Abstract
The purpose of this study was to measure and assess comparative study of technical and environmental efficiency of agriculture sector in South Asia using balanced panel data for the period 2002–2016. The translog stochastic frontier analysis approach was applied to estimate output-oriented technical efficiency and input-oriented environmental efficiency. Results of translog production model give that output elasticity w.r.t. land, labor, capital and fertilizer is 2.13, 1.26, 0.01 and 0.17, respectively. Log likelihood test shows that there is technical inefficiency in the agriculture sector of South Asian countries. The average value of output-oriented technical efficiency for South Asian countries was 0.92 ranging from 0.82 to 0.97. This suggests that agricultural production of South Asian region could be increased up to 8 percent by eliminating the effects of technical inefficiency. Moreover, the findings show that Sri Lanka is the most technically efficient country having efficiency 0.99 followed by India (0.98), Bhutan (0.93), Bangladesh (0.89), Nepal (0.88) and Pakistan (0.85). The results of input-oriented environmental efficiency score of South Asia were 0.77 ranging from 0.57 to 0.97. This shows that there is opportunity to enhance environmental efficiency of South Asian countries by 23%. Sri Lanka achieved the highest environmental efficiency (0.97) followed by India (0.96), Bhutan (0.72), Bangladesh (0.71), Pakistan (0.67) and Nepal (0.57). It is recommended that there must be collaboration among the South Asian countries in research and development especially in agriculture sector on priority bases.
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References
Abas, N., Kalair, A., Khan, N., & Kalair, A. R. (2017). Review of GHG emissions in Pakistan compared to SAARC countries. Renewable and Sustainable Energy Reviews, 80, 990–1016.
Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.
Ajibefun, A. I., & Adenegan, K. O. (2008). Impact of policy changes on technical efficiency on farmers: empirical evidence from Nigerian small-scale food crop farmers. Journal of Rural Economics and Development, 17(1), 13–23.
Battese, G. E., & Broca, S. S. (1997). Functional forms of stochastic frontier production functions and models for technical inefficiency effects: a comparative study for wheat farmers in Pakistan. Journal of Productivity Analysis, 8(4), 395–414.
Battese, G. E., & Coelli, T. J. (1988). Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data. Journal of Econometrics, 38(3), 387–399.
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.
Carrer, M. J., de Souza Filho, H. M., Batalha, M. O., & Rossi, F. R. (2015). Farm management information systems (FMIS) and technical efficiency: An analysis of citrus farms in Brazil. Computers and Electronics in Agriculture, 119, 105–111.
Coelli, T. J., Rao, D. S. P., & Battese G.E., (1998). An introduction to efficiency and productivity analysis (pp. 183–218). Massachusetts: Kluwer Academic Publishers.
Coelli, T. (1995a). Estimators and hypothesis tests for a stochastic frontier function: A Monte Carlo analysis. Journal of Productivity Analysis, 6(3), 247–268.
Coelli, T. J. (1995b). Recent developments in frontier modelling and efficiency measurement. Australian Journal of Agricultural Economics, 39(3), 219–245.
Coelli, T. J. (1996). A guide to FRONTIER version 4.1: a computer program for stochastic frontier production and cost function estimation (Vol. 7, pp. 1–33). CEPA Working papers.
Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Berlin: Springer.
Cropper, M. L., & Oates, W. E. (1992). Environmental economics: A survey. Journal of Economic Literature, 30(2), 675–740.
Davidova, S., & Latruffe, L. (2020). Technical efficiency and farm financial management in countries in transition. INRA-ESR Remes, 3(10), 1–38.
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.
FAO (2016). The United Nations Framework Convention on Climate Change (UNFCCC) on Issues relating to agriculture: agricultural practices and technologies (No. CCC/SBSTA/2014/L.14). Rome. https://unfccc.int/files/documentation/submissions.
FAO (2018). Country Indicators. http://www.fao.org/faostat/en/#data.
