Application of Genetic Algorithm in Determination of Optimal Land use Pattern Corresponding with Sustainable Agriculture: A Case Study of Sari Goharbaran

Document Type : Research Paper

Authors

1 M.A. in Agricultural Economics, Sari University of Agricultural Science and Natural Resources, Sari.

2 Associate Professor, Department of Agricultural Economic, Sari Agricultural Science and Natural Resources University

3 Assistant Professor, Department of Agricultural Economic, Sari University of Agricultural Science and Natural Resources.

4 PHD Student in Agricultural Economics, Sari University of Agricultural Science and Natural Resources.

Abstract

Introduction:Agriculture as one of the basic pillars of development, has an important role in economic development.  Accordingly,  using cropping pattern optimization is a proper way for agricultural development. Therefore, in the present study, optimal cropping pattern in Goharbaran region of Sari city has been evaluated in terms of multi-objective planning has been done using non-linear programming and genetic algorithm and finally compared each other.
Materials and Methods:Required data for this study has been collected with interview 250 of farmers during the 2014-2015.
Findings:Comparison The results of this study showed that the optimal pattern of non-linear genetic algorithm is superior compared to ordinary non-linear programming model. Because increasing the profit of the genetic algorithm is about 0.2% higher than normal nonlinear planning, while reduced risk the by about 6 percent. Also, the amount of production increases by about 18 percent in the genetic algorithm and the consumption of chemical fertilizer is 7 percent lower than normal nonlinear programming. Based on the results, all four sustainable farming objectives in the framework of multi-objective model in the model obtained from the genetic model have a superiority to the typical nonlinear planning model.
Conclusion:Since the proposed cropping pattern of genetic algorithm causes to increase farmers' gross margin compared to the ordinary nonlinear programming, therefore, the government's encouragement and support is mandatory of the farmers in applying the results of such models.

Keywords


1. Barzgari M, Hajiabadi M, and Ghezel Soflu A. Optimization of urban water distribution network using genetic algorithm (Case study of Salami city). National Conference on Civil Engineering and Needs-Based Research. 2015; 1-11.

https://civilica.com/doc/461182/  

2. Rezaee Z, Dourandish A, Nobahar A. Determination of Cultivation pattern Under Three strategies of economic, social, environmental with application of genetic algorithms: (Case Study of Mashhad). Biennial Conference of Agricultural Economics. 2012; 1607- 1615. https://www.wwjournal.ir/article_2464.html

3. Kohansal MR,  Firooz Zarea A, Determining optimal cultivation model corresponding with organic agriculture Application of Multiple-objective Linear Fuzzy Fractional Programming (Case study: North Khorasan province). Agricultural Economics and Development.  2008;  16(62):1-32.

https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=135355

4. Bagherian A, Saleh I, Paykani Gh, Optimization of Cultivation pattern in Kazeroun region using of linear programming. Sixth Iranian Agricultural Economics Conference. 2007; November 8 and 9.  . https://civilica.com/doc/46812/

5. Gopi A, Venkata S, Kandukuri N. Land allocation strategies through genetic algorithm approach–A case study. Global journal of research in engineering. Global Journal of Research in Engineering. 2011; ,11(4): 6-14.

https://www.semanticscholar.org/paper/LAND-ALLOCATION-STRATEGIES-THROUGH-GENETIC-CASE-Annepu-Subbaiah/d1453686c2679db8f338348f92fca302a4583014#citing-papers

6. Shabani M, Honar T. Determination of optimal cropping pattern in irrigation canals using IPM model. Water and Soil Journal. 2008; 22(2): 95-106. https://www.sid.ir/en/journal/ViewPaper.aspx?ID=141462

7. Kiafar H, Sadradini AA. Optimal water allocation for Sufi-Chay Irrigation and Drainage network in East Azarbaijan province of Iran using genetic algorithm. Fourth Conference on Water Resources Management.  2011; 5: 52-61.

https://www.sid.ir/en/journal/ViewPaper.aspx?id=387490

8. Zraatkish Y. Water Economical Valuation in Agriculture with Environmental Approach.
Agricultural Economic and Development. 2016; 47-2(1):  269-295.

https://sustainagriculture.tabrizu.ac.ir/article_12312.html

9. Raju K S, & Kumar D.N. Irrigation planning using genetic algorithms. Water Resour Manage. 2004; 18(2): 163-176. https://doi.org/10.1023/B:WARM.0000024738.72486.b

10. Dutta S, Sahoo B, Mishra R, & Acharya S.. Fuzzy stochastic genetic algorithm forobtaining optimum crops pattern and water balance in a farm. Water
    Resources Management. 2016; 30:4097–4123.

https://doi.org/10.1007/s11269-016-1406-7

11. Godarzi A. Optimization of water absorption cycle and solar bromide lithium using genetic algorithm. 2009.

http://library.sharif.ir/parvan/search/

12. Cochran CB. Sampeling Techniques, John Wiley, New York. 1977.

https://www.wiley.com/en-us/Sampling+Techniques%2C+3rd+Edition-p-9780471162407