Methods for interpreting intricate soil variation models: beyond prediction
Abstract
Many soil science subdisciplines are based on the idea that understanding the spatial variation of soil characteristics is essential. In most soil mapping studies, a soil map is created by predicting the values of a statistical or non-statistical model that has been calibrated with measured values of the soil property and environmental covariates for which maps are available. The field has gradually shifted focus in recent years to more sophisticated statistical and algorithmic tools from the domain of machine learning. These models are especially helpful because they can predict outcomes and are frequently more accurate than conventional models, but they are difficult to understand and their operation is difficult to visualize.
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