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
3 Agricultural Engineering Research Institute (AERI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
Abstract
Mechanized rice transplanting can improve production efficiency by reducing labor dependence and increasing operational speed. This study aimed to assess and rank the development of mechanized rice transplanting across 17 counties in Guilan Province, Iran, using an integrated FAHP–TOPSIS model. Five main criteria, comprising technology development and adoption, production capacity and land structure, infrastructure and operational capacity, human resources, and technological development trends, were evaluated through 15 subcriteria. Criteria weights were determined using the Fuzzy Analytic Hierarchy Process (FAHP), based on aggregated expert judgments and fuzzy pairwise comparisons, while counties were ranked using TOPSIS. The results identified technology development and adoption as the most important criterion (0.4314). Mechanized transplanting area (0.236), rice cultivation area (0.131), and the number of active rice transplanters (0.130) were the highest-weighted subcriteria. Rasht ranked first, with a TOPSIS closeness coefficient of 0.8302, whereas Rudbar ranked 17th (0.0420), indicating spatial disparities in technological development. Sensitivity analysis involving 90 scenarios, generated by independently varying the global weights of 15 subcriteria by ±10%, ±20%, and ±30%, revealed a maximum rank shift of one position. The highest and lowest ranks remained unchanged, and the minimum Spearman rank correlation coefficient was 0.9951. Validation using VIKOR demonstrated a very high agreement in county rankings, with a Spearman rank correlation coefficient of 0.9951 (ρ=0.9951). These findings can support prioritizing assessments of equipment needs, strengthening mechanization services, and developing county-specific regional mechanization strategies.
Keywords
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