Determinants of Poverty among Farming Household in South-East Nigeria
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Abstract
The study specifically profiled the socioeconomic characteristics, determined the level of poverty among farming households, and identified the determinant of poverty among farming households with descriptive statistics and a logistics regression model. General household survey secondary data was used. The data obtained was analyzed with descriptive statistics, such as mean, which was used to describe socio-economic characteristics of farming households, and the dimension of poverty among the farmers of 5 states was related by classifying the respondents into poor, moderately poor, and non-poor using the poverty line, and a logit model was used to examine the determinant of poverty among farming households in the southeast of Nigeria. Findings from the study revealed the average age of the respondent to be approximately 48 years. The mean household size is about 2, and the average income earned is about N24125. Age is negative and significantly influences household poverty status. Household size was found to be positive, and it significantly affected the poverty status of the farming household by 1%. Income from non-farm activities is positive, and it’s significant at 1%. Access to extension is positive and significant at 5%. Also, the source of electricity is positive, and it’s significant at 5%. Finally, the water source is positive, and it’s significant at 10%. The study recommends that governments and other stakeholders should provide incentives and support to encourage more youth to participate in farming activities. Farmers should prioritize their farming activities as their main source of income and livelihood. Farmers should ensure that their farming activities are not neglected or compromised in any way. The government should also reduce the cost of tariffs; transformers, solar, and other sources of electricity should be provided; and drill boreholes for farmers.
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Copyright (c) 2024 Afolabi Oluwatobi Christopher, Babarinde Taofeek Olajide, Offor Miriam Ngozi, Omonigba Toha Adewale, Olachi Deborah Azino, Aremu Waliyat Titilayo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Afolabi Oluwatobi Christopher, Department of Agricultural Economics, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Department of Agricultural Economics,
Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Babarinde Taofeek Olajide, Department of Agricultural Economics, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Department of Agricultural Economics,
Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Offor Miriam Ngozi, Department of Agricultural Economics, Federal University of Technology, 1526, PMB, Owerri, Nigeria.
Department of Agricultural Economics,
Federal University of Technology, 1526, PMB, Owerri, Nigeria.
Omonigba Toha Adewale, Department of Agricultural Economics, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Department of Agricultural Economics,
Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Olachi Deborah Azino, Department of Energy Economics, University of Ibadan, Box 4078, Ibadan, Nigeria.
Department of Energy Economics,
University of Ibadan, Box 4078, Ibadan, Nigeria.
Aremu Waliyat Titilayo, Department of Agricultural Economics, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
Department of Agricultural Economics,
Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria.
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