عنوان مقاله [English]
Virtual water trade as a new approach to global water resources, has attracted global attention and allocation of 90 percent of the agricultural sector to this trade has revealed special attention to it. The present study with the aim of optimizing the trade of virtual water in 23 cities located in the Isfahan province was done in 2014. For this purpose a multi-criteria regional cropping pattern with the ability to transfer crops was designed and implemented. The results of this study showed that Isfahan province has 118.7 million cubic meters net exports of virtual water in the current cropping pattern and implementation of the multi-criteria model led to 17.8 million cubic meters net import of virtual water. Therefore, improvement of net import of virtual water was 136.5 million cubic meters that it shows the stored virtual water during the optimization process of exchanges. Renewable water per capita index (Falkenmark) with an average of 119 cubic meters per year improved by multi-criteria. The results can help to decision-making managers in the optimization field of virtual water trade. From this point of view can be cited determination of regional net virtual water trade in order to deal with inappropriate temporal and spatial distribution of precipitation in Isfahan province by transport of agricultural products.
1. Dehghanian Menshadi, H., Nik Sokhan, M. and Ardestani, M. (2013). Estimation of virtual water of watershed and its role in inter-basin water transmission systems. Journal of Water Resources Engineering, 6:101–107. (Persian)
2. Nikouei, A., Torkamani, J. and Mamanpoush, A. (2010). Management of water consumption at different levels of salinity in order to achieve multiple goals of Zayandehrood basin farmers. Journal of Irrigation and Drainage, 4:143–155. (Persian)
3. Chapagain, A.K. and Hoekstra, A.Y. (2003). Virtual water flows between nations in relation to trade in livestock and livestock products. Delft, The Netherlands: UNESCO-IHE Institute for water Education (Value of Water Research Report).
4. Safi, R. and Mirlotfi, S. (2015). Assessment of sugarcane cultivation in Khuzestan province from the virtual water viewpoint. Journal of Water Resources Engineering, 8:87–96. (Persian)
5. Allan, J.A. (1988). Virtual water: a strategic resource. Ground Water, 36(4):545–547.
6. Hoekstra, A.Y. and Hung, P.Q. (2005). Globalisation of water resources: international virtual water flows in relation to crop trade. Global Environmental Change, 15(1):45–56.
7. Babazadeh, H. and Saraei Tabrizi, M. (2012). Evaluation of agricultural situation of Hormozgan province for virtual water perspective. Water Research in Agriculture (Soil and Water Sciences), (26):485–489. (Persian)
8. Dabrowski, J.M., Murray, K., Ashton, P.J. and Leaner, J.J. (2009). Agricultural impacts on water quality and implications for virtual water trading decisions. Ecological Economics, 68(4):1074–1082.
9. Wichelns, D. (2001). The role of “virtual water” in efforts to achieve food security and other national goals, with an example from Egypt. Agricultural Water Management, 49(2):131–151.
10.Yang, H. and Zehnder, A. (2016). “Virtual water”: An unfolding concept in integrated water resources management. Water Resource Research, 43(12):109-123.
11.Galan, A., Pozo, C., Guillen-Gosalbez, G., Anton Vallejo, A. and Jimenez Esteller, L. (2016). Multi-stage linear programming model for optimizing cropping plan decisions under the new Common Agricultural Policy. Land Use Policy, 48:515–524.
12.Mandal, R. (2014). Flood, cropping pattern choice and returns in agriculture: A study of Assam plains, India. Economic Analysis and Policy, 44(3):333–344.
13.Li, M. and Guo, P. (2016). A coupled random fuzzy two-stage programming model for crop area optimization—A case study of the middle Heihe River basin, China. Agricultural Water Management, 155:53–66.
14.Garg, N.K. and Dadhich, S.M. (2014). Integrated non-linear model for optimal cropping pattern and irrigation scheduling under deficit irrigation. Agricultural Water Management, 140:1–13.
