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1. Using Virtual Research Environments in Agro-Environmental Research.

Author: Rob M. Lokers, M. J. R. Knapen, Leonardo Candela, Steven Hoek, and Wouter Meijninger
Published: 2020
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2. The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise.

Author: Daniel Wallach 0001, Taru Palosuo, Peter J. Thorburn, Zvi Hochman, Emmanuelle Gourdain, Fety Andrianasolo, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cecile Garcia, Sebastian Gayler, Afshin Ghahramani, Santosh Hiremath, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Per-Erik Jansson, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Elisabet Lewan, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, Gloria Padovan, Jørgen Eivind Olesen, Arne Poyda, Eckart Priesack, Johannes W. M. Pullens, Budong Qian, Niels Schütze, Vakhtang Shelia, Amir Souissi, Xenia Specka, Amit Kumar Srivastava, Tommaso Stella, Thilo Streck, Giacomo Trombi, Evelyn Wallor, Jing Wang, Tobias K. D. Weber, Lutz Weihermüller, Allard de Wit, Thomas Wöhling, Liujun Xiao, Chuang Zhao, Yan Zhu 0005, and Sabine J. Seidel
Published: 2021
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3. The chaos in calibrating crop models:Lessons learned from a multi-model calibration exercise

Author: Eckart Priesack, Johannes Wilhelmus Maria Pullens, Heidi Horan, Anne Klosterhalfen, Elisabet Lewan, Marco Moriondo, Emmanuelle Gourdain, Roberto Ferrise, Tobias K. D. Weber, Camilla Dibari, Neil M.J. Crout, Daniel Wallach, Amir Souissi, Jing Wang, Eric Justes, Kurt Christian Kersebaum, Benjamin Dumont, Mohamed Jabloun, Niels Schütze, Qi Jing, G. Padovan, Bernardo Maestrini, Steven Hoek, Mingxia Huang, Sebastian Gayler, Giacomo Trombi, Gerrit Hoogenboom, Qunying Luo, Jørgen E. Olesen, Chuang Zhao, Evelyn Wallor, Per-Erik Jansson, Tommaso Stella, Peter J. Thorburn, Santosh Hiremath, Arne Poyda, Thomas Wöhling, Amit Kumar Srivastava, Thomas Gaiser, Sabine J. Seidel, Budong Qian, Vakhtang Shelia, Henrike Mielenz, Afshin Ghahramani, Allard de Wit, Senthold Asseng, Fety Andrianasolo, Bruno Basso, Liujun Xiao, Zvi Hochman, Taru Palosuo, Yan Zhu, Marie Launay, Cécile Garcia, Xenia Specka, Thilo Streck, Lutz Weihermüller, Hasti Nariman Zadeh, Samuel Buis, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland (LUKE), CSIRO Agriculture and Food (CSIRO), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), ARVALIS - Institut du végétal [Paris], ARVALIS - Institut du Végétal [Boigneville], University of Florida [Gainesville] (UF), Michigan State University [East Lansing], Michigan State University System, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Nottingham, UK (UON), Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Gembloux Agro-Bio Tech [Gembloux], Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, University of Hohenheim, University of Southern Queensland (USQ), Aalto University School of Science and Technology [Aalto, Finland], Wageningen University and Research [Wageningen] (WUR), China Agricultural University (CAU), Royal Institute of Technology [Stockholm] (KTH ), Agriculture and Agri-Food Canada, Saskatoon Research Centre, Agriculture and Agri-Food [Ottawa] (AAFC), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Global Change Research Institute (CAS), Institute of Bio- and Geosciences [Jülich] (IBG), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Swedish University of Agricultural Sciences (SLU), Hillridge Technology Pty Ltd, Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI), Institute of Bioeconomy (IBE), Consiglio Nazionale delle Ricerche (CNR), Aarhus University [Aarhus], Kiel University, Helmholtz-Zentrum München (HZM), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Université de Carthage - University of Carthage, Helmholtz-Gemeinschaft = Helmholtz Association, Lincoln Agritech Ltd, Nanjing Agricultural University, Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modelling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), Academy of Finland through projects AICropPro (316172) and DivCSA (316215), National Science Foundation for Distinguished Young Scholars (31725020), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council, Agriculture and Agri-Food Canada's Project 1387 under the Canadian Agricultural Partnership, DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, National Key Research and Development Program of China (2016YFD0300105), Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, Academy of Finland through project AI-CropPro (315896), JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), Natural Resources Institute Finland (Luke) through a strategic project BoostIA, BonaRes project 'Soil3' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 – 390732324 EXC (PhenoRob), Project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), INRA ACCAF meta-programme, German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure 'Soil as a Sustainable Resource for the Bioeconomy – BonaRes', project 'BonaRes (Module B): BonaRes Centre for Soil Research, subproject B' (grant 031B0511B), and National Key Research and Development Program of China (2017YFD0300205)
Subjects: Earth Observation and Environmental Informatics, Environmental Engineering, 010504 meteorology & atmospheric sciences, Computer science, Process (engineering), Process-based models, Model parameters, Machine learning, computer.software_genre, 01 natural sciences, paramètre, Software, F01 - Culture des plantes, Component (UML), Aardobservatie en omgevingsinformatica, Calibration, Parameter estimation, Applied Ecology, 0105 earth and related environmental sciences, U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, business.industry, Estimation theory, Ecological Modeling, Toegepaste Ecologie, Calibration recommendations, Experimental data, 04 agricultural and veterinary sciences, PE&RC, [STAT]Statistics [stat], CHAOS (operating system), Phenology, Calibration Recommendations, Process-based Models, Parameter Estimation, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Artificial intelligence, ddc:004, Phénologie, Modèle végétal, business, computer
Abstract: International audience; Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.
Published: 2021
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4. The chaos in calibrating crop models

