Location of greenhouses for optimization in agriculture with sustainable development approach (Case study: Asadabad plain)
Subject Areas : GISAbouzar Ramezani 1 , Moslem Darvishi 2 , Davod Nejat 3
1 - Assistant Professor of GIS, Department of Engineering, Sayyed Jamaleddin Asadabadi University, Hamadan, Asadabad, Hamzeie Blvd, Rezaeian Blvd, Postal Code: 6541861841. *(Corresponding Author)
2 - Lecturer of Geomatics, Department of Engineering, Sayyed Jamaleddin Asadabadi University.
3 - Lecturer of Geomatics, Department of Engineering, Sayyed Jamaleddin Asadabadi University.
Keywords: location, greenhouse, sustainable development, optimization, agriculture.,
Abstract :
Backgeround and Objective: Greenhouse refers to a limited space that has the ability to control the appropriate environmental conditions for the growth of plants in different areas during different seasons of the year. Rapid economic and cultural growth, population growth, soil and water constraints, the community's need for food, the existence of large consumer markets and the interest in producing off-season crops in recent years have led to the development of greenhouse crops. But choosing the wrong place to build a greenhouse leads to a waste of capital and the failure of these goals. The purpose of this study is to find the optimal location of the greenhouse in Asadabad plain with a sustainable development approach. Material and Methodology: Due to the existence of numerous and effective parameters –for the performance of greenhouses, the technique of multi-criteria analysis has been used to find a suitable location. Also, due to the uncertainty in the behavior of natural parameters, fuzzy logic has been used to model the effect of the parameters. Findings: The results show that of the total area of Asadabad plain, 10% are in a very good condition, 39% are in a good condition (suitable with restrictions) and 51% are in a bad condition in terms of greenhouse construction. Discussion and Conclusion: Relying on spatial analysis can reduce the investment risk for greenhouses and lead to environmental sustainability.
1. Cockshull K, Graves C, Cave CR. The influence of shading on yield of glasshouse tomatoes. Journal of Horticultural Science. 1992;67(1):11-24.
2. Sivapalan S. Benefits of treating a sandy soil with a crosslinked-type polyacrylamide. Australian Journal of Experimental Agriculture. 2006; 46(4):579-84.
3. JW Jr ARB. Greenhouse engineering. Natural Resource Agriculture and Engineering Service (NRAES)-33 Ithaca NY. 1994.
4. Lorenzo P, Castilla N, editors. Bell pepper yield response to plant density and radiation in unheated plastic greenhouse. I International Symposium on Solanacea for Fresh Market 412; 1995.
5. Rezaeiniya N, Ghadikolaei AS, Mehri-Tekmeh J, Rezaeiniya H. Fuzzy ANP approach for new application: Greenhouse Location selection; a case in Iran. Journal of mathematics and computer Science. 2014;8(1):1-20.
6. Khosrobeygi Z, Rafiee S, Mohtasebi S, Nasiri A. Simultaneous localization and mapping in greenhouse with stereo vision. 2020.
7. Shaemi Barzuk A, Nik Andish N, Hisseini M. Optimum location of greenhouse cultivation of vegetables high consume in Qom province (with an emphasis on minimal energy needs). Journal of Climate Research. 2020;1398(37):101-10.
8. Iddio E, Wang L, Thomas Y, McMorrow G, Denzer A. Energy efficient operation and modeling for greenhouses: A literature review. Renewable and Sustainable Energy Reviews. 2020;117:109480.
9. Kouchaksaraei RH, Zolfani SH, Golabchi M. Glasshouse locating based on SWARA-COPRAS approach. International Journal of Strategic Property Management. 2015; 19(2):111-122.
10. Tavana M, Arteaga FJS, Mohammadi S, Alimohammadi M. A fuzzy multi-criteria spatial decision support system for solar farm location planning. Energy strategy reviews. 2017;18:93-105.
11. Ozdemir S, Sahin G. Multi-criteria decision-making in the location selection for a solar PV power plant using AHP. Measurement. 2018;129:218-26.
12. Dweiri F, Khan SA, Almulla A. A multi-criteria decision support system to rank sustainable desalination plant location criteria. Desalination. 2018;444:26-34.
13. Kabak M, Erbaş M, Çetinkaya C, Özceylan E. A GIS-based MCDM approach for the evaluation of bike-share stations. Journal of cleaner production. 2018;201:49-60.
14. Erol İ, Sencer S, Özmen A, Searcy C. Fuzzy MCDM framework for locating a nuclear power plant in Turkey. Energy Policy. 2014;67:186-97.
15. Żak J, Węgliński S. The selection of the logistics center location based on MCDM/A methodology. Transportation Research Procedia. 2014;3:555-64.
16. Tadić S, Zečević S, Krstić M. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications. 2014;41(18):8112-28.
17. Chen S, Li Z, Liu F, Yang S, Li M. Risk evaluation of solar greenhouse cucumbers low temperature disaster based on GIS spatial analysis in Tianjin, China. Geomatics, Natural Hazards and Risk. 2019;10(1):576-98.
18. Manzano A. Gasification of greenhouse residues for obtaining electrical energy in the south of Spain: localization by GIS. Interciencia. 2007;32, 6-131, (2)
19. Kim S-B, Shin H-J, Yun D-K, Hong S-W, Kim S-J. Estimation of greenhouse damaged area by heavy snowfall using GIS and remote sensing technique. KCID journal. 2011;18(2):111-21.
20. Gupta MJ, Chandra P. Effect of greenhouse design parameters on conservation of energy for greenhouse environmental control. Energy. 2002;27(8):777-94.
21. Parlato MC, Valenti F, Porto SM. Covering plastic films in greenhouses system: A GIS-based model to improve post use suistainable management. Journal of Environmental Management. 2020;263:110389.
22. Sánchez-Lozano JM, Teruel-Solano J, Soto-Elvira PL, García-Cascales MS. Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain. Renewable and sustainable energy reviews. 2013;24:544-56.
23. Khodaparasti S, Bruni M, Beraldi P, Maleki H, Jahedi S. A multi-period location-allocation model for nursing home network planning under uncertainty. Operations Research for Health Care. 2018.
24. Malek MR, Twaroch F. An introduction to intuitionistic fuzzy spatial region: na; 2004.
25. Kruse R, Gebhardt JE, Klowon F. Foundations of fuzzy systems: John Wiley & Sons, Inc.; 1994.
