How does eliminating the use of pesticides affect technical efficiency in Italian farms
Keywords:
agri-environmental policy, DEA, separability, two-stage DEA, type of farmingAbstract
Through specific legislation such as Regulation no. 2078/92 and policies like Agenda 2000, the European Union has directed financial support towards farms that are adapting their production processes to use less fertilisers and pesticides and which, consequently, have a lower impact on the environment. In this vein, European farmers are being incentivised to follow organic farming methods and participate in other agri-environmental policies aimed at reducing the use of chemicals in agriculture. In the literature, this concern with the use of pesticides is reflected by several studies that have used a quantitative approach to investigate the environmental effects of chemical inputs and the impact of agri-environmental subsidies on technical efficiency in farms. Nevertheless, there is a gap in knowledge in assessing the effect on technical efficiency in farms of completely eliminating the use of chemical products. The purpose of this research, then, was to analyse the effect on technical efficiency of a radical reduction in the use of pesticides in Italian farms, using sample data from the Farm Accountancy Data Network (FADN) for the period 2004 to 2019, investigated in function of the different specialisation and economic size of the farms included in the data set. The level of technical efficiency in the farms included in the sample has been assessed through a non-parametric approach, using Data Envelopment Analysis in two stages, both with and then without the use of pesticides. The research findings have shown that a radical reduction in pesticides in Italian farms has had a direct impact on farms, reducing their level of technical efficiency. In particular, the results show that farms of a small economic size have suffered the greatest drop in technical efficiency. Meanwhile, the specialisation of the farm production has differently influenced the effect of a reduction in the use of chemical pesticides and fertilisers.
References
Aigner, D., Lovell, C. K. & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37.
Asmild, M., Hougaard, J. L., Kronborg, D. & Kvist, H. K. (2003). Measuring inefficiency via potential improvements. Journal of Productivity Analysis, 19(1), 59-76.
Bădin, L., Daraio, C. & Simar, L. (2012). How to measure the impact of environmental factors in a nonparametric production model. European Journal of Operational Research, 223(3), 818-833.
Bakker, L., Sok, J., Van Der Werf, W. & Bianchi, F. J. J. A. (2021). Kicking the habit: what makes and breaks farmers’ intentions to reduce pesticide use? Ecological Economics, 180, 106868.
Balezentis, T. (2014). On measures of the agricultural efficiency: a review. Transformations in Business & Economics, 13(3), 110-131.
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.
Barzman, M. & Dachbrodt-Saaydeh, S. (2011). Comparative analysis of pesticide action plans in five European countries. Pest Management Science, 67(12), 1481-1485.
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(1-2), 153-169.
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(2), 325-332.
Bertoni, D. & Olper, A. (2012). The political economy of agrienvironmental measures: an empirical assessment at the EU regional level. Applied Studies in Agribusiness and Commerce, 6(3-4), 71-82.
Bogetoft, P. & Hougaard, J. L. (2003). Rational inefficiencies. Journal of Productivity Analysis, 20(3), 243-271.
Bogetoft, P., & Otto, L. (2010). Benchmarking with DEA, SFA, and R (Vol. 157). Springer Science & Business Media, 1-347.
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, 429-444.
Chèze, B., David, M. & Martinet, V. (2020). Understanding farmers’ reluctance to reduce pesticide use: A choice experiment. Ecological Economics, 167, 106349.
Coelli, T. J., Rao, D. S. P., O’Donnell, C.J. & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Springer Verlag, Berlin ,1-349
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(3), 151-163.
Cowan, R. & Gunby, P. (1996). Sprayed to death: path dependence, lock-in and pest control strategies. The Economic Journal, 106(436), 521-542.
Damalas, C. A. (2021) Farmers’ intention to reduce pesticide use: the role of perceived risk of loss in the model of the planned behaviour theory. Environ. Sci. Pollut. Res., 28, 35278–35285.
Daraio, C. & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: a probabilistic approach. Journal of Productivity Analysis, 24(1), 93-121.
Daraio, C., Simar, L. & Wilson, P. W. (2018). Central limit theorems for conditional efficiency measures and tests of the ‘separability’condition in non-parametric, two-stage models of production. The Econometrics Journal, 21(2), 170-191.
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.
Donati, A., Giolitti, A., Marchettini, N., Rossi, C., Tiezzi, E. & Ulgiati, S. (1993). Environmental aspects of pesticide use in Italian agriculture. Science of the Total Environment, 129(1-2), 125-135.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
Galluzzo, N. (2013). Farm dimension and efficiency in Italian agriculture: a quantitative approach. American Journal of Rural Development, 1(2), 26-32.
Galluzzo, N. (2019). An analysis of technical efficiency in Icelandic dairy and sheep farms. Studies in Agricultural Economics, 121(3), 144-150.
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.
Gren, I. M. (1994). Regulating the Farmers’ use of Pesticides in Sweden. In: Opschoor, H. & Turner, K. (Eds.) Economic Incentives and Environmental Policies. Environment, Science and Society, Vol 1. Springer, Dordrecht.
