Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/5769
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dc.contributor.authorAdebayo, C.O , Akogwu, G.O and Yisa, E.S-
dc.date.accessioned2021-07-01T14:41:30Z-
dc.date.available2021-07-01T14:41:30Z-
dc.date.issued2012-12-
dc.identifier.issn0794-5213-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/5769-
dc.description.abstractFarm households in Nigeria have many challenges which include poverty and income variability. One of the strategies often adopted to tackle this is income diversification. This study was therefore designed to identify determinants of income diversification among farm households in Ikara, Makarfi and Giwa Local Government Areas of Kaduna State. A three stage random sampling technique was used to collect primary data from two hundred and twenty-two (222) households distributed across the study area. Analytical tools used were descriptive statistics and Tobit regression model. The Tobit model was used to capture determinants of income diversifications (agricultural and non- agricultural enterprises) among farm households. Majority (43%) of respondents had an average non-farm income of N199, 950.45 per year with 11% of respondents in full time farming. The significant variables that increase income diversification strategies of farm households were educational level, farm size, membership of cooperatives and non-farm income while farm size decreases the income diversification of households with the highest elasticity of 0.41. This study suggests the need for provision of basic infrastructure in the farming communities to increase their non-farm activities.en_US
dc.language.isoenen_US
dc.publisherproduction agricultural and Technologyen_US
dc.subjectfarm household, income diversification, determinantsen_US
dc.titleDeterminants of Income Diversification Among Farm Households In Kaduna State: Application of Tobit Regression Modelen_US
dc.typeArticleen_US
Appears in Collections:Agricultural Economics and Farm Management

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