192 results on '"Hochman, Zvi"'
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2. Proposal and extensive test of a calibration protocol for crop phenology models
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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.
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- 2023
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3. Data fusion using climatology and seasonal climate forecasts improves estimates of Australian national wheat yields
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Mitchell, Patrick J., Waldner, François, Horan, Heidi, Brown, Jaclyn N., and Hochman, Zvi
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- 2022
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4. Improving early-season wheat yield forecasts driven by probabilistic seasonal climate forecasts
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Jin, Huidong, Li, Ming, Hopwood, Garry, Hochman, Zvi, and Bakar, K Shuvo
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- 2022
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5. The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise
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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.
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- 2021
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6. The future of farming: Who will produce our food?
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Giller, Ken E., Delaune, Thomas, Silva, João Vasco, Descheemaeker, Katrien, van de Ven, Gerrie, Schut, Antonius G.T., van Wijk, Mark, Hammond, James, Hochman, Zvi, Taulya, Godfrey, Chikowo, Regis, Narayanan, Sudha, Kishore, Avinash, Bresciani, Fabrizio, Teixeira, Heitor Mancini, Andersson, Jens A., and van Ittersum, Martin K.
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- 2021
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7. Multi-model evaluation of phenology prediction for wheat in Australia
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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
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- 2021
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8. Climate drivers provide valuable insights into late season prediction of Australian wheat yield
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Dayal, Kavina, Brown, Jaclyn N., Waldner, François, Lawes, Roger, Hochman, Zvi, Donohue, Randall, Horan, Heidi, and Chen, Yang
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- 2020
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9. All pixels are useful, but some are more useful: Efficient in situ data collection for crop-type mapping using sequential exploration methods
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Fowler, Jared, Waldner, François, and Hochman, Zvi
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- 2020
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10. Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods
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Kamir, Elisa, Waldner, François, and Hochman, Zvi
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- 2020
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11. Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods
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Waldner, François, Chen, Yang, Lawes, Roger, and Hochman, Zvi
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- 2019
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12. Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed
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Waldner, François, Bellemans, Nicolas, Hochman, Zvi, Newby, Terence, de Abelleyra, Diego, Verón, Santiago R., Bartalev, Sergey, Lavreniuk, Mykola, Kussul, Nataliia, Maire, Guerric Le, Simoes, Margareth, Skakun, Sergii, and Defourny, Pierre
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- 2019
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13. Vertical farms bear fruit
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O’Sullivan, Cathryn A., McIntyre, C. Lynne, Dry, Ian B., Hani, Susan M., Hochman, Zvi, and Bonnett, Graham D.
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- 2020
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14. Seasonal climate forecasts provide more definitive and accurate crop yield predictions
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Brown, Jaclyn N., Hochman, Zvi, Holzworth, Dean, and Horan, Heidi
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- 2018
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15. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations
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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
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- 2023
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16. Scales of development for wheat and barley specific to either single culms or a population of culms
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Celestina, Corinne, primary, Hunt, James, additional, Brown, Hamish, additional, Huth, Neil, additional, Andreucci, Mariana, additional, Hochman, Zvi, additional, Bloomfield, Maxwell, additional, Porker, Kenton, additional, McCallum, Melissa, additional, Harris, Felicity, additional, Matthews, Mary, additional, Biddulph, Ben, additional, Al Yaseri, Ghazwan, additional, Nicol, Dion, additional, Hyles, Jessica, additional, Wang, Enli, additional, Zheng, Bangyou, additional, Zhao, Zhigan, additional, and Kohout, Michele, additional
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- 2023
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17. Governing through representatives of the community: A case study on farmer organizations in rural Australia
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Wang, Jingpei, Hochman, Zvi, Taylor, Bruce, Darbas, Toni, van Rees, Harm, Carberry, Peter, and Ren, Dapeng
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- 2017
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18. Corrigendum to “Scales of development for wheat and barley specific to either single culms or a population of culms” [Eur. J. Agron. 147 (2023) 126824]
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Celestina, Corinne, Hunt, James, Brown, Hamish, Huth, Neil, Andreucci, Mariana, Hochman, Zvi, Bloomfield, Maxwell, Porker, Kenton, McCallum, Melissa, Harris, Felicity, Matthews, Mary, Biddulph, Ben, Al Yaseri, Ghazwan, Nicol, Dion, Hyles, Jessica, Wang, Enli, Zheng, Bangyou, Zhao, Zhigan, and Kohout, Michele
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- 2023
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19. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment
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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).