Author: Daniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman, Emmanuelle Gourdain, Fety Andrianasolo, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cecile Garcia, Sebastian Gayler, Afshin Ghahramani, Santosh Hiremath, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Per-Erik Jansson, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Elisabet Lewan, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, Gloria Padovan, Jørgen Eivind Olesen, Arne Poyda, Eckart Priesack, Johannes Wilhelmus Maria Pullens, Budong Qian, Niels Schütze, Vakhtang Shelia, Amir Souissi, Xenia Specka, Amit Kumar Srivastava, Tommaso Stella, Thilo Streck, Giacomo Trombi, Evelyn Wallor, Jing Wang, Tobias K.D. Weber, Lutz Weihermüller, Allard de Wit, Thomas Wöhling, Liujun Xiao, Chuang Zhao, Yan Zhu, Sabine J. Seidel, INRAE, Luke Natural Resources Institute Finland, CSIRO, Arvalis Institut du Végétal, Technical University of Munich, Michigan State University, University of Nottingham, University of Florence, University of Liege, University of Bonn, University of Hohenheim, University of Southern Queensland, Department of Computer Science, Wageningen University and Research Centre, University of Florida, China Agricultural University, KTH Royal Institute of Technology, Ottawa Research and Development Centre, Centre de coopération internationale en recherche agronomique pour le développement, Leibniz Centre for Agricultural Landscape Research, Jülich Research Centre, Swedish University of Agricultural Sciences, Hillridge Technology Pty Ltd, Institute for Crop and Soil Science, CNR-ENEA-EURATOM Association, Aarhus University, Kiel University, Helmholtz Zentrum München - German Research Center for Environmental Health, Technische Universität Dresden, University of Carthage, Nanjing Agricultural University, Aalto-yliopisto, and Aalto University
Subjects: Structure (mathematical logic), Estimation, Mathematical optimization, Process modeling, Estimation theory, Computer science, Calibration recommendations, Experimental data, Process-based models, CHAOS (operating system), Phenology, Calibration, Range (statistics), Parameter estimation
Abstract: Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation ( DFG , Grant Agreement SFB 1253/1 2017 ), the Academy of Finland through projects AICropPro ( 316172 ) and DivCSA ( 316215 ) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' ( BOMA 03037514 ) of the Federal Ministry of Education and Research ( BMBF ), Germany, the Deutsche Forschungsgemeinschaft ( DFG , German Research Foundation ) under Germany's Excellence Strategy - EXC 2070–390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B ) funded by the Federal Ministry of Education and Research ( BMBF , Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research ( BMBF ) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): BonaRes Centre for Soil Research , subproject B” (grant 031B0511B ), the National Key Research and Development Program of China ( 2017YFD0300205 ), the National Science Foundation for Distinguished Young Scholars ( 31725020 ), the Priority Academic Program Development of Jiangsu Higher Education Institutions ( PAPD ), the 111 Project ( B16026 ), and China Scholarship Council , the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133 ) and USDA / NIFA HATCH grant N. MCL02368 , the National Key Research and Development Program of China ( 2016YFD0300105 ), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries , the Academy of Finland through project AI-CropPro ( 315896 ), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies ( D.M. 24064/7303/15 of 6/Nov/2015), 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 (project no. CZ.02.1.01/0.0/0.0/16_019/000797 ). The order in which the donors are listed is arbitrary. Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AICropPro (316172) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070?390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure ?Soil as a Sustainable Resource for the Bioeconomy ? BonaRes?, project ?BonaRes (Module B): BonaRes Centre for Soil Research, subproject B? (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project (B16026), and China Scholarship Council, the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ?Agricultural Landscapes under Global Climate Change ? Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the Academy of Finland through project AI-CropPro (315896), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), 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 (project no. CZ.02.1.01/0.0/0.0/16_019/000797). The order in which the donors are listed is arbitrary. Publisher Copyright: © 2021 Elsevier Ltd Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.
Published: 2021

