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

“土壤质量”和“土壤健康”是与“土壤安全”相关的指标的通用术语。然而，引号中的这些术语都不容易定义。它们也不容易以避免争议的方式严格量化。尽管如此，这三个任期都对政策制定者、土地管理者和监管者有吸引力。指标提供了基准，用于对不同的地方或做法进行排名，并决定在哪些方面作出努力，以尽可能有效和经济地实现变革，而且指标还提供了一种手段，用于事后评估这种变革是否实际实现以及在多大程度上实现了变革，因此，这类指标对利益攸关方具有吸引力。指标往往依赖于专家的意见，但专家意见不一。即使是明显客观的生物物理测量也会出现误差，更糟糕的是，土壤本身因地而异，甚至因时而异。目前尚不清楚如何消除偏见或如何衡量不同类型的信息-意见和测量。因此，有空间开发一个严格的，科学的方法SQH，结合专家得出的意见与物理为基础的测量，在我们的理解土壤质量和健康（SQH）以科学的方式。贝叶斯信念网络是基于图形的，定向网络可以合并这些各种数据的概率分布。从本质上讲，直接性从多个数据中得出结论-在我们的情况下，SQH评级。该网络是自我学习的，因为任何额外的土壤和质量评估数据将重新加强决定质量评级的途径。在使用中，如果网络默认为数据缺失的平均值，则仍然可以获得包含部分数据的其他土壤的SQH评级。为了适应操作SQH所需的各种功能和尺度，将需要一组贝叶斯置信网络，该网络考虑土壤特性与SQH的相互作用，以及环境变化和土地利用和管理对土壤质量的影响。BBN具有许多优点：它们可以考虑和整合生物，经济和社会因素，并已被有效地用于确定土地利用决策行为中的土地管理决策的后果。贝叶斯建模方法是一个严格的框架，其中土壤质量的耦合和变异性的完整表征是基于物理定律，经验关系，但可以很容易地将专家知识正式和其他种类的软数据。

'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.