Estimation of the technological gap ratio of different rice varieties in Guilan province

Document Type : Research Paper

Authors

• Reza Esfanjari Kenari ¹
• Seyedeh Sodabeh Hashemi Chafchiri ²
• Mohamad Hosein Menhaj ³

¹ Assistant Professor, Department of Agricultural Economics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

² MSc Student, Department of Agricultural Economics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

³ Associate Professor, Department of Agricultural Economics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

10.30495/jae.2022.24379.2145

Abstract

The main purpose of this study was to evaluate the efficiency and technological gap ratio (TGR) of different rice cultivars in Guilan province For this purpose, metafrontier method was used to determine the TGR. The statistical population of the study was all rice farmers in Guilan province in 2017. Sample size was selected by stratified sampling method. The results showed that the mean technical efficiency range of different rice varieties was between 76% and 93%. In fact, if the gap between the farmers in the study is filled. In fact, the average yield of the Hashemi, Domsiah, Ali Kazemi, Jamshidjo, Shiroudi and Khazar could be 47, 7, 23, 12, 8 and 15 present increases respectively. The results also showed that the highest technological gap ratio for the studied varieties was related to Hashemi (0.96) and the lowest technology gap ratio was related to Khazar (0.45). Based on the results income, mechanization index, farmer's main occupation, ownership, experience and land size have a positive and significant effect on farmers' technical efficiency.

Keywords

• Technical Efficiency
• Technological Gap Ratio
• Rice
• Guilan

---

References

1. Abdi A, Dashti GH, Ghahramanzadeh M, Hosseinzad J. Analyzing the technical efficiency and technology gap of poultry units in Sanandaj County. Sci. J. 2016; 26 (3): 49-61.
2. Amidi Sampling theory and its applications. Academic Press Center. 2016; 78-83.
3. Battese GE, Rao DSP, O’Donnell C. A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Product. Anal. 2004; 21: 91–103.
4. Battese J, Rao DS. Technology gap, efficiency and stochastic metafrontier function. bus. econ. 2002; 1: 87-93.
5. Bowlin Measuring performance: An introduction to data envelopment analysis (DEA). J. Cost Anal. 1998; 15 (2):3–27.
6. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 1978; 2 (60): 429-444.
7. Esfanjari Kenari R, Ahmadpour M, Keikha M. Investigation of energy technology gap ratio of major crops in Sari. Agric. Econ. Res. 2018; 10 (1): 206-187.
8. Esfanjari Kenari R, Eskandari M, Mehrabi Boshrabadi h. Economic analysis of transformation of traditional irrigation systems to modern systems of wheat production in the Fars Province. 2015; 2 (2): 229-245.
9. Hakimipour n, Kani K. Comparative analysis of the efficiency of large industries sectors in provinces of Iran: using stochastic frontier function model. Knowl. Dev. 2008; 24: 138-187.
10. Jalali A, Shirzadi S, Esfanjari Kenari R. Metafrontier analysis of technology gap of Saffron farms. J.Saffron. Res. 2015; 4 (2): 187-198.
11. Karmol P, Villano R, Fleming E, kristnsen, P. Technical efficiencieng and technology gaps on ‘clean and safe’ vegetable. Farms in northern Thailand: a comparsion of different technologies. Australian Agricultural and Resource Economics Society (AARES) 54th Annual Conference. Adelaide, Australia.
12. Ministry of Agriculture Jihad of Guilan. 2018. https://www.jkgc.ir.
13. Ministry of Industry, Mine and Trade. 2012. https://www.mimt.gov.ir.
14. Moreira VH, Bravo-Ureta BE. Technical efficiency and metatechnology ratios for dairy farms in three southern cone contries: A stochastic metafronitier model. Product. Anal. 2010; 33: 33-45.
15. Nekamleu GB, Nyemeck J, Sanogo T. Metafrontier analysis of technology gap and productivity difference in African agriculture. j. food agric. econom. 2006; 1 (2): 111-120.
16. O'Donnell CJ, Rao DSP, Battese, GE. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. empir. econ. 2008; 4: 231-255.
17. Rahbar Dehghan AR, Esmaeeli Dastjerdipour A, Dhmardeh N. Calculating Types of efficiency and returns to scale in the milk industry (case study: Kerman province). J. Plan. .Budg. 2016; 17 (4): 145-159.
18. Rao DS, O’Donnell C, Battese G. Metafrontier functions for the study of interregional productivity differences, Centre for Efficiency and Productivity Analysis. School of Economics, University of Queensland, Australia, Working Paper Series. 2003; No. 01/2003.

 

19. Sabouhi Determining the efficiency of dairy farms in Fars Province, M.Sc thesis, Shiraz University. 1995; Iran.
20. Shabanzadeh M, Esfanjari Kenari R, Rezaie, A. Investigation of energy consumption pattern of tomato crop in Khorasan Razavi Province. Agric. Machin. 2016; 6 (2): 524-536.
21. Shahraki A, Dahmardeh N, Karbasi AR. Calculating efficiency and returns to scale of Grape producers in Sistan region using data envelopment analysis. Oper. Res. Applic. 2012; 3 (34): 77-90.
22. Tinaprilla Efisiensi usahatani padi antar wilayah sentra produksi di Indonesia: pendekatan stochastic metafrontier production function. (PhD Dissertation), Institut Pertanian Bogor, Bogor (ID). 2012.
23. Villano R, Fleming E, Fleming P. Measuring regional productivity differences and resource economics society, Conference (52nd), February 5-8, 2008, Canberra, Australia.
24. Wongchai A, Liu WB, Peng KC. DEA metafrontier analysis on technical efficiency differences of national universities in Thailand. IJONTE. 2012; 3 (3): 1-13.