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

Author: Allard de Wit, Emmanuelle Gourdain, Chuang Zhao, Bruno Basso, Tommaso Stella, Sebastian Gayler, Qi Jing, Eric Justes, Marco Moriondo, Arne Poyda, Zvi Hochman, Kurt Christian Kersebaum, Neil M.J. Crout, Eckart Priesack, Niels Schütze, Sabine J. Seidel, T. Palosuo, Heidi Horan, Amit Kumar Srivastava, Amir Souissi, Anne Klosterhalfen, Giacomo Trombi, Gerrit Hoogenboom, Vakhtang Shelia, Tobias K. D. Weber, Evelyn Wallor, Daniel Wallach, Yan Zhu, Mohamed Jabloun, Budong Qian, Cécile Garcia, Johannes Wilhelmus Maria Pullens, Xenia Specka, Benjamin Dumont, Qunying Luo, Jing Wang, Camilla Dibari, Peter J. Thorburn, Roberto Ferrise, Bernardo Maestrini, Jørgen E. Olesen, Afshin Ghahramani, Senthold Asseng, Lutz Weihermüller, Marie Launay, Thomas Gaiser, Thilo Streck, Thomas Wöhling, Liujun Xiao, Henrike Mielenz, Steven Hoek, Mingxia Huang, Samuel Buis, Hasti Nariman Zadeh, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland (LUKE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), ARVALIS - Institut du végétal [Paris], The University of Florida College of Medicine, Michigan State University [East Lansing], Michigan State University System, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Nottingham, UK (UON), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Unité de recherche TERRA [Gembloux], Gembloux Agro-Bio Tech [Gembloux], Université de Liège-Université de Liège, University of Hohenheim, ARVALIS - Institut du Végétal [Ouzouer le Marché] (ARVALIS), University of Southern Queensland (USQ), CSIRO Agriculture and Food (CSIRO), University of North Florida [Jacksonville] (UNF), University of Florida [Gainesville] (UF), China Agricultural University Library, University of Nottingham Ningbo [China], Agriculture and Agri-Food Canada, Saskatoon Research Centre, Agriculture and Agri-Food [Ottawa] (AAFC), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Inst Landscape Biogeochem, Leibniz Ctr Agr Landscape Res, Muncheberg, Germany, Partenaires INRAE, ∗Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany, Institute of Bio- and Geosciences [Jülich] (IBG), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association-Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Hillridge Technology Pty Ltd, Wageningen University and Research [Wageningen] (WUR), Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI), Aalto University School of Science and Technology [Aalto, Finland], Aarhus University [Aarhus], Kiel University, German Res Ctr Environm Hlth, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Université de Carthage - University of Carthage, University of Bonn, Université de Florence, China Agricultural University (CAU), Helmholtz-Gemeinschaft = Helmholtz Association, Nanjing Agricultural University, Institut für Genetik - Universität Bonn / Institute of Genetics - University of Bonn, and German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) BonaRes Center for Soil Research, subproject ‘Sustainable Subsoil Management – Soil3’ (grant 031B0151A), project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B)BonaRes Centre for Soil Research, subproject B' (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020),Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 project (B16026)National Institute of Food and Agriculture (award no. 2015-68007-23133) USDA/NIFA HATCHgrant No. MCL02368, the National Key Research and Development Program of China (2016YFD0300105),Forestry Policies (D.M. 24064/7303/15 of 26/Nov/2015)
Subjects: 0106 biological sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Computer science, Calibration (statistics), Mean squared prediction error, Extrapolation, Climate change, Soil Science, Plant Science, Target population, Model evaluation Wheat, 01 natural sciences, Crop, Statistics, Aardobservatie en omgevingsinformatica, Crop Model, Phenology Prediction, Model Evaluation, Wheat, Crop model, Plant phenology, Crop management, Applied Ecology, Model evaluation, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Observational error, Phenology, Emphasis (telecommunications), Toegepaste Ecologie, Experimental data, 04 agricultural and veterinary sciences, 15. Life on land, PE&RC, Plant development, Agronomy, Current management, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, ddc:640, Agronomy and Crop Science, Phenology prediction, 010606 plant biology & botany
Abstract: Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Since climate change is projected to alter crop phenology worldwide, there is a large effort to predict phenology as a function of environment. Many studies have focused on comparing model equations for describing how phenology responds to weather but the effect of crop model calibration, also expected to be important, has received much less attention. The objective here was to obtain a rigorous evaluation of prediction capability of wheat crop phenology models, and to analyze the role of calibration. The 27 participants in this multi-model study were provided experimental data for calibration and asked to submit predictions for sites and years not represented in those data. Participants were instructed to use and document their 99usual99 calibration approach. Overall, the models provided quite good predictions of phenology (median of mean absolute error of 6.1 days) and did much better than simply using the average of observed values as predictor. Calibration was found to compensate to some extent for differences between models, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches led to major differences in prediction error between models with the same structure. Given the large diversity of calibration approaches and the importance of calibration, there is a clear need for guidelines and tools to aid with calibration. Arguably the most important and difficult choice for calibration is the choice of parameters to estimate. Several recommendations for calibration practices are proposed. Model applications, including model studies of climate change impact, should focus more on the data used for calibration and on the calibration methods employed.
Published: 2021
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6. Multi-model evaluation of phenology prediction for wheat in Australia

