Predicting operational and environmental efficiency of primary sectors of EU countries, by implementing Artificial Neural Networks

Abstract

One of the most important policy reforms for the European Union (EU) agriculture was the implementation of the Agenda 2000, which establishes a new framework for subsidies management, decoupled from both crop and animal production for the vast majority of products. One of the main goals of this new policy framework is the improvement of its environmental impact. Additionally, there is a need for the implementation of new efficiency assessment and prognostication tools for the evaluation of EU farming, because the influence of market forces has been increased substantially. Regarding prognostication of crop and animal output, as well as Green House Gas (GHG) emissions, the application of Artificial Neural Networks (ANNs) is being proposed, succeeding satisfactory quality characteristics for the models being proposed for operational and environmental predictions in EU agriculture. Copyright © 2017 for this paper by its author