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- 2022
20. The nitrogen price of improved wheat yield under climate change
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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
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- 2023
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21. Uncertainty in crop phenology simulations is driven primarily by parameter variability
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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
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- 2023
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22. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations
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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.
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- 2023
23. High temporal resolution of leaf area data improves empirical estimation of grain yield
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Waldner, François, Horan, Heidi, Chen, Yang, and Hochman, Zvi
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- 2019
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24. Yield potential determines Australian wheat growers’ capacity to close yield gaps while mitigating economic risk
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Monjardino, Marta, Hochman, Zvi, and Horan, Heidi
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- 2019
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25. Socio-psychological and management drivers explain farm level wheat yield gaps in Australia
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Zhang, Airong, Hochman, Zvi, Horan, Heidi, Navarro, Javier Garcia, Das, Bianca Tara, and Waldner, François
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- 2019
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26. Evidence for increasing global wheat yield potential
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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
27. APSIM – Evolution towards a new generation of agricultural systems simulation
- Author
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Holzworth, Dean P., Huth, Neil I., deVoil, Peter G., Zurcher, Eric J., Herrmann, Neville I., McLean, Greg, Chenu, Karine, van Oosterom, Erik J., Snow, Val, Murphy, Chris, Moore, Andrew D., Brown, Hamish, Whish, Jeremy P.M., Verrall, Shaun, Fainges, Justin, Bell, Lindsay W., Peake, Allan S., Poulton, Perry L., Hochman, Zvi, Thorburn, Peter J., Gaydon, Donald S., Dalgliesh, Neal P., Rodriguez, Daniel, Cox, Howard, Chapman, Scott, Doherty, Alastair, Teixeira, Edmar, Sharp, Joanna, Cichota, Rogerio, Vogeler, Iris, Li, Frank Y., Wang, Enli, Hammer, Graeme L., Robertson, Michael J., Dimes, John P., Whitbread, Anthony M., Hunt, James, van Rees, Harm, McClelland, Tim, Carberry, Peter S., Hargreaves, John N.G., MacLeod, Neil, McDonald, Cam, Harsdorf, Justin, Wedgwood, Sara, and Keating, Brian A.
- Published
- 2014
- Full Text
- View/download PDF
28. Participatory approaches to address climate change: perceived issues affecting the ability of South East Queensland graziers to adapt to future climates
- Author
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Brown, Peter R., Hochman, Zvi, Bridle, Kerry L., and Huth, Neil I.
- Published
- 2015
- Full Text
- View/download PDF
29. Proposal and extensive test of a calibration protocol for crop phenology models
- Author
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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
30. Scope for improved eco-efficiency varies among diverse cropping systems
- Author
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Carberry, Peter S., Liang, Wei-li, Twomlow, Stephen, Holzworth, Dean P., Dimes, John P., McClelland, Tim, Huth, Neil I., Chen, Fu, Hochman, Zvi, and Keating, Brian A.