Färe, R., & Lovell, C. A. K. (1978). Measuring the Technical Efficiency of Production. Journal of Economic Theory, 19(1), 150–162.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253–281.
Farrell, M. J., & Fieldhouse, M. (1962). Estimating efficient production functions under increasing returns to scale. Journal of the Royal Statistical Society: Series A (General), 125(2), 252–267.
Färe, R., Grosskopf, S., & Kokkelenberg, E. C. (1989). Measuring plant capacity, utilization and technical change: A nonparametric approach. International Economic Review, 30(3), 655–666.
Ferrara, G., & Vidoli, F. (2017). Semiparametric stochastic frontier models: A generalized additive model approach. European Journal of Operational Research, 258(2), 761–777.
Gatimbu, K. K., Ogada, M. J., & Budambula, N. L. (2020). Environmental efficiency of small-scale tea processors in Kenya: an inverse data envelopment analysis (DEA) approach. Environment, Development and Sustainability, 22(4), 3333–3345.
Graham, M. (2004). Environmental efficiency: meaning and measurement and application to Australian dairy farms (No. 415–2016–26217).
Hjalmarsson, L., Kumbhakar, S. C., & Heshmati, A. (1996). DEA, DFA and SFA: a comparison. Journal of Productivity Analysis, 7(2–3), 303–327.
ILO (2018). Data Tools and Labor Statistics ILOSTAT, https://ilostat.ilo.org/data/.
Johnson, S. R., Bouzaher, A., Carriquiry, A., Jensen, H., & Lakshminarayan, P. G. (1994). Production efficiency and agricultural reform in Ukraine. American Journal of Agricultural Economics, 76(3), 629–635.
Kopp, R. J. (1981). The measurement of productive efficiency: A reconsideration. The Quarterly Journal of Economics, 96(3), 477–503.
Kuo, H. F., Chen, H. L., & Tsou, K. W. (2014). Analysis of farming environmental efficiency using a DEA model with undesirable outputs. Apcbee Procedia, 10, 154–158.
Le, T. L., Lee, P. P., Peng, K. C., & Chung, R. H. (2019). Evaluation of total factor productivity and environmental efficiency of agriculture in nine East Asian countries. Agric. Econ., 65, 249–258.
Liu, J., Rahman, S., Sriboonchitta, S., & Wiboonpongse, A. (2017). Enhancing productivity and resource conservation by eliminating inefficiency of Thai rice farmers: a zero-inefficiency stochastic frontier approach. Sustainability, 9(5), 1–18.
Liu, Y., Yan, B., Wang, Y., & Zhou, Y. (2019). Will land transfer always increase technical efficiency in China?—A land cost perspective. Land Use Policy, 82, 414–421.
Long, X., Luo, Y., Sun, H., & Tian, G. (2018). Fertilizer using intensity and environmental efficiency for China’s agriculture sector from 1997 to 2014. Natural Hazards, 92, 1573–1591. https://doi.org/10.1007/s11069-018-3265-4.
Mathijs, E., & Swinnen, J. F. (2001). Production organization and efficiency during transition: An empirical analysis of East German agriculture. Review of Economics and Statistics, 83(1), 100–107.
Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444.
Moreno-Moreno, J. J., Morente, F. V., & Diaz, M. T. S. (2018). Assessment of the operational and environmental efficiency of agriculture in Latin America and the Caribbean. Agricultural Economics, 64(2), 74–88.
Ouyang, W., & Yang, J. B. (2020). The network energy and environment efficiency analysis of 27 OECD countries: A multiplicative network DEA model. Energy, 197(C), 117161.
Pais-Magalhães, V., Moutinho, V., & Marques, A. C. (2020). Scoring method of eco-efficiency using the DEA approach: evidence from European waste sectors. Environment, Development and Sustainability,. https://doi.org/10.1007/s10668-020-00709-x.
Pitt, M. M., & Lee, L. F. (1981). The measurement and sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9(1), 43–64.
Pittman, R. W. (1981). Issue in pollution control: interplant cost differences and economies of scale. Land Economics, 57(1), 1–17.
Pittman, R. W. (1983). Multilateral productivity comparisons with undesirable outputs. The Economic Journal, 93(372), 883–891.
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.
Reinhard, S., Lovell, C. K., & Thijssen, G. J. (2000). Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. European Journal of Operational Research, 121(2), 287–303.
Sriboonchitta, S., Liu, J., Wiboonpongse, A., & Denoeux, T. A. (2017). Double-copula stochastic frontier model with dependent error components and correction for sample selection. International Journal Approximate Reasoning, 80, 174–184.
Tirado, R., Englande, A.J., Promakasikorn, L., & Novotny, V. (2017). Use of agrochemicals in Thailand and its consequences for the environment. Greenpeace Research Laboratories Technical Note. 2008. Available on line: https://www.greenpeace.to/publications/GPSEA_agrochemical-use-in-thailand.pdf.
Trang, N., Khai, H., Tu, H., & Hong, N. (2018). Environmental efficiency of transformed farming systems: A case study of change from sugarcane to shrimp in the Vietnamese Mekong Delta. Forestry Research and Engineering: International Journal, 2, 54–60.
Tsionas, E. G. (2003). Combining DEA and stochastic frontier models: An empirical Bayes approach. European Journal of Operational Research, 147(3), 499–510.
Tu, V. H. (2017). Resource use efficiency and economic losses: implications for sustainable rice production in Vietnam. Environment, development and sustainability, 19(1), 285–300.
Tu, V. H., Can, N. D., Takahashi, Y., Kopp, S. W., & Yabe, M. (2018). Modelling the factors affecting the adoption of eco-friendly rice production in the Vietnamese Mekong Delta. Cogent Food & Agriculture, 4(1), 1–24.
Tu, V. H., Can, N. D., Takahashi, Y., Kopp, S. W., & Yabe, M. (2019). Technical and environmental efficiency of eco-friendly rice production in the upstream region of the Vietnamese Mekong delta. Environment, Development and Sustainability, 21(5), 2401–2424.
Ullah, A., Khan, D., & Zheng, S. (2017). The determinants of technical efficiency of peach growers: evidence from Khyber Pakhtunkhwa Pakistan. Custos e Agronegocio On Line, 13(4), 211–238.
Ullah, A., & Khan, D. (2020). Testing environmental Kuznets curve hypothesis in the presence of green revolution: a cointegration analysis for Pakistan. Environmental Science and Pollution Research, 27(10), 11320–11336.
Vo, H. T., Yabe, M., Trang, N. T., & Khai, H. V. (2015). Environmental efficiency of ecologically engineered rice production in the Mekong Delta of Vietnam. Journal of the Faculty of Agriculture Kyushu University, 60(2), 493–500.
Vu, T. H., Peng, K. C., & Chung, R. H. (2019). Evaluation of Environmental Efficiency of Edible Canna Production in Vietnam. Agriculture, 9(11), 1–14.
Wang, K., Yu, S., & Zhang, W. (2013). China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation. Mathematical and Computer Modelling, 58(5–6), 1117–1127.
World Bank (2019). Data Bank. Retrieved from https://databank.worldbank.org/.
Yang, L., Ma, C., Yang, Y., Zhang, E., & Lv, H. (2020). Estimating the regional eco-efficiency in China based on bootstrapping by-production technologies. Journal of Cleaner Production, 243, 118550.
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Bibi, Z., Khan, D. & Haq, I.u. Technical and environmental efficiency of agriculture sector in South Asia: a stochastic frontier analysis approach. Environ Dev Sustain 23, 9260–9279 (2021). https://doi.org/10.1007/s10668-020-01023-2
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DOI: https://doi.org/10.1007/s10668-020-01023-2