From Indigenous to Improved Breeds: Adoption Dynamics and Intensity of Artificial Insemination Among Farmers in Western Kenya

Adoption of Artificial Insemination (AI) has the potential to upgrade local dairy breeds for improved milk production in western Kenya. This study employed the double hurdle probit model to analyze factors influencing the adoption and intensity of AI technology. A multistage random sampling technique was employed to identify sample units among adopters and non-adopters of AI technology. Data was obtained from cross-sectional survey of 378 farmer households. Results of the probit model showed that age, education level, experience, milk sales, AI cost, worker’s skill on heat detection, semen type, AI reliability, and availability of the inseminator positively and significantly influenced AI technology adoption. Only training on livestock production negatively and significantly influenced AI technology adoption. Results of the truncated regression showed that age, education level, experience, and training on livestock production positively and significantly influenced the intensity of AI technology use. Group membership and the availability of the inseminator negatively and significantly influenced the intensity of AI technology adoption. It is concluded information is the most critical factor influencing adoption of AI. Building more trust and confidence about AI technologies will lead to increased adoption. The study recommends the improvement of farmer education through introduction of effective farmer training and information sessions. There is need to conduct training needs assessments before the trainings are carried out so as to capture the farmers’ interest together with their socio-economic environments.