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

Daniel Wallach, Taru Palosuo, Peter Thorburn, Emmanuelle Gourdain, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cécile Garcia, Sebastian Gayler, Afshin Ghahramani, Zvi Hochman, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, 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
doi: https://doi.org/10.1101/708578
Daniel Wallach
1INRA, UMR AGIR, Castanet Tolosan, France
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  • For correspondence: daniel.wallach@inra.fr
Taru Palosuo
2Natural Resources Institute Finland (Luke), Helsinki, Finland
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Peter Thorburn
3CSIRO Agriculture and Food, Brisbane, Queensland, Australia
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Emmanuelle Gourdain
4ARVALIS - Institut du végétal Paris, France
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Senthold Asseng
5Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida
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Bruno Basso
6Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan
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Samuel Buis
7INRA, UMR 1114 EMMAH, Avignon, France
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Neil Crout
8School of Biosciences, University of Nottingham, Loughborough, UK
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Camilla Dibari
9Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Italy
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Benjamin Dumont
10Department Terra & AgroBioChem, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium
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Roberto Ferrise
9Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Italy
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Thomas Gaiser
11Institute of Crop Science and Resource Conservation, University of Bonn, Germany
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Cécile Garcia
6Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan
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Sebastian Gayler
12Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
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Afshin Ghahramani
13Centre for Sustainable Agricultural Systems, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, Queensland, Australia
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Zvi Hochman
3CSIRO Agriculture and Food, Brisbane, Queensland, Australia
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Steven Hoek
14Environmental Sciences Group, Wageningen University & Research, Wageningen, The Netherlands
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Heidi Horan
3CSIRO Agriculture and Food, Brisbane, Queensland, Australia
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Gerrit Hoogenboom
5Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida
15Institute for Sustainable Food Systems, University of Florida, Gainesville, Florida
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Mingxia Huang
16College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
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Mohamed Jabloun
8School of Biosciences, University of Nottingham, Loughborough, UK
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Qi Jing
17Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
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Eric Justes
18CIRAD, UMR SYSTEM, Montpellier, France
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Kurt Christian Kersebaum
19Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
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Anne Klosterhalfen
20Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany
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Marie Launay
21INRA, US 1116 AgroClim, Avignon, France
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Qunying Luo
22Hillridge Technology Pty Ltd, Sydney, Australia
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Bernardo Maestrini
6Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan
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Henrike Mielenz
23Institute for Crop and Soil Science, Federal Research Centre for cultivated Plants, Julius Kühn-Institut (JKI), Braunschweig, Germany
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Marco Moriondo
24CNR-IBIMET, Firenze, Italy
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Hasti Nariman Zadeh
25Aalto University School of Science, Espoo, Finland
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Jørgen Eivind Olesen
26Department of Agroecology, Aarhus University, Tjele, Denmark
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Arne Poyda
27Grass and Forage Science / Organic Agriculture, Institute of Crop Science and Plant Breeding, Kiel University, Kiel, Germany
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Eckart Priesack
28Institute of Biochemical Plant Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
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Johannes Wilhelmus Maria Pullens
26Department of Agroecology, Aarhus University, Tjele, Denmark
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Budong Qian
17Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
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Niels Schütze
29Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany
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Vakhtang Shelia
5Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida
15Institute for Sustainable Food Systems, University of Florida, Gainesville, Florida
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Amir Souissi
30National Institute of Agronomic Research of Tunisia (INRAT), Agronomy Laboratory, University of Carthage, Tunis, Tunisia
31National Agronomy Institute of Tunisia (INAT), University of Carthage, Tunis, Tunisia
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Xenia Specka
19Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
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Amit Kumar Srivastava
11Institute of Crop Science and Resource Conservation, University of Bonn, Germany
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Tommaso Stella
19Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
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Thilo Streck
12Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
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Giacomo Trombi
9Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Italy
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Evelyn Wallor
19Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
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Jing Wang
16College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
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Tobias K.D. Weber
12Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany
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Lutz Weihermüller
20Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany
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Allard de Wit
14Environmental Sciences Group, Wageningen University & Research, Wageningen, The Netherlands
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Thomas Wöhling
29Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany
32Lincoln Agritech Ltd., Hamilton, New Zealand
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Liujun Xiao
5Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida
33National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China
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Chuang Zhao
5Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida
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Yan Zhu
33National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China
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Sabine J. Seidel
11Institute of Crop Science and Resource Conservation, University of Bonn, Germany
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ABSTRACT

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 30, 2020.
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How well do crop modeling groups predict wheat phenology, given calibration data from the target population?
Daniel Wallach, Taru Palosuo, Peter Thorburn, Emmanuelle Gourdain, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cécile Garcia, Sebastian Gayler, Afshin Ghahramani, Zvi Hochman, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, 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
bioRxiv 708578; doi: https://doi.org/10.1101/708578
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How well do crop modeling groups predict wheat phenology, given calibration data from the target population?
Daniel Wallach, Taru Palosuo, Peter Thorburn, Emmanuelle Gourdain, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cécile Garcia, Sebastian Gayler, Afshin Ghahramani, Zvi Hochman, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, 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
bioRxiv 708578; doi: https://doi.org/10.1101/708578

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