Determinants Of Loan Repayment Ability Of Farmers In Kogi State

This study evaluates socio-economic factors that could predict repayment ability, quantify the effects of those factors and device a method to curb or manage default in agriculture and/or other credit lending. Data for the analysis were obtained by interviewing a sample of loan beneficiary farmers cooperative society members and non cooperative society members in three local government areas in Kogi State. The major factors that affect repayment ability were identified and the extent of the effect was assessed using the marginal and elasticity of probability. Participation in cooperative societies, non-farm income; farming experience had major effect on repayment ability while family size, farm size and return on investment, had a minimal effect on repayment ability. Poor record keeping, low literacy, and fear of high interest rates were some of the problems and constraints encountered by the farmers and the credit institutions. it is recommended that farmers should be encouraged to keep good records, financial institutions should also ease the process of loan acquisition to enable farmers with low educational background better access to funds. It is also recommended that credit worthiness of to be beneficiaries should be calculated in advance to reduce the frequency of loan default

Forcasting Acha production in Nigeria: A comparison of grafted polynomial and linear models

Paucity of functional forms is one of the problems associated with forecasting economic time series data because not all economic time series correlate linearly to trend over an entire sample period. This paper attempt to investigate the performance of grafted polynomials in forecasting Acha production in Nigeria. The observed trend in Acha production in Nigeria from 1970-2004 provides the database for the study. A grafted model (Quadratic-Quadratic-Linear) was estimated to forecast Acha output for periods outside the sample. The estimated structural parameters gave an R squared values of 0.81 and 0.99 for the linear and grafted respectively. Forecast were generated to evaluate the predictive performance of the estimated models, the grafted model performed better than the linear because the forecast were closer in values to the observed values.Theil inequality coefficient of 0.015714 and 0.129091 was obtained for the grafted and linear respectively, this further reinforces the superiority of the grafted polynomial. It is better therefore to fuse the entire local trend in forecasting of this nature to achieve a better time series prediction.br> Journal of Agriculture, Forestry and the Social Sciences Vol. 3 (2) 2005: pp.42-5