Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes

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Multi-criteria characterization of recent digital soil mapping and modeling approaches (2009, methodology): “Vasques et al. (2008) compared five different methods to predict SOC using VNIR spectra and Minasny and McBratney (2007b) compared four different methods to predict clay, cotton yield, pH and Zn.”
Machine learning for predicting soil classes in three semi-arid landscapes (2015, methodology): “Additionally, using taxonomic 537 distance (Minasny and McBratney, 2007) instead of misclassification error as the loss function to 538 minimize during model training may result in increased model accuracy.”