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

Autor: 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
Přispěvatelé: 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, 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)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
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
Zdroj: Wallach, D, Palosuo, T, Thorburn, P, Gourdain, E, Asseng, S, Basso, B, Buis, S, Crout, N, Dibari, C, Dumont, B, Ferrise, R, Gaiser, T, Garcia, C, Gayler, S, Ghahramani, A, Hochman, Z, Hoek, S, Hoogenboom, G, Horan, H, Huang, M, Jabloun, M, Jing, Q, Justes, E, Kersebaum, K C, Klosterhalfen, A, Launay, M, Luo, Q, Maestrini, B, Mielenz, H, Moriondo, M, Nariman Zadeh, H, Olesen, J E, Poyda, A, Priesack, E, Pullens, J W M, Qian, B, Schütze, N, Shelia, V, Souissi, A, Specka, X, Srivastava, A K, Stella, T, Streck, T, Trombi, G, Wallor, E, Wang, J, Weber, T K D, Weihermüller, L, de Wit, A, Wöhling, T, Xiao, L, Zhao, C, Zhu, Y & Seidel, S J 2021, ' How well do crop modeling groups predict wheat phenology, given calibration data from the target population? ', European Journal of Agronomy, vol. 124, 126195 . https://doi.org/10.1016/j.eja.2020.126195
Eur. J. Agron. 124:126195 (2021)
European Journal of Agronomy 124 (2021)
European Journal of Agronomy
European Journal of Agronomy, 2021, 124, ⟨10.1016/j.eja.2020.126195⟩
European journal of agronomy 124, 126195-(2021). doi:10.1016/j.eja.2020.126195
European Journal of Agronomy, 124
ISSN: 1161-0301
Popis: 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.
Databáze: OpenAIRE
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