Optimization of Water Allocation and Optimal Cropping Pattern in Irrigation and Drainage Network of Ghazvin Plain

Document Type : Research Paper


1 Ph.D. Candidate, Department of Agricultural Systems engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Associate Professor, Department of Agricultural Systems engineering , Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Associate Professor, Department of Irrigation, Soil and Water Research Institute, Agricultural Research, Education and Promotion Organization.

4 Associate Professor of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, and Caspian Sea Basin Research Center, Rasht, Iran.


Water scarcity has become a major constraint for the growth of societies in the world over the last few decades. The agricultural sector, as the largest water user, is prioritizing the optimal and sustainable allocation of water resources. In the present study, an optimization model was developed to maximize the profit and minimize the stability of water resources in the main canal and grade 1 and 2 channels in the irrigation and drainage network of Qazvin plain (Figure 1). The optimization model was proposed using a multi-objective genetic algorithm with two separate objective functions. In order to study the model farm, the major and important plants with their culture ratio in L1, L2, L3, L4, L4A, L5, L6, L7, L8, L9, L10, L20 canals in Qazvin plain, cost production and sales price of products were collected in the year 1394. The results of the optimization exercise indicate a 64% increase in net profit compared to the existing cropping pattern by reducing the level of cropping of products with low net profit and increasing the level of high-yielding products. On the other hand, according to the coefficients applied to water quotas, the definition of the second target function has reduced water consumption by optimal conditions from 100% of the water quota to 80%.


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Volume 44, Issue 3
November 2021
Pages 103-116
  • Receive Date: 06 December 2018
  • Revise Date: 19 June 2019
  • Accept Date: 23 June 2019
  • Publish Date: 23 September 2021