A Top-Down Approach to the State Factor Paradigm for Use in Macroscale Soil Analysis

This article explains the need for an alternative conceptual framework for conducting macroscale soil geographic research. A top-down approach to the classic state factor paradigm is then described and illustrated by revisiting the idea of soil zonality to investigate the relationship between climate and soil character across the contiguous United States. Although many macroscale soil maps suggest strong relationships between soil properties and climate, few studies have explicitly demonstrated those relationships using unclassified point observations. Data from 10,661 sample points were analyzed using geographically weighted regression (GWR) models to describe the geography of the relationship between A-horizon soil properties and climate. In addition, macroscale soil property surfaces were interpolated from measured point samples visually to assess the validity of the zonal soil concepts. Results suggest many interesting relationships between climate and surface soil character. Local-scale soil variability related to topography and parent material was not found to exceed macroscale variability related to climate. Three of the four surface soil properties analyzed exhibited strong zonality, thus lending support to the concept of zonal soils. GWR was found to be an excellent tool for use in macroscale geographic soil research because of the coefficient maps that the procedure yields.