A Bayesian sequential updating approach to predict phenology of silage maize

Viswanathan, Michelle; Weber, Tobias K. D.; Gayler, Sebastian; Mai, Juliane; Streck, Thilo

doi:https://doi.org/10.5194/bg-19-2187-2022

Articles | Volume 19, issue 8

https://doi.org/10.5194/bg-19-2187-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-19-2187-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 8

Research article

|

22 Apr 2022

Research article |

| 22 Apr 2022

A Bayesian sequential updating approach to predict phenology of silage maize

Michelle Viswanathan, Tobias K. D. Weber, Sebastian Gayler, Juliane Mai, and Thilo Streck

Supplement

https://doi.org/10.5194/bg-19-2187-2022-supplement

Data sets

Regional climate change observational data FOR 1695 T. K. D. Weber, J. Ingwersen, P. Högy, A. Poyda, H. D. Wizemann, M. S. Demyan, K. Bohm, R. Eshonkulov, S. Gayler, P. Kremer, Y. F. Nkwain, C. Troost, I. Witte, G. Cadisch, T. Müller, A. Fangmeier, V. Wullmeyer, and T. Streck https://doi.org/10.20387/bonares-a0qc-46jc

Model code and software

Expert-N version 5.12 C. Klein, F. Heinlein, X. Duan, S. Gayler, and P. Priesack, P. https://expert-n.uni-hohenheim.de/fileadmin/einrichtungen/expert-n/Precompiled/expertn5.12.zip

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Short summary

We analysed the evolution of model parameter uncertainty and prediction error as we updated parameters of a maize phenology model based on yearly observations, by sequentially applying Bayesian calibration. Although parameter uncertainty was reduced, prediction quality deteriorated when calibration and prediction data were from different maize ripening groups or temperature conditions. The study highlights that Bayesian methods should account for model limitations and inherent data structures.