan, Kaiyu, additional, Hoogenboom, Gerrit, additional, Jiang, Qianjing, additional, Kim, Soo-Hyung, additional, Kisekka, Isaya, additional, Lizaso, Jon, additional, Masia, Sara, additional, Meng, Huimin, additional, Mereu, Valentina, additional, Mukhtar, Ahmed, additional, Perego, Alessia, additional, Peng, Bin, additional, Priesack, Eckart, additional, Qi, Zhiming, additional, Shelia, Vakhtang, additional, Snyder, Richard, additional, Soltani, Afshin, additional, Spano, Donatella, additional, Srivastava, Amit, additional, Thomson, Aimee, additional, Timlin, Dennis, additional, Trabucco, Antonio, additional, Webber, Heidi, additional, Weber, Tobias, additional, Willaume, Magali, additional, Williams, Karina, additional, van der Laan, Michael, additional, Ventrella, Domenico, additional, Viswanathan, Michelle, additional, Xu, Xu, additional, and Zhou, Wang, additional

Published
2023
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22. 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
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23. 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

24. 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

26. 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
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27. 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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30. 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
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31. 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

32. Multi-site, multi-crop measurements in the soil-vegetation-atmosphere continuum: a comprehensive dataset from two climatically contrasting regions in southwestern Germany for the period 2009-2018

Author

Weber, Tobias K. D., Ingwersen, Joachim, Hoegy, Petra, Poyda, Arne, Wizemann, Hans-Dieter, Demyan, Michael Scott, Bohm, Kristina, Eshonkulov, Ravshan, Gayler, Sebastian, Kremer, Pascal, Laub, Moritz, Nkwain, Yvonne Funkiun, Troost, Christian, Witte, Irene, Reichenau, Tim, Berger, Thomas, Cadisch, Georg, Mueller, Torsten, Fangmeier, Andreas, Wulfmeyer, Volker, Streck, Thilo, Weber, Tobias K. D., Ingwersen, Joachim, Hoegy, Petra, Poyda, Arne, Wizemann, Hans-Dieter, Demyan, Michael Scott, Bohm, Kristina, Eshonkulov, Ravshan, Gayler, Sebastian, Kremer, Pascal, Laub, Moritz, Nkwain, Yvonne Funkiun, Troost, Christian, Witte, Irene, Reichenau, Tim, Berger, Thomas, Cadisch, Georg, Mueller, Torsten, Fangmeier, Andreas, Wulfmeyer, Volker, and Streck, Thilo

Abstract

We present a comprehensive, high-quality dataset characterizing soil-vegetation and land surface processes from continuous measurements conducted in two climatically contrasting study regions in southwestern Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 degrees C and annual precipitation of 890 mm and the cooler and wetter Swabian Alb with mean temperature 7.5 degrees C and annual precipitation of 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance (EC) stations which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site years containing observations with a multitude of crops, as well as considerable variation in local growing-season climates. The presented multi-site, multi-year dataset is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass, and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30 min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1200 soil samples were taken to study changes of carbon and nitrogen contents. The dataset is available at https://doi.org/10.20387/bonares-a0qc-46jc (Weber et al., 2021). One field in each region is still fully set up as continuous observatories for state variables and fluxes in intensively managed agricultural fields.

Published
2022

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

Author

Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García 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, Ji, Zhao, Zhigan, Asseng, Senthold, Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García 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, Ji, Zhao, Zhigan, 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. (2022-02-11).

Published
2022

34. Evidence for increasing global wheat yield potential

Author

Guarin, Jose Rafael, 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, Rezaei, Ehsan Eyshi, 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., 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, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose Rafael, 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, Rezaei, Ehsan Eyshi, 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., 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, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

Published
2022

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

Author

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), Academy of Finland, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García 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., 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, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, 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), Academy of Finland, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García 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., 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, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre

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.

Published
2022

36. Evidence for increasing global wheat yield potential

Author

International Wheat Yield Partnership, International Maize and Wheat Improvement Center, Comisión Nacional de Investigación Científica y Tecnológica (Chile), Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), National Natural Science Foundation of China, Ministry of Education, Youth and Sports (Czech Republic), Biotechnology and Biological Sciences Research Council (UK), Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García 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, International Wheat Yield Partnership, International Maize and Wheat Improvement Center, Comisión Nacional de Investigación Científica y Tecnológica (Chile), Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), National Natural Science Foundation of China, Ministry of Education, Youth and Sports (Czech Republic), Biotechnology and Biological Sciences Research Council (UK), Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García 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

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

Published
2022

37. Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations

Author

Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., 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, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, 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, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., 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, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, 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, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre

Abstract

The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models.

Published
2022

38. 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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40. Multi-site, multi-crop measurements in the soil–vegetation–atmosphere continuum: a comprehensive dataset from two climatically contrasting regions in southwestern Germany for the period 2009–2018

Author

Weber, Tobias K. D., primary, Ingwersen, Joachim, additional, Högy, Petra, additional, Poyda, Arne, additional, Wizemann, Hans-Dieter, additional, Demyan, Michael Scott, additional, Bohm, Kristina, additional, Eshonkulov, Ravshan, additional, Gayler, Sebastian, additional, Kremer, Pascal, additional, Laub, Moritz, additional, Nkwain, Yvonne Funkiun, additional, Troost, Christian, additional, Witte, Irene, additional, Reichenau, Tim, additional, Berger, Thomas, additional, Cadisch, Georg, additional, Müller, Torsten, additional, Fangmeier, Andreas, additional, Wulfmeyer, Volker, additional, and Streck, Thilo, additional

Published
2022
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42. 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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43. 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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50. 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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