Technical efficiency and fertilizers use in Italian farms using a machine learning approach
Nicola Galluzzo
Abstract: The Common Agricultural Policy has supported a less intense use of fertilizers and chemicals in agriculture in the next five years (2023 – 2027) because of the European Green Deal proposals that are environmental protection-oriented. The most common consequence of input reduction in farms was a direct effect to the technical efficiency in farm. The main purpose of this research was to assess the technical efficiency in a sample of Italian farm part of the Farm Accountancy Data Network (FADN) dataset using the Data Envelopment Analysis (DEA) input-oriented approach and by the machine learning approach such as the iterative decision tree evaluating which quantity of chemical fertilizer in terms of nitrogen (N), phosphorus (P) and potassium (K) has to be reduced to improve the technical efficiency. Results have pointed out that between a reduction of chemical fertilizers and technical efficiency there is a fundamental link and the drop in chemical fertilizers has impacted the technical efficiency in Italian farms part of FADN dataset. Based on these results emerged the need of putting into practice some actions towards farmers to compensate the reduction in technical efficiency and the produced output in the productive process as well.
Keywords: DEA; FADN; interactive decision tree; nitrogen; type of farmings
Citation: Galluzzo, N. (2024). Technical efficiency and fertilizers use in Italian farms using a machine learning approach. Bulgarian Journal of Agricultural Economics and Management, 69(2) 30-45.
| References: (click to open/close) | Abu, O. (2011). Fertilizer usage and technical efficiency of rice farms under tropical conditions: a Data Envelopment Analysis (DEA). Journal of Agricultural Sciences, 2(2), 83-87.
Arru, B., Furesi, R., Madau, F. A., & Pulina, P. (2019). Recreational services provision and farm diversification: a technical efficiency analysis on Italian agritourism. Agriculture, 9(2), 42.
Asmild, M., Hougaard, J. L., Kronborg, D., & Kvist, H. K. (2003). Measuring inefficiency via potential improvements. Journal of productivity analysis, 19, 59-76.
Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of productivity analysis, 3, 153-169.
Bianchi, M., del Valle, I., & Tapia, C. (2020). Measuring eco-efficiency in European regions: Evidence from a territorial perspective. Journal of Cleaner Production, 276, 123246.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, New York.
Bogetoft, P. & Otto, L. (2011). Benchmarking with DEA, SFA, and R. Springer, New York.
Bogetoft, P., & Hougaard, J. L. (2003). Rational inefficiencies. Journal of Productivity Analysis, 20, 243-271.
Bravo-Ureta, B. E., & Pinheiro, A. E. (1993). Efficiency analysis of developing country agriculture: a review of the frontier function literature. Agricultural and resource economics Review, 22(1), 88-101.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Chavas, J. P., & Aliber, M. (1993). An analysis of economic efficiency in agriculture: A nonparametric approach. Journal of Agricultural and Resource Economics, 18(1), 1-16.
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, New York.
Cooper, W. W., Seiford, L. M., Tone, K., & Zhu, J. (2007). Some models and measures for evaluating performances with DEA: past accomplishments and future prospects. Journal of Productivity Analysis, 28, 151-163.
De Koeijer, T. J., Wossink, G. A. A., Struik, P. C., & Renkema, J. A. (2002). Measuring agricultural sustainability in terms of efficiency: the case of Dutch sugar beet growers. Journal of environmental management, 66(1), 9-17.
De Mauro, A. (2019). Big Data Analytics: Analizzare e interpretare dati con il machine learning. Apogeo Editore, Milano.
Expósito, A., & Velasco, F. (2020). Exploring environmental efficiency of the European agricultural sector in the use of mineral fertilizers. Journal of Cleaner Production, 253, 119971-119986.
Farrel, M. J. (1957). The measure of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253-290.
Fusco, G., Campobasso, F., Laureti, L., Frittelli, M., Valente, D., & Petrosillo, I. (2023). The environmental impact of agriculture: An instrument to support public policy. Ecological Indicators, 147, 109961-109970.
