Fuzzy Linear Programming Model for the Optimization of Cropping Pattern in Zarrinehroud Basin

Document Type : Original Article

Authors

1 Department of irrigation and drainage, University of Tehran

2 Associate Professor, Department of Irrigation and Drainage Engineering, Abouraihan Campus, University of Tehran, Tehran, Iran.

3 Assistant Professor, Department of Irrigation and Drainage Engineering, Abouraihan Campus, University of Tehran, Tehran, Iran.

Abstract

Agricultural sector is the largest water consumer, and uncertainty is an inevitable aspect of water management in this sector. In this study, fully fuzzy linear programming using two different solutions were applied for multi-objective optimizing of cropping pattern and net benefit in the uncertain conditions for the Zarrinehroud Basin. The uncertainties within the optimized cropping pattern were considered using a fuzzy method. Moreover, in order to consider the uncertainties in the amount of available water limitation, three different hydrological conditions were applied to determine maximum, average and minimum of fuzzy bond. The results of optimization by the fully fuzzy linear programming model showed an increase of 2.53% in net benefit comparing to the crisp optimization and an increase of 36.34 percent comparing to the present cropping pattern through decreasing low income crops and increasing high income crops. It is also shown that applying fully fuzzy linear programming instead of crisp linear programming leads to a greater saving in water consumption with the amount of 88.22%. Considering 10% and 20% uncertainty bands for optimization parameters causes more water saving and net benefits from optimal cropping pattern. Therefore, optimization of cropping pattern through considering uncertainties based on fuzzy method leads to a more optimal planning for agricultural water.

Keywords

Main Subjects

References

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Iran-Water Resources Research
Volume 14, Issue 1
April 2018
Pages 13-24

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History

  • Receive Date: 04 March 2017
  • Revise Date: 21 June 2017
  • Accept Date: 27 June 2017

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