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Research Article Open access CC BY 4.0

Impact of Betel Leaf Farming on Livelihood and Income Generation in Some Selected Areas of the Rajshahi District of Bangladesh

Eshrat Jahan Mahfuza, Md. Shishir Ahamed, Md. Fuad Hassan

South Asian Journal of Social Studies and Economics · pp. 250–259 · Published 7 Oct 2023

10.9734/sajsse/2023/v20i3728

Abstract

Betel leaf is popularly known as “paan’’ in Bangladesh. As income and livelihood are greatly impacted by the different production practices, the goal of this study was to determine betel leaf farming's impact on livelihood and income generation in some areas of the Rajshahi district of Bangladesh. A total of 108 farmers were randomly selected from three Upazilas (Bagmara, Durgapur, and Mohanpur Upazila) of the Rajshahi district using a multistage sampling technique.  Primary data were collected through a face-to-face survey during the month of December 2021. The CARE livelihood approach was carried out to determine the impact of betel leaf farming on livelihood. To better understand the livelihood and income status of the respondent, Focus Group Discussion (FGD) and different observation techniques were employed. The results of the study showed that as a cash crop, the continual production of betel leaves throughout the year ensures a minimum level of return to most of the farmers, and a large group of people directly earned their livelihood through it. In addition, it was revealed that about 77% of the farmers thought that they had a better living standard due to the betel leaf production though 17% were of the opinion that there were no changes in their living standard. Inadequate capital, market risks, production risks, institutional risks, human risks, and financial risks were the main problems and constraints of betel leaf farming in the study area. Finally based on the results, significant policy recommendations were given regarding betel leaf farming in Bangladesh.

Betel leaf cash crop livelihood risk factors Rajshahi district

Cited by 2

Implementation of an EfficientNet-B4 Model Architecture with a Convolutional Block Attention Module (CBAM) for Betel Leaf Disease Classification

Rima Tri Wahyunigrum, Indah Agustien Siradjuddin, Triasmi Dwi Farawati · Engineering, Technology & Applied Science Research · 2025

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