Bayesian selection of mixed covariates from a latent layer: application to hierarchical modeling of soil carbon dynamics
Soil carbon is important not only to ensure food security via soil fertility, but also to potentially mitigate global warming via increasing soil carbon sequestration. There is an urgent need to understand the response of the soil carbon pool to climate change and agricultural practices. Biophysical models have been developed to study Soil Organic Matter (SOM) for some decades. However, there still remains considerable uncertainty about the mechanisms that affect SOM dynamics from the microbial level to global scales. In this paper, we propose a statistical Bayesian selection approach to study which forcing conditions influence soil carbon dynamics by looking at the depth distribution of radiocarbon content for 159 profiles under different conditions of climate (temperature, precipitation, etc.) and environment (soil type, land-use). Stochastic Search Variable Selection (SSVS) is here applied to latent variables in a hierarchical Bayesian model. The model describes variations of radiocarbon content as a function of depth and potential covariates such as climatic and environmental factors. SSVS provides a probabilistic judgment about the joint contribution of soil type, climate and land use on soil carbon dynamics. We also discuss the practical performance and limitations of SSVS in presence of categorical covariates and collinearity between covariates in the latent layers of the model.