Social Economic Determinants of Adoption of Fish Farming in Gem Sub-County, Siaya County, Kenya

The demand for fish in Kenya has been steadily increasing, prompting the exploration of alternative methods such as fish farming to address this rising demand. However, the adoption of fish farming in Gem Sub-county, Siaya County, Kenya, has faced several challenges, resulting in a low success rate of only 13.9% in fish farming projects, leading to insufficient fish supply. This study aimed to analyze the factors influencing the adoption of fish farming in Gem Sub-county. The research was guided by four objectives; determining whether training influence farmers’ adoption of fish farming, determining the availability of fish farming resources on the adoption of fish farming, investigating the availability of fish markets on the adoption of fish farming and assessing fish farmers' attitude on the adoption of fish farming in Gem Sub-county, Siaya County, Kenya. The study employed the theory of innovation diffusion and a descriptive survey research design to investigate the adoption of fish farming in a specific area. The target population included 140 fish farmers, 9 area Chiefs, and 9 Officials from the Fishery Department, totaling 158 individuals. To form a representative sample, 3 out of 9 locations (30%) were selected, resulting in 42 respondents (30% of 140 fish farmers). Purposive sampling was used to select these locations, with the Area Chiefs and Fishery Department Officials coming from these sampled areas due to their expertise on fish farming projects. Data collection methods varied: questionnaires were used for fish farmers, while interviews were conducted with officials from the Fishery Department and Area Chiefs. To validate research instruments, a pilot study involving 4 fish farmers from a specific location was conducted to ensure reliability and validity. The data analysis process began with identifying common themes, assigning codes and labels to relevant data, and then calculating frequencies to provide descriptive information about the respondents. Quantitative data was analyzed using descriptive statistics like frequencies and percentages, as well as inferential analysis employing Pearson's Product Moment Correlation Analysis, with the aid of Statistical Packages for Social Science (SPSS 23). The findings were presented using tables and charts. Qualitative data was analyzed qualitatively, aligned with the specific research objectives, and presented in narrative forms. The study established that there was a positive association between the frequency of farmers' training and the number of farmers who adopted fish farming (r = 0.991 and a significant level (p-value) of 0.000. In addition, correlation between the frequency with which the government markets fish and fish products and the number of farmers who have undertaken fish farming was positive (r = 0.976 and a significant level (p-value) of 0.000). Lastly, the association between fish farmers' interest in fish farming and the number of farmers who adopted fish farming was positive (r = 0.948 and a significant level (p-value) of 0.014. The study concluded that fish farmers in the studied area lacked meaningful training, negatively impacting their ability to adopt fish farming due to a lack of necessary skills and knowledge. Additionally, the unavailability of essential resources hindered effective fish farming implementation, while the limited access to fish markets, marked by intense competition, both locally and internationally, further discouraged fish farming adoption. In response to these findings, the study recommended that the Fishery Furthermore, it advised that the government, particularly through the Ministry of Agriculture and Fisheries, allocate more funds to support fish farming and explore new markets for local fish farmers, as incentives for the wider adoption of fish farming practices.