Assignment Name: Baseline Survey in Chittagong Hill Tracts, Barind Tract, and Saline-Prone Region for the “Building Climate Resilient Livelihoods in Vulnerable Landscapes in Bangladesh (BCRL)” Project

Country: Bangladesh

Client Name: Food and Agriculture Organization (FAO) Bangladesh

Department of Agricultural Extension (DAE), and the Department of Environment (DoE) with technical support from the Food and Agriculture Organization of the United Nations (FAO) have been implementing a project entitled Building Climate Resilient Livelihoods in Vulnerable Landscapes in Bangladesh (BCRL). The overarching objective of this project is to improve the resilience of people, communities, and ecosystems to climate change, and improve livelihoods through increased value addition in the agricultural food systems of Bangladesh. The project will target three different landscapes across some of Bangladesh’s most vulnerable geographies, covering drought-impacted zone in the northwest, salinity and waterloggingprone coastal areas in the south-west, and extreme rainfall and erosion-prone areas in the south-east. The targeted upazilas are:

• High Barind Tract (HBT): Nachole, Godagari, and Bholahat upazilas;
• Waterlogged/Saline: Paikgachha, Dumuria, and Batiaghata upazilas;
• Chittagong Hill Tracts (CHT): Manikchhari, Khagrachari Sadar, and Kawkhali upazilas.

The Baseline Survey in the Chittagong Hill Tracts (CHT), Barind Tract, and Saline-prone Region was conducted as part of the Building Climate Resilient Livelihoods in Vulnerable Landscapes in Bangladesh project. The project, initiated and funded by FAO and GoB, aimed to foster climate resilience among communities in these ecologically vulnerable regions by promoting adaptive livelihood strategies and enhancing their capacity to respond to climate-related risks.

The baseline survey was designed to provide a comprehensive assessment of the socio-economic conditions, livelihoods, and climate vulnerability in these regions. The study covered agricultural practices, water management, and community resilience to extreme climate events such as droughts, floods, and salinity intrusion. The survey’s findings were intended to guide interventions aimed at enhancing the livelihoods of marginalized communities and supporting them in transitioning to climate-resilient farming and natural resource management practices.

The survey contributed to the economic empowerment of vulnerable communities through sustainable livelihood models and pro-poor development in the context of climate change adaptation.

The overall objectives of this survey were to assess the socio-economic, environmental, and climatic conditions in the target geographies of the BCRL project and generate the latest data and information for informed decision-making, village selection, and finalizing the mechanism of delivering climate resilience building support at the field level.

The scope of work and specific tasks of the Service Provider (SP) included:

• Conduct household surveys (based on the survey distribution outlined in Table 1) in the aforementioned nine upazilas of HBT, CHT, and Waterlogged/Saline prone regions of Bangladesh using KOBO Toolbox.
• Process, and analyze the collected data, and deliver an analytical report with region-specific and combined assessment.

Perform data analysis with statistical or econometric software (i.e SPSS, STATA, R) from different perspectives such as climate vulnerability, gender, food and livelihood security, farm management, climate resilience, etc. The utilization of an appropriate econometric model would be preferred in data analysis.

DM WATCH conducted the following tasks:

Comprehensive Baseline Survey

• Conducted a detailed baseline survey across three distinct vulnerable landscapes: the High Barind Tract, saline-prone coastal areas, and the Chittagong Hill Tracts. The survey captured socio-economic conditions, climate vulnerabilities, agricultural practices, and community resilience strategies, providing a foundational dataset for the project.

Extensive Qualitative and Quantitative Data Collection

• Quantitative Data: Collected data from households using structured surveys via KOBO Toolbox to ensure efficiency and accuracy in the field.
• Qualitative Data: Conducted key informant interviews (KIIs) and focus group discussions (FGDs) with community stakeholders, farmers, and local authorities to gather in-depth insights on challenges and opportunities in the regions.

Econometric and Statistical Data Analysis

• Utilized advanced statistical and econometric software such as SPSS and STATA to analyze data from multiple perspectives, including gender, food security, climate resilience, and livelihood trends. Key insights were drawn using climate vulnerability indices and econometric models.

Integration of Gender and Climate Resilience Perspectives

• Incorporated a gender-sensitive lens in the analysis, highlighting disparities and adaptive capacities of marginalized groups, especially women. The study also focused on climate resilience strategies employed by communities in different ecological zones.

Customized Reporting and Deliverables

• Delivered detailed region-specific and combined assessment reports tailored to the needs of the Food and Agriculture Organization (FAO) and the Government of Bangladesh.
• Provided a complete dataset with coding files in Excel and STATA formats, ensuring transparency and reusability for future reference.

 Stakeholder Engagement

• Facilitated meaningful interactions with key stakeholders, including community leaders, government officials, and local organizations, to validate findings and ensure practical applicability of recommendations.

Knowledge Dissemination

• Presented findings and recommendations in workshops and meetings with FAO and project stakeholders. Insights from the study were geared towards informing climate-adaptive policies and sustainable livelihood interventions.

Capacity Building for Field Teams

• Conducted training sessions for enumerators and field staff, emphasizing the use of digital tools for data collection and maintaining data quality through cross-validation and supervision.