Optimizing Cropping Pattern with Emphasis on Water Resource Restrictions: A Case Study of Seidan-Farough Plain, Marvdasht Township

Document Type : Research Paper

Authors

• E. F
• S. KH
• M. A

Abstract

Protection of vital water resources along with economic considerations has made a major revision in decision making of agricultural systems. Under such circumstances, it is necessary to use efficient planning approaches which allow decision makers to access multiple objectives. Accordingly, the present study applied multi-objective programming, cluster analysis, and compromise programming to develop an optimal cropping pattern providing gross margin maximization and water use minimization in Seidan-Farough Plain, Marvdasht Township. The required data is related to crop year 2010-2011 and collected from 106[A1]  questionnaires using two stage cluster sampling. Considering different combinations of weights for the objective functions, the optimal cropping patterns were presented in three scenarios.  All patterns proposed by the model made it possible to increase gross margin and reduce water consumption. Moreover, when the objective weight of minimizing water use across patterns increased, crop combination changed in favor of the crops with lower water requirement. Comparison of current and optimal cropping patterns showed that farmers in both small and large farms had relatively similar performance in achieving the objective of gross margin maximization, while the objective of water use minimization was more successfully provided in large farms than that of small farms which in turn represents more efficient management of water use in large-scale farms. In addition, the tradeoffs between objectives, especially in small farms, indicated that decreasing a certain amount of gross margin would result in a more substantial reduction in water consumption providing farmers with a variety of acceptable options in order to reduce water use.

Keywords

• Optimal Cropping Pattern
• Water
• Multi-Objective Programming
• compromise programming
• Agriculture

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