The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale |
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| Authors: | K Taalab R Corstanje T M Mayr M J Whelan R E Creamer |
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| Institution: | 1. Environmental Science and Technology Department, National Soil Resources Institute, School of Applied Sciences, Cranfield University, Cranfield, Bedfordshire MK4 30A, UK;2. CSAFI, Environmental Science and Technology, School of Energy, Environment and AgriFood, Cranfield University, Bedfordshire, UK |
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| Abstract: | This paper investigates the use of expert knowledge as a resource for digital soil mapping. To do this, three models of topsoil soil bulk density (Db) were produced: (i) a random forest model formulated and cross‐validated with the limited data available (which served as the benchmark), (ii) a naïve Bayesian network (BN) where the conditional probabilities that define the relations between Db and explanatory landscape variables were derived from expert knowledge rather than data and (iii) a ‘hierarchical’ BN where model structure was also defined by expert knowledge. These models were used to generate spatial predictions for mapping topsoil Db at a landscape scale. The results show that expert knowledge‐based models can identify the same spatial trends in soil properties at a landscape scale as state‐of‐the‐art mapping algorithms. This means that they are a viable option for soil mapping applications in areas that have limited empirical data. |
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