Factors influencing adoption and intensity of use of artificial insemination technology among farmers in Alego-Usonga Sub-County, Siaya County, Kenya

Abstract

Artificial Insemination (AI) is a breeding technology that can be used in dairy cows to help in upgrading of the local breeds within their own setting to increase milk production. Despite the positive benefits of A.I technology such as the ability to get superior genes with a capacity to improve milk productivity, the adoption level has been low in Alego-Usonga Sub-county where an observable peak of 40.72% A.I service in 2016 was realized among smallholder dairy farmers. Therefore this study aimed at establishing the factors influencing A.I technology adoption and intensity among smallholder dairy farmers in Alego-Usonga Sub-County. The specific objectives were to determine the effect of Social, Economic, Technical and Institutional factors on adoption of AI technology among smallholder dairy farmers in Alego-Usonga Subcounty. The Innovation Diffusion Theory guided this study. The study area was Alego- Usonga Sub-County, Siaya County, Kenya. The study population was all smallholder dairy farmers in Alego- Usonga Sub-County. Cross-sectional survey design was used in this study. Multistage random sampling techniques was employed to sample 378 dairy farmers from a population of 22965 dairy farmers from the six wards. Structured questionnaires were used to collect primary data. Double Hurdle Model was used analyze factors influencing adoption and intensity of AI technology. Multivariate data analysis was performed using STATA Software. Results of the probit model showed that age of the respondents (β=0.253, p=0.013), education level (β=0.201, p=0.000), experience (β=0.121, p=0.041), milk sales (β=0.001, p=0.003), AI cost (β=0.542, p=0.008), worker’s skill on heat detection (β=0.198, p=0.047), semen type (β=0.345, p=0.000), AI reliability (β=1.862, p=0.000), and availability of the inseminator (β=0.85, p=0.000) positively and significantly influenced AI technology adoption in the study area. On the other hand, only training on livestock production (β=-0.496, p=0.028) negatively and significantly influenced AI technology adoption in the study area. Results of the truncated regression showed that age of the respondents (β=0.05, p=0.000), education level (0.042, p=0.000), experience (β=0.058, p=0.001), and training on livestock production (β=0.056, p=0.009) positively and significantly influenced the intensity of AI technology use in the study area. On the other hand, group membership (β=-0.038, p=0.03), and availability of the inseminator (β=-0.048, p=0.024) negatively and significantly influenced the intensity of AI technology adoption in the study area. The study recommends the introduction of adult learning sessions for farmers in a bid to improve their literacy levels. There is need to conduct training needs assessments before the trainings are carried out so as to capture the farmers’ interest together with the environment. Farmers should also enhance the skills of their workers by allowing them also to attend trainings. The government should step in by subsidizing the cost of AI and funding trainings and workshops as this will encourage many farmers who were unable to take up the technology to adopt it