This article examines the presence of spatial spillovers in farms' dynamic technical inefficiency scores using Data Envelopment Analysis and a second-stage spatial truncated bootstrap regression model. Dynamic inefficiency is measured in terms of variable input contraction and gross investment expansion, while the secondstage model allows an individual's dynamic inefficiency to be influenced by both own and neighbours' characteristics. The empirical application focuses on the panel data of specialised Dutch dairy farms observed over the period 2009-2016 and for which exact geographical coordinates of latitude and longitude are available. The results confirm the existence of spatial spillovers in farmers' dynamic technical inefficiency levels. Although changes in neighbours' subsidies do not significantly influence an individual's inefficiency, an increase in neighbours' age reduces an individual's performance, while an increase in neighbours' levels of intensification improves an individual's dynamic efficiency.

We find spatial dependence in landowners' stated intentions to make land available for bioenergy crops. Our data are generated from a contingent valuation survey of 599 owners of marginal land in southern Michigan. Employing a Bayesian framework and using these spatially explicit data, we estimate and compare non-spatial probit and spatial Durbin probit models to examine the presence of spatial dependence in land rental intentions. Results show that intentions to rent land for bioenergy crop production are spatially dependent. This spatial dependence arises both from the land supply intentions of nearby landowners and from spatial spillover effects of landowner characteristics and attitudes towards environmental amenities and the disamenities of land rental. We show that ignoring spatial dependence in the intentions of neighbouring landowners to participate in land rental markets for bioenergy feedstocks can lead to distortions that underestimate total effects. Our finding implies that studies of land use and crop supply should test for spatial interactions in order to make accurate inferences.

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In parametric efficiency studies, two alternative approaches exist to provide an estimate of the long-run efficiency of firms: the dynamic stochastic frontier model and the generalised true random-effects model. We extend the former in order to allow for heterogeneity in the long-run technical efficiency of firms. This model is based on potential differences in firm-specific characteristics and in firms' inefficiency persistence. The model is applied to an unbalanced micro-panel of German dairy farms over the period 1999 to 2009. Estimation of long-run technical efficiency and inefficiency persistence is based on an output distance function representation of the production technology and estimated in a Bayesian framework. The results suggest that heterogeneity in long-run technical efficiency of farms is mostly attributed to discrepancies in farm-specific factors rather than differences in farms' inefficiency persistence. Farm size is positively related to long-run technical efficiency while subsidies exert a negative effect on the long-run technical efficiency of farms. Inefficiency persistence is found to be very high, but heterogeneity in this persistence is low.

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