Preprints
https://doi.org/10.5194/bg-2021-238
https://doi.org/10.5194/bg-2021-238

  13 Oct 2021

13 Oct 2021

Review status: this preprint is currently under review for the journal BG.

A Bayesian sequential updating approach to predict phenology of silage maize

Michelle Viswanathan1, Tobias K. D. Weber1, Sebastian Gayler1, Juliane Mai2, and Thilo Streck1 Michelle Viswanathan et al.
  • 1Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, 70593, Germany
  • 2Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Abstract. Crop models are tools used for predicting year to year crop development on field to regional scales. However, robust predictions are hampered by factors such as uncertainty in crop model parameters and in the data used for calibration. Bayesian calibration allows for the estimation of model parameters and quantification of uncertainties, with the consideration of prior information. In this study, we used a Bayesian sequential updating (BSU) approach to progressively incorporate additional data at a yearly time-step to calibrate a phenology model (SPASS) while analysing changes in parameter uncertainty and prediction quality. We used field measurements of silage maize grown between 2010 and 2016 in the regions of Kraichgau and Swabian Alb in southwestern Germany. Parameter uncertainty and model prediction errors were expected to progressively reduce to a final, irreducible value. Parameter uncertainty reduced as expected with the sequential updates. For two sequences using synthetic data, one in which the model was able to accurately simulate the observations, and the other in which a single cultivar was grown under the same environmental conditions, prediction error mostly reduced. However, in the true sequences that followed the actual chronological order of cultivation by the farmers in the two regions, prediction error increased when the calibration data was not representative of the validation data. This could be explained by differences in ripening group and temperature conditions during vegetative growth. With implications for manual and automatic data streams and model updating, our study highlights that the success of Bayesian methods for predictions depends on a comprehensive understanding of inherent structure in the observation data and model limitations.

Michelle Viswanathan et al.

Status: open (until 24 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Michelle Viswanathan et al.

Michelle Viswanathan et al.

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Short summary
We analysed the evolution of model parameter uncertainty and prediction error as we updated a maize phenology model based on yearly observations, by applying Bayesian calibration sequentially. Even though parameter uncertainty reduced, prediction quality deteriorated when data used for calibration and prediction were from different maize ripening groups or temperature conditions. Bayesian calibration methods should account for model limitations and inherent structure in the data.
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