Evaluation of Synchronization Protocols and Semen Quality Characteristics on Reproductive Efficiency and Fertility Outcomes in Zimbabwean Dairy Cattle Production.
Keywords:
Artificial Insemination, Heat synchronization, semen quality, Reproductive performance, Zimbabwean dairy cattle industryAbstract
This review addresses the technical challenges affecting the success rate of artificial
insemination (AI) in dairy cattle, with a specific focus on heat synchronization protocols and
semen quality for artificial insemination. Both heat synchronization and semen quality play
significant roles in determining artificial insemination success rates. Furthermore, the influence
of body condition score on reproductive performance and AI outcomes has been reviewed,
particularly in dairy cattle in Zimbabwe. In Zimbabwe, artificial insemination success rates are
notably low, particularly on communal farms, despite over 80% of the country's cattle
population being located in the smallholder sector. A systematic literature review was
conducted from January 2022 to January 2024 utilizing Google Scholar and PubMed to assess
how heat synchronization protocols, semen quality, and body condition scores affect artificial
insemination success, mainly in dairy cattle in Zimbabwe. The review also considered the
impact of body condition scores on offspring sex determination. A total of 62 full articles were
included, consisting of 45 research papers and 17 narrative reviews. In Zimbabwe, the demand
for artificial insemination services has significantly risen over the past four years across both
communal and commercial farms. However, many farmers are unaware of the factors
influencing artificial insemination success. Various elements contribute to the low artificial
insemination success rates in the country which are poor heat detection and timing, inseminator
skill,animal healh and nutrition and semen handling technics. In conclusion, systematically
identifying the factors that affect AI success in cattle can aid AI technicians and farmers in
better understanding the animal requirements and technical procedures involved, fostering
cooperation to enhance AI outcomes.
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Copyright (c) 2025 Cornelius Dennis Magwede, Fungai Primrose Chatiza, Stanley Marshall Makuza

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