Galluzzo, N. (2021a). Estimation of the impact of CAP subsidies as environmental variables on Romanian farms. Economia Agro-alimentare/Food Economy, 23(3), 1-24.
Galluzzo, N. (2021b). A quantitative analysis on Romanian rural areas, agritourism and the impacts of European Union’s financial subsidies. Journal of Rural Studies, 82, 458-467.
Galluzzo, N. (2022). The relationship between agritourism and social capital in Italian regions. Journal of Rural Studies, 94, 218-226.
Galluzzo, N. (2023). The role of CAP subsidies in all European Union member states: an analysis of technical inefficiency. Bulgarian Journal of Agricultural Economics & Management/Ikonomika i Upravlenie na Selskoto Stopanstvo, 68(3), 3-13.
Huang, W., & Jiang, L. (2019). Efficiency performance of fertilizer use in arable agricultural production in China. China Agricultural Economic Review, 11(1), 52-69.
Kočišová, K. (2015). Application of the DEA on the measurement of efficiency in the EU countries. Agricultural Economics, 61(2), 51-62.
Korhonen, P. J., & Luptacik, M. (2004). Eco-efficiency analysis of power plants: An extension of data envelopment analysis. European journal of operational research, 154(2), 437-446.
Kuhn, M., Weston, S., Keefer, C., & Kuhn, M. M. (2023). Package ‘Cubist’. Rule and Instance Based Regression Modeling. R Package Version 0.4,1 on CRAN package Rsoftware.
Kuosmanen, T., & Kortelainen, M. (2005). Measuring eco‐efficiency of production with data envelopment analysis. Journal of industrial ecology, 9(4), 59-72.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674-2705.
Lovell, C. K. (1993). Production frontiers and productive efficiency. The measurement of productive efficiency: techniques and applications, 3, 67.
Milborrow, S., & Milborrow, M. S. (2022). Package rpart. plot on CRAN package Rsoftware.
Picazo-Tadeo, A. J., Gómez-Limón, J. A., & Reig-Martínez, E. (2011). Assessing farming eco-efficiency: a data envelopment analysis approach. Journal of environmental management, 92(4), 1154-1164.
Prandecki, K., Wrzaszcz, W., & Zieliński, M. (2021). Environmental and climate challenges to agriculture in Poland in the context of objectives adopted in the European Green Deal strategy. Sustainability, 13(18), 10318.
Reinhard, S., Lovell, C. K., & Thijssen, G. (1999). Econometric estimation of technical and environmental efficiency: an application to Dutch dairy farms. American Journal of Agricultural Economics, 81(1), 44-60.
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), 210-229.
Storm, H., Baylis, K., & Heckelei, T. (2020). Machine learning in agricultural and applied economics. European Review of Agricultural Economics, 47(3), 849-892.
Sutton, M. A., Howard, C. M., Erisman, J. W., Billen, G., Bleeker, A., Grennfelt, P., Van Grinsven, H., & Grizzetti, B. (Eds.). (2011). The European nitrogen assessment: sources, effects and policy perspectives. Cambridge University Press, Cambridge.
Therneau, T., Atkinson, B., Ripley, B., & Ripley, M. B. (2015). Package rpart. Available online: cran Rsoftware.
Toma, P., Miglietta, P. P., Zurlini, G., Valente, D., & Petrosillo, I. (2017). A non-parametric bootstrap-data envelopment analysis approach for environmental policy planning and management of agricultural efficiency in EU countries. Ecological indicators, 83, 132-143.
Yadava, A. K. (2023). Technical efficiency of chemical fertilizers use and agricultural yield: Evidence from India. Iranian Economic Review, Forthcoming, 27(1), 121-134.
Yang, Z., & Han, H. (2011). Technical efficiency of fertilizer and its influencing factors: based on wheat and corn empirical study. Journal of China Agricultural University, 16(1), 140-147.
Zhang, B., Bi, J., Fan, Z., Yuan, Z., & Ge, J. (2008). Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological economics, 68(1-2), 306-316.
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| Date published: 2024-06-28
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