Document Type : Original Article
Authors
1 Research Assistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran.
2 Imam Khomeini Higher Education Center, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Abstract
Traction power resources play a role in improving the productivity of rice production systems. This study evaluated and ranked 17 counties in Guilan Province, Iran, in terms of traction power resource development using an integrated FAHP–TOPSIS model. Four main criteria—traction power resource adequacy, development trends, resource structure and balance, and productivity, production structure, and mechanization potential—were evaluated through 12 sub-criteria. Criteria and sub-criteria weights were determined using the Fuzzy Analytic Hierarchy Process (FAHP), and counties were ranked using TOPSIS. To assess ranking stability, sensitivity analyses examined 24 scenarios involving independent changes in four main criteria weights and 792 scenarios involving simultaneous changes in two sub-criteria weights, at ±10%, ±20%, and ±30%. Rankings were validated using VIKOR, with sensitivity assessed at five compromise parameter values. Traction power resource adequacy was the most important criterion, with a weight of 0.523. According to TOPSIS, Astaneh-ye Ashrafiyeh ranked first, with a closeness coefficient of 0.8119 and a very favorable development level, while Rasht ranked 17th, with a coefficient of 0.1982 and a weak development level. Astaneh-ye Ashrafiyeh maintained first position across all weight sensitivity scenarios and examined VIKOR compromise parameter values, although some counties changed ranks. The Spearman rank correlation coefficient between TOPSIS and VIKOR was 0.8431. The findings indicate that traction power resource development rankings, particularly among middle-ranked counties, are sensitive to criteria weights and decision-making methods. These results can support regional prioritization and allocation of rice mechanization resources, thereby improving the evidence base for county-specific mechanization planning and investment.
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