A Pragmatic Assessment of Government Support for Organic Agriculture in Ireland

Drawing on a pragmatic approach, this paper provides an analysis of governmental support for organic farming in Ireland. There are varying levels of encouragement and programmes provided to farmers in their conversion from conventional to organic production, and in their maintenance of organic production. Support policies vary across regions and are linked to European Union legislation, thus it is challenging to document the many types of support in place. This research investigates relevant technical, financial, and policy support available to organic farmers in Ireland. This exploratory study develops an assessment of Ireland within eight key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, and future developments. Information and data from the Irish Department of Agriculture, Fisheries and Food (DAFF), the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies were utilized to assess the level of support in each category. Following the pragmatic approach, this assessment provides key findings which allow policymakers, organizations and citizens to better understand the current situation and set a path for the future development of organic farming in Ireland

Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of parameters of exogenous variables is inconsistent and determine its asymptotic bias. I provide simulation results to evaluate the performance of the MLE. The simulation results indicate that the MLE imposes a substantial amount of bias on both autoregressive and moving average parameters