Applying social cognitive theory to examine farmer migration in response to groundwater salinity: The case of Qaenat, Iran

Shakib, S.H., Tabesh, M., & Yazdanpanah, M. (2025). Applying social cognitive theory to examine farmer migration in response to groundwater salinity: The case of Qaenat, Iran. Agricultural Water Management 317 e109594. 10.1016/j.agwat.2025.109594.

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Abstract

Adverse climatic conditions and excessive groundwater extraction have jeopardized agriculture and water resources, caused the salinization of agricultural wells and prompted widespread rural-to-urban migration. To develop effective decentralization policies, it is crucial to analyze farmers' migration behavior in response to increasing water salinity. This study applies the Extended Social Cognitive Theory (ESCT) to explore these dynamics. This study employs a descriptive-survey research method, with the statistical population comprising farmers in Qaenat County, South Khorasan Province. A proportional random sample of 300 farmers was selected and surveyed using a researcher-developed questionnaire. The validity of the questionnaire was confirmed through expert opinions, while its reliability was assessed using Cronbach's alpha coefficient, ranging from 0.6 to 0.95. The Structural Equation Modeling (SEM) analysis revealed that the perception of others' behavior (POB), attitudes, and perceived barriers significantly predict the willingness to adapt to water salinity through both technical and non-technical methods. Furthermore, the findings revealed that the ESCT accounted for 48.4 % of the variance in farmers' migration intentions and 29.5 % of the variance in their actual migration behavior. Among the examined factors, perceived behavior of others, attitudes, and social capital emerged as key determinants of migration behavior, while moral norms exerted the strongest influence on migration intentions. These results underscore the critical role of socio-cognitive variables in shaping adaptive responses to the challenges posed by water salinity. Future research could apply various behavioral models to investigate both technical and non-technical adaptation strategies, offering a more comprehensive understanding of how individuals respond to environmental challenges across different contexts.

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