Articles | Volume 19, issue 8
Biogeosciences, 19, 2187–2209, 2022
Biogeosciences, 19, 2187–2209, 2022
Research article
22 Apr 2022
Research article | 22 Apr 2022

A Bayesian sequential updating approach to predict phenology of silage maize

Michelle Viswanathan et al.

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Cited articles

Adnan, A. A., Diels, J., Jibrin, J. M., Kamara, A. Y., Shaibu, A. S., Craufurd, P., and Menkir, A.: CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria, F. Crop. Res., 253, 107826,, 2020. 
Alderman, P. D. and Stanfill, B.: Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis, Eur. J. Agron., 88, 1–9,, 2017. 
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Borchers, H. W.: pracma: Practical Numerical Math Functions, version 2.2.9, CRAN [code],, 2020. 
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.
Final-revised paper