Abstract
Abstract Background and Objective: Understanding climatic data and examining their effects on agricultural crops are among the key factors influencing site selection and, consequently, increasing production. The aim of this study is to evaluate the climatic and environmental potentials of the Arshaq region in Ardabil Province for rainfed wheat cultivation using the AHP and WASPAS decision making methods.
Methods and Material: uch as precipitation (annual, germination, tillering, wintering, regrowth, flowering, and grain formation), mean annual temperature, elevation, and slope were used based on a 30 year statistical period (1994–2023). The research process included data collection, statistical calculations and analysis using Excel, and determination of the weighted indices of the criteria through the Analytic Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS) methods.
Results and Discussion: The data were analyzed using the AHP and WASPAS models. The research findings, utilizing the AHP model across the wheat growth stages, indicated that the most important effective factors for wheat cultivation in the Arshaq region were, in order of weighted criterion values: effective precipitation in the grain formation stage (0.195), precipitation during flowering (0.175), annual precipitation (0.132), and precipitation during germination (0.114). Based on the WASPAS method, the stations ranked as the most suitable for wheat cultivation in the Arshaq region were: Salavat (5.713), Khoshabad (5.219), Doustbeiglou (5.132), Qousheh Sofla (5.096), Kangarlou (5.026), Khalifehlou (5.023), Tazakand-e-Arshaq (4.928), Amirkandi (4.802), Arbabkandi (4.649), Samian (4.646), and Moshiran (4.374), respectively.
Conclusion: The final results of this study indicate that in over 60% of the Arshaq region, rainfed wheat cultivation is suitable and economically valuable, while in 25% of the area, cultivation is possible at a moderate to low level but still feasible.
Keywords: Climate, Arshaq region, rainfed wheat, and AHP, WASPAS, and GIS models