Author: Elisabet Lewan, Xenia Specka, Arne Poyda, Bernardo Maestrini, Liujun Xiao, Amir Souissi, Sabine J. Seidel, Roberto Ferrise, G. Padovan, Steven Hoek, Tobias K. D. Weber, Thilo Streck, Mingxia Huang, Qunying Luo, Niels Schütze, Jørgen E. Olesen, Samuel Buis, Qi Jing, Budong Qian, Yan Zhu, Marie Launay, Allard de Wit, Thomas Wöhling, Sebastian Gayler, Fety Andrianasolo, Eckart Priesack, Bruno Basso, Senthold Asseng, Benjamin Dumont, Heidi Horan, Eric Justes, Thomas Gaiser, Mohamed Jabloun, Giacomo Trombi, Santosh Hiremath, Lutz Weihermüller, Daniel Wallach, Jing Wang, Zvi Hochman, Taru Palosuo, Amit Kumar Srivastava, Marco Moriondo, Vakhtang Shelia, Peter J. Thorburn, Gerrit Hoogenboom, Evelyn Wallor, Kurt Christian Kersebaum, Johannes Wilhelmus Maria Pullens, Neil M.J. Crout, Chuang Zhao, Per-Erik Jansson, Tommaso Stella, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland (LUKE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), ARVALIS - Institut du Végétal [Boigneville], ARVALIS - Institut du végétal [Paris], University of Florida [Gainesville] (UF), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Michigan State University [East Lansing], Michigan State University System, DEPARTMENT OF EARTH AND ENVIRONMENTAL SCIENCES MICHIGAN STATE UNIVERSITY USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Nottingham, UK (UON), Université de Liège - Gembloux, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Bonn, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, University of Hohenheim, Institute of Soil Science and Land Evaluation, Soil Biology Section, Aalto University School of Science and Technology [Aalto, Finland], Wageningen University and Research [Wageningen] (WUR), CSIRO Agriculture and Food (CSIRO), Food Systems Institute [Gainesville] (UF|IFAS), China Agriculture University [Beijing], College of Resources and Environmental Sciences, China Agricultural University (CAU), Royal Institute of Technology [Stockholm] (KTH ), Agriculture and Agri-Food [Ottawa] (AAFC), Ottawa Research and Development Center, Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Global Change Research Institute (CAS), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Swedish University of Agricultural Sciences (SLU), Hillridge Technology Pty Ltd, Institute of Bioeconomy (IBE), Consiglio Nazionale delle Ricerche (CNR), Aarhus University [Aarhus], Department of Agroecology, Aarhus University, Tjele, Denmark, Kiel University, Institute of Crop Science and Plant Breeding, Christian-Albrechts University of Kiel, Helmholtz-Zentrum München (HZM), German Res Ctr Environm Hlth, Partenaires INRAE, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Université de Carthage - University of Carthage, Institut National de la Recherche Agronomique de Tunisie (INRAT), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Institute of Bio- and Geosciences [Jülich] (IBG), Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Lincoln Agritech Ltd, Nanjing Agricultural University, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modelling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes projects 'Soil3' (BOMA 03037514) and 'I4S' (031B0513I) of the Federal Ministry of Education and Research (BMBF), Germany, the German Research Foundation (DFG) under Germany's Excellence Strategy -EXC 2070 -390732324, the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the EU funded SustEs project (CZ.02.1.01/0.0/0.0/16_019/0000797), the INRAE ACCAF metaprogramme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure 'Soil as a Sustainable Resource for the Bioeconomy -BonaRes', project 'BonaRes (Module B): BonaRes Centre for Soil Research, subproject B' (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 project (B16026), and China Scholarship Council, the Agriculture and AgriFood Canada's Project 1387 under the Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 `Agricultural Landscapes under Global Climate Change -Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture (USDA), National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 26/Nov/2015). The field work was jointly funded by CSIRO and the Grains Research and Development Corporation (GRDC) under the 'Adding Value to GRDC's National Variety Trial Network' project (CSA00027). The order in which the donors are listed is arbitrary., INRAE, Luke Natural Resources Institute Finland, CSIRO, Arvalis Institut du Végétal, University of Florida, Michigan State University, University of Nottingham, University of Liege, University of Florence, Department of Computer Science, Wageningen University and Research Centre, China Agricultural University, KTH Royal Institute of Technology, Ottawa Research and Development Centre, Leibniz Centre for Agricultural Landscape Research, Swedish University of Agricultural Sciences, National Research Council of Italy, Aarhus University, Helmholtz Zentrum München - German Research Center for Environmental Health, Technische Universität Dresden, University of Carthage, Forschungszentrum Jülich, Aalto-yliopisto, and Aalto University
Subjects: 0106 biological sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, Calibration (statistics), Structure uncertainty, 01 natural sciences, F01 - Culture des plantes, Aardobservatie en omgevingsinformatica, Statistics, Range (statistics), ddc:550, Evaluation, Applied Ecology, Triticum, Mathematics, 2. Zero hunger, Global and Planetary Change, U10 - Informatique, mathématiques et statistiques, Phenology, Toegepaste Ecologie, Forestry, 04 agricultural and veterinary sciences, Australia, Parameter Uncertainty, Structure Uncertainty, Wheat, technique de prévision, PE&RC, [SDE]Environmental Sciences, Incertitude, Phénologie, Earth Observation and Environmental Informatics, F40 - Écologie végétale, Parameter uncertainty, Benchmark (surveying), Baseline (configuration management), Selection (genetic algorithm), 0105 earth and related environmental sciences, Modélisation des cultures, cultivar selection [EN], Global change, 15. Life on land, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Stage (hydrology), Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Model predictions depend not only on model structure but also on the parameter values. This study is thus an evaluation of modeling groups, which choose the structure and fix or estimate the parameters, rather than an evaluation just of model structures. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. For a given modeling group, MAE for the evaluation environments was significantly correlated with MAE for the calibration environments, which suggests that it would be of interest to test ensemble predictors that weight individual modeling groups based on performance for the calibration data. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. Finally, there was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
Published: 2021
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7. Developing agro-meteorological services in Sub-Saharan Africa: a scoping study

Author: Hendrik Boogaard, Steven Hoek, Alterra Earth informatics, and T. Ceccarelli
Subjects: Earth Observation and Environmental Informatics, Sub saharan, Geography, Aardobservatie en omgevingsinformatica, Life Science, Scoping study, Environmental planning
Abstract: This study provides some guidelines for Dutch private and public parties on opportunities and constraints in developing agro-meteorological services in Sub-Saharan African countries. We focused on agro-meteorological services where there was evidence that weather and climate information was combined with agronomic centered advices for farmers. The potential for developing these services was analysed from the perspective of the institutional, technical and business environment. Several business development “pathways” were suggested varying from a public orientated business model (B2G), to public-private partnerships with an opening towards commercial partnerships (B2B, B2C), a balanced role of private and public actors, and the “right” local partners also in the perspective of combining more services (“bundling”). Deze studie geeft Nederlandse publieke en private partijen inzicht in de kansen en belemmeringen van het opzetten van agro-meteorologische services in sub-Sahara Afrikaanse landen. Wij hebben met name services bestudeerd die weer en klimaat combineren met agronomische adviezen voor boeren. Wij hebben gekeken naar institutionele, technische en markt aspecten. Mogelijke bedrijfsmodellen voor het in de markt zetten van van agro-meteorologische services variëren van een publiek georiënteerd model (B2G), een publiek-private samenwerking met zowel commerciële partijen (B2B, B2C) als publieke partijen, de juiste lokale partners en gericht op het bundelen van verschillende services.
Published: 2021
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8. Using virtual research environments in agro-environmental research

Author: Steven Hoek, W.M.L. Meijninger, Rob Lokers, M.J.R. Knapen, Leonardo Candela, Wageningen Environmental Research (Alterra), Istituto di Scienza e Tecnologie dell'Informazione 'A. Faedo' (ISTI), Consiglio Nazionale delle Ricerche [Roma] (CNR), Ioannis N. Athanasiadis, Steven P. Frysinger, Gerald Schimak, Willem Jan Knibbe, TC 5, and WG 5.11
Subjects: Earth Observation and Environmental Informatics, Crop growth modelling, business.industry, Process (engineering), Computer science, Scale (chemistry), Big data, 020206 networking & telecommunications, Usability, Cloud computing, Agro-climatic modelling, 02 engineering and technology, Data science, Virtual research environment, Workflow, Aardobservatie en omgevingsinformatica, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Use case, [INFO]Computer Science [cs], business
Abstract: International audience; Tackling some of the grand global challenges, agro-environmental research has turned more and more into an international venture, where distributed research teams work together to solve complex research questions. Moreover, the interdisciplinary character of these challenges requires that a large diversity of different data sources and information is combined in new, innovative ways. There is a pressing need to support researchers with environments that allow them to efficiently work together and co-develop research. As research is often data-intensive, and big data becomes a common part of a lot of research, such environments should also offer the resources, tools and workflows that allow to process data at scale if needed. Virtual research environments (VRE), which combine working in the Cloud, with collaborative functions and state of the art data science tools, can be a potential solution. In the H2020 AGINFRA+ project, the usability of the VREs has been explored for use cases around agro-climatic modelling. The implemented pilot application for crop growth modelling has successfully shown that VREs can support distributed research teams in co-development, helps them to adopt open science and that the VRE’s cloud computing facilities allow large scale modelling applications.
Published: 2020
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9. CST, a freeware for predicting crop yield from remote sensing or crop model indicators: Illustration with RSA and Ethiopia

