
Maiyaki Damisa
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Papers by Maiyaki Damisa
tackling poverty related issues among the rural farmers in sub-Saharan Africa through an innovation
focussed approach referred to as Integrated Agricultural Research for development (IAR4D). This
paper employs some baseline data of the Sudan Savanna Task Force in analysing household livelihood
strategies and their poverty status. Stratified random sampling technique was employed in collecting
data from the respondents. The respondents were classified on the basis of whether the farmers are
future IAR4D (intervention), conventional (ARD) or clean sites (little intervention). This is necessary
for the end-line survey and for the impact assessment of the Challenge Programme. A total of 600
households were surveyed for the study. Descriptive statistics, estimated poverty line and the Tobit
regression model were employed in the analysis. The result showed that sales of crops and livestock
constitute the highest proportion of household income in all the IP sites implying that the households
are highly dependent on agriculture for their livelihood. There was no significant difference (P<0.05)
in the number of households below the estimated poverty line in all the treatments however, the
poverty gap index was highest for the IAR4D sites and least for some R & D sites; Results of the Tobit
analysis explaining the factors determining the intensity of household poverty in the region (regales of
village type) shows that eight variables affect household poverty intensity (P < 0.05) viz: Household
Head Education, Child Dependency Ratio, Household Size, Farm Income, Household Production
Enterprise Portfolio, Non Farm Income, Household total farm area and Extension Contact. According
to the results obtained from the elasticity coefficients, the important factors that reduce household
poverty intensity in the study area were farm income (P < 0.05), household total farm area (P < 0.10)
and non-farm income in order of importance (P < 0.10). Factors that significantly increase poverty
intensity were household size and child dependency ratio (P < 0.05). The results thus imply that given
that IAR4D is designed to act directly on farm income through adoption of sustainable agricultural
technologies, taskforce interventions are likely to contribute to reduction of poverty in the area.
food shortage experienced by the households becomes harsh by January /February and the shortage becomes severe by
March /June. This cycle of seasonal food shortage by farming households keeps occurring yearly. This paper therefore,
investigated the farming household perception and response strategies to the seasonal food shortages in the Northern
Guinea Savanna of Nigeria. The study was carried out in four villages randomly selected from two Local Government
Areas in Kaduna and Katsina States. A total of 230 farming households were interviewed. The double-hurdle model was
employed in the analysis. The result showed that more than 70 percent of households experience severe food shortage
and the factors that influenced household perception of food shortage are different from factors that influence the
household response strategies to food shortage.
tackling poverty related issues among the rural farmers in sub-Saharan Africa through an innovation
focussed approach referred to as Integrated Agricultural Research for development (IAR4D). This
paper employs some baseline data of the Sudan Savanna Task Force in analysing household livelihood
strategies and their poverty status. Stratified random sampling technique was employed in collecting
data from the respondents. The respondents were classified on the basis of whether the farmers are
future IAR4D (intervention), conventional (ARD) or clean sites (little intervention). This is necessary
for the end-line survey and for the impact assessment of the Challenge Programme. A total of 600
households were surveyed for the study. Descriptive statistics, estimated poverty line and the Tobit
regression model were employed in the analysis. The result showed that sales of crops and livestock
constitute the highest proportion of household income in all the IP sites implying that the households
are highly dependent on agriculture for their livelihood. There was no significant difference (P<0.05)
in the number of households below the estimated poverty line in all the treatments however, the
poverty gap index was highest for the IAR4D sites and least for some R & D sites; Results of the Tobit
analysis explaining the factors determining the intensity of household poverty in the region (regales of
village type) shows that eight variables affect household poverty intensity (P < 0.05) viz: Household
Head Education, Child Dependency Ratio, Household Size, Farm Income, Household Production
Enterprise Portfolio, Non Farm Income, Household total farm area and Extension Contact. According
to the results obtained from the elasticity coefficients, the important factors that reduce household
poverty intensity in the study area were farm income (P < 0.05), household total farm area (P < 0.10)
and non-farm income in order of importance (P < 0.10). Factors that significantly increase poverty
intensity were household size and child dependency ratio (P < 0.05). The results thus imply that given
that IAR4D is designed to act directly on farm income through adoption of sustainable agricultural
technologies, taskforce interventions are likely to contribute to reduction of poverty in the area.
food shortage experienced by the households becomes harsh by January /February and the shortage becomes severe by
March /June. This cycle of seasonal food shortage by farming households keeps occurring yearly. This paper therefore,
investigated the farming household perception and response strategies to the seasonal food shortages in the Northern
Guinea Savanna of Nigeria. The study was carried out in four villages randomly selected from two Local Government
Areas in Kaduna and Katsina States. A total of 230 farming households were interviewed. The double-hurdle model was
employed in the analysis. The result showed that more than 70 percent of households experience severe food shortage
and the factors that influenced household perception of food shortage are different from factors that influence the
household response strategies to food shortage.