ProjectA Bayesian Belief Network
to operationalize the concepts of Soil Quality and Health
Though soil health and soil quality are important to the concept of soil security no strict definition exists for either.
Aims & Objectives
Though soil health and soil quality are important to the concept of soil security no strict definition exists for either. This is partly because they derive from opinion and measurement. Bayesian Belief Networks can successfully combine these types of hard and soft data to:
- investigate determinants of soil health and quality
- build a naïve Bayesian Belief Network that connects the most important components identified above in a logical fashion in order to explain differences between soils and to elicit opinions from experts that reinforce or modify this network
- populate the network with data and produce preliminary inferences of how soil health and quality are related, quantifiably, with soil properties and land-use. To iterate with the expert opinion data in order to develop a robust network and infer a distribution, i.e. index of SQH
- explore how SQH changes in time and incorporate these dynamics in networks that reflect the changes that matter.
Determine the consequence of land management decisions
agricultural systems modeller and soil scientist
Andy Whitmore is an agricultural systems modeller and soil scientist. He graduated in chemistry but has worked since on mathematical models both in the UK and in the Netherlands. Recent work has looked at resilience of soils, the fate and effect of organic matter added to soil and greenhouse gas emissions from agriculture. As well as this Soil Security project, he leads work looking at possible ways of implementing the agricultural Sustainable Development Goals and he leads a network of scientists. You can find out more about Andy here.
Joanna Zawadzka is an environmental scientist, whose primary interests are in Ecosystem Goods and Services Assessment and digital soil mapping. Her previous activities have focussed on research related to carbon sequestration within various types of environment as well as digital terrain analysis. Joanna has experience in spatial data analysis in the context of the environmental mapping and modelling. She is familiar with a wide range of techniques including GIS, remote sensing and statistical methods to extract, analyse, and interpret data on natural environment
Kirsty Hassall is a scientific statistician whose research focuses on application of statistical methods and techniques to a wide variety of biological data. She has previous experience with time series data and spatial statistics, in particular Bayesian inference of state space models and spatio-temporal models for biological processes. Examples of Kirsty’s current research includes work on multiplicity issues in ‘omics data and modelling expert opinion through Bayes Nets. She is familiar with a range of statistical techniques including multivariate approaches and the analysis of designed experiments.