Author: H. Boogaard, H. Kerdiles, F. Rembold, O. Leo, and Steven Hoek
Subjects: 0301 basic medicine, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Yield (finance), Crop yield, Regression analysis, Vegetation, food security, 01 natural sciences, Regression, Normalized Difference Vegetation Index, 03 medical and health sciences, remote sensing, 030104 developmental biology, Outlier, Aardobservatie en omgevingsinformatica, Scenario analysis, crop yield prediction, 0105 earth and related environmental sciences, Mathematics, Remote sensing
Abstract: CST (Crop Statistics Tool) is a standalone freeware for predicting crop yield statistics using indicators derived from crop models, weather or remote sensing data. The principle of CST is that years similar to the target year (e.g. the current year) should have similar yields, or similar yield deviations from a technological time trend. In practice, CST guides the crop analyst through standard steps: After data screening to identify possible outliers and analysis of time trend, the crop analyst has the choice between the following two approaches to forecast yield: (1) multiple regression analysis in which a linear relationship is calibrated between historical yield data and yield indicators, while accounting for a time trend if present; (2) scenario analysis, whereby CST looks for the years most similar (according to the indicators) to the current year to estimate a yield deviation from the time trend or the average yield. CST allows to assess models with standard statistics and tests as well as warnings, which is especially useful when many indicators are available. Moreover, thanks to batch processing, the crop analyst can test a given model for various dekads, regions or crops. This paper illustrates the interest of CST with two case studies made over Africa and based on the regression approach between crop yields and NDVI or cumulated rainfall at a given dekad. In the first one, South African maize yields at province level over 1987-2015 were found to be well correlated with Vegetation/ProbaV NDVI or CHIRPS rainfall for two of the three main maize producing provinces; for each province, we tested indicators from the 15 dekads between January and May. In the second study, we regressed the 1999-2014 maize yields from the main 26 crop production zones against also NDVI and cumulated rainfall with two different start dates (April and June); we tested 3000 models (26 zones, 15 dekads, 3 single indicators without and with time trend, all indicators together, and finally trend alone) and obtained mixed results: A strong dominance of the time trend and for the indicators, unstable relationships and sometimes wrong slope signs. Beyond these contrasting results that could be partly due to the quality of yield statistics or the relevance of the selected indicators, CST combined with the SPIRITS tool for extracting indicators at region level from raster time series, should help crop analysts predict crop yield, in particular where many indicators derived from remote sensing data or crop models are to be tested.
Published: 2017
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10. Supplementary material to 'Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications'

Author: Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, Christian Folberth, Michael Glotter, Steven Hoek, Toshichika Iizumi, Roberto C. Izaurralde, Curtis Jones, Nikolay Khabarov, Peter Lawrence, Wenfeng Liu, Stefan Olin, Thomas A. M. Pugh, Deepak Ray, Ashwan Reddy, Cynthia Rosenzweig, Alexander C. Ruane, Gen Sakurai, Erwin Schmid, Rastislav Skalsky, Carol X. Song, Xuhui Wang, Allard de Wit, and Hong Yang
Published: 2016
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11. Statistical analysis of large simulated yield datasets for studying climate change effects