Hansson, H., Manevska-Tasevska, G. & Asmild, M. (2019). Rationalising inefficiency in agricultural production – the case of Swedish dairy agriculture. European Review of Agricultural Economics, 47(1), 1-24.
Kourtesi, S., Fousekis, P. & Polymeros, A. (2012). Conditional efficiency estimation with environmental variables: evidence from Greek cereal farms. Scientific Bulletin-Economic Sciences, 11(1), 43-52.
Kumbhakar, S. C., Wang, H. J. & Horncastle, A. P. (2015). A practitioner’s guide to stochastic frontier analysis using Stata. Cambridge University Press,1-359
Lankoski, J. & Thiem, A. (2020). Linkages between agricultural policies, productivity and environmental sustainability. Ecological Economics, 178, 106809.
Latruffe, L. & Desjeux, Y. (2016). Common Agricultural Policy support, technical efficiency and productivity change in French agriculture. Review of Agricultural, Food and Environmental Studies, 97(1), 15-28.
Lee, R., den Uyl, R. & Runhaar, H. (2019). Assessment of policy instruments for pesticide use reduction in Europe; Learning from a systematic literature review. Crop Protection, 126, 104929.
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.
Mennig, P. & Sauer, J. (2020). The impact of agri-environment schemes on farm productivity: a DID-matching approach. European Review of Agricultural Economics, 47(3), 1045-1093.
Mikus, O., Vrtar, D., Hadelan, L., Susac, M. Z. & Rogelj, M. J. (2021). Policy impact and factors of farmers’ participation in agri-Environmental measures. Scientific Papers: Management, Economic Engineering in Agriculture & Rural Development, 21(1), 517-524.
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.
Möhring, N., Ingold, K., Kudsk, P., Martin-Laurent, F., Niggli, U., Siegrist, M., Studer, B., Walter, A. & Finger, R. (2020). Pathways for advancing pesticide policies. Nature Food, 1(9), 535-540.
Montanarella, L. & Panagos, P. (2021). The relevance of sustainable soil management within the European Green Deal. Land Use Policy, 100, 104950.
Nowak, A., Kijek, T. & Domańska, K. (2015): Technical efficiency and its determinants in the European Union. Agric. Econ. Czech, 61, 275-283.
Nowak, A. & Kubik, R. (2019). Changes in agricultural productivity in new and old member states of the European Union. European Research Studies Journal, 22(4), 101-114.
Onãte, J. J., Andersen, E., Peco, B. & Primdahl, J. (2000). Agrienvironmental schemes and the European agricultural landscapes: the role of indicators as valuing tools for evaluation. Landscape Ecology, 15, 271-280.
Petrescu-Mag, R. M., Banatean-Dunea, I., Vesa, S. C., Copacinschi, S. & Petrescu, D. C. (2019). What do Romanian farmers think about the effects of pesticides? Perceptions and willingness to pay for bio-pesticides. Sustainability, 11(13), 3628.
Simar, L. & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31-64.
Simar, L. & Wilson, P. W. (2011). Two-stage DEA: caveat emptor. Journal of Productivity Analysis, 36(2), 205-218.
Simar, L. & Wilson, P.W. (2015). Statistical approaches for nonparametric frontier models: a guided tour. International Statistical Review, 83(1), 77-110.
Singbo, A. G., Lansink, A. O. & Emvalomatis, G. (2015). Estimating shadow prices and efficiency analysis of productive inputs and pesticide use of vegetable production. European Journal of Operational Research, 245(1), 265-272.
Skevas, T. & Lansink, A. O. (2014). Reducing pesticide use and pesticide impact by productivity growth: the case of Dutch arable farming. Journal of Agricultural Economics, 65(1), 191-211.
Skevas, T., Lansink, A. O. & Stefanou, S. E. (2012). Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms. European Journal of Operational Research, 223(2), 550-559.
Skevas, T., Stefanou, S.E., & Lansink, A.O. (2014). Pesticide use, environmental spillovers and efficiency: A DEA risk-adjusted efficiency approach applied to Dutch arable farming. European Journal of Operational Research, 237(2), 658-664.
Vanloqueren, G. & Baret, P. (2009). How agricultural research system shape a technological regime that develops genetic engineering but locks out agro-ecological innovations. Research Policy, 38, 971-983.
Wang, H. J. & Schmidt, P. (2002). One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis, 18(2), 129-144.
Wrzaszcz, W. & Prandecki, K. (2020). Agriculture and The European Green Deal. Zagadnienia Ekonomiki Rolnej/ Problems of Agricultural Economics, 4(365), 156-179.
Zheng, W., Luo, B. & Hu, X. (2020). The determinants of farmers’ fertilizers and pesticides use behavior in China: An explanation based on label effect. Journal of Cleaner Production, 272, 123054.
Zimmermann, A. & Britz, W. (2016). European farms’ participation in agri-environmental measures. Land Use Policy, 50, 214-228.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Bulgarian Journal of Agricultural Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
