A Hierarchical Model for Estimating Distribution Profiles of Soil Texture

Abstract

The MLRA (Major Land Resource Area) 107 pilot project involved implementation of a multi-phase probability sampling design to update the soil surveys for two counties in western Iowa. We consider estimation of distribution profiles of soil texture using a hierarchical model and data from the pilot project. Soil texture measurements are recorded for each horizon (or layer) of soil. Soil horizon profiles are modeled as realizations of Markov chains. Conditional on the horizon profile, transformed field and laboratory determinations of soil texture are modeled as a multivariate mixed model with normal errors. The posterior distribution of unknown model parameters is numerically approximated using a Gibbs sampler. The hierarchical model provides a comprehensive framework which may be useful for analyzing many other variables of interest in the pilot project.