A Bayesian Belief network to operationalize the concepts of Soil Quality and Health

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'Soil Quality' and 'Soil Health' are general terms for indicators that are associated with 'Soil Security'. None of these terms within quotation marks is easy to define, however. Neither are they easy to quantify rigorously in a way that avoids dispute. Nonetheless all three terms have traction with policy makers and with land managers and regulators. Indicators provide benchmarks for ranking different places or practices and deciding where to deploy effort to bring about change as effectively and economically as possible and they provide a means to assess afterwards whether or not and to what extent this change has actually been brought about.As a result, indicators of this kind are attractive to stakeholders. Indicators often rely on expert opinion for their derivation, but experts differ. Even apparently objective biophysical measurements are subject to error and worse, the soil itself varies from place to place and even time to time. It is not clear how to eliminate bias or how to weight the different kinds of information - opinion and measurement.There is therefore scope for developing a rigorous, scientific approach to SQH that incorporates expert-derived opinion alongside physically-based measurements in our understanding of Soil Quality and Health (SQH) in a scientific manner.Bayesian Belief Networks are graph-based, directional networks that can incorporate probability distributions of these various kinds of data. Essentially the directedness leads from multiple pieces of data to a conclusion - in our case a rating of SQH. The network is self-learning in that any additional soils and data for which quality assessments are available will re-inforce the pathways that decide the quality rating. In use, SQH ratings for additional soils that contain even partial data can still be obtained if the net defaults to mean values where data is missing.To accommodate the various functions and scales needed to operationalise SQH, will require a set of Bayesian Belief Networks that considers the interactions of soil properties with SQH but also the impact of environmental change and land use and management on soil quality. There a numerous advantages to using BBNs: they can consider and integrate biological, economic and sociological factors and have effectively been use to determine the consequence of land-management decisions in land use decision behaviour. Bayesian modelling methods are a rigorous framework in which a complete characterization of the coupling and variability of soil quality is based on physical laws, empirical relationships but can easily incorporate expert knowledge formally and other kinds of soft data.