The objective of this project was to determine the genetic control of conception rate, or pregnancy percentage in Angus beef heifers. Producers from 6 herds in 5 states provided 3,144 heifer records that included breeding dates, breeding contemporary groups, service sires, and pregnancy check information. Two hundred fourteen sires of the heifers were represented; with 104 sires having less than 5 progeny, and 14 sires having greater than 50 progeny. These data were combined with performance and pedigree information, including actual and adjusted birth weights, weaning weights, and yearling weights, from the American Angus Association database. Heifer pregnancy rate varied from 75 to 95% between herds, and from 65 to 100% between sires, with an overall pregnancy rate of 93%, measured as the percentage of heifers pregnant at pregnancy check after the breeding season. Pregnancy was analyzed as a threshold trait with an underlying continuous distribution. A generalized linear animal model, using a relationship matrix, was fitted. This model included the fixed effects of contemporary group, age of dam, and first AI service sire, and the covariates of heifer age at the beginning of breeding, adjusted birth weight, adjusted weaning weight, and adjusted yearling weight. The relationship matrix included 4 generations of pedigree. The heritability of pregnancy and first-service conception rates on the underlying scale was 0.13 +/- 0.07 and 0.03 +/- 0.03, respectively. Estimated breeding values for pregnancy rate on the observed scale ranged from -0.02 to 0.05 for sires of heifers. Including growth traits with pregnancy rate as 2-trait analyses did not change the heritability of pregnancy rate. As expected for a reproductive trait, the heritability of pregnancy rate was low. Because of its low heritability, genetic improvement in fertility by selection on heifer pregnancy rate would be expected to be slow.

Fertility is a critically important factor in cattle production because it directly relates to the ability to produce the offspring necessary to offset costs in production systems. Female fertility has received much attention and has been enhanced through assisted reproductive technologies, as well as genetic selection; however, improving bull fertility has been largely ignored. Improvements in bull reproductive performance are necessary to optimize the efficiency of cattle production. Selection and management to improve bull fertility not only have the potential to increase conception rates but also have the capacity to improve other economically relevant production traits. Bull fertility has reportedly been genetically correlated with traits such as average daily gain, heifer pregnancy, and calving interval. Published studies show that bull fertility traits are low to moderately heritable, indicating that improvements in bull fertility can be realized through selection. Although female fertility has continued to progress according to increasing conception rates, the reported correlation between male and female fertility is low, indicating that male fertility cannot be improved by selection for female fertility. Correlations between several bull fertility traits, such as concentration, number of spermatozoa, motility, and number of spermatozoa abnormalities, vary among studies. Using male fertility traits in selection indices would provide producers with more advanced selection tools. The objective of this review was to discuss current beef bull fertility measurements and to discuss the future of genetic evaluation of beef bull fertility and potential