Creation and Interpolation of Continuous Soil Layer Classes in the Lower Namoi Valley

Abstract

The major errors associated with soil classification and mapping are due to subjective allocation of individuals to classes and incongruities between the classification system and the natural continuous variability of the soil mantle. Fuzzy clustering algorithms can be applied to resolve both errors. In this study we numerically classified 1419 soil horizon samples using fuzzy k‐means (FKM) and fuzzy k‐means with extragrade (FKME) analysis. Each sample was characterized by 12 chemical and textural attributes that were used for the numerical classification. The fuzzy classes produced were mapped at various depths using a method that considered the unity of class membership and local kriging. The use of a confusion index enabled the representation of the continuous nature of membership between the classes mapped and highlighted areas where the collection of additional information may be appropriate. The resulting classes reflect sensible and practical groupings that are easily related to the natural structure of the landscape. Silt and clay contents were the most distinguishing attributes in identifying the various geological and geomorphic components. Differences in soil‐forming process were well highlighted by organic C (Org. C), P, electrical conductivity (EC1:5), pH, and Cl− content. We concluded that the fuzzy clustering algorithms and geostatistical techniques provide a worthwhile approach to soil classification and representation of the soil continuum.