Creation and interpolation of continuous soil layer classes in the lower Namoi valley

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 soi l mantle, Fuzzy clustering algorithms can be applied to resolve both errors . In this study we numerically classified 1419 soil horizon samples using f uzzy k-means (FKM) and fuzzy k-means with extragrade (FKME) analysis. Each sample was characterized by 12 chemical and textural attributes that were u sed for the numerical classification. The fuzzy classes produced were mappe d at various depths using a method that considered the unity of class membe rship and local kriging, The use of a confusion index enabled the represent ation of the continuous nature of membership between the classes mapped and highlighted areas where the collection of additional information may be ap propriate. The resulting classes reflect sensible and practical groupings t hat are easily related to the natural structure of the landscape. Silt and clay contents were the most distinguishing attributes in identifying the va rious geological and geomorphic components, Differences in soil-forming pro cess were well highlighted by organic C (Org. C), P, electrical conductivit y (EC1:5), pH, and Cl- content. We concluded that the fuzzy clustering algo rithms and geostatistical techniques provide a worthwhile approach to soil classification and representation of the soil continuum,