Household Food Security of Farmers in Coastal Bangladesh: Insights into the Effects of Climate Change Perceptions, Agricultural Technology Use, and Weather Parameter Fluctuations

Background: Coastal regions are particularly vulnerable to the impacts of climate change on food production. In Bangladesh, with over 170 million people, food insecurity due to climate change shocks and extreme events is a growing concern. This study investigates climate change perceptions, agricultural technology use, socio-economic conditions, and household food security among farmer households in coastal Bangladesh.

Methodology: To explore the connections between food security, climate change shocks, and agricultural technology use, we applied various statistical tests to analyze predictive and explanatory variables. Using binary logistic regression, we examined the causes and dynamics of climate change risk perceptions and agricultural technology adoption. Key indicators included the Food Consumption Score (FCS) and the Household Food Insecurity Assessment Scale (HFIAS), which relate to farmers’ adaptation to climate change, asset management, climate change risks, and socio-demographic factors. Our survey covered 406 farmer households in the Khulna and Bagerhat districts of Bangladesh. We employed cluster and stratified sampling strategies for data collection. Additionally, we analyzed temporal data from 1991 to 2021, focusing on annual average mean and maximum temperatures, and rainfall patterns to assess weather trends.

Results: The binary logistic regression reveals significant differences between food-insecure and food-secure individuals in terms of gender, education, occupation, family size, HFIAS scores, household income, and farmland area, while age, distance to market, and agricultural income show no significant differences. For technology use among farmers, significant differences are found in gender, agricultural income, food security, household income, and farmland area, but not in age, distance to market, family size, or education. Correlation values (R=0.35) and (P=0.0058) indicate a moderate positive correlation between year and temperature, showing a statistically significant warming trend over the past three decades in the Khulna-Bagerhat region. The values (R=0.28) and (P=0.029) indicate a weak positive correlation between year and maximum temperature, suggesting a slight but statistically significant warming trend with year-to-year fluctuations. Annual and maximum precipitation show variability but are not statistically significant over the past decades.

Conclusion: The results show that farmers in Khulna and Bagerhat districts struggling with climate change need support from policymakers to adopt more resilient practices. This study can help design local training programs, raise climate change awareness, and improve sustainable farming techniques, which can be replicated in similar areas.