A Dynamic Network Data Envelopment Analysis Model to Calculate the Efficiency of Wheat Farms
محورهای موضوعی :
Computer Engineering
Shahin Rajaei Qazlue
1
,
Ahmad Mehrabian
2
,
Kaveh Khalili-Damghani
3
,
Mohammad Amirkhan
4
1 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.
2 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
3 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.
تاریخ دریافت : 1402/04/25
تاریخ پذیرش : 1402/07/06
تاریخ انتشار : 1402/06/10
کلید واژه:
Efficiency score,
Performance Assessment,
Network data envelopment analysis,
Dynamic network data envelopment analysis,
Wheat farms efficiency score,
چکیده مقاله :
Wheat is a strategic crop; the prosperity of production improves any country's economy. In recent years, Iran has faced a significant decrease in wheat production, so it is important to conduct a study in this sector and provide a suitable solution to improve the concerns. A dynamic network model is presented to calculate efficiency for agricultural farmlands over 6 years. This model considers the every-other-year farming method, indirect input part of that is used in one year and the rest in the following years, and the complex relations of wheat production. Finally, to prove the applicability of the presented model, a case study of wheat fields in the northwest of Iran has been selected, and the desired model has been implemented. The results show Zanjanrod performs at the highest efficiency level, followed by Takestan.
منابع و مأخذ:
Charnes, A., Cooper, W. and Rods, E., 1978. Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), pp.429-444.
Helfland, S. and Levine, E., 2004. Farm size and the determinants of productive efficiency in the Brazilian Center-West. Agricultural Economics, 31(2-3), pp.241-249.
Sueyoshi, T. and Sekitani, K., 2005. Returns to scale in dynamic DEA. European Journal of Operational Research, 161(2), pp.536-544.
Cui, Q., Wei, Y. and Li, Y., 2016. Exploring the impacts of the EU ETS emission limits on airline performance via the Dynamic Environmental DEA approach. Applied Energy, 183, pp.984-994.
Sueyoshi, T., Hasebe, T., Ito, F., Sakai, J. and Ozawa, W., 1998. DEA-Bilateral Performance Comparison: An Application to Japan Agricultural Co-operatives (Nokyo). Omega, 26(2), pp.233-248.
Frija, A., Wossink, A., Buysse, J., Speelman, S. and Van Huylenbroeck, G., 2011. Irrigation pricing policies and its impact on agricultural inputs demand in Tunisia: A DEA-based methodology. Journal of Environmental Management, 92(9), pp.2109-2118.
Toma, E., Dobre, C., Dona, I. and Cofas, E., 2015. DEA Applicability in Assessment of Agriculture Efficiency on Areas with Similar Geographically Patterns. Agriculture and Agricultural Science Procedia, 6, pp.704-711.
Angulo-Meza, L., González-Aray, M., Iriarte, A., Rebolledo-Leiva, R. and Soares de Mello, J., 2017. A multi objective DEA model to assess the eco-efficiency of agricultural practices within the CF + DEA method. Computers and Electronics in Agriculture, 161, pp.151-161.
Li, N., Jiang, Y., Mu, H. and Yu, Z., 2018. Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA). Energy, 164, pp.1145-1160.
Chen, Y., Miao, J. and Zhu, Z., 2023. Measuring green total factor productivity of China's agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO2 emissions. Journal of Cleaner Production, 318, p.128543.
Wu, H., Wang, B., Lu, M., Irfan, M., Miao, X., Luo, S., and Hao, Y., 2023. The strategy to achieve zero‑carbon inagricultural sector: Does digitalization matter under the background of COP26 targets? Energy Economics, 126, 106916.
Hassan, W., Hao, G., Yasmeen, R., Yan, H., 2023. Role of China's agricultural water policy reforms and production technology heterogeneity on agriculture water usage efficiency and total factor productivity change. Agricultural Water Management, 287, 108429
Chen, Y., Cook, W., Kao, C. and Zhu, J., 2013. Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures. European Journal of Operational Research, 226(3), pp.507-515.