- Published
- 2013
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. 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
33. 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
34. 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
35. 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
36. 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
37. Using Seasonal Climate Forecasts to Manage Dryland Crops in Northern Australia — Experiences from the 1997/98 Seasons
- Author
-
Meinke, Holger, Hochman, Zvi, Sadourny, Robert, editor, Hammer, G. L., editor, Nicholls, N., editor, and Mitchell, C., editor
- Published
- 2000
- Full Text
- View/download PDF
38. Australian Grains Baseline and Mitigation Assessment. Main Report
- Author
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Sevenster, Maartje, Bell, Lindsay, Anderson, Brook, Jamali, Hizbullah, Horan, Heidi, Simmons, Aaron, Cowie, Annette, and Hochman, Zvi
- Published
- 2022
- Full Text
- View/download PDF
39. Exploiting genotype × management interactions to increase rainfed crop production: a case study from south-eastern Australia
- Author
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Hunt, James R, primary, Kirkegaard, John A, additional, Harris, Felicity A, additional, Porker, Kenton D, additional, Rattey, Allan R, additional, Collins, Marisa J, additional, Celestina, Corinne, additional, Cann, David J, additional, Hochman, Zvi, additional, Lilley, Julianne M, additional, and Flohr, Bonnie M, additional
- Published
- 2021
- Full Text
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40. Design of sustainable dryland crop rotations require value judgements and efficient trade-offs
- Author
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Hochman, Zvi, primary, Garcia, Javier Navarro, additional, Horan, Heidi, additional, Whish, Jeremy, additional, and Bell, Lindsay, additional
- Published
- 2021
- Full Text
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41. How well do crop models predict phenology, given calibration data from the target population?
- Author
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Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., 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, and Justes, Eric
- Subjects
F40 - Écologie végétale ,U10 - Informatique, mathématiques et statistiques ,Modélisation des cultures ,Analyse de données ,Modèle de simulation ,technique de prévision ,blé ,F01 - Culture des plantes ,Phénologie ,Triticum - Abstract
Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.
- Published
- 2021
42. The future of farming: Who will produce our food?
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Environmental Sciences, Giller, Ken E., Delaune, Thomas, Silva, João Vasco, Descheemaeker, Katrien, van de Ven, Gerrie, Schut, Antonius G.T., van Wijk, Mark, Hammond, James, Hochman, Zvi, Taulya, Godfrey, Chikowo, Regis, Narayanan, Sudha, Kishore, Avinash, Bresciani, Fabrizio, Teixeira, Heitor Mancini, Andersson, Jens A., van Ittersum, Martin K., Environmental Sciences, Giller, Ken E., Delaune, Thomas, Silva, João Vasco, Descheemaeker, Katrien, van de Ven, Gerrie, Schut, Antonius G.T., van Wijk, Mark, Hammond, James, Hochman, Zvi, Taulya, Godfrey, Chikowo, Regis, Narayanan, Sudha, Kishore, Avinash, Bresciani, Fabrizio, Teixeira, Heitor Mancini, Andersson, Jens A., and van Ittersum, Martin K.
- Published
- 2021
43. How well do crop modeling groups predict wheat phenology, given calibration data from the target population?
- Author
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Wallach, Daniel, primary, Palosuo, Taru, additional, Thorburn, Peter, additional, Gourdain, Emmanuelle, additional, Asseng, Senthold, additional, Basso, Bruno, additional, Buis, Samuel, additional, Crout, Neil, additional, Dibari, Camilla, additional, Dumont, Benjamin, additional, Ferrise, Roberto, additional, Gaiser, Thomas, additional, Garcia, Cécile, additional, Gayler, Sebastian, additional, Ghahramani, Afshin, additional, Hochman, Zvi, additional, Hoek, Steven, additional, Hoogenboom, Gerrit, additional, Horan, Heidi, additional, Huang, Mingxia, additional, Jabloun, Mohamed, additional, Jing, Qi, additional, Justes, Eric, additional, Kersebaum, Kurt Christian, additional, Klosterhalfen, Anne, additional, Launay, Marie, additional, Luo, Qunying, additional, Maestrini, Bernardo, additional, Mielenz, Henrike, additional, Moriondo, Marco, additional, Nariman Zadeh, Hasti, additional, Olesen, Jørgen Eivind, additional, Poyda, Arne, additional, Priesack, Eckart, additional, Pullens, Johannes Wilhelmus Maria, additional, Qian, Budong, additional, Schütze, Niels, additional, Shelia, Vakhtang, additional, Souissi, Amir, additional, Specka, Xenia, additional, Srivastava, Amit Kumar, additional, Stella, Tommaso, additional, Streck, Thilo, additional, Trombi, Giacomo, additional, Wallor, Evelyn, additional, Wang, Jing, additional, Weber, Tobias K.D., additional, Weihermüller, Lutz, additional, de Wit, Allard, additional, Wöhling, Thomas, additional, Xiao, Liujun, additional, Zhao, Chuang, additional, Zhu, Yan, additional, and Seidel, Sabine J., additional
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- 2021
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44. Evaluation of nitrogen bank, a soil nitrogen management strategy for sustainably closing wheat yield gaps
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Meier, Elizabeth A., primary, Hunt, James R., additional, and Hochman, Zvi, additional
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- 2021
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45. Final report to the Grains Research and Development Corporation. Project CSP1904-005RXT: The adaptation of pulses (chickpea and lentil) across the northern grains region
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Peake, Allan, Dreccer, Fernanda, Whish, Jeremy, and Hochman, Zvi
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- 2020
- Full Text
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46. The chaos in calibrating crop models
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Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, de Wit, A.J.W., Seidel, Sabine J., Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, de Wit, A.J.W., and Seidel, Sabine J.
