How well do crop modeling groups predict wheat phenology, given calibration data from the target population?
2021
Wallach, Daniel | Palosuo, Taru | Thorburn, Peter | Gourdain, Emmanuelle | Asseng, Senthold | Basso, Bruno | Buis, Samuel | Crout, Neil | Dibari, Camilla | Dumont, Benjamin | Ferrise, Roberto | Gaiser, Thomas | Garcia, Cécile | Gayler, Sebastian | Ghahramani, Afshin | Hochman, Zvi | Hoek, Steven | Hoogenboom, Gerrit | Horan, Heidi | Huang, Mingxia | Jabloun, Mohamed | Jing, Qi | Justes, Eric | Kersebaum, Kurt Christian | Klosterhalfen, Anne | Launay, Marie | Maestrini, Bernardo | Qian, Budong | Luo, Qunying | Mielenz, Henrike | Moriondo, Marco | Nariman Zadeh, Hasti | Olesen, Jørgen Eivind | Poyda, Arne | Priesack, Eckart | Pullens, Johannes Wilhelmus Maria | Schütze, Niels | Shelia, Vakhtang | Souissi, Amir | Specka, Xenia | Srivastava, Amit Kumar | Stella, Tommaso | Streck, Thilo | Trombi, Giacomo | Wallor, Evelyn | Wang, Jing | Weber, Tobias K.D. | Weihermüller, Lutz | de Wit, Allard | Wöhling, Thomas | Xiao, Liujun | Zhao, Chuang | Zhu, Yan | Seidel, Sabine | AGroécologie, Innovations, teRritoires (AGIR) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Natural Resources Institute Finland (LUKE) | CSIRO Agriculture and Food (CSIRO AF) ; Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | ARVALIS - Institut du végétal [Paris] | Technische Universität Munchen - Technical University Munich - Université Technique de Munich (TUM) | 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 = Université de Florence (UniFI) | Gembloux Agro-Bio Tech [Faculté universitaire des sciences agronomiques de Gembloux] ([FUSAGx]) ; Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège) | Universität Bonn = University of Bonn | Institute of Crop Science and Resource Conservation [Bonn] (INRES) ; Rheinische Friedrich-Wilhelms-Universität Bonn | Universität Hohenheim = University of Hohenheim | Institute of Soil Science and Land Evaluation, Soil Biology Section ; Universität Hohenheim = University of Hohenheim | University of Southern Queensland (USQ) | Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | Wageningen University and Research [Wageningen] (WUR) | University of Florida [Gainesville] (UF) | China Agriculture University [Beijing] | College of Resources and Environmental Sciences ; China Agricultural University (CAU) | Agriculture and Agri-Food Canada Eastern Cereal and Oilseed Research Centre ; Agriculture and Agri-Food (AAFC) | Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Agrosystèmes Biodiversifiés (UMR ABSys) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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 de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - 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) | Leibniz-Center for Agricultural Landscape Research Muencheberg (ZALF) | Inst Bio & Geosci IBG Agrosphere 3 ; Forschungszentrum Jülich = Research Center Juelich (FZ Juelich) | Agroclim (AGROCLIM) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Agriculture and Agri-Food Canada, Saskatoon Research Centre ; Agriculture and Agri-Food (AAFC) | Hillridge Technology Pty Ltd | Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI) | CNR-IBE | Aalto University | Aarhus University [Aarhus] | Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel (CAU) | German Res Ctr Environm Hlth ; Partenaires INRAE | Technische Universität Dresden = Dresden University of Technology (TU Dresden) | Florida Agricultural and Mechanical University (FAMU) ; University of Florida [Gainesville] (UF) | Université de Carthage (Tunisie) = University of Carthage (UCAR) | Inst Landscape Biogeochem, Leibniz Ctr Agr Landscape Res, Muncheberg, Germany ; Partenaires INRAE | Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | China Agricultural University (CAU) | Agrosphere, IBG-3 ; Forschungszentrum Jülich GmbH | Centre de recherche de Jülich | Jülich Research Centre (FZJ) ; Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association | Lincoln Agritech Ltd | 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) | Nanjing Agricultural University (NAU)
International audience
Afficher plus [+] Moins [-]anglais. 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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