Modeling the spatial distribution of soil properties by Generalized Least Squares regression

Assessing the spatial distribution of soil properties has achieved considerable interest among soil scientists, both for testing hypotheses about the soil formation processes and for predicting the properties of soils at non-sampled locations (mapping). In our article recently accepted for publication in Journal of Soil and Water Conservation we discuss the main approaches to modeling spatial variates, and we propose a modeling framework that is able to incorporate the most important effects usually found in spatial variates, including fixed and random spatial effects, spatial trends and heteroscedasticity. We provide a case study of the analysis of eight soil properties in a mountain catchment in the Spanish Pyrenees. We focus on the importance of model selection in order to determine which effects are relevant for modeling each soil parameter.

On this post I provide supplementary material to the article. The file analysis.R includes an example of the code used for performing the analysis described in the article. It is written in R, for more information visit: . The data needed are included in the file arnas.csv.

Download supplementary material.

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