Socioeconomic Determinants of Modern Agricultural Technology Adoption among Farmers in Rivers State: A Case Study of Etche Local Government Area.

Abstract

This research examined the socio-economic determinants of modern agricultural technology adoption among farmers in Rivers State. The specific objectives were to examine how education; gender; farm size; marital status and annual farm income influences the adoption of modern agricultural technology. Primary data was collected and inferential statistics (linear regression, Probit regression and Logistic regression) were used in the analysis of collected data. The results showed that 72% of the respondents were female, 30% being majority of the respondents fell between the age category of 36-43 years, 64% were married, 34% being majority have primary education as their highest educational qualification, 46% of the farmers earn ?120,000 and above annually. The results also revealed that only 14% of the farmers adopted high yielding varieties as their modern agricultural technology while 86% reported not adopting any agricultural technology. 30 % of the farmers grow cassava as their main farm crop. None of the farmers reported using reported using inorganic fertilizers; only 5% reported using poultry droppings. 76% reported not using high yielding varieties, while only 24% reported using high yielding varieties. 86% reported getting their high yielding varieties from other sources other than ADP and government sources. 100% of the respondents reported the absence of extension training to be a limiting factor to adoption of modern agricultural technology. 38% reported lack of awareness. The logistic regression results had an R 2 of 74% indicating that a 74% variation in modern agricultural technology adoption is determined by variations in the independent variables. The independent variables had significance values of 0.04 for level of education, 0.00 for f.,[arm size, annual farm income has a p-value of 0.03, 0.714 for gender and 0.701 for age. Only annual farm income, farm size and educational level had significant influence on farmers’ adoption of