Document Type : Research Paper
Authors
1 PhD student, Water science and Engineering Department, Bu-Ali Sina University
2 Associate Professor, Water science and Engineering Department, Bu-Ali Sina University
3 Professor, Water science and Engineering Department, Bu-Ali Sina University
Abstract
In recent years, the conflict between environmental interests and farmers' livelihoods has emerged as one of the most crucial issues in the environmental and agricultural governance of the Zayandeh-Rood watershed. Hence, achieving a balance between economic profitability through the use of an optimal cultivation model and preventing the excessive extraction of groundwater resources holds great importance for policymakers in this field. In this study, the environmental function was modeled by considering water resources limitation, while the economic function was modeled based on the economic profit derived from the products cultivated in the plain of Kohpaye-Segzi in the period of 1996-2011. The Nash function was optimized through constrained nonlinear optimization, taking into account the yearly limitations of water resources. After optimizing the Nash function, the crop cultivation pattern was determined using both the game theory method and the linear programming method. The results indicated a 30% reduction in water consumption with the game theory method, and a 17% reduction with the linear programming method. Additionally, the profit reductions for these two methods were 16% and 3% respectively, compared to the base case. Furthermore, the results revealed that the groundwater level in the representative hydrograph of the plain decreased by 3.94 and 2.23 meters in the game theory and linear programming methods, respectively. Conversely, in the optimized game theory method with an economic function weight of 0.5, the groundwater level of the plain increased by 8.67 meters. Taking into account the reduction in water consumption, profit reduction, and the increase in groundwater level, the optimized game theory method with a weight of 0.5 was found to be superior to the linear programming method.
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