Technical efficiency and assessment of input excess in all European farms by a non-parametric methodology and the conditional inference tree
Nicola Galluzzo
Резюме: 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.
Ключови думи: conditional inference tree; DEA; machine learning; Multi-directional Efficiency Analysis; type of farming
Цитиране: 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.
Литература: (click to open/close) | Alemdar, T., & Necat Oren, M. (2006). Determinants of technical efficiency of wheat farming in Southeastern Anatolia, Turkey: a nonparametric technical efficiency analysis. Journal of Applied sciences, 6(4), 827-830. Alvarez, A., & Arias, C. (2004). Technical efficiency and farm size: a conditional analysis. Agricultural Economics, 30(3), 241-250. Asmild, M., Hougaard, J. L., Kronborg, D., & Kvist, H. K. (2003). Measuring inefficiency via potential improvements. Journal of productivity analysis, 19, 59-76. Badunenko, O., & Mozharovskyi, P. (2016). Nonparametric frontier analysis using Stata. The Stata Journal, 16(3), 550-589. Bakucs, L. Z., Latruffe, L., Fertő, I., & Fogarasi, J. (2010). The impact of EU accession on farms’ technical efficiency in Hungary. Post-communist economies, 22(2), 165-175. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092. Baráth, L., Fertő, I., & Bojnec, Š. (2020). The effect of investment, LFA and agri-environmental subsidies on the components of total factor productivity: the case of Slovenian farms. Journal of Agricultural Economics, 71(3), 853-876. Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical economics, 20, 325-332. Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, Berlin. Bogetoft, P., & Hougaard, J. L. (2003). Rational inefficiencies. Journal of Productivity Analysis, 20(3), 243-271. Bojnec, Š., & Fertő, I. (2013). Farm income sources, farm size and farm technical efficiency in Slovenia. Post- Communist Economies, 25(3), 343-356. Bojnec, Š., & Fertő, I. (2019). Do CAP subsidies stabilise farm income in Hungary and Slovenia? Agricultural Economics, 65(3), pp. 103-111. Bojnec, Š., & Latruffe, L. (2009). Determinants of technical efficiency of Slovenian farms. Post-Communist Economies, 21(1), 117-124. Bojnec, Š., & Latruffe, L. (2013). Farm size, agricultural subsidies and farm performance in Slovenia. Land use policy, 32, 207-217. Bravo-Ureta, B. E., Solís, D., Moreira López, V. H., Maripani, J. F., Thiam, A., & Rivas, T. (2007). Technical efficiency in farming: a meta-regression analysis. Journal of productivity Analysis, 27, 57-72. Chebil, A., Frija, A., & Thabet, C. (2015). Economic efficiency measures and its determinants for irrigated wheat farms in Tunisia: a DEA approach. New Medit, 14(2), 32-38. Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79-96. Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Springer science & business media. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. New York: Springer. De Castris, M., & Di Gennaro, D. (2017). What is below the CAP? Evaluating spatial patterns in agricultural subsidies. In: XXXVIII Annual Scientific Conference of the AIS Re. Italian Association of Regional Science, Cagliari, Italy. De Mauro, A. (2019). Big data analytics: guida per iniziare a classificare e interpretare dati con il machine learning. Apogeo, Milano. Dhungana, B. R., Nuthall, P. L., & Nartea, G. V. (2004). Measuring the economic inefficiency of Nepalese rice farms using data envelopment analysis. Australian Journal of Agricultural and Resource Economics, 48(2), 347-369. Fraser, I., & Cordina, D. (1999). An application of data envelopment analysis to irrigated dairy farms in Northern Victoria, Australia. Agricultural Systems, 59(3), 267-282. Galluzzo, N. (2013). Farm dimension and efficiency in Italian agriculture: a quantitative approach. American Journal of Rural Development, 1(2), 26-32. Galluzzo, N. (2018). Impact of the Common Agricultural Policy payments towards Romanian farms. Bulgarian Journal of Agricultural Science, 24(2), pp. 199-205. Galluzzo, N. (2019). A long-term analysis of the common agricultural policy financial subsidies towards Italian farms. Ukrainian journal of veterinary and agricultural sciences, 2(1), 12-17. Galluzzo, N. (2020). A technical efficiency analysis of financial subsidies allocated by the CAP in Romanian farms using stochastic frontier analysis. European Countryside, 12(4), 494-505. Galluzzo, N. (2021). A quantitative analysis on Romanian rural areas, agritourism