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Analysis of efficiency in organic wine and olive farms in the Italian FADN dataset

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  • Galluzzo, Nicola

Abstract

Over the recent years there has been in Italy a growth of organic farms with a positive consequence in increasing the farmer’s income by a direct commercialization of products. The analysis has used a quantitative model of investigation in a dataset of organic and conventional farms belonging to the Farm Accountancy Data Network (FADN). The organic farms have underscored an inferior level of efficiency than conventional ones underling as land capital and labor force may be two pivotal variables in improving the level of economic and allocative efficiency in organic farms.

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  • Galluzzo, Nicola, 2014. "Analysis of efficiency in organic wine and olive farms in the Italian FADN dataset," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182978, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaae14:182978
    DOI: 10.22004/ag.econ.182978
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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    2. Laure Latruffe & Céline Nauges, 2014. "Technical efficiency and conversion to organic farming: the case of France," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 41(2), pages 227-253.
    3. Cisilino, Federica & Madau, Fabio A., 2007. "Organic and Conventional Farming: a Comparison Analysis through the Italian FADN," MPRA Paper 21786, University Library of Munich, Germany.
    4. Madau, Fabio A., 2007. "Technical Efficiency in Organic and Conventional Farming: Evidence from Italian Cereal Farms," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 8(1), pages 1-17, January.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Battese, George E., 1992. "Frontier production functions and technical efficiency: a survey of empirical applications in agricultural economics," Agricultural Economics, Blackwell, vol. 7(3-4), pages 185-208, October.
    7. Tzouvelekas, Vangelis & Pantzios, Christos J. & Fotopoulos, Christos, 2002. "Empirical Evidence of Technical Efficiency Levels in Greek Organic and Conventional Farms," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 3(2), pages 1-12, August.
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