Biogeosciences
Biogeosciences
BG
Articles | Volume 19, issue 8
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

Viewed

Total article views: 2,833 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,924 819 90 2,833 255 83 92
  • HTML: 1,924
  • PDF: 819
  • XML: 90
  • Total: 2,833
  • Supplement: 255
  • BibTeX: 83
  • EndNote: 92
Views and downloads (calculated since 13 Oct 2021)
Cumulative views and downloads (calculated since 13 Oct 2021)

Viewed (geographical distribution)

Total article views: 2,833 (including HTML, PDF, and XML) Thereof 2,696 with geography defined and 137 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint