Measuring Economic Efficiency of Kidney Bean Production using Non-Discretionary Data Envelopment Analysis

Document Type: Research Paper


1 Department of Agricultural Engineering, Faculty of Agriculture, Yasouj University, Yasouj, Iran.

2 Indian Institute of Management, Lucknow, India



Efficient use of assets in agriculture is a goal for policy-makers and farmers. Agricultural input resources are scarce therefore optimum use of inputs in different agricultural operations is important. Mathematical programming technique such as data envelopment analysis (DEA) is a well-known approach for estimation efficiency of agricultural DMUs. In this study, efficiency of kidney bean production in twelve provinces of Iran has been studied. Inputs were cost of tillage, planting, cultivation, harvesting and land. Output included total production value of kidney bean. Land cost is a non-controllable variable. Therefore; a non-discretionary DEA approach was applied to estimate efficiency of kidney bean production. The average value of technical efficiency score of kidney bean production was 0.74. Results showed that 58 percent of DMUs were efficient and the rest were inefficient. In optimum condition based on the proposed model, tillage, planting, cultivation and harvesting costs is decreased by 34.48%, 11.92%, 27.87% and 7.27%, respectively, without decreasing kidney bean production level.


[1].  Khoshroo, A., Energy use pattern and greenhouse gas emission of wheat production: a case study in Iran, Agricultural Communications, 2014, 2(2), P. 9-14.

[2].  Charnes, A., Cooper, W. W., Rhodes, E., Measuring the efficiency of decision making units, European journal of operational research, 1978, 2(6), P. 429-444.

[3].  Tone, K., Toloo, M., Izadikhah, M., A modified slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 2020, 287(2), P. 560-571.

[4].  Kao, C., Measuring efficiency in a general production possibility set allowing for negative data, European Journal of Operational Research, 2020, 282(3), P. 980-988.

[5].  Izadikhah, M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 27-41.

[6].  Emrouznejad, A., Yang, G.-l., A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016, Socio-Economic Planning Sciences, 2018, 61, P. 4-8.

[7].  Jafari, M., Mousavi, M., Performance analysis and rating of insurance companies using DEA in Iran capital market, Advances in Mathematical Finance and Applications, 2017, 2(3), P. 41-50.

[8].  Akbari, S., Heydari, J., Keramati, M., Keramati, A., Designing A Mixed System of Network DEA for Evaluating the Efficiency of Branches of Commercial Banks in Iran, Advances in Mathematical Finance and Applications, 2019, 4(1), P. 1-13.

[9].  Muñiz, M. A., Separating managerial inefficiency and external conditions in data envelopment analysis, European Journal of Operational Research, 2002, 143(3), P. 625-643.

[10].  Khoshroo, A., Mulwa, R., Emrouznejad, A., Arabi, B., A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production, Energy, 2013, 63, P. 189-194.

[11].  Khoshroo, A., Mulwa, R., Improving Energy Efficiency Using Data Envelopment Analysis: A Case of Walnut Production. In Managing Service Productivity, 2014, pp 227-240, Springer,

[12].  Mulwa, R., Emrouznejad, A., Muhammad, L., Economic efficiency of smallholder maize producers in Western Kenya: a DEA meta-frontier analysis, International Journal of Operational Research, 2009, 4(3), P. 250-267.

[13].  Izadikhah, M., Khoshroo, A., Energy management in crop production using a novel Fuzzy Data Envelopment Analysis model, RAIRO-Operations Research, 2018, 52(2), P. 595-617.

[14].  Khoshroo, A., Izadikhah, M., Improving efficiency of farming products through benchmarking and data envelopment analysis, International Journal of Management and Decision Making, 2019, 18(1), P. 15-30.

[15].  Mousavi-Avval, S. H., Rafiee, S., Mohammadi, A., Optimization of energy consumption and input costs for apple production in Iran using data envelopment analysis, Energy, 2011, 36(2), P. 909-916.

[16].  Mohammadi, A., Rafiee, S., Jafari, A., Dalgaard, T., Knudsen, M. T., Keyhani, A., Mousavi-Avval, S. H., Hermansen, J. E., Potential greenhouse gas emission reductions in soybean farming: a combined use of life cycle assessment and data envelopment analysis, Journal of Cleaner Production, 2013, 54, P. 89-100.

[17].  Izadikhah, M., Saen, R. F., Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data, Transportation Research Part D: Transport and Environment, 2016, 49, P. 110-126.

[18].  Izadikhah, M., Saen, R. F., A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis, Journal of Cleaner Production, 2016, 137, P. 1347-1367.

[19].  Ruggiero, J., Non-discretionary inputs in data envelopment analysis, European Journal of Operational Research, 1998, 111(3), P. 461-469.

[20].  Khoshroo, A., Emrouznejad, A., Ghaffarizadeh, A., Kasraei, M., Omid, M., Sensitivity analysis of energy inputs in crop production using artificial neural networks, Journal of cleaner production, 2018, 197, P. 992-998.

