Farmers’ intention to adapt to climate change, application of an extended version of integrated planned behavior and protection motivation theories

Introduction
Global climate change has increasingly emerged as one of the most significant global challenges threatening the environment and worldwide food security, particularly in less developed regions (Adu et al., 2018). The direct impacts of climate change on the agricultural sector include food insecurity (Cox and Bastiaans, 2007), an increase in pests and diseases that negatively affect agricultural productivity (Dehghanpour et al., 2020), enhanced erosion, and a decline in soil fertility (Gabrielle et al., 2017). Therefore, it is essential to mitigate these impacts by empowering farmers to adapt and manage the adverse effects on agriculture and their livelihoods (Goyal et al., 2016). Effective adaptation can significantly reduce the magnitude of climate change impacts (Nakuja et al., 2012). Adaptation refers to a set of actions taken to maintain a system's ability to cope with climate change (James et al., 2018). It can considerably reduce vulnerability to change and assist rural communities in mitigating potential risks and coping with the negative consequences of change (Bryan et al., 2009). Various theories and models have been developed to analyze people's intentions and behaviors. Ajzen's Theory of Planned Behavior (TPB) (Ajzen, 1991) is among the most widely used theories, positing that a person's intention to engage in a specific behavior is influenced by their attitudes, subjective norms, and perceived behavioral control. Protection Motivation Theory (PMT), developed by Davis et al. (2015), has been utilized to assess how motivation influences individuals' health behaviors. PMT consists of two components: threat appraisal, measured by perceived threat and perceived severity, and coping appraisal, measured by self-efficacy, response efficacy, and response costs. This study employed an extended model integrating TPB and PMT to investigate farmers' intentions and behaviors regarding climate change adaptation.

Methodology
This study was conducted in Meshkinshahr County. A cross-sectional survey method utilizing a multi-stage sampling strategy was employed for data collection. The statistical population comprised orchard farmers in the county, from which a sample of 220 farmers was selected using the Cochran formula. The research instrument was a questionnaire developed based on the study's theoretical framework and a comprehensive literature review, focusing on the research objectives. The questionnaire included the socioeconomic characteristics of the respondents and eight constructs of the model. It was validated by a panel of experts. A pilot study was conducted with 30 farmers outside the sample area to test the validity of the questionnaire and make any necessary corrections. The calculated alpha values for all constructs were above 0.7, indicating acceptable reliability of the research instrument. SPSS version 22 was used for initial statistical analyses, and Structural Equation Modeling (SEM) with PLS software was utilized to model and predict the effects of factors influencing farmers' intentions and behaviors regarding adaptation. The goodness of fit for the model was assessed first. The reliability of the research model was confirmed using composite reliability coefficients and Cronbach's alpha. Indicators of discriminant validity, as well as convergent and divergent validity, were employed to evaluate the model's validity.

Results and Discussion
The results indicated that, with the exception of perceived severity, all hypotheses of the study were confirmed, explaining 84% of the variance in intention. Perceived vulnerability had the most significant impact on farmers' intentions, suggesting that their willingness to adapt to climate change largely depends on their perception of vulnerability (Gabrielle et al., 2017). The significant effect of attitude on intention implies that a positive attitude toward new ideas leads to better preparation and a greater intention to implement them (Rezaei and Ghofranfarid, 2017). Self-efficacy also demonstrated a significant effect on intention, indicating that individuals with higher perceived self-efficacy are more likely to seek coping strategies and take adaptation measures when facing challenges such as climate change (An et al., 2022; Yi et al., 2018). The significant influence of trust on intention shows that farmers are more inclined to adapt when they trust government officials and agricultural experts. Furthermore, the positive effect of belief in climate change on intention highlights that as farmers increasingly recognize changing climate conditions, they are more willing to adapt. This finding underscores the necessity for extension services to provide relevant information that aids farmers in adapting to climate change. Conversely, the lack of a significant effect of perceived severity on intention may stem from inadequate policy interventions and farmers' limited understanding of climate change. Additionally, behavioral intention positively affected actual adaptation behavior, explaining 56% of the variance in that behavior, thus confirming the foundational premise of the TPB. Moreover, significant differences in socioeconomic variables were evident among farmers based on gender, education, family size, and place of residence. Male and more educated farmers, as well as those with larger families in rural areas, were more likely to adapt to climate change.

Conclusion
Utilizing an extended model of the integrated TPB-PMT, this study explored farmers' intentions and behaviors related to climate change adaptation in Meshkinshahr County. The model successfully explained variance in farmers’ intentions, which in turn accounted for 56% of the variance in behavior. The findings offer new insights into the effectiveness of these theories in predicting farmers’ intentions and their adoption of climate change adaptation strategies, with important implications for agricultural policy and extension services. Among the constructs of the model, perceived vulnerability emerged as the most crucial factor influencing intention, while perceived severity showed no significant impact, indicating that farmers are somewhat aware of their vulnerability to climate change but lack sufficient knowledge of the severity of its consequences. As climate change continues to affect the agricultural sector, it is vital to conduct extension campaigns to educate and prepare farmers for adaptation.