A Bayesian approach to analyzing the long-term soil fertility experiment

The treatment effects in the agricultural long-term experiments (LTEs) have been widely analyzed using conventional Frequentist approaches. In contrast, the Bayesian approach based on Bayes’ Theorem incorporates prior information on model parameters and provides greater flexibility on the models and parameters. The aim of this talk is to show how the Bayesian approach can be applied to the LFTs datasets and how results and interpretations differ between the two approaches, using the long-term soil fertility experiment in Sweden as an example. The effects of crop rotation and fertilization on soil carbon (SOC) content in 0-20 cm topsoil were assessed using both the conventional mixed-effects model and a Bayesian approach. The results showed similar trends that rotation with ley slowed SOC loss compared to rotation without ley, and nitrogen fertilization reduced SOC losses.

This seminar is a part of an online seminar series about Baysian data analysis, given by the SLU Center of Statistics. The full program for the seminar series can be seen here.

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Mohammad Ghorbani