[21].  Dhungana, B. R., Nuthall, P. L., Nartea, G. V., Measuring the economic inefficiency of Nepalese rice farms using data envelopment analysis, Australian Journal of Agricultural and Resource Economics, 2004, 48(2), P. 347-369.

[22].  Al-Mezeini, N. K., Oukil, A., Al-Ismaili, A. M., Investigating the efficiency of greenhouse production in Oman: A two-stage approach based on Data Envelopment Analysis and double bootstrapping, Journal of Cleaner Production, 2020, 247, P. 119160.

[23].  Maina, F., Mburu, J., Gitau, G., VanLeeuwen, J., Negusse, Y., Economic efficiency of milk production among smallscale dairy farmers in Mukurweini, Nyeri County, Kenya, Journal of Development and Agricultural Economics, 2018, 10(5), P. 152-158.

[24].  Khoshroo, A., Izadikhah, M., Emrouznejad, A., Improving energy efficiency considering reduction of CO2 emission of turnip production: A novel data envelopment analysis model with undesirable output approach, Journal of Cleaner Production, 2018, 187, P. 605-615.

[25].  Ebrahimi, R., Salehi, M., Investigation of CO2 emission reduction and improving energy use efficiency of button mushroom production using Data Envelopment Analysis, Journal of Cleaner Production, 2015, 103, P. 112-119.

[26].  Hosseinzadeh-Bandbafha, H., Safarzadeh, D., Ahmadi, E., Nabavi-Pelesaraei, A., Hosseinzadeh-Bandbafha, E., Applying data envelopment analysis to evaluation of energy efficiency and decreasing of greenhouse gas emissions of fattening farms, Energy, 2017, 120, P. 652-662.

[27].  Harrison, J., Rouse, P., Armstrong, J., Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models, Journal of Productivity Analysis, 2012, 37(3), P. 261-276.

[28].  Lotfi, F. H., Jahanshahloo, G. R., Esmaeili, M., Sensitivity analysis of efficient units in the presence of non-discretionary inputs, Applied mathematics and computation, 2007, 190(2), P. 1185-1197.

[29].  Syrjänen, M. J., Non-discretionary and discretionary factors and scale in data envelopment analysis, European journal of operational research, 2004, 158(1), P. 20-33.

[30].  Saen, R. F., A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscretionary factors, Annals of Operations Research, 2009, 172(1), P. 177-192.

[31].  Azizi, H., Ajirlu, H. G., Measurement of the worst practice of decision-making units in the presence of non-discretionary factors and imprecise data, Applied Mathematical Modelling, 2011, 35(9), P. 4149-4156.

[32].  Aliakbarpoor, Z., Izadikhah, M., Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis, International Journal of Automation and Computing, 2012, 9(6), P. 609-615.

[33].  Khoshandam, L., Amirteimoori, A., Matin, R. K., Marginal rates of substitution in the presence of non-discretionary factors: A data envelopment analysis approach, Measurement, 2014, 58, P. 409-415.

[34].  Shabani, A., Torabipour, S. M. R., Saen, R. F., Khodakarami, M., Distinctive data envelopment analysis model for evaluating global environment performance, Applied Mathematical Modelling, 2015, 39(15), P. 4385-4404.

[35].  Soltani, N., Lozano, S., Potential-based efficiency assessment and target setting, Computers & Industrial Engineering, 2018, 126, P. 611-624.

[36].  Taleb, M., Ramli, R., Khalid, R., Developing a two-stage approach of super efficiency slack-based measure in the presence of non-discretionary factors and mixed integer-valued data envelopment analysis, Expert Systems with Applications, 2018, 103, P. 14-24.

[37].  Galagedera, D. U., Modelling social responsibility in mutual fund performance appraisal: A two-stage data envelopment analysis model with non-discretionary first stage output, European Journal of Operational Research, 2019, 273(1), P. 376-389.

[38].  Dibachi, H., Behzadi. MH, Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology, 2015, 8 (8), P. 549–555, Doi: 10.17485/ijst/2015/v8iS8/71505

[39].  Queiroz, M. V. A. B., Sampaio, R. M. B., Sampaio, L. M. B., Dynamic efficiency of primary education in Brazil: Socioeconomic and infrastructure influence on school performance, Socio-Economic Planning Sciences, 2020, 70, P. 100738.

[40].  Chambers, R. G., Chung, Y., Färe, R., Profit, directional distance functions, and Nerlovian efficiency, Journal of optimization theory and applications, 1998, 98(2), P. 351-364.

[41].  Izadikhah, M., Using goal programming method to solve DEA problems with value judgments, Yugoslav Journal of Operations Research, 2016, 24 (2), P. 267 – 282, Doi: 10.2298/YJOR121221015I

[42].  Allahyar, M., Rostamy-Malkhalifeh, M., Negative data in data envelopment analysis: Efficiency analysis and estimating returns to scale, Computers & Industrial Engineering, 2015, 82, P. 78-81.