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Agricultural Economics and Management Journal   ISSN 0205-3845
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Technical efficiency and assessment of input excess in all European farms by a non-parametric methodology and the conditional inference tree
Nicola Galluzzo
Abstract: The technical efficiency is a fundamental tool in order to evaluate the performances of farms. The main purpose of this study was to investigate the technical efficiency in all European farms part of the FADN dataset using a non-parametric approach such as the DEA. The estimation of the technical efficiency has pointed out a dichotomy between new and older member states of the European Union. The further stage of this research has been addressed in evaluating the excess of input able to impact to technical inefficiency patterns using a new approach such as the Multi-directional Efficiency Analysis. The Multi-directional Efficiency Analysis is a novelty in the literature because it estimates the percentage of excess in input involved in the inefficiency overcoming the main bottleneck in the estimation on efficiency using the DEA. By the estimation of the technical efficiency, it has been possible also to assess the impact of financial subsides allocated by the CAP. Results have corroborated as CAP subsidies have reduced the technical efficiency in farms. In the third stage of this research, it has been possible to assess by the machine learning using the conditional inference tree the excess of input and how the excess impacts on the technical efficiency with an accuracy of more 90%. The conditional inference tree is also a novelty in the literature about the technical efficiency estimation in farms combining the estimation of the technical efficiency with the patterns and reasons of inefficiency.
Keywords: conditional inference tree; DEA; machine learning; Multi-directional Efficiency Analysis; type of farming
Citation: Galluzzo, N. (2023). Technical efficiency and assessment of input excess in all European farms by a non-parametric methodology and the conditional inference tree. Bulgarian Journal of Agricultural Economics and Management, 68(4), 3-16.
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DOI: https://doi.org/10.61308/DWJS6911
Date published: 2023-12-22
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