Full Length Research Paper
ABSTRACT
This paper examined the socio-economic factors affecting early maturing maize varieties adoption in Safana Local Government Area of Katsina State, Nigeria. Using random sampling techniques, 300 maize farmers were selected across 10 communities in the Local Government area. Out of the 300 respondents sampled 163 were non-adopters and 137 were adopters. Data obtained were analyzed using descriptive statistics, adoption index and Probit regression models. The major findings showed that 88% of respondents were male headed, average age of household head was 44 years, average household size was 11 persons, dependency ratio was 1.49, level of education was Islamic education, average years of schooling was 5 years and average years of farming was 25 years. About 65% of farmers had access to extension agent, only about 10% had access to credit and labor force was mostly family labor. Results of probit model showed that farmers’ size of land for maize cultivation (1%), farmers’ participation in an association (1%), number of extension contacts (10%), age of farmer (5%) and income from sales of maize (1%) influenced the adoption of early maturing maize varieties. The adoption of early maturing maize varieties has contributed in increasing the income of maize farming households as well as enhancing the status of maize farming households.
Key words: Socio-economic factor, adoption, early maturing maize varieties.
INTRODUCTION
METHODOLOGY
Study area
This study was conducted in Safana LGA Katsina State, Nigeria. Safana Local Government Area(LGA) has a projected population of about 183,779 based on 3.2% growth rate (NPC, 2006) and an area of 282 km2 (KTARDA, 2012). The Local Government is located at 12° N and 7°E of the equator. April is warmest with an average temperature of 37.9°C at noon. December is coldest with an average temperature of 13°C at night. Safana has no distinct temperature seasons; the temperature is relatively constant during the year.
Sampling procedure
The target populations for this study were male and female maize farmers from all the 10 communities of the Sorghum/Legume/Livestock platform in Safana LGA. These communities are Mai Jaura, Kunamawa A, Kunamawa B, Dogon Ruwa, Kanbiri, Sabon Garin Baure, Sabon Garin Gamji, Doga, Takatsaba, Kwayawa. There was no complete list of farmers in these communities but a list of maize farmers was generated with the help of both the village heads and extension agents in these communities. From each of the ten communities, 30 respondents were randomly selected giving a total of 300 respondents. Out of the 300 respondents sampled 163 were non-adopters and 137 were adopters.
Data collection
Primary data were used for this study. Data were collected using structured questionnaire administered by trained enumerators. The information collected was on sex, age, marital status farm size and family size based on 2012 farming season. The survey was conducted in March 2013.
Data analysis
The analytical tool that was employed for this study was probit regression model. The specification of the probit model follows that in the process of planting early maturing maize varieties, farmers have to decide between two choices, and if Y is the outcome from the choice, then:
Yi = 1, if the farmer plants the early maturing maize varieties introduced.
Yi = 0, if the farmer does not plant the early maturing maize varieties introduced
Either choice yields a utility index, Ui, that the individual farmer, I, acts to maximize. If Ui* is the critical or threshold level, at which decision to plant occurs, then:
Yi = 1 if Ui > Ui*
Yi = 0 if Ui ≤Ui* (1)
The non-observable underlying utility function which ranks the preference of the ith farmer can be expressed thus:
Where, Xni = the nth variable of the ith observation and Bn = the nth parameter to be estimated.
The probability Pi for the farmer i to adopt the varieties is then:
Pi = P [Y =1] = P [Ui> = Ui*] = P [Ui* < = Ui]
Since Ui* is a discrete random variables, if F [*] is its cumulative distribution function, then:
P [Y = 1] = P [Ui* < = Ui] F [Ui]
P[Y-1] = 1 – F [Ui] (3)
The form of F [*] is determined by the probability density function of the random variable Ui. Equation [ii] is a form of generalized linear models which can be rewritten as follows:
The Linear form of the model is specified as:
Yi = α + + + + + + + + + + µ (4)
= Age of the farmer (in years)(-); Years of formal education (+); = Number of years of farming experience (+); = Previous season farm income for maize (Naira) (+); = Farm size (hectares cultivated for maize per season) (+); = Access to credit (Amount of credit accessed during production season) (+); = Extension contact (Number of extension contacts during the production period) (+); = Household size (number of person in the household) (+); = membership of association (Years spent in association) (+); α = constant term; µ = disturbance term or error term, and are the regression coefficients of the independent variables.
RESULTS AND DISCUSSION
CONCLUSION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
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