Ethiopia has one of the largest livestock populations in Africa; however, its dairy productivity remains critically low. Artificial insemination (AI) is a key technology for genetic improvement, but its efficiency and constraints within the smallholder systems of specific regions like South Wollo are not well documented. This study employed a mixed-methods approach to evaluate AI efficiency and reproductive outcomes, combining cross-sectional surveys of 384 smallholder farms with a five-year retrospective analysis (2019-2023) of AI and reproductive records from three districts: Legambo, Tenta, and Mekdela. The mean conception rate was 58.7%, with significant district-level variation. Crossbred cattle performed better than local Zebu, showing earlier age at first calving (40.7 vs. 52.3 months) and shorter calving intervals (13.8 vs. 16.5 months). Reproductive performance was superior in highland agro-ecology compared to midland areas, evidenced by higher conception rates (52.4% vs. 42.9%) and shorter calving intervals, which were attributed to longer green feed availability. Logistic regression identified body condition score (OR = 2.34) as the strongest positive predictor of conception, while concurrent disease (OR = 0.31) and poor semen storage (OR = 0.38) were key negative predictors. The major constraints identified were technician skill gaps, geographical inaccessibility (> 72% of farmers lived > 5 km from an AI centre), and poor farmer knowledge of oestrus detection. In conclusion, while crossbreeding through AI holds great promise, its success is fundamentally constrained by managerial and infrastructural limitations. Therefore, a holistic strategy that integrates improved nutrition, reliable AI service delivery, and comprehensive farmer training is imperative to unlock the full genetic potential and enhance dairy productivity in smallholder systems of Ethiopia.
Zemedkun Diffe (Wed,) studied this question.