A systematic PLS-SEM approach on assessment of indigenous knowledge in adapting to floods; A way forward to sustainable agriculture

Abstract

The present study was conducted in one of the major agriculture areas to check farmers indigenous knowledge about the impacts of floods on their farming lives, food security, sustainable development, and risk assessment. In the current study, primary data was used to analyze the situation. A semi-structured questionnaire was distributed among farmers. We have collected a cross-sectional dataset and applied the PLS-SEM dual-stage hybrid model to test the proposed hypotheses and rank the social, economic, and technological factors according to their normalized importance. Results revealed that farmers’ knowledge associated with adaption strategies, food security, risk assessment, and livelihood assets are the most significant predictors. Farmers need to have sufficient knowledge about floods, and it can help them to adopt proper measurements. A PLS-SEM dual-stage hybrid model was used to check the relationship among all variables, which showed a significant relationship among DV, IV, and control variables. PLS-SEM direct path analysis revealed that AS (b = −0.155;p0.001), FS (b = 0.343;p0.001), LA (b = 0.273;p0.001), RA (b = 0.147;p0.006), and for FKF have statistically significant values of beta, while SD (b = −0.079NS) is not significant. These results offer support to hypotheses H1 through H4 and H5 being rejected. On the other hand, age does not have any relationship with farmers’ knowledge of floods. Our study results have important policy suggestions for governments and other stakeholders to consider in order to make useful policies for the ecosystem. The study will aid in the implementation of effective monitoring and public policies to promote integrated and sustainable development, as well as how to minimize the impacts of floods on farmers’ lives and save the ecosystem and food.