Agricultural census data and land use modelling

Modelling land use change is often constrained by imperfect and incomplete data sources. This paper explores three modelling methodologies and their ability to predict agricultural land use on the basis of information from the Scottish Agricultural Census. This dataset, which contains information on ownership, land use and employment statistics for the majority of Scotland, is restricted by law concerning the level of detail which can be provided, and as such is both the best available source of information for agricultural practice in Scotland and is partial and incomplete. It is demonstrated that the methodologies applied to the problem (neural network, Bayesian network and decision tree), with a limited number of relevant drivers included in the modelling process, are capable of use for the prediction of changes in land use, suitable for policy analysis. The reasons for selecting these particular modelling approaches included a need to deal with a large amount of noisy, inaccurate data, and the fact that each is capable of successfully investigating and quantifying unknown relationships between dataset variables. The greatest success, measured as a combination of accuracy, data-handling flexibility and ease of model comprehension by the user, was achieved by the decision tree method.