An expert system to predict intricate saline - sodic subsoil patterns in upland South Australia

Abstract

Digital soil mapping (DSM) offers apparent benefits over more labour-intensive and costly traditional soil survey. Large cartographic scale (e.g. 1 : 10 000 scale) soil maps are rare in Australia, especially in agricultural areas where they are needed to support detailed land evaluation and targeted land management decisions. We describe a DSM expert system using environmental correlation that applies a priori knowledge from a key area (128 ha) soil–landscape with a regionally repeating toposequence to predict the distribution of saline–sodic subsoil patterns in the surrounding upland farming region (2275 ha) in South Australia. Our predictive framework comprises interrelated and iterative steps, including: (i) consolidating a priori knowledge of the key area soil–landscape; (ii) refining existing mentally held and graphic soil–landscape models; (iii) selecting suitable environmental covariates compatible with geographic information systems (GIS) by interrogation via 3D visualisation using a GIS; (iv) transforming the existing soil–landscape models to a computer model; (v) applying the computer model to the environmental variables using the expert system; (vi) performing the predictive mapping; and (vii) validation. The environmental covariates selected include: digital terrain attributes of slope gradient, topographic wetness index and plan curvature, and airborne gamma-radiometric K%. We apply selected soil profile physiochemical data from a prior soil survey to validate mapping. Results showed that we correctly predicted the saline–sodic subsoils in 10 of 11 reference profiles in the region.