Author: David Makowski, Senthold Asseng, Frank Ewert, Simona Bassu, Jean-Louis Durand, Pierre Martre, Myriam Adam, Pramod K. Aggarwal, Carlos Angulo, Christian Baron, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Hendrik Boogaard, Kenneth J. Boote, Nadine Brisson, Davide Cammarano, Andrew J. Challinor, Sjakk J. G. Conijn, Marc Corbeels, Delphine Deryng, Giacomo De Sanctis, Jordi Doltra, Sebastian Gayler, Richard Goldberg, Patricio Grassini, Jerry L. Hatfield, Lee Heng, Steven Hoek, Josh Hooker, Tony L. A. Hunt, Joachim Ingwersen, Cesar Izaurralde, Raymond E. E. Jongschaap, James W. Jones, Armen R. Kemanian, Christian Kersebaum, Soo-Hyung Kim, Jon Lizaso, Christoph Müller, Naresh S. Kumar, Claas Nendel, Garry J. O'Leary, Jorgen E. Olesen, Tom M. Osborne, Taru Palosuo, Maria V. Pravia, Eckart Priesack, Dominique Ripoche, Cynthia Rosenzweig, Alexander C. Ruane, Fredirico Sau, Mickhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Edmar I. Teixeira, Peter Thorburn, Denis Timlin, Maria Travasso, Reimund Rötter, Katharina Waha, Daniel Wallach, Jeffrey W. White, Jimmy R. Williams, Joost Wolf, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, University of Florida [Gainesville] (UF), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Michigan State University [East Lansing], Michigan State University System, Agroclim (AGROCLIM), German Research Center for Environmental Health, Centre for Geo-Information, University of Leeds, International Center for Tropical Agriculture, Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Sciences [Changchun Branch] (CAS), University of East Anglia, Catabrian Agricultural Research and Training Center (CIFA), University of Tübingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), University of Nebraska [Lincoln], University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), University of Reading (UOR), University of Guelph, University of Hohenheim, Joint Global Change Research Institute, Instituto Nacional de Investigación Agropecuaria (INIA), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), Department of Environment and Primary Industries, Landscape and Water Sciences, Aarhus University [Aarhus], Agrifood Research Finland, Pennsylvania State University (Penn State), Penn State System, Rothamsted Research, FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Washington State University (WSU), Plant & Food Research, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS : Agricultural Research Service, Institute for Climate and Water, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Hillel, D., Rosenzweig, C., Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Clermont Auvergne ( UCA ), Université Blaise Pascal (Clermont Ferrand 2) ( UBP ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Michigan State University, UE Agroclim ( UE AGROCLIM ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Catabrian Agricultural Research and Training Center ( CIFA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska Lincoln ( UNL ), International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Indian Agricultural Research Institute ( IARI ), Aarhus University, PennState University [Pennsylvania] ( PSU ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), New Zealand Institute for Plant and Food Research Limited, Commonwealth Scientific and Industrial Research Organisation, United States Department of Agriculture - Agricultural Research Service, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), AgroParisTech-Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville], Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Blaise Pascal (Clermont Ferrand 2) (UBP), UE Agroclim (UE AGROCLIM), Wageningen University and Research Center (WUR), Food and Agricultural Organization (FAO), Helmholtz Zentrum München = German Research Center for Environmental Health, University of East Anglia [Norwich] (UEA), University of Nebraska–Lincoln, Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: analyse de données, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Yield (finance), data analysis, Climate change, 01 natural sciences, Agro Water- en Biobased Economy, statistical analysis, Effects of global warming, Aardobservatie en omgevingsinformatica, Life Science, Alterra - Centrum Bodem, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, global change, 0105 earth and related environmental sciences, 2. Zero hunger, changement climatique, WIMEK, Mathematical model, analyse statistique, Crop yield, Soil Science Centre, Global change, Statistical model, 04 agricultural and veterinary sciences, 15. Life on land, PE&RC, Climate resilience, Climate Resilience, Plant Production Systems, Klimaatbestendigheid, 13. Climate action, Plantaardige Productiesystemen, Climatology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.
Published: 2015
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12. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Author: Joost Wolf, Xinyou Yin, Pierre Martre, Zhengtao Zhang, H. K. Soo, Manuel Marcaida, Nadine Brisson, Patrick Bertuzzi, Soo-Hyung Kim, Yan Zhu, Roberto C. Izaurralde, L. A. Hunt, Maria I. Travasso, Christian Baron, James W. Jones, R.E.E. Jongschaap, T. Palosuo, Daniel Wallach, Jerry L. Hatfield, Christian Biernath, G. De Sanctis, Senthold Asseng, H. Yoshida, Donald S. Gaydon, Edmar Teixeira, Davide Cammarano, Alex C. Ruane, C. Nendel, T. Hasegawa, Thilo Streck, Garry O'Leary, Upendra Singh, Frank Ewert, Delphine Deryng, R. Goldberg, Bas A. M. Bouman, Peter J. Thorburn, Tao Li, Roberto Confalonieri, Myriam Adam, Jes Olesen, Reimund P. Rötter, Tamon Fumoto, Patricio Grassini, Joachim Ingwersen, Robert F. Grant, Katharina Waha, James Williams, Fulu Tao, Eckart Priesack, Pramod K. Aggarwal, Liang Tang, Sebastian Gayler, Jordi Doltra, L. Heng, Christoph Müller, J.G. Conijn, Iwan Supit, S. Naresh Kumar, Iurii Shcherbak, Jeffrey W. White, Hendrik Boogaard, Kenneth J. Boote, David Makowski, Federico Sau, Jean-Louis Durand, Mikhail A. Semenov, Claudio O. Stöckle, Marc Corbeels, Steven Hoek, Simone Bregaglio, Hiroshi Nakagawa, Philippe Oriol, Anthony Challinor, R. A. Kemanian, Carlos Angulo, Pasquale Steduto, Bruno Basso, Kurt Christian Kersebaum, Cynthia Rosenzweig, Dennis Timlin, J. Hooker, Samuel Buis, Maria Virginia Pravia, Françoise Ruget, Dominique Ripoche, Simona Bassu, Pierre Stratonovitch, Jon I. Lizaso, Balwinder Singh, Tom M. Osborne, Paul W. Wilkens, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Int Rice Res Inst, Los Banos, Philippines, Université Paris Diderot - Paris 7 (UPD7), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), International Water Management Institute, Research Program on Climate Change, Agriculture and Food Security, CGIAR, Institute of Crops Science and Resource Conservation INRES, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Geological Sciences [East Lansing], Agroclim (AGROCLIM), German Research Center for Environmental Health, Institute of Soil Ecololgy, Helmholtz-Zentrum München (HZM), Center for Geo-information, Alterra, Department of Agronomy, University of Florida [Gainesville] (UF), Cassandra Lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, CGIAR ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Plant Research International, Wageningen University and Research [Wageningen] (WUR), Embrapa Cerrados, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, School of Environmental Science, University of East Anglia [Norwich] (UEA), European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Tsukuba, National Institute of Agro-Environmental Sciences (NIAES), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), WESS Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Departement of Renewable Resources, University of Alberta, Department of Agronomy and Horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), Centre for Geo-Information, Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Instituto Nacional de Investigación Agropecuaria (INIA), Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), College of the Environment, School of Environmental and Forest Sciences, University of Washington, Department Produccion Vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), National Agriculture and Food Research Organization (NARO), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Department of Economic Development Jobs, Transport and Resources, Grains Innovation Park, Department of Agroecology, Aarhus University [Aarhus], Walker Institute, NCAS Climate, Natural Resources Institute Finland, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, German Research Center for Environmental Health, Institute of Soil Ecology, Department Biologia Vegetal, Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences and W.K. Kellogg Biological Station, International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), International Fertilizer Development Center (IFDC), College of the Environment, School of Environmental and Forest Science, University of Washington [Seattle], FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Biological Systems Engineering, Washington State University (WSU), Plant Production Systems and Earth System Science, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, CIRN, Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agriculture, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Arid-Land Agricultural Research Center, Texas AgriLife Research and Extension, Texas A&M University System, Centre for Crop Systems Analysis, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), Metaprogramme ACCAF, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Helmholtz Zentrum München = German Research Center for Environmental Health, Università degli Studi di Milano = University of Milan (UNIMI), University of Nebraska–Lincoln, Université de Toulouse (UT)-Université de Toulouse (UT), Natural Resources Institute Finland (LUKE), Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Nanjing Agricultural University (NAU), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation INRES, International Rice Research Institute, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Eberhard Karls Universität Tübingen, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), National Agriculture and Food Research Organization, International Maize and Wheat Improvement Centre (CIMMYT), Food and Agricultural Organization (FAO), New Zealand Institute for Plant and Food Research Limited, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Beijing Normal University, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ) -AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Department of Geological Sciences, W.K. Kellogg Biological Station, Michigan State Univ, Dept Geol Sci, E Lansing, MI 48823 USA, UE Agroclim ( UE AGROCLIM ), Helmholtz-Zentrum München ( HZM ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Invergowrie, School of Earth and Environment [Leeds] ( SEE ), Wageningen University and Research Centre [Wageningen] ( WUR ), Agro-ecologyand Sustainable Intensification of Annual Crops, Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), University of East Anglia [Norwich] ( UEA ), European Commission - Joint Research Centre [Ispra] ( JRC ), National Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation, NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska-Lincoln, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), UMR 1248 Agrosystèmes et Développement Territorial (AGIR), Agro-ecology and Sustainable Intensification of Annual Crops, Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), PennState University [Pennsylvania] ( PSU ), W.K. Kellogg Biological Station, Department of Geological Sciences, International Maize and Wheat Improvement Centre ( CIMMYT ), International Fertilizer Development Center ( IFDC ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, and Texas A and M University ( TAMU )
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, F62 - Physiologie végétale - Croissance et développement, 01 natural sciences, Statistics, Aardobservatie en omgevingsinformatica, Climate change, Crop model, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, Triticum, Mathematics, 2. Zero hunger, Global and Planetary Change, Mathematical model, Air, Forestry, Regression analysis, 04 agricultural and veterinary sciences, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, Rendement des cultures, Plant Production Systems, Statistical model, Modèle mathématique, Atmosphère, Earth Observation and Environmental Informatics, Yield, Crop Physiology, P40 - Météorologie et climatologie, [SDE.MCG]Environmental Sciences/Global Changes, Oryza sativa, Zea mays, Earth System Science, Emulator, Agro Water- en Biobased Economy, Alterra - Centrum Bodem, Precipitation, Croissance, 0105 earth and related environmental sciences, Meta-model, Changement climatique, Hydrology, Modélisation des cultures, Crop yield, Simulation modeling, Soil Science Centre, 15. Life on land, Température, Laboratorium voor Phytopathologie, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Yield (chemistry), Plantaardige Productiesystemen, Laboratory of Phytopathology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leerstoelgroep Aardsysteemkunde, Plante de culture, Agronomy and Crop Science, Dioxyde de carbone
Abstract: Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.
Published: 2015
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13. How do various maize crop models vary in their responses to climate change factors?