and the impacts of European Union’s financial subsidies. Journal of Rural Studies, 82, 458-467. Garrone, M., Emmers, D., Lee, H., Olper, A., & Swinnen, J. (2019). Subsidies and agricultural productivity in the EU. Agricultural Economics, 50(6), 803-817. Gunes, E., & Guldal, H. T. (2019). Determination of economic efficiency of agricultural enterprises in Turkey: a DEA approach. New Medit, 18(4), 105-115. Guth, M., & Smędzik-Ambroży, K. (2020). Economic resources versus the efficiency of different types of agricultural production in regions of the European Union. Economic research-Ekonomska istraživanja, 33(1), 1036-1051. Guth, M., Smędzik-Ambroży, K., Czyżewski, B., & Stępień, S. (2020). The economic sustainability of farms under common agricultural policy in the European Union countries. Agriculture, 10(2), 34. Hansson, H., Manevska-Tasevska, G., Asmild, M. (2020). Rationalising inefficiency in agricultural production – the case of Swedish dairy agriculture. European Review of Agricultural Economics, 47(1), 1-24. Igwe, O. O., Nwaogu, D. C., & Onyegbule, F. (2017). Technical efficiency of poultry enterpreneurs in Abia state: a stochastic frontier approach. Scientific Papers: Management, Economic Engineering in Agriculture & Rural Development, 17(1). Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda. Kovács, K., & Emvalomatis, G. (2011). Dutch, Hungarian and German dairy farms technical efficiency comparison. APSTRACT: Applied Studies in Agribusiness and Commerce, 5(1033-2016-84134), 121-128. Kovács, K., Juračak, J., Očić, V., Burdiuzha, A., & Szűcs, I. (2022). Evaluation of technical efficiency of Hungarian and Croatian livestock sectors using DEA on FADN data. Journal of Central European Agriculture, 23(4), 909-920. Kumbhakar, S. C., Wang, H. J., Horncastle, A. P. (2015). A practitioner’s guide to stochastic frontier analysis using Stata. Cambridge University Press. Cambridge. Latruffe, L., Balcombe, K., Davidova, S., & Zawalinska, K. (2004). Determinants of technical efficiency of crop and livestock farms in Poland. Applied economics, 36(12), 1255-1263. Latruffe, L., Bravo-Ureta, B. E., Carpentier, A., Desjeux, Y., & Moreira, V. H. (2017). Subsidies and technical efficiency in agriculture: Evidence from European dairy farms. American Journal of agricultural economics, 99(3), 783-799. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. Manevska-Tasevska, G., Hansson, H., Asmild, M., & Surry, Y. (2021). Exploring the regional efficiency of the Swedish agricultural sector during the CAP reforms‒ multi-directional efficiency analysis approach. Land Use Policy, 100, 104897. Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1, 100010. Minviel, J. J., & Latruffe, L. (2017). Effect of public subsidies on farm technical efficiency: a meta-analysis of empirical results. Applied Economics, 49(2), 213-226. Nowak, A., Kijek, T., & Domańska, K. (2015). Technical efficiency and its determinants in the European Union. Agricultural Economics, 61(6), 275-283. Pallathadka, H., Mustafa, M., Sanchez, D. T., Sajja, G. S., Gour, S., & Naved, M. (2023). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proceedings, 80, 2803-2806. Popescu, A., Dinu, T. A., & Stoian, E. (2019). Efficiency of the agricultural land use in the European Union. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development, 19(3), 475-486. Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), pp. 210-229. Stetter, C., Mennig, P., & Sauer, J. (2022). Using machine learning to identify heterogeneous impacts of agri-environment schemes in the EU: a case study. European Review of Agricultural Economics, 49(4), 723-759. Storm, H., Baylis, K., & Heckelei, T. (2020). Machine learning in agricultural and applied economics. European Review of Agricultural Economics, 47(3), 849-892. Todorović, S., Papić, R., Ciaian, P., & Bogdanov, N. (2020). Technical efficiency of arable farms in Serbia: do subsidies matter? New Medit: Mediterranean Journal of Economics, Agriculture and Environment = Revue Méditerranéenne dʹEconomie Agriculture et Environment, 19(4). Yu, X., & Maruejols, L. (2023). Prediction, pattern recognition and machine learning in agricultural economics. China Agricultural Economic Review, 15(2), 375-378. Zhu, X., & Lansink, A. O. (2010). Impact of CAP subsidies on technical efficiency of crop farms in Germany, the Netherlands and Sweden. Journal of Agricultural Economics, 61(3), 545-564. |
|
| Дата на публикуване: 2023-12-22
Свали пълен текст