Abstract:This study aims to analyze the direct and indirect impact of future climate changes on agricultural production and macroeconomic aggregates. A dynamic general equilibrium model of the Tunisian economy has been developed, which takes into account the effects of future climate shocks from 2020 to 2050 to assess the impact of future climate change on agricultural production and macroeconomic aggregates. The model is used to simulate various scenarios. The results of the climate shock simulations clearly show that… Show more
Automation has always played a significant role in the productivity and efficiency of agriculture. This paper explores the role of automation on farms' technical efficiency (TE) in an expanding dairy sector. We use a representative sample of Irish dairy farms that includes detailed data on automated technologies. We apply a latent class stochastic frontier model to assess technology heterogeneity amongst Irish dairy farms to obtain their TE scores. We identify two classes of farms: smaller, less intensive and larger, more intensive farms. We find significant differences between the classes in relation to farm characteristics, input use, labour efficiency and TE scores. Larger, more intensive farms produce closer to their stochastic frontier. Our findings also suggest that automation has a heterogeneous effect on farms' TE depending on farms' classification. Specifically, adopting automated cluster removers and scrapers is associated with higher TE on smaller, less intensive farms. In contrast, automated parlour feeders is positively associated with larger, more intensive farms' TE. Finally, the implications of adopting automated technologies on Irish farms are discussed.
Automation has always played a significant role in the productivity and efficiency of agriculture. This paper explores the role of automation on farms' technical efficiency (TE) in an expanding dairy sector. We use a representative sample of Irish dairy farms that includes detailed data on automated technologies. We apply a latent class stochastic frontier model to assess technology heterogeneity amongst Irish dairy farms to obtain their TE scores. We identify two classes of farms: smaller, less intensive and larger, more intensive farms. We find significant differences between the classes in relation to farm characteristics, input use, labour efficiency and TE scores. Larger, more intensive farms produce closer to their stochastic frontier. Our findings also suggest that automation has a heterogeneous effect on farms' TE depending on farms' classification. Specifically, adopting automated cluster removers and scrapers is associated with higher TE on smaller, less intensive farms. In contrast, automated parlour feeders is positively associated with larger, more intensive farms' TE. Finally, the implications of adopting automated technologies on Irish farms are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.