Aims: In past, labour was extensively used in agriculture sector and there was huge surplus labour in agriculture sector. However, such trend has recently been changed where surplus labour in agricultural sector has reduced significantly and agriculture sector compete with non-agricultural sector in terms of hiring labour. Thus, the present study was undertaken to analyze the determinants of labour migration from agriculture sector to non-agricultural sector in Gopalganj district of Bangladesh. Place and Duration of Study: The study was conducted at 12 villages of three upazilas (Gopalganj Sadar, Tungipara and Kotalipara) in Gopalganj district. For the study, data were collected during the period from January to March in 2021. Methodology: To this end, primary data were collected from agricultural labours. Descriptive statistics and simple random sampling technique were used in this study. Binary logistic model was used to analyze the collected data. In addition, five point likert scale was used to rank the barrier towards internal labour migration. Results: Results found from the logit model indicate that factors like family size, education, past experience, access to available information, transportation facilities, and savings are positively related with the log of odd ratio in favor of labour migration from agriculture sector to non-agricultural sector while wage rate, age, off-farm income and farm holdings are inversely related with labour transfer from agriculture sector to non-agricultural sector. In addition, respondents in the study area have recognized lack of proper technical training as the major constraint in labour migration with a mean value of 4.48. Conclusion: The present study recommends that government should take initiatives to open skill development institutions in rural level so that agricultural labour can take training. Regarding necessary information on non-agricultural jobs, it can be recommended that government, local agents and NGOs, in case of migration, should take proper initiatives so that agricultural labours can easily get information about non-agricultural jobs.

Aims: Although the income of the people of other sectors in the economy is relatively stable but the income of farmers are comparatively unstable. In addition, most of the previous studies analyzed partially the determinants of farmer’s income.Thus, the aim of the current study is to investigate the pattern of farmers’ income in Gopalganj district of Bangladesh. Place and Duration of Study: The present study was conducted in Gopalganj district of Bangladesh where 10 unions, 20 villages and 400 respondents were selected randomly. Data were collected during the period from January to May in 2022. Methodology: A multistage sampling technique was applied for the study where district and upazilas were selected purposively while unions, villages and respondents were selected using simple random sampling techniques. Primary data were collected from 400 farmers using a well-structured questionnaire. Descriptive statistics and the log linear regression model were used as the analytical tools. Results: Result found from the descriptive statistics indicates that mean age of the respondents was 49.02 years whereas average educational attainment of the farmers in the study area was 5.72. In regards to the     farming experience, it is found that average farming experience of the farmer was 5.07 years with maximum and minimum values were 25 years and 3 years, respectively. Regarding the household size of the respondents, the study showed that the mean family member was 4.82 with a standard deviation of 1.02. The study also indicates that 35.12% average income of the farmer obtained from non-farm sources while 64.88% average income obtained from farm sources.In regards to the farm income, it is found from the regression analysis that farm income is positively related with age, household size, education, farm size, agricultural training, access to credit facilities, membership of agricultural cooperative and distance to nearest market whereas the same is influenced by non-farm income. The findings suggest that variables like household size, education, household expenditure, access to internet and access to technical and vocational training has positive impact on non-farm income. In contrast, variables like age, farm size, distance from town, farm income have negative impact on non-farm income of the respondents in the study area. Finally, some policy recommendations are made based on the findings obtained from the study.