A PLS-SEM approach to determine farmers' awareness about climate change mitigation and adaptation strategies: pathway toward sustainable environment and agricultural productivity

This research was conducted in a significant agricultural region to gauge farmers' knowledge of climate change adaption strategies. We employed a semi-structured questionnaire based on the literature; it was broken up into sections, and used certain statistical techniques (PLS-SEM) to examine the results. Farmers who had sufficient assets and resources thought they were safer and could withstand the adverse effects of climate change. A total of 900 completed questionnaires were gathered to investigate the link between the control, moderator, and DV variables in the future. As a consequence, the PLS-SEM path analysis findings showed that our model is fit. PLS-SEM direct path analysis revealed AM >FACC, UA- >FACC, SA- >FACC, FS- >FACC, PR- >FACC, and SI- >FACC are significant. The established hypotheses H1-H6 are strengthened by these findings. We also examined the respondents' ages and genders to use as controls; whereas gender showed no correlation with FACC, there was a strong link between age and the dependent variable. There is no statistically significant correlation between gender and climate change awareness, but older people tend to have a broader understanding of the topic and its consequences. Education significantly moderates the relationship of farmer's awareness (climate change) associated with AM, UA, SA, FS, PR, and SI. depicts the moderation role of education on the relationship between AM*Education->FACC, UA*Education->FACC, SA*Education- >FACC, FS*Education- >FACC, PR*Education- >FACC, and SI*Education- >FACC. H2a and H5a in this study showed significant correlations with education as a moderator; however, H1a, H3a, H4, and H6a did not demonstrate any moderator relationships. There is a medium to strong correlation between various factors, and the correlation values of a few chosen variables are significant when compared to all other variables in the current study. Highly significant correlations were found between PR, SA, SI, and UA with FACC. Governmental policies and effective monitoring systems will be developed as a result of the research to enable integrated and sustainable water development.