15.Anonymous. (2014). Crop pattern project in the pilot study of Shahreza city located in Isfahan province. Isfahan Agricultural and Natural Resources Research Center, Isfahan Agricultural Jihad Organization. (Persian)
16.Julaei, R., Azar, A. and Chizari, A. (2005). Multi-regional planning models and their applications in agriculture: Case study of Fars province. Journal of Agricultural Economics and Development, 1:87–112. (Persian)
17. Zoppi, C. and Lai, S. (2015). Determinants of land take at the regional scale: a study concerning Sardinia (Italy). Environmental Impact Assessment Review, 55:1–10.
18. Mirzavand, M. and Eimani, R. (2015). Determination of optimal cropping pattern based on the concept of virtual water and economic profitability for dealing with water deficit: Case study: Kashan plain, Isfahan province. Water Resources and Development, 4:51–9. (Persian)
19. Anonymous. (2015). Land expansion and strategic document for development of Isfahan Province. Isfahan Science and Research Campus, Isfahan University of Technology, Iran. (Persian)
20. Mardani, M., Kenari, R.E., Babaei, M. and Asemani, E. (2013). Application of Meta-goal programming approach to determine optimal cropping pattern. International Journal of Agronomy and Plant Production, 4(8):1928–35.
21. Masson, R., Lahrichi, N. and Rousseau, L.M. (2016). A two-stage solution method for the annual dairy transportation problem. European Journal of Operational Research, 251(1):36–43.
22. Nikouei, A. and Ward, F.A. (2016). Pricing irrigation water for drought adaptation in Iran. Journal of Hydrology, 503:29–46.
23. Karami, E. (2016). Appropriateness of farmers’ adoption of irrigation methods: The application of the AHP model. Agricultural Systems, 87(1):101–19.
24. Fernandez, E. and Olmedo, R. (2016). An outranking-based general approach to solving group multi-objective optimization problems. European Journal of Operational, 225(3):497–506.
25. Hannan, E.L. (1981). On Fuzzy Goal Programming. Decision Sciences, 12(3):522–531.
26. Singh, S.K. and Yadav, S.P. (2015). Modeling and optimization of multi objective non-linear programming problem in intuitionistic fuzzy environment. Applied Mathematical Modelling, 39(16):4617–4629.
27. Yalcin, G. and Erginel, N. (2015). Fuzzy multi-objective programming algorithm for vehicle routing problems with backhauls. Expert Systems with Applications, 42(13):5632–5644.
28. Mardani, M., Sakhdari, H. and Sabouhi, M. (2011). Application of multi-objective programming and degree of conservative controller parameters in agricultural planning, the case study: Mashhad district. Agricultural Economics Research, (3):158–63. (Persian)
29. Bender, M.J. and Simonovic, S.P. (2000). A fuzzy compromise approach to water resource systems planning under uncertainty. Fuzzy Sets and Systems, 115(1):35–44. (Persian)
30. Jones, D.D. and Barnes, E.M. (2000). Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management. Biological Systems Engineering: Papers and Publications, 65:137–58.
31. Zeng, X., Kang, S., Li, F., Zhang, L. and Guo, P. (2010). Fuzzy multi-objective linear programming applying to crop area planning. Agricultural Water Management, 98(1):134–142.
32. Falkenmark, M. and Widstrand, C. (1992). Population and water resources: a delicate balance. Popul Bull, 47(3):1–36.
33. Hess, T., Andersson, U., Mena, C. and Williams, A. (2015). The impact of healthier dietary scenarios on the global blue water scarcity footprint of food consumption in the UK. Food Policy, 50:1–10.
34. Brooke, A., Kendrick, K. and Meeraus, A. (1998). GAMS: A users’s guide. The Scientific Press.
35. Sabouhi, M. and Soltani, G. (2008). Optimization of grop patterns in the basin with emphasis on social benefits and net imports of virtual water: A case study of Khorasan region. Water and Soil Science (Science and Technology of Agriculture and Natural Resource), 12:197–302. (Persian)
36. Dehghanpur, H. and Bakhshode, M. (2008). Investigating the constraints of virtual trade in Marvdasht region. Economics and Agricultre Development (Agricultural Sciences and Technology), 22:126–37. (Persian)
37. Mubako, S., Lahiri, S. and Lant, C. (2013). Input–output analysis of virtual water transfers: Case study of California and Illinois. Ecological Economics, 93:230–238.