Integrated Assessment of Technical and Environmental Efficiency in Irrigated Wheat Production in Iran

Document Type : Original Research

Author
Department of Agricultural Economics, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, Islamic Republic of Iran.
Abstract
In Iran's arid and semi-arid regions, ensuring wheat production while reducing pressure on natural resources is a major challenge. This study evaluates the technical and environmental efficiency of irrigated wheat production across 377 Iranian counties during the 2023 cropping season. Technical efficiency was estimated using an input-oriented Data Envelopment Analysis (DEA) model under variable returns to scale, while environmental efficiency was assessed by incorporating undesirable agrochemical inputs through an inverse transformation approach. To capture agrochemical-related environmental pressure, an Environmental Performance Index (EPI) was constructed using Principal Component Analysis (PCA) of fertilizer and pesticide use, where higher values indicate lower environmental pressure. Counties were grouped by hierarchical clustering based on environmental efficiency and EPI, and provincial benchmarking was conducted using an equal-weighted TOPSIS framework. The novelty of this study lies in integrating DEA, PCA, hierarchical clustering, spatial analysis, and TOPSIS within a unified framework applied to 377 counties, providing one of the first nationwide county-level assessments of technical and environmental efficiency in Iran's irrigated wheat sector. The average technical and environmental efficiency scores were 0.744 and 0.929, respectively, indicating considerable potential for input savings while maintaining current production levels. Nitrogen fertilizer was identified as the main driver of environmental pressure, and substantial spatial disparities were observed across counties. Overall, the findings highlight the importance of region-specific strategies that improve environmental sustainability while maintaining agricultural productivity.
Keywords
Subjects

