Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures
2.4 Discussion
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constrained so as to control the false discovery rate (FDR, loosely, the expected pro-portion of rejected null hypotheses that are actually true). Lark et al. (2007) propose an approach to model selection that uses the FDR, in combination with an indepen-dent prior selection of variables based on expert knowledge. Expert judgement is used both to determine the size of the pool of models that is searched (matching it to the strength of evidence for the existence of good models) and to ensure that the searched subset of possible models includes those that make sense given our knowledge of the soil. In trials it was found that this method selected good pre-dictor models, and avoided overfitting in cases where uncontrolled model selection methods fell into this particular trap.
Another example is the incorporation of the soil taxonomic distance in the data-mining algorithm for spatial prediction of soil classes (Minasny and McBratney, 2007). Current methods for predicting soil classes only consider the minimisation of the misclassification error. Soil classes at any taxonomic level have taxonomic re-lationships between each other, and no statistical procedure so far has been utilised to account for these relationships. Incorporating taxonomic distance between soil classes in a supervised classification routine such as decision tree, allows more meaningful prediction and effective integration of soil science knowledge.
2 Digital Soil Mapping Technologies for Countries with Sparse Data Infrastructures 29
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