Determinants of Risk Status of Small Scale Farmers in Niger State, Nigeria

Aims: To ascertain the risk status of farming households and whether the risk status is accentuated by some factors. The specific objective is to determine the relationship between their risk status and socio-economic characteristics and food security status in the study area.
Study Design: Cross-sectional study.
Place and Duration of Study: Department of Agricultural Economics and Extension Technology, Federal University of Technology, Minna, Nigeria, between March 2011 and February, 2012.
Methodology: The population for the study comprised farming households in Niger State. In order to obtain the sample for the study, two Local Government Areas (Suleja and Bosso) where randomly selected from where five farming communities were randomly selected and then ten farm families were randomly selected to give a total of 50 household from each Local Government Areas and 100 respondents for the study. The primary data covering background information, scale of production and yield, agricultural input use and access to credit, household food security and risk status were collected with structured questionnaire. Data analysis included the description of the socio-economic characteristics of the respondents using descriptive statistics and multinomial logistic regression used to confirm the determinants of risk status of the respondents.
Results: With an LRI of 0.3451, the estimates of the explanatory variables show that the set of significant explanatory variables and their sign vary across the groups. The model, through the explanatory variables included predicted correctly 46.17% of risk neutral respondents, only 0.31% of the risk seekers and 53.53% of risk-averse respondents. The overall prediction was 53.8%. In this particular study, sex, primary educational status, years of farming experience, marital status, household size, credit, membership of cooperative, land acquisition by inheritance and total investment capital are the factors found to have determined risk status at different levels of significance but with differing signs relative to the base outcome.
Conclusion: The model specified correctly predicted the probability of the risk status and has highlighted that there are more than just the observed socio-economic variables that explain the risk attitude of farmers, hence risk attitudes could only be explained by individual social, economic, cultural and psychological factors and it may be important to estimate individual risk preferences or identify factors that affect the individual’s capacity to bear risk or consider their risk environment.