Adoption of Improved Processing Technologies by Fish Farmers in Akure North and South Local Government Areas, Ondo State, Nigeria

FELIX OLAYINKA OLADIPO1; ADEGOKE ABIDEMI ADEYELU1,2; OLUFEMI BOLARIN1

1Department of Agricultural extension and rural development, University of Ilorin, PMB 1515, Ilorin, Nigeria

2Department of Agricultural extension and management, Rufus Giwa Polytechnic, PMB 1019, Owo, Nigeria

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Abstract

The study was conducted to determine the adoption of improved processing technologies by fish farmers in Akure North and South LGAs, Ondo state, Nigeria.  A two stage sampling technique was employed to select 150 fish farmers interviewed for the study, data were analyzed by using descriptive statistics and Tobit regression model. Results showed that majority of the farmers (68.0%) were males with mean age of 38.5 years old. Majority (80.7%) of respondents were married while  (90%) had various degree of formal education, oil drum, smoking kiln and mud oven were the improved fish processing technologies available in the study area. Furthermore, cooperatives (92.7%) was ranked 1st as the most preferred source of information by the farmers, majority of  respondents (94.7%) were  aware of oil drum.  The mean  levels of adoption was 2.50, values below and above the mean were regarded as low and high respectively, the use of oil drum (2.58), smoking kiln (1.47) and mud oven (1.33) were the adoption scores of the respondents in the study area.  Moreover, age (4.201), education level (2.105), household size (1.791), income (3.021), gender (1.781), pond size (2.511) and other income generating activities (2.256) were factors that were statistically significant thus affecting the rate of adoption of improved fish processing technologies. The study concludes that respondents were aware of some of the improved technologies but low adoption, the study therefore recommends the need for capacity building and advisory services by extension agents and other stakeholders for fish processors.

Keywords: Fish, processing, technology, processor, Tobit regression model.

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