Abstract:
The World Reference Base for Soil Resources (WRB) (FAO, IUSS Working Group WRB, 2007) at present does not acknowledge the spatial soil continuum, but provides a sound basis to do so. Using methods from statistical learning theory to develop digital soil maps is much more efficient and precise while regionalising soil diagnostic properties instead of complex entities such as the soil units assigned by the WRB. Particularly in
providing spatial soil information displayed in digital soil maps, any aggregation of this spatial soil information to soil units means a loss of information.
The soil landscape can be systematically described in its spatial continuum simply by the vertical order and extent of the WRB diagnostic horizons. The diagnostic horizons are related in their thickness to a standard depth and listed from top to bottom in order of appearance.
Typical diagnostic horizon thickness and occurrence probability were predicted from terrain parameters by classification and regression trees (CART), throughout the research area in southern Ecuador. The two disadvantages of CART, abrupt prediction class boundaries and dependence on the dataset, were addressed by hundredfold model runs on different data subsets, leading to a range of possible predictions. Prediction uncertainty was included in the digital soil maps by calculating these predictions' means and standard deviations as well as by horizon occurrence probability prediction. Model performance was evaluated by means of hundredfold external cross validation.
Terrain parameters were found to have a strong influence on diagnostic topsoil properties. However, no influence on the vertical profile differentiation was observed. Hence predicting horizon thickness and subsoil diagnostic properties was difficult. The systematic description of the soil continuum of this particular soillandscape resulted in histic and stagnic soil parts dominating the first 100 cm of the soil column for most of the area.
