ABSTRACT

This study on the determinants of labour supply and utilization of cassava farmers in Afikpo North Local Government Area of Ebonyi State examined the socio-economic characteristics of the farmers, factors affecting labour supply and the determinants of cassava productivity in the study area. Simple random sampling technique was employed in selecting sixty cassava farmers in the study area while both simple descriptive statistics like tables and frequencies and other econometric tools like multiple regression were used to analyze the data.75% of the respondents were males which accounted for the majority of the farmers against females who were a minority (25%). Marital status revealed clearly that they were majorly married given the high value of 62% in comparison to 30% for the single. 42% of the cassava farmers had no kind of education and were illiterates and thus could not read and write. The result showed that majority of the cassava farmers representing a percentage of 50 were between household sizes 4-6 which was moderately large. Cassava farmers in the study area were also involved in other businesses as trading, transportation and civil. Inheritance accounted for about 50% of all the land used for farming in the study area. The R2value of 0.673 means that 67.3% variation in labour supply was accounted for by changes in the independent variables included in the model Four explanatory variables were included and regressed against the data for labour supply measured by the total man days of labour during the farming season. The variables household size and wages were all significant and positively related to labour supply at 5% and 1% respectively while farm size and farm income were significant at 1% and 5% respectively but negatively related to labour supply. These all conformed to economic theory. The result of the multiple regression for the determinants of cassava farmers' productivity in the study area showed that 77.% of variation in the dependent variable (cassava output) was explained by the changes in the independent variables included in the model. The result showed that farm size was positively related to cassava output at 1% level of significance. This conforms to a priori expectation and simply means that as farm size increased, cassava output increased as well. Farming experience showed a 1% positive significance level. This implies that farmers would get more output from their cassava farms as they got more experience. Fertilizer usage influenced cassava output positively at 5% level of significance. This indicates that cassava output will increase with an increase in fertilizer application. The distance farmers travelled to their farms from their homes had a negative toll on cassava productivity at 10% significance. The implication of this is that as farmers located their farms farther away from their residential areas, farm output decreased. A 10% significant positive relationship existed between the number of extension visits witnessed by cassava farmers and their output. This means that farm output will increase with the frequency of extension visits. Household size had a negative relationship with cassava output at 10% meaning that farm output will decrease as household size increases.