Development of a novel prediction method to determine future reproductive success of dairy cows

J. Reid, Brian Evans, G. O'Gorman, C. Browne

Research output: Contribution to conferencePoster


Background: Managing fertility in the dairy sector is fundamental in maintaining sustainability. In the United Kingdom there has been a significant decrease in reproductive performance in dairy cattle, which is thought to be attributed to the specific breeding of high yielding progeny. Days to first service (DFS) has been frequently used by producers to measure of herd fertility (Fricke et al., 2014).

Material and methods: A scoring system was designed to predict the reproductive performance of dairy cattle, recording; heart rate (HR), rectal temperature (RT), body condition score (BCS), visual vaginal score (VV) and calving score (CS) during post-calving checks on 51 Holstein-Friesian cattle. Data was analysed by generalised linear model to determine the impact on DFS.
Results: The results of the generalised linear model found that both VV (B=6.208, p=0.000) and CS (7.305, p=0.030) were significant predictors of DFS, whereas BCS (B=2.836, p=0.446), HR (B=1.520, p=0.197) and RT (B=0.509, p=0.877) were not significant predictors. Based on the results of the coefficients (Table 1), CS had the most substantial impact. A 1-point score increase in CS caused a predicted impact on 7.32 days increase in DFS. CS scores ranged from 1 to 4 and had a mean score of 1.55.

Discussion and conclusion: Abnormal parturition and births where assistance is required, results in a greater bacterial contamination of the uterine environment. Failure to resolve these infections increases the development of various forms of uterine disease (Leblanc, 2008), and decreasing reproductive performance. Using this system it may be possible to identify animals at risk of future reproductive failure, allowing better treatment and management decisions to be made.
Original languageEnglish
Publication statusPublished - Jan 2016
EventGlobal Farm Platform Conference 2016 - University of Bristol, Bristol, United Kingdom
Duration: 12 Jan 201615 Jan 2016


ConferenceGlobal Farm Platform Conference 2016
Country/TerritoryUnited Kingdom


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