Articles | Volume 19, issue 8
Biogeosciences, 19, 2187–2209, 2022
https://doi.org/10.5194/bg-19-2187-2022
Biogeosciences, 19, 2187–2209, 2022
https://doi.org/10.5194/bg-19-2187-2022
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-238', Anonymous Referee #1, 19 Nov 2021
    • AC1: 'Reply on RC1', Michelle Viswanathan, 11 Jan 2022
  • RC2: 'Comment on bg-2021-238', Justin Sexton, 26 Nov 2021
    • AC2: 'Reply on RC2', Michelle Viswanathan, 11 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (28 Jan 2022) by Trevor Keenan
AR by Michelle Viswanathan on behalf of the Authors (02 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 Feb 2022) by Trevor Keenan
RR by Anonymous Referee #1 (08 Mar 2022)
ED: Publish as is (08 Mar 2022) by Trevor Keenan
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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.
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