The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale

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.