The Effect of Synchronized Breeding on Genetic Evaluations of Fertility Traits in Dairy Cattle

Authors

  • Gerson Oliveira University of Guelph
  • Larry R. Schaeffer
  • Flavio Schenkel
  • Christine Baes

Abstract

Estrus detection is labor-intensive and time-consuming, with decreased expression in many high-producing dairy cows. To overcome this issue, some producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (timed AI). The objective of this study was to assess the potential bias that timed AI might add to the estimated genetic parameters of female reproductive traits. A Holstein population with 400 sires and 3 000 dams was simulated over 20 years, resulting in 30 000 cows randomly distributed in 200 herds. The simulated traits mimicked calving to first service (CTFS), first service to conception (FSTC) and days open (DO), assuming these to be the most affected traits by hormone synchronization. A total of 13 scenarios were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. To simulate the effect of timed AI, cows had their phenotypes masked by setting CTFS and DO to the mean of CTFS, and FSTC was set to zero. Four parameters were used to indirectly measure the presence of bias: 1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); 2) the differences in the mean EBV of top 25, 50, 75 and 100 sires; 3) changes in correlation between TBV’s and EBV’s ranks; and 4) the changes in the genetic trend. The accuracy within each class of animals (bulls, dams, and cows) decreased proportionally with the increase of the use of timed AI. The average EBV of the top sires went toward zero when increasing the number of hormonal synchronized animals. The sires’ rank correlation of EBVs and TBVs followed similar behaviour, with smaller correlation for scenarios with more timed AI animals. The genetic trend was also more affected by scenarios that considered more intense use of hormonal synchronization. This simulation study indicated that genetic evaluations that included herds that used timed AI are likely biased, and the amount of bias is proportional to the number of animals on timed AI.

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Published

2019-10-17

Issue

No. 55 (2019): Proceedings of the 2019 Interbull Meeting

Section

Articles

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