Post-2013 EU Common Agricultural Policy: predictive models of land use change
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This article presents a multi-temporal uncertainty-based method that incorporates
a statistical regression model with a view to establishing the risk (probability)
of land cover changes as a function of a set of environmental and socio-economic
driving factors. The morphologic, climatic and socio-economic variables were examined
using an Artificial Neural Network (ANN) model and the Multi-Layer Perceptron
(MLP). Following the analysis, maps indicating the suitability to future changes
were generated on the basis of observed transitions. From these maps two possible
land use scenarios were built, applying the Markov chain principle. The region of
Basilicata, in southern Italy, was selected for the analysis. The results highlight: a) a
good inclination to change towards specialised crop systems, provided there is sufficient
water supply; b) that some cropping patterns are not suitable for changes, partly
because they are found in a context with severe limitations for alternative uses.