Utilization and Adoption of Artificial Insemination among dairy cattle farmers in Tanzania: Historical background, Challenges, Opportunities and Future Prospects

Artificial insemination (AI) and other reproductive biotechnologies are the essential tools for accelerating genetic improvement, controlling diseases and decreasing breeding expense in dairy cattle. In Tanzania, AI technology has been considered as one of the main strategies for national dairy cattle improvement programs which aim to enhance productivity, adaptability, and hence improve farmers livelihoods. Milk production in Tanzania in 2024 totaled 4,009,985,321.20 liters, of which 2,587,651,655.20 liters came from indigenous cattle and 1,387,620,068 liters from dairy cattle, while an additional 10,089,767.50 liters were imported. There have been several initiatives through the National Artificial Insemination Centre (NAIC) and Tanzania Livestock Research Institute (TALIRI), in collaboration with International and local stakeholders which have been geared to increase the uptakes of this technology among the farmers and increase milk production in the country. The projects such as mobile insemination camps funded by Tanzanian government, Africa Dairy Genetic Gains (ADGG) and Heifer in Trust Schemes have helped to build capacity and integrate genomic data into national breeding strategies. Despite the presence of different initiatives aimed to increase AI adoption rate in the country, in 2024 only 72,731 cows out of 39.2 million cattle were inseminated which was equivalent to 0.18% of all cattle in the country, whereas the target was to inseminate 2 million cows. Total semen dose produced at NAIC in 2024 was 110,434 whereas the target was to produce 1,500,000 semen doses, giving a production efficiency of 0.7%. Studies shows that AI adoption among smallholder farmers who are the producers of more than 95% of total milk produced in the country remains relatively low. The main reasons for low AI technology adoption rate have been reported to be high cost of AI services, limited technician skills, unreliable cold-chain systems, poor heat detection which leads to increased number of services per conception, unsupportive infrastructural and logistics, and weak government policies. This review paper has tried to elaborate and highlight the historical development of AI technology in the country, AI adoption rate and constraints, fertility performances, and the potential emerging opportunities for proper utilization of AI in the country. In conclusion, to ensure sustainable AI delivery services and utilization, Tanzania must continue to invest in infrastructure, technician training, formulate supportive policies, digital data management systems, AI and genomic data integration and smallholder farmers awareness creation.