✦ LIBER ✦

A neural network for evaluating environmental impact of decoupling in rural systems

✍ Scribed by Andrea Bonfiglio

Publisher: Elsevier Science
Year: 2011
Tongue: English
Weight: 515 KB
Volume: 35
Category: Article
ISSN: 0198-9715
DOI: 10.1016/j.compenvurbsys.2010.06.004

No coin nor oath required. For personal study only.

✦ Synopsis

This paper aims to assess the environmental impact of a specific disposition of the 2003 Common Agricultural Policy reform of the European Union aimed at decoupling governmental payments from production in the agricultural economy of an Italian rural region during the 2005-2007 period. Effects from the removal of policy income support are also evaluated. For this purpose, a Multilayer Feedforward Neural Network is applied to model farmers' behaviour over the period 2003-2007. Results indicate that the choice of decoupling direct payments from production, compared with the hypothesis of maintaining the previous policy regime, allowed a reduction in the use of chemicals in the regional case analysed. Positive environmental effects were relatively higher in farms that introduced crops only for obtaining short-term gains. Moreover, results show that the total removal of direct payments would have brought about stronger environmental benefits than decoupling, by pushing the most penalised farmers to reorient their production towards alternative activities.

📜 SIMILAR VOLUMES

Evaluation of Equilibria with a Use of A

Evaluation of Equilibria with a Use of Artificial Neural Networks (ANN): I. Artificial Neural Networks and Experimental Design as a Tool in Electrochemical Data Evaluation for Fully Inert Metal Complexes

✍ Ignacy Cukrowski; Josef Havel 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 511 KB

MINIMISATION OF DECISION ERRORS IN A PRO

MINIMISATION OF DECISION ERRORS IN A PROBABILISTIC NEURAL NETWORK FOR CHANGE POINT DETECTION IN MECHANICAL SYSTEMS

✍ G.M. LLOYD; M.L. WANG; T.L. PAEZ 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 298 KB

Probabilistic neural nets have been applied in the detection of structural damage. These networks, which rely upon approximating the multivariate density of the training data, have been shown to be e!ective in some applications. However, quanti"cation of decision errors, which must ultimately be use

Integration of neural networks in a geog

Integration of neural networks in a geographical information system for the monitoring of a catchment area

✍ Frédérik Thiery; Stéphane Grieu; Adama Traoré; Mathieu Barreau; Monique Polit 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 850 KB

A layered neural network interfaced with

A layered neural network interfaced with a cochlear model for the study of speech encoding in the auditory system

✍ H. Sheikhzadeh; L. Deng 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 599 KB

Our recent development of a computational cochlear-nucleus-like network model for the study of speech encoding mechanisms associated with parts of the auditory system central to the auditory nerve is described in this paper. This network model is based on physiological grounds, and is designed to gr

Neural network approach for robust and f

Neural network approach for robust and fast calculation of physical processes in numerical environmental models: Compound parameterization with a quality control of larger errors

✍ Vladimir M. Krasnopolsky; Michael S. Fox-Rabinovitz; Hendrik L. Tolman; Alexei A 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 814 KB

Selection of the most significant parame

Selection of the most significant parameters for duration modelling in a Spanish text-to-speech system using neural networks

✍ Ricardo Córdoba; Juan M. Montero; Juana M. Gutiérrez; José A. Vallejo; Emilia En 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 163 KB

Accurate prediction of segmental duration from text in a text-to-speech system is difficult for several reasons. One which is especially relevant is the great quantity of contextual factors that affect timing and it is difficult to find the right way to model them. There are many parameters that aff