1.   Banker, R.D., Charnes, A. and Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30:1078–1092.
2.   Bravo-Ureta, B.E., Solís, D., Moreira López, V.H., Maripani, J.F., Thiam, A. and Rivas, T., 2007. Technical efficiency in farming: A meta-regression analysis. J. Prod. Anal., 27:57–72.
3.   Charnes, A., Cooper, W.W. and Rhodes, E., 1978. Measuring the efficiency of decision-making units. Eur. J. Oper. Res., 2:429–444.
4.   Coelli, T., 1996. A guide to DEAP version 2.1: A data envelopment analysis (computer) program. Center for Efficiency and Productivity Analysis, University of New England, Armidale, Australia.
5.   Coelli, T., Rao, D.S.P., O’Donnell, C. and Battese, G., 2005. An introduction to efficiency and productivity analysis. 2nd ed. Springer, New York.
6.   Cooper, W.W., Seiford, L.M. and Zhu, J., 2011. Handbook on data envelopment analysis. 2nd ed. Springer, New York.
7.   Dastan, S., Rezvani Moghaddam, P. and AghaAlikhani, M., 2021. Assessing environmental impacts of irrigated wheat systems in Iran. J. Clean. Prod., 320:128883.
8.   Dinno, A., 2015. Nonparametric pairwise multiple comparisons in independent groups using Dunn’s test. Stata J., 15:292–300.
9.   Ehsani, M., Esmaeili, A. and Maleki, A., 2020. Resource efficiency and environmental sustainability in Iran’s agriculture: A regional perspective. Sustainability, 12:10095.
10.  Esmaeili, A. and Ehsani, M., 2019. Environmental efficiency and sustainability in Iranian wheat production: A DEA-based analysis. Environ. Sci. Pollut. Res., 26:17715–17724.
11.  Everitt, B.S., Landau, S., Leese, M. and Stahl, D., 2011. Cluster analysis. 5th ed. Wiley, Chichester.
12.  FAO, 2022. FAOSTAT: Crops and livestock data. Food and Agriculture Organization of the United Nations, Rome.
13.  Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E., 2019. Multivariate data analysis. 8th ed. Cengage, Boston.
14.  Halkos, G.E. and Tzeremes, N.G., 2013. Estimating the degree of environmental inefficiency in the presence of heterogeneity. Econ. Lett., 118:384–387.
15.  Hwang, C.L. and Yoon, K., 1981. Multiple attribute decision making: Methods and applications. Springer-Verlag, Berlin.
16.  Jolliffe, I.T. and Cadima, J., 2016. Principal component analysis: A review and recent developments. Philos. Trans. R. Soc. A, 374:1–16.
17.  Kazemi, N., Meysam, F. and Ghanbari, M., 2021. Spatial variability of agroclimatic conditions across Iran’s wheat belt. Agric. Water Manag., 255:107015.
18.  Kruskal, W.H. and Wallis, W.A., 1952. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc., 47:583–621.
19.  Latruffe, L., 2010. Competitiveness, productivity and efficiency in the agricultural and agri-food sectors. OECD Publishing, Paris.
20.  Madau, F.A., 2011. Efficiency and environmental efficiency assessments in the Italian agricultural sector: A non-parametric approach. Agric. Resour. Econ. Rev., 40:33–47.
21.  Maleki, A., Ghadimi, P. and Khosravi, F., 2022. Spatial distribution of sustainable agriculture indicators in Iran. Environ. Dev. Sustain., 24:6631–6654.
22.  Ministry of Agriculture-Jihad (MAJ), 2023. Annual report on agricultural production statistics. Tehran, Iran.
23.  Omrani, H., Alizadeh, S. and Soltani, A., 2020. Integration of DEA and PCA approaches for regional sustainability assessment in agriculture. Ecol. Indic., 119:106857.
24.  R Core Team, 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
25.  Reinhard, S., Lovell, C.A.K. and Thijssen, G., 2000. Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur. J. Oper. Res., 121:287–303.
26.  Runfola, D.M., Anderson, A., Baier, H., Crittenden, M., Dowker, E., Fuhrig, S. and Yale, K., 2020. geoBoundaries: A global database of political administrative boundaries. PLoS ONE, 15:e0231866.
27.  Seiford, L.M. and Zhu, J., 2002. Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res., 142:16–20.
28.  Shayanmehr, M., Rezaei, R. and Ghorbani, R., 2020. Regional analysis of irrigation water productivity and sustainability in Iranian agriculture. Water Resour. Manag., 34:3895–3912.
29.  Shiferaw, B., Smale, M., Braun, H.J., Duveiller, E., Reynolds, M. and Muricho, G., 2013. Crops that feed the world 10: Past successes and future challenges to the role of wheat in global food security. Food Secur., 5:291–317.
30.  Sueyoshi, T. and Goto, M., 2012. DEA environmental assessment: A numerical example using Japanese industrial sector data. Energy Econ., 34:669–676.
31.  Sutton, M.A., Raghuram, N., Adhya, T.K., Baral, B.R. and Zhang, F., 2021. Our nutrient world: The challenge to produce more food and energy with less pollution. Centre for Ecology & Hydrology, Edinburgh.
32.  Tilman, D., Balzer, C., Hill, J. and Befort, B.L., 2011. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA, 108:20260–20264.
33.  Tomczak, M. and Tomczak, E., 2014. The need to report effect size estimates revisited. Trends Sport Sci., 21:19–25.
34.  Vyas, S. and Kumaranayake, L., 2006. Constructing socio-economic status indices: How to use principal components analysis. Health Policy Plan., 21:459–468.
35.  Wang, T.C. and Lee, H.D., 2009. Developing a fuzzy TOPSIS approach based on subjective and objective weights. Expert Syst. Appl., 36:8980–8985.
36.  Ward, J.H., 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 58:236–244.
37.  Zhang, X., Davidson, E.A., Mauzerall, D.L., Searchinger, T.D., Dumas, P. and Shen, Y., 2015. Managing nitrogen for sustainable development. Nature, 528:51–59.
38.  Zhou, P., Ang, B.W. and Poh, K.L., 2008. A survey of data envelopment analysis in energy and environmental studies. Eur. J. Oper. Res., 189:1–18.

Articles in Press, Accepted Manuscript
Available Online from 08 August 2026