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Georeferenced data on biophysical and socio-economic attributes are increasingly being used for decisions regarding priorities of land uses. However, research on the methodological approaches to using spatially referenced biophysical digital data in agricultural and resource economics is limited. Whether this is due to a failure to recognise the full versatility of these data or to some genuine limitations imposed by the data is one of the questions this article addresses. We also review some recent developments in the field and point to research directions in the use of such data in agricultural and resource economics as well as the choice of empirical approaches, such as econometric or programming models, static or dynamic models, and stochastic or deterministic models.


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