Färe, R. and Grosskopf, S., 2000. Network DEA. Socio-Economic Planning Sciences, 34(1), pp.35-49.
Yang, Z., 2006. A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies. Mathematical and Computer Modelling, 43(7-8), pp.910-919.
Yu, M. and Lin, E., 2008. Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega, 36(6), pp.1005-1017.
Yu, M. and Fan, C., 2009. Measuring the performance of multimode bus transit: A mixed structure network DEA model. Transportation Research Part E: Logistics and Transportation Review, 45(3), pp.501-515.
Yang, W., Shao, Y., Qiao, H. and Wang, S., 2014. An Empirical Analysis on Regional Technical Efficiency of Chinese Steel Sector based on Network DEA Method. Procedia Computer Science, 31, pp.615-624.
Zhang, W., Wu, X., and Shi, J., 2023. Cross efficiency model of network DEA and its application on low carbon efficiency evaluation of multimodal transport. Ocean & Coastal Management, 244, 106778.
Meng, M., Pang, T., and Li, X., 2023. Assessing the total factor productivity of China’s thermal power industry using a network DEA approach with cross-efficiency. Energy Reports, 9, 5196–5205.
Khalili-Damghani, K. and Shahmir, Z., 2015. Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Computers & Industrial Engineering, 88, pp.131-150.
Keskin, N., 2023. An illustration of dynamic network DEA in commercial banking including robustness tests. Omega, 55, pp.141-150.
Liu, Q., Shang, J., Wang, J., Niu, W., and Qiao, W., 2023. Evaluation and prediction of the safety management efficiency of coal enterprises based on a DEA-BP neural network. Resources Policy, 83, 103611.
Gao, X., Ye, Y., Su, W., and Chen, L., 2023. Assessing the comprehensive importance of power grid nodes based on DEA. International Journal of Critical Infrastructure Protection, 42, 100614.
Tone, K. and Tsutsui, M., 2010. Dynamic DEA: A slacks-based measure approach☆. Omega, 38(3-4), pp.145-156.
Khalili-Damghani, K., Tavana, M., Santos-Arteaga, F. and Mohtasham, S., 2015. A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry. Energy Economics, 51, pp.320-328.
Chen, L. and Wang, K., 2022. spatial spillover effect of low-carbon city pilot scheme on green efficiency in China's cities: Evidence from a quasi-natural experiment. Energy Economics, 110, p.106018.
Wang, Z., Zhang, Z. and Johny, N., 2023. Measurement of innovation resource allocation enterprises. Kybernetes, 49(3), pp.835-851.
Gan, L., Wan, X., Ma, Y., and Lev, B., 2023. Efficiency evaluation for urban industrial metabolism through the methodologies of emerge analysis and dynamic network stochastic block model. Sustainable Cities and Society, 90, 104396.
Tone, K. and efficiency in civil–military integration Tsutsui, M., 2014. Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), pp.124-131.
Kao, H., Wu, D. and Huang, C., 2017. Evaluation of cloud service industry with dynamic and network DEA models. Applied Mathematics and Computation, 315, pp.188-202.
Tavana, M., Khalili-Damghani, K., Santos Arteaga, F. and Hosseini, A., 2019. A fuzzy multi-objective multi-period network DEA model for efficiency measurement in oil refineries. Computers & Industrial Engineering, 135, pp.143-155.
Yu, A., Shi, Y., You, J. and Zhu, J., 2021. Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach. European Journal of Operational Research, 292(1), pp.199-212.
Gazori-Nishabori, A., Khalili-Damghani, K. and Hafezalkotob, A., 2022. A Nash bargaining game data envelopment analysis model for measuring efficiency of dynamic multi-period network structures. Journal of Modelling in Management
Luo, K., Liu, Y., Chen, P. and Zeng, M., 2023. Assessing the impact of digital economy on green development efficiency in the Yangtze River Economic Belt. Energy Economics, p.1061
