Soil parent material exerts a fundamental control on many environmental processes. Nevertheless, resulting from the separate mapping programmes of the geological and soil surveys, parent material is currently poorly mapped in the United Kingdom. This research develops and tests four methods of predicting soil parent material using three study areas in England. The qualities of desirable parent material maps were stated, and then used to create new map value metrics to assess the success of the four methodologies. Firstly, translations of surface and bedrock geology maps to parent material maps were tested, using international and national parent material classifications. Secondly, qualitative expert knowledge of parent material, captured from published literature, was formalised into inputs for a corrected probability model. Parent material likelihood was predicted using three map evidence layers: geology, slope and soil. Thirdly, extensive data mining was used to create fully quantitative inputs for the same probability model, and the results were compared. The final method provided a quantitative framework for the expert knowledge model inputs by the incorporation of sparse data sampling. The expert knowledge method created parent material maps of higher value than those created by the translation of geological maps. However, the inputs derived from qualitative expert knowledge were demonstrated to benefit from the addition of quantitative sample data. The resulting maps achieved overall accuracies between 60% and 90% and contained numerous detailed classes with explicit probabilities of prediction. Extensive parent materials were shown to be predicted well, and physically and chemically distinctive parent materials could be effectively predicted irrespective of their extent. Parent material class confusion arose between units where the evidence datasets were unable to provide the sufficient geographic or descriptive detail necessary for differentiation. In such cases, class amalgamation was used to overcome consistent misclassification. Recommendations are provided for the application of this research
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