Gastro-intestinal Parasitism and Determinants of Daily Milk Yield in Smallholder Dairy Cattle Farms in Kabete Sub-county, Kiambu County

Abstract

The dairy industry in Kenya is pivotal to the economy, and a substantial percentage of small-scale farmers carry out dairy farming as a livelihood. Currently, a significant proportion of the milk produced is from the small-scale farmers, who form a bulk of the milk producers and have great potential to grow the industry and change their economic status. Optimum production in these small-scale farms is hindered by many factors, including inappropriate breed selection, poor nutrition, endemic infectious diseases and gastro-intestinal parasitism. Optimum milk yield can only be achieved if the lactating cows are in good health condition and receive good husbandry. In addition, understanding the farmer's knowledge, attitudes and practices on endoparasite control is helpful in planning and executing helminth management and control programs. This study was carried out in Kabete sub county within Kiambu county with the following specific objectives: 1). to determine the knowledge, attitudes and practices on control of gastro-intestinal parasitism and the use of anthelmintics in dairy cows, 2). to determine the prevalence, egg/oocyst count of gastro-intestinal parasites in lactating dairy cows, and 3). to determine the factors associated with daily milk yield in lactating dairy cows in the Kabete sub-county of Kiambu county. This was a cross-sectional study involving 196 lactating dairy cows from 62 farms. On-farm questionnaires were administered to the principal farmers to capture farm and animal-level data. Selected cows were assessed for clinical abnormalities and sampled for feces and milk. Milk samples from every quarter were subjected to California mastitis test to evaluate subclinical mastitis. Fecal samples from the rectum were analyzed for the presence of helminth eggs and/or oocysts using simple fecal floatation and the McMaster method. In analyzing the data, descriptive statistics and multilevel mixed-effects linear regression were considered.