Modeling and predicting agricultural land use in England based on spatially high-resolution data

This paper addresses various statistical and empirical challenges associated with modelling farmers' decision-making processes concerning agricultural land-use. These include (i) use of spatially high-resolution data so that idiosyncratic effects of physical environment drivers, e.g. soil textures, can be explicitly modelled; (ii) modelling land-use shares as censored responses, which enables consistent estimation of the unknown parameters; (iii) incorporating spatial error dependence and heterogeneity, which leads to accurate formulation of the variances for the parameter estimates and more effective statistical inferences; and (iv) reducing the computational burden and improving estimation accuracy by introducing an alternative GMM/QML hybrid estimation procedure. We also provide extensive evidence, which suggests that our approach can construct more accurate land-use predictions than existing methods in the literature. We then apply our method to empirically investigate how the climatic, economic, policy and physical determinants influence the land-use patterns in England over time and spatial space. We are also interested in examining whether environmental schemes and grants have assisted in freeing up land used for arable, rough grazing, temporary and permanent grasslands and converting it to bio-energy crops to help to achieve deep emission reductions and prepare for climate change.