Assessment of the factors affecting the use and adoption of artificial insemination in cattle in Busitema Sub-county, Busia District
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A baseline research study was carried out in Busitema sub-county, Busia district in the Eastern region of Uganda with the aim of determining the adoption rate and factors influencing the adoption of Artificial Insemination among cattle farmers. The study was conducted through semi-structured interviews which targeted cattle farmers, area extension workers and inseminators. A total of 66 cattle farmers, 2 animal husbandry officers and 1 A.I. technician were interviewed during the study period. The data obtained was analyzed to establish the performance of A.I. and the different factors influencing its adoption and performance. A.I. adoption was established as 9.1% and was found to be highly associated with the herd type, the grazing system used, the purpose of cattle production and the level of education of the farmer (p-Value <0.05). There was however no statistical relationship between A.I. adoption and Age of the farmer, Marital status, Occupation, time spent keeping cattle, Religion, Sex or Size of the herd (p-Value >0.05). it was determined that A.I. was only being used in dairy breeds and their crosses and most farmers keeping indigenous cattle breeds admitted that they were un aware that A.I. could be used in indigenous cattle and they blamed the government for not organizing seminars where they could obtain useful knowledge. Among A.I. adopters, the performance was established as 2 SC-1 which is good performance though farmers complained of the extra cost incurred. The study also determined that farmers were ignorant of sexually transmitted diseases that could be spread through use of borrowed bulls. Farmers keeping indigenous cattle breeds displayed ignorance about fertility management in cattle which can severely prolong inter calving intervals. Farmers complained about lack of A.I. technicians i.e. some farmers admitted that they got their inseminators from Kenya due to failure to reach out to the district A.I. technician who also stated work overload as one of his main constraints in service provision in addition to other challenges such as poor network. There is need therefore to strengthen extension services in addition to training more A.I. technicians and equipping them with the necessary tools and equipments needed for better A.I. performance.