Digital soil assessment – enhancing the provisional Agricultural Land Classification Map for England

Agricultural Land Classification (ALC) for England provides an assessment of the quality of agricultural land by determining physical and chemical limitations to agricultural production. It distinguishes six grades of land based on climate, topography and soil characteristics of a site.  The original "Provisional" ALC map was created during the 1960s-70s before the National Soil Map was created, plus new guidelines were introduced in 1988. A new "Predictive" ALC map has now been produced following the 1988 guidelines and drawing on not only the National Soil Map at 1:250,000 scale but also more recent detailed soil mapping that covers some 25% of the country. Whilst this new Predictive ALC map benefits from geospatial representations of ALC within a GIS environment, the map is produced solely by combination of reclassified parameters, it has boundary artefacts and missing nuances in soil information and climate  that can affect the decision making about ALC at a local level. Digital Soil Mapping, through its capability to leverage machine learning methods, can capture the intricate nonlinear relationships between ALC and input climate, soil, and topographic variables, thereby enhancing the existing methods for directly predicting soil function for agricultural production through ALC. In this study, an alternative ALC map for England was produced by leveraging the detailed soil maps and boosted classification trees. Nine climate features, four soil features, and 17 topographic features were used as predictors and stratified random sampling technique was used to extract the training data from detailed soil maps. We achieved 76% accuracy on training and 74% on validation and testing data and applied the model to generate ALC for the whole country. Comparison to the predictive ALC map revealed some grade changes and improved continuity of ALC grades in some areas and an estimate of the uncertainty that was not available to users of the provisional map. Spatially explicit assessment of uncertainty allows for the efficient allocation of resources for additional soil surveys needed to improve portions of the predictive ALC map, which is one of the primary advantages of using digital soil mapping approaches in soil assessment.