Using distance metrics to determine the appropriate domain of pedotransfer function predictions

The development of pedotransfer functions continues unabated, in part due to the inability of users to identify functions suitable to their soil of interest. In this paper, a protocol is proposed that allows users to determine the similarity between a functions calibration data and a subject of interest. The protocol uses standard distance metrics, Standardized Euclidean and Mahalanobis distances, to determine the distance from the mean of the calibration data and the subject. Distances exceeding a designated cutoff limit are deemed distinct from the calibration data and as such unsuitable for function application. An example is provided whereby a function to predict Cation Exchange Capacity is trained on a local dataset and applied to a second dataset with large pedodiversity. Those subjects suitable for functions application are identified via the proposed protocol. The proposed protocol is dependent on the provision of robust estimates of mean, standard deviation and covariance matrix of the calibration data.