In recent years, an increasing interest has been paid to modelling farm data through spatial econometric techniques. Spatial econometric models deal with the use of both exogenous and endogenous information to capture interaction as well as spillover effects among the data. In this chapter, we focus our attention on models that address spatial dependence and spatial heterogeneity in the analysis of technical efficiency in the context of stochastic frontier.

The first section is devoted to an introduction to the use of spatial models in agricultural economics and to the estimation of agricultural frontier production functions. Then, a spatial stochastic frontier approach is explained as a way to account for production efficiencies while dealing with spatial dependence. The last section describes two ways, namely spatial regimes and spatially constrained clustering, to fit and test spatial models when farm data exhibit spatial heterogeneity. In both the last two sections, the reader’s understanding is supported by the provision of R codes.