2025 : 4 : 22
safar marofi

safar marofi

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 18134033500
HIndex:
Faculty: Faculty of Agriculture
Address:
Phone: 09183143686

Research

Title
Optimizing cropping pattern to improve the performance of irrigation network using system dynamics—Powell algorithm
Type
JournalPaper
Keywords
Crop yield · Cropping pattern · Economic profitability · Irrigation network · Modeling · VENSIM software
Year
2022
Journal ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
DOI
Researchers Saeed Azadi ، Hamed Nozari ، Behzad Ghanbarian ، safar marofi

Abstract

One of the strategies for agricultural development is the optimal use of irrigation and drainage networks, which leads to higher productivity and economic benefits. In this regard, quantitative and qualitative studies of drainage water from networks are essential for efficient water management. In the present study, we develop a model using a system dynamics approach to simulate the cropping pattern of an irrigation and drainage network as well as the discharge and salinity of drainage water from network farms. We apply the Powell algorithm to optimize the economic profitability of cultivated crops by considering the salinity and discharge of drainage water from the fields. With three aims, i.e., (1) maximizing benefit–cost ratio, (2) minimizing drainage water salinity and discharge of network, and (3) economic and environmental considerations simultaneously, the optimization of cropping pattern within the Kosar irrigation and drainage network is performed. Results based on five consecutive years under different scenarios showed that some crops, such as watermelon, are not economically recommened for production due to high costs, water consumption, and low selling price causes environmental pollution. On the other hand, wheat, grain maize, silage maize, sorghum, and alfalfa have different conditions, and their production is suitable by considering all scenarios. By comparing with experimental data, we find that the proposed model is accurate to simulate and optimize the irrigation network and to detect its cropping pattern.