Author: Federico Sau, Sjaak Conijn, Delphine Deryng, Jean-Louis Durand, Katharina Waha, Edmar Teixeira, Iurii Shcherbak, R.E.E. Jongschaap, James W. Jones, Kenneth J. Boote, Maria Virginia Pravia, Jerry L. Hatfield, Alex C. Ruane, Christian Biernath, Patricio Grassini, H.L. Boogaard, Steven Hoek, K. Christian Kersebaum, Fulu Tao, Christian Baron, David Makowski, Claas Nendel, Sebastian Gayler, Dennis Timlin, Marc Corbeels, Christoph Müller, Nadine Brisson, Jon I. Lizaso, Naresh S. Kumar, Cynthia Rosenzweig, Simona Bassu, Armen R. Kemanian, Cesar Izaurralde, Bruno Basso, Giacomo De Sanctis, Myriam Adam, Soo-Hyung Kim, Eckart Priesack, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Department of agronomy, University of Florida [Gainesville], Department Produccion vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Department of agricultural and biological engineering, GISS Climate impacts group, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC)-NASA Goddard Space Flight Center (GSFC), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department crop systems, forestry and environmental sciences, University of Basilicata, Centre for Geo-Information, ALTERRA, WUR-Plant research international, Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), School of Environmental Sciences [Norwich], UE Agroclim (UE AGROCLIM), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen, Department of agronomy and horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, Department of plant science, University of Pensylvania, Institute of Lanscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of environmental and forest sciences, University of Washington [Seattle], Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of landscape systems analysis, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Universidad Politécnica de Madrid ( UPM ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ) -NASA Goddard Space Flight Center ( GSFC ), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Wageningen University and Research Centre [Wageningen] ( WUR ), Annual cropping systems, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Tyndall Centre for climate change research and school of environmental sciences, University of East Anglia [Norwich] ( UEA ), UE Agroclim ( UE AGROCLIM ), Water and earth system science (WESS) competence cluster, University of Nebraska-Lincoln, Leibniz Centre for Agricultural Landscape Research, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), University of Florida [Gainesville] (UF), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Wageningen University and Research [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agroclim (AGROCLIM), and Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, nitrogen dynamics, Atmospheric sciences, maize, 01 natural sciences, Standard deviation, F01 - Culture des plantes, wheat, Aardobservatie en omgevingsinformatica, water-use efficiency, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, General Environmental Science, agriculture, 2. Zero hunger, Global and Planetary Change, elevated co2, Ecology, Geography, Phenology, U10 - Informatique, mathématiques et statistiques, Agricultura, 04 agricultural and veterinary sciences, simulation, Rendement des cultures, model intercomparison, climate change, CO2, Crop simulation model, Modèle mathématique, Crops, Agricultural, simulation-model, Earth Observation and Environmental Informatics, air co2 enrichment, P40 - Météorologie et climatologie, Agmip, Climate, Maize, Model Intercomparison, Simulation, Temperature, Uncertainty, Climate change, carbon-dioxide, Models, Biological, Zea mays, Agro Water- en Biobased Economy, Environmental Chemistry, Water-use efficiency, climate, 0105 earth and related environmental sciences, Changement climatique, systems simulation, Modélisation des cultures, Crop yield, Simulation modeling, Water, temperature, Modèle de simulation, Carbon Dioxide, yield, Température, Agronomy, 13. Climate action, Yield (chemistry), 040103 agronomy & agriculture, AgMIP, 0401 agriculture, forestry, and fisheries, Environmental science, Dioxyde de carbone
Abstract: Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Published: 2014
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14. Building an operational system for crop monitoring and yield forecasting in Morocco

