A Quantitative Property-Based Layer and Profile Numerical Soil Classification System for Australia

Abstract

Most soil classification systems rely on the identification of genetic horizons, delineated through visual observations guided by theories of soil development. However, these systems often differ across countries, creating challenges for information transfer and comparison. In this study, we explore the application of numerical soil classification as a means of establishing a more universally applicable soil classification system. Using a comprehensive set of relevant soil properties—such as available water capacity, bulk density, cation exchange capacity (CEC), effective CEC, pH (in both water and calcium chloride), organic carbon content and soil texture (sand, silt and clay percentages)—clustering analysis was performed using the k-means algorithm. This method generated 40 layer classes and 100 profile classes, offering an innovative perspective on soil variation. The spatial distribution of layer classes exhibited depth-dependent variation, although it was less pronounced than the east-to-west variation across Australia. Notably, the spatial distribution of numerical profile classes aligned well with existing Australian soil classification maps. This approach marks a significant step toward developing a fully quantitative system for soil classification, not only within Australia but also for global applications, enhancing consistency and comparability in soil science.