Title: Modelling farm structural change A feasibility study for ex-post modelling utilizing FADN and FSS data in Germany and developing an ex-ante forecast module for the CAPRI farm type layer baseline
Authors: GOCHT AlexanderRODER NorbertNEUENFELDT SebastianSTORM HugoHECKELEI Thomas
Editors: ESPINOSA GODED MARIA
GOMEZ Y PALOMA Sergio
Publisher: Publications Office of the European Union
Publication Year: 2012
JRC N°: JRC75524
ISBN: 978-92-79-27047-5
ISSN: 1831-9424
Other Identifiers: EUR 25555 EN
OPOCE LF-NA-25555-EN-N
URI: http://ipts.jrc.ec.europa.eu/publications/pub.cfm?id=5759
http://publications.jrc.ec.europa.eu/repository/handle/JRC75524
DOI: 10.2791/17545
Type: EUR - Scientific and Technical Research Reports
Abstract: The present study aims to develop a prototype analytical tool to assess structural changes at the farm level in EU-27 using the Farm Accountancy Data Network (FADN) combined with the Farm Structure Survey (FSS). For the purpose of this study, farm structural change is related to the change in production systems, therefore a change in farm size and farm entry/exit into one sector/farm typology. In the ex-post analysis of structural change two methodologies are presented, one in which structural change is analysed from a discrete perspective using a Markov approach, whereas the second uses the continuous perspective to evaluate the type of farming over time using MCI (Multiplicative Competitive Interaction) models. The methodolgies are applied in selected German regions and the goodness of fit in the out of sample prediction is compared. In the ex-ante methodology, the existing farm module of CAPRI (Common Agricultural Policy Regionalised Impact System) is expanded by considering the findings of the statistical ex-post-analysis when projecting farm-type structural change in the baseline trends. Results show that the Markov prediction may outperform naïve prediction methods but that the quality of the prediction is critically dependent on the model specification. A higher in-sample fit does not necessarily lead to better out-of-sample prediction, which potentially indicates that the effects of specific explanatory variables may change over time. In addition, introducing structural change into the CAPRI farm type baseline improve policy impact assessment and hence a more reliable and consistent farm grid for simulations is constructed.
JRC Directorate:Growth and Innovation

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