Search

Your search keyword '"Hillridge Technology Pty Ltd"' showing total 4 results
Start Over Author "Hillridge Technology Pty Ltd"
4 results on '"Hillridge Technology Pty Ltd"'

1. 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
Full Text
View/download PDF

2. 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

3. 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
Full Text
View/download PDF

4. 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
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results