Impact of climate-smart agriculture practices on multidimensional poverty among coastal farmers in Bangladesh

Agriculture employs over 70% of the rural population in developing countries, yet food security remains a critical challenge due to urbanization, climate change, and population growth. By 2050, food production needs to increase by 70% to meet the demands of a projected 9 billion people. Climate change exacerbates this issue through extreme weather events, significantly impacting agricultural yields. Bangladesh, particularly its southwestern coastal areas, is highly vulnerable to climate change impacts like salinity, cyclones, and flooding, leading to substantial rice losses. The agricultural sector in Bangladesh is dominated by small and marginal farmers, many of whom experience food insecurity and poverty rates exceeding the national average due to frequent climate shocks. Climate-smart agriculture (CSA) practices, which increase productivity, build resilience, and reduce emissions, are proposed as a solution to reduce yield losses caused by adverse climatic conditions. The study aims to identify factors influencing CSA adoption and to evaluate its impact on multidimensional poverty among coastal farm households in Bangladesh.

Existing research has explored factors associated with CSA adoption, such as land size, prior experience with climate shocks, land fertility, and market distance. Studies have also demonstrated positive impacts of CSA on yield and food security. However, the link between CSA and multidimensional poverty in coastal contexts remains under-researched. While some studies highlight increased agricultural output and improved livelihoods among CSA adopters, a comprehensive analysis of its impact on multidimensional poverty, particularly in vulnerable coastal areas, is lacking. This study addresses this gap by investigating the causal relationship between CSA adoption and multidimensional poverty, providing technology-specific recommendations for coastal farmers.

The study was conducted in the Khulna district of Bangladesh, a southwestern coastal zone vulnerable to climate change. A multi-stage sampling technique was employed to select 351 farm households across three sub-districts (Dacope, Paikgachha, and Batiaghata). Data were collected through face-to-face interviews using a structured questionnaire that covered socioeconomic characteristics, CSA adoption, crop diversification, poverty indicators, demographic factors, input variables, institutional factors, agricultural productivity, and land-specific variables. The questionnaire was pre-tested, and adjustments were made based on feedback. Data collection took place in May 2022. The study used an endogenous switching regression (ESR) model with full information maximum likelihood (FIML) estimation to address the endogeneity of the CSA adoption decision. Multidimensional poverty was measured using the Alkire-Foster multidimensional poverty index (MPI), which encompasses nine indicators across three dimensions: education, health, and standard of living. Seven CSA technologies were analyzed: crop diversification, agroforestry, farming decisions based on weather forecasting, on-farm diversification, conservation tillage, contingent crop planning, and rainwater harvesting/drip irrigation. The ESR model analyzed the factors influencing CSA adoption and the impact of adoption on MPI. Counterfactual analyses were conducted to estimate the potential poverty reduction if non-adopters had adopted CSA and if current adopters had not. Separate ESR regressions were also run for each specific CSA technology to assess their individual effects on MPI.

The ESR analysis revealed that crop vulnerability, crop income, access to extension services, and training on input management positively and significantly influenced the decision to adopt CSA. The counterfactual analysis showed a significant impact of CSA on multidimensional poverty. Current CSA adopters experienced a 41-percentage point lower MPI compared to a scenario where they did not adopt CSA (p<0.01). If current non-adopters had adopted CSA, their MPI would have been reduced by 15 percentage points (p<0.01). The simple treatment effect showed that current adopters had a 10-percentage point lower MPI than non-adopters. Analysis of specific CSA technologies demonstrated that agroforestry, on-farm diversification, conservation tillage, contingent crop planning, and rainwater harvesting/drip irrigation significantly reduced MPI (p<0.01). However, crop diversification and farming decisions based on weather forecasting showed a positive relationship with MPI. This might be due to the lower economic productivity of small farms, which compromises economies of scale, and the limited access to reliable weather forecasting information, respectively. Robustness checks using alternative MPI measures and a binary MPI variable yielded consistent results, confirming the significant impact of CSA on multidimensional poverty reduction.

The study's findings strongly support the hypothesis that CSA adoption significantly influences multidimensional poverty among coastal farm households in Bangladesh. The significant reduction in MPI among CSA adopters highlights the potential of CSA to mitigate climate change impacts on agriculture and improve livelihoods. The positive association between crop vulnerability and CSA adoption suggests that farmers are motivated to adopt CSA to protect against climate-related risks. The effectiveness of specific CSA technologies in poverty reduction provides valuable information for targeted interventions. The study emphasizes the importance of enhancing access to extension services and providing training on input management to promote wider adoption of effective CSA practices.

This study demonstrates the substantial contribution of climate-smart agriculture (CSA) to reducing multidimensional poverty among coastal farmers in Bangladesh. The findings underscore the need for targeted interventions focusing on specific CSA technologies such as agroforestry, on-farm diversification, and conservation tillage. Expanding access to extension services and providing training on input management are crucial for enhancing CSA adoption. Future research could explore the long-term impacts of CSA, investigate the role of gender and social capital in CSA adoption, and examine the effectiveness of different policy interventions to promote CSA in climate-vulnerable regions.

The cross-sectional nature of the study limits the ability to establish definitive causal relationships over time. The study focuses on a specific region in Bangladesh; therefore, the generalizability of the findings to other contexts needs further investigation. The sample size, while sufficient for the statistical analysis conducted, might not fully capture the heterogeneity of all farming practices and situations in the study area.