Author: Dong Qinghan, Riad Ballaghi, Allard de Wit, Tarik El Hairech, and Steven Hoek
Subjects: Earth Observation and Environmental Informatics, Food security, business.industry, Agroforestry, Crop yield, Agricultural engineering, Remote sensing, Operational system, Morocco, Risk analysis (business), Agriculture, Aardobservatie en omgevingsinformatica, Environmental science, Production (economics), Agricultural productivity, Crop model, business, Yield forecasting, Productivity
Abstract: Morocco has an agricultural production system for cereals which is dominated by rainfed, low yielding cereal production which is highly vulnerable to fluctuations in rainfall. Crop yield forecasting systems could play a significant role to reduce the vulnerability of the Moroccan agriculture to weather risks in the framework of a food security strategy. This paper describes the efforts that were carried out for adapting the European Crop Growth Monitoring System to Moroccan conditions for crop monitoring and regional yield forecasting. Results demonstrate that the correlations between CGMS output and regional reported yields can be strongly improved by relatively simple changes to the parameterization of the crop model and a different strategy for the initialization of the soil water balance.
Published: 2013

15. Impacts of Problem-Based Instruction on Students’ Beliefs about Physics and Learning Physics

Author: May Lee, Cormac J. K. Larkin, and Steven Hoekstra
Subjects: problem-based instruction, higher education, introductory physics, Education
Abstract: To help prepare students to address future challenges in Science, Technology, Engineering, and Mathematics (STEM), they need to develop 21st-century skills. These skills are mediated by their beliefs about the nature of scientific knowledge and practices, or epistemological beliefs. One approach shown to support students’ development of these beliefs and skills is problem-based instruction (PBI), which encourages collaborative self-directed learning while working on open-ended problems. We used a mixed-method qualitative approach to examine how implementing PBI in a physics course taught at a Dutch university affected students’ beliefs about physics and learning physics. Analysis of the responses to the course surveys (41–74% response rates) from the first implementation indicated students appreciated opportunities for social interactions with peers and use of scientific equipment with PBI but found difficulties connecting to the Internet given the COVID-19 restrictions. The Colorado Learning Attitudes towards Science Survey (CLASS), a validated survey on epistemological beliefs about physics and learning physics, was completed by a second cohort of students in a subsequent implementation of PBI for the same course; analysis of the students’ pre- and post-responses (28% response rate) showed a slight shift towards more expert-like perspectives despite challenges (e.g., access to lab). Findings from this study may inform teachers with an interest in supporting the development of students’ epistemological beliefs about STEM and the implementation of PBI in undergraduate STEM courses.
Published: 2023
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16. Mesoscopic interference for metric and curvature & gravitational wave detection

Author: Ryan J Marshman, Anupam Mazumdar, Gavin W Morley, Peter F Barker, Steven Hoekstra, and Sougato Bose
Subjects: Stern–Gerlach interferometry, general relativity, gravitational waves, Science, Physics, QC1-999
Abstract: A compact detector for space-time metric and curvature is highly desirable. Here we show that quantum spatial superpositions of mesoscopic objects could be exploited to create such a detector. We propose a specific form for such a detector and analyse how asymmetries in its design allow it to directly couple to the curvature. Moreover, we also find that its non-symmetric construction and the large mass of the interfered objects, enable the detection gravitational waves (GWs). Finally, we discuss how the construction of such a detector is in principle possible with a combination of state of the art techniques while taking into account the known sources of decoherence and noise. To this end, we use Stern–Gerlach interferometry with masses ∼10 ^−17 kg, where the interferometric signal is extracted by measuring spins and show that accelerations as low as 5 × 10 ^−15 ms ^−2 Hz ^−1/2 , as well as the frame dragging effects caused by the Earth, could be sensed. The GW sensitivity scales differently from the stray acceleration sensitivity, a unique feature of the proposed interferometer. We identify mitigation mechanisms for the known sources of noise, namely gravity gradient noise, uncertainty principle and electro-magnetic forces and show that it could potentially lead to a metre sized, orientable and vibrational noise (thermal/seismic) resilient detector of mid (ground based) and low (space based) frequency GWs from massive binaries (the predicted regimes are similar to those targeted by atom interferometers and LISA).
Published: 2020
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16 results on '"Steven Hoek"'

1. Using Virtual Research Environments in Agro-Environmental Research.

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

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

4. The chaos in calibrating crop models

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

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

7. Developing agro-meteorological services in Sub-Saharan Africa: a scoping study

8. Using virtual research environments in agro-environmental research

9. CST, a freeware for predicting crop yield from remote sensing or crop model indicators: Illustration with RSA and Ethiopia

10. Supplementary material to 'Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications'

11. Statistical analysis of large simulated yield datasets for studying climate change effects

12. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

13. How do various maize crop models vary in their responses to climate change factors?

14. Building an operational system for crop monitoring and yield forecasting in Morocco

15. Impacts of Problem-Based Instruction on Students’ Beliefs about Physics and Learning Physics

16. Mesoscopic interference for metric and curvature & gravitational wave detection

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