Socio-economic determinants of income among cowpea farmers in Bama local government area of Borno state, Nigeria.

The paper analyzed the socioeconomic determinants of income among cowpea farmers in Bama Local Government Area (LGA) of Borno State, Nigeria. Primary data were mainly used for the study. This was collected using structured questionnaires administered to 120 cowpea farmers sampled from six villages spread across the LGA. The study employed both descriptive and inferential statistics to analyze the data. The findings of the study revealed that about 78% of the respondents were males while 22% were females. Majority of the farmers (63.3%) had formal education and 73.3% cultivated farm land of not more than 2 hectares. The regression analysis result indicated that the variables specified in the model explain 75% of the income generated by the cowpea farmers in the study area. Educational level, number of productive members per household, access to extension agents and years of farming experience were significantly and positively related to income of cowpea farmers. Major constraints reported by the cowpea farmers in the study area include high cost of inputs and inadequate storage facilities. Based on the results of the study, it was recommended that policy measures aimed at providing relevant training opportunities and education schemes to the farmers should be encouraged; further more, farmers should be encouraged to join or form cooperative societies to take advantage of economics of scale in purchasing inputs and also to obtain good price for their produce.KEY WORDS: Socio-economic, Determinants, Income, Cowpea, Bama, Nigeria

The relationship between social factors and the poverty experienced by farming households in Borno state, Nigeria.

Many social characteristics of households relates to the poverty experienced by households. Hence, this study examined the poverty profile and social factors that relate with it among the farming households in Borno State, Nigeria. Using multistage sampling technique, 360 farming households were randomly sampled from 12 villages spread across six Local Government Areas of the three agro-ecological zones in the State. Primary data generated from farming households through well-structured questionnaires were mainly used for the study. The data were analysed using descriptive statistics and Foster, Greer and Thorbecke (FGT) P alpha measures of poverty. The monthly mean per adult equivalent household expenditure (MPAEHE) of the households was N2,972.77 out of which a poverty line of N1,982.84 was estimated. The FGT poverty measures showed that 62% of the farming households of the study area were poor; the average depth of the poor households from the poverty line was 44% of the poverty line, while 18% of the poor farming households were critically or severely poor. The findings revealed that poverty level among farming households increased with increase in the age of household heads, years of farming experience, household size; child dependency ratio and adult dependency ratio. On the other hand, poverty level decreased with increase in the household heads’ years of formal education and number of extension contacts per season. The study further revealed that poverty level in the study area was relatively higher among households headed by males, married persons and among households whose heads were not member of any cooperative society. Based on these findings the study recommended that policies aimed towards increasing access of households to educational facilities and provision of better family planning should be given adequate attention.Keywords: Markov games; Queuing; Virtual reality strategies; nash equilibrium, MSC CLASSIFICATION: 91A4

Poverty and its determinants among farming households in West Africa: empirical evidence from Borno State, Nigeria

The study examines the extent of poverty and its determinants among farming households in Borno State of Nigeria. Primary data were obtained from 1,998 farming households using well-structured questionnaires in 2004. Mean monthly per adult equivalent household expenditure poverty line was used to characterize the households into poor and non-poor, while Tobit regression model was used to determine the factors associated with poverty experienced by the farming households in the study area. About 67% of the households were categorized as poor. The Tobit regression analysis reveals that 15 out of the 23 household livelihood-related variables included in the model had their coefficients significant at between (P<0.01) and (P<0.10), representing about 63 percent of the variables. The Tobit result reveals that increases in farm size, amount of credit, agricultural production diversification and extent of agricultural output commercialization contributed to reduce the poverty level among farming households. Poverty intensity is high among households having large size, high child dependency ratio and expenditure on education. Therefore, policy measures should aim at enhancing the rural farmers’ access to improved agricultural inputs, land and credit; thereby increasing agricultural productivity and production so as to meet home consumption and generate an increased surplus for the market