Assessing the value of Digital Soil Assessment: a bridge too far?

Digital Soil Mapping (DSM) is a demonstrated viable approach to generate spatial predictions of soil properties but Digital Soil Assessment (DSA) methods are not widely applied as the translational step from DSM to DSA remains challenging. The purpose, in generic terms, of DSA is the conversion of quantitative data on soil properties obtained through DSM to a spatial assessment of the capacity of a soil to fulfil a particular function. However, the interpretation and value of soil information to users for effective decision making is often qualitative rather than quantitative, expressing the capacity and capability of soil to deliver particular services or perform particular functions. We identify three challenges to implementing DSA for decision making and illustrate these with several case studies: 1) Bridging the gap between quantitative DSM to qualitative DSA.  Soil Quality and Health (SQH) are general terms for indicators that are associated with soil security which are neither easy to define, nor easy to quantify. Through combining the UK national soils datasets, and the SQH Bayesian inference, we were then able to predict SQH for soils across Great Britain.  We show that we are able to describe both aleatoric uncertainty and, equally important, epistemic uncertainty through a description of the experts confidence and through using multiple experts. 2) Cascading the uncertainty generated from DSM into DSA. We demonstrate using a stochastic simulation technique and specified threshold values for soil constraints for crop growth that the uncertainty can be incorporated into the resulting value assessments. We show that DSA can be used to quantify the potential contributions of soil constraints versus socio-economic, farm management and other factors, and the importance of allowing for uncertainties and having appropriate constraint criteria is illustrated by the sensitivity of our constraint estimates to the various criteria we tested. 3) Incorporating temporally dynamic environmental data. We developed a DSA that integrated a dynamic modelling approach to determine land suitability under future climatic variability.  The DSA outputs highlighted where best to grow food in the future based on soil and climate interactions, however decision making needs to address potential trade-offs in other soil services before deciding when and where to protect the best quality land. Although we illustrate, through these examples, that informative and useful spatial data about the soil can be obtained through DSA, in each case the process is elaborate and complex, with significant modelling challenges. Unlike DSM, DSA introduces a value judgement on the soil which can be difficult to capture through quantitative modelling processes.