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

Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and Gourdain, Emmanuelle and Asseng, Senthold and Basso, Bruno and Buis, Samuel and Crout, Neil and Dibari, Camilla and Dumont, Benjamin and Ferrise, Roberto and Gaiser, Thomas and Garcia, Cecile and Gayler, Sebastian and Ghahramani, Afshin ORCID: https://orcid.org/0000-0002-9648-4606 and Hochman, Zvi and Hoek, Steven and Hoogenboom, Gerrit and Horan, Heidi and Huang, Mingxia and Jabloun, Mohamed and Jing, Qi and Justes, Eric and Kersebaum, Kurt Christian and Klosterhalfen, Anne and Launay, Marie and Luo, Qunying and Maestrini, Bernardo and Mielenz, Henrike and Moriondo, Marco and Zadeh, Hasti Nariman and Olesen, Jorgen Eivind and Poyda, Arne and Priesack, Eckart and Pullens, Johannes Wilhelmus Maria and Qian, Budong and Schutze, Niels and Shelia, Vakhtang and Souissi, Amir and Specka, Xenia and Srivastava, Amit Kumar and Stella, Tommaso and Streck, Thilo and Trombi, Giacomo and Wallor, Evelyn and Wang, Jing and Weber, Tobias K. D. and Weihermuller, Lutz and de Wit, Allard and Wohling, Thomas and Xiao, Liujun and Zhao, Chuang and Zhu, Yan and Seidel, Sabine J. (2021) How well do crop modeling groups predict wheat phenology, given calibration data from the target population? European Journal of Agronomy, 124:126195. pp. 1-10. ISSN 1161-0301


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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 18 Jan 2021 04:27
Last Modified: 18 Jan 2021 04:27
Uncontrolled Keywords: Crop model; Phenology prediction; Model evaluation; Wheat
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070107 Farming Systems Research
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300205 Agricultural production systems simulation
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9606 Environmental and Natural Resource Evaluation > 960699 Environmental and Natural Resource Evaluation not elsewhere classified
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1899 Other environmental management > 189999 Other environmental management not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.eja.2020.126195
URI: http://eprints.usq.edu.au/id/eprint/40531

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