Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration. (February 2021) - Davoudabadi, Mohammad Javad; Pagendam, Daniel; Drovandi, Christopher; Baldock, Jeff; White, Gentry
Abstract: Sequestering carbon into the soil can mitigate the atmospheric concentration of greenhouse gases, improving crop productivity and yield financial gains for farmers through the sale of carbon credits. In this work, we develop and evaluate advanced Bayesian methods for modelling soil carbon sequestration and quantifying uncertainty around predictions that are needed to fit more complex soil carbon models, such as multiple-pool soil carbon dynamic models. This paper demonstrates efficient computational methods using a one-pool model of the soil carbon dynamics previously used to predict soil carbon stock change under different agricultural practices applied at Tarlee, South Australia. We focus on methods that can improve the speed of computation when estimating parameters and model state variables in a statistically defensible way. This paper also serves as a tutorial on advanced Bayesian methods for fitting complex state-space models, which will be of interest to soil scientists and other environmental scientists more generally. Highlights: Compared the performance of standard and advanced particle filters for fitting complex state-space models. Improved the computational efficiency of the Rao-Blackwellised particle filter for fitting complex state-space models. Correlated pseudo-marginal method outperforms the standard pseudo-marginal method in parameter estimation.