Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration

Carbon accounting is now an important activity for governments throughout the world who have made commitments under the Kyoto Protocol and the Paris Agreement on Climate Change. 

For farmer and landowners, they need to estimate the amount of carbon they have isolated, or sequestered, on their farmlands so that they can be provided with markets to sell carbon credits. To estimate carbon sequestration and the amount of carbon credits to sell, computationally efficient methods are required so that these can be incorporated into decision support systems.

The methods presented in this paper are useful in estimating carbon stocks in agricultural production systems. Specifically, we have developed and evaluated advanced Bayesian methods for fitting complex state-space models which serves as a tutorial for soil scientists and other environmental scientists more generally who work on complex state-space models in their research.