- Abstract
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields.
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- 2020
47. Leaping the Climate Projection Uncertainty Chasm with Agricultural Vulnerability Maps
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Brown, Jaclyn, primary, Gobbett, David L., additional, Chen, Chao, additional, Fletcher, Andrew, additional, Hochman, Zvi, additional, and Clarke, John, additional
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- 2021
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48. The chaos in calibrating crop models
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Wallach, Daniel, primary, Palosuo, Taru, additional, Thorburn, Peter, additional, Hochman, Zvi, additional, Gourdain, Emmanuelle, additional, Andrianasolo, Fety, additional, Asseng, Senthold, additional, Basso, Bruno, additional, Buis, Samuel, additional, Crout, Neil, additional, Dibari, Camilla, additional, Dumont, Benjamin, additional, Ferrise, Roberto, additional, Gaiser, Thomas, additional, Garcia, Cecile, additional, Gayler, Sebastian, additional, Ghahramani, Afshin, additional, Hiremath, Santosh, additional, Hoek, Steven, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Huang, Mingxia, additional, Jabloun, Mohamed, additional, Jansson, Per-Erik, additional, Jing, Qi, additional, Justes, Eric, additional, Kersebaum, Kurt Christian, additional, Klosterhalfen, Anne, additional, Launay, Marie, additional, Lewan, Elisabet, additional, Luo, Qunying, additional, Maestrini, Bernardo, additional, Mielenz, Henrike, additional, Moriondo, Marco, additional, Nariman Zadeh, Hasti, additional, Padovan, Gloria, additional, Olesen, Jørgen Eivind, additional, Poyda, Arne, additional, Priesack, Eckart, additional, Pullens, Johannes Wilhelmus Maria, additional, Qian, Budong, additional, Schütze, Niels, additional, Shelia, Vakhtang, additional, Souissi, Amir, additional, Specka, Xenia, additional, Srivastava, Amit Kumar, additional, Stella, Tommaso, additional, Streck, Thilo, additional, Trombi, Giacomo, additional, Wallor, Evelyn, additional, Wang, Jing, additional, Weber, Tobias K.D., additional, Weihermüller, Lutz, additional, de Wit, Allard, additional, Wöhling, Thomas, additional, Xiao, Liujun, additional, Zhao, Chuang, additional, Zhu, Yan, additional, and Seidel, Sabine J., additional
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- 2020
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49. Cropping system yield gaps can be narrowed with more optimal rotations in dryland subtropical Australia
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Hochman, Zvi, primary, Horan, Heidi, additional, Navarro Garcia, Javier, additional, Hopwood, Garry, additional, Whish, Jeremy, additional, Bell, Lindsay, additional, Zhang, Xiying, additional, and Jing, Haichun, additional
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- 2020
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50. Global priorities of environmental issues to combat food insecurity and biodiversity loss
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Scherer, Laura, primary, Svenning, Jens-Christian, additional, Huang, Jing, additional, Seymour, Colleen L., additional, Sandel, Brody, additional, Mueller, Nathaniel, additional, Kummu, Matti, additional, Bekunda, Mateete, additional, Bruelheide, Helge, additional, Hochman, Zvi, additional, Siebert, Stefan, additional, Rueda, Oscar, additional, and van Bodegom, Peter M., additional
- Published
- 2020
- Full Text
- View/download PDF
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