Key variables for the identification of soil management classes in the aeolian landscapes of north–west Europe

At present, spatially very detailed data sets can be obtained about soil, landscape and crop variability. However, there is a need to select independent key properties to identify management classes needed for precise land management. In a previous study performed in the European loess belt, topsoil pH, apparent electrical conductivity (ECa) and elevation were identified as key properties. In this study we enlarged the number of soil properties by including gamma ray measurements and employed a similar methodology to a field in the sand belt of northern Europe. Based on a principal component analysis we identified the same three variables as key properties. This was surprising given the big differences in landscape topology and pedogenesis between the loess and sand areas. These three key variables were used to delineate management classes using a fuzzy k-means with extragrade classification procedure. This classification was evaluated by mapping the wheat grain yield in the year 2006. A multiple regression model could be constructed that predicted yield from ECa and elevation well (Radj2 = 0.88). To analyse the influence of ECa on crop yield in depth a boundary line analysis was conducted. The boundary line could be modelled with an excellent Radj2 of 0.98. It was concluded that ECa, elevation and pH are generic key variables for the delineation of management classes of the aeolian landscapes of north–west Europe. Given its integral nature and strong relationship with crop performances, the authors plea to upgrade ECa from a “secondary” (proxy) source of information to a “primary” variable which can be used directly as a basis for detailed soil mapping of the bulk soil.

► Three variables were identified as key variables: ECa, elevation and pH.
► The same key variables were identified for the loess and sand areas of Europe.
► Management classes are best created with a method containing an ‘extragrade’ class.
► An excellent relationship was found between key variables and wheat yield.
► It is recommended to upgrade ECa from a proxy to a primary soil property.