Characterization And Control Of Pathogenic Parasites In Nile Tilapia (Oreochromis Niloticus Linnaeus 1758) In Fish-Farming Systems In Bungoma County, Kenya

ABSTRACT

In Kenya, fish farming has been expanding in recent years, with Government support through financial and input subsidies to small scale farmers. The sub sector generates a variety of benefits such as food and nutrition and develops trade. However, the sub sector faces the challenges of fish diseases, given that the fish production facilities support high-density living conditions that are favorable for spread of parasitic diseases. Besides, when fish that is infected is consumed by humans, the humans may be infected, especially when eaten fresh. Infected fish lowers their market value and sunctions may also be imposed on fish exports. Knowledge of fish parasites in aquaculture systems as well as treatment options available would enlighten fish farmers on the specific actions to take in order to harvest quality fish. The study was cross sectional carried out in Bungoma County from August 2015 to Decemebr 2017. The main objective of the study was to characterize and evaluate control options of pathogenic parasites present in fish farming systems in Bungoma County. Bungoma County was chosen for this study because it is one of the Counties that has high potential for fish farming. The County hosts one out of the public fish farms and hatcheries at Chwele besides authenticated private fish hatcheries. He hatcheries are sources of fish fingerlings as well as training and demonstration centres for fish farmers. At every fish pond, six water quality parameters were assessed using a portable auto sampler. Farmed Nile tilapia was randomly sampled to determine the pathogenic parasites and bacteria present in various organs. Commercial fish feeds were also purchased from two fish feed millers in Bungoma County and assessed for parasite presence. Polymerase Chain Reaction (PCR) and genetic sequencing analysis were conducted in order to assess the genes responsible for antimicrobial resistance. The data was entered into Micro Soft Excel (Windows 10) spreadsheet and analysed by SPSS Software. It was found that there was significant difference only in temperature of the pond water sampled across the sub counties (F=15.5; df=5; p