Back in time, the biggest objection to using gender-sorted semen was the comparison of higher pregnancy rates with conventional semen. It is well known from field results that sorting technology has evolved, proving its repeatable accuracy rates over gender. Now, recent field results also show that gender-sorted semen significantly improved its pregnancy rates, proven in a reproducible way in different systems around the world.
A new study has compared the major results of gender-sorted semen pregnancy rates on Irish dairy farms from 2013 to its most recent performance in 2022, including a wide range of A.I. services (Agriland, 2023). The statistical analysis indicated that pregnancy rate for gender-sorted semen in 2022 is at 60%, compared to the standard conventional A.I., which is at 63%. This indicates a relative pregnancy rate of gender-sorted semen that is 95% closer to the target rates of conventional semen.
This independent commercial field study, undertaken by ICBF, Teagasc and the partner A.I. companies (i.e., Dovea A.I., Eurogene/A.I. Services, Munster Bovine and Progressive Genetics), also showed that gender-sorted semen increased overall performance by >10% relative points, rising from 84% in 2013 to 95% in 2022 (Read Ultraplus launch Media Release).
The period of significant improvements shown in this study overlaps with the commercial availability of 4M™ and posteriorly Ultraplus™ (2022) semen, reflecting the effect of these technologies on field results for pregnancy rates. In addition to better conception, it is well established that gender-sorted semen has also shown high gender accuracy, producing more female calves to generate the expected cost-benefit ratio for farms using this technology.
The analysis was based on over 1.83M conventional inseminations and 86K gender-sorted semen inseminations completed over a 5-year period (2017 to 2022) and is the most comprehensive analysis of the performance of gender-sorted semen in a commercial field setting ever undertaken. All inseminations were completed by A.I. technicians from the partner A.I. companies and within a time frame that allowed them to be validated against a subsequent birth event. Statistical analysis was undertaken of the field data, with important factors such as year, herd, parity, days in milk and cow genetic merit all accounted for in the model
