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An Analysis of the Pattern of Farmer’s Income in Gopalganj District: An Empirical Investigation

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.