Abstract:
This paper investigated factors affecting innovative behaviour of rice farmers in North-Eastern Nigeria. Cross sectional data was collected from a sample of 285 rice farmers using interview schedule. However, only 270 were used for analyses. The remaining 15 were rejected due to inconsistencies in the responses. Descriptive statistics, logistic regression were used as analytical tools. The results of the study revealed that high adopters of modern rice production technologies obtained significantly (P < 0.05) higher yields than the low adopters’ counterpart. Also, significant (P < 0.05) differences existed in the two adopter categories with regards to farmers’ income and literacy level. However, no significant (P > 0.05) differences were found with respect to household size and farming experience between the two adopter categories. The results of the logistic regression revealed that farm income, extension contact, access to credit, literacy level of the decision maker, family size and membership of cooperative societies significantly (P < 0.05) were the factors that influenced farmers’ innovative behaviour. The study concluded knowledge of households’ socio-economic and institutional attributes is invaluable when designing and targeting technologies for smallholder farmers. Hence, policies that will ensure continued access to credit facilities and effective extension system should be vigorously pursued. This is crucial for increased productivity in rice production in particular and food security in general. Static model to adoption studies using cross sectional data was employed in this research. Adoption decision is a dynamic process involving changes in farmers’ perceptions and attitudes as acquisition of better information progresses and farmers’ ability and skill improve in applying new methods. Therefore, there is need to know which rice technologies have been adopted and why they are still in use or already abandoned after introduction. This kind of study will require the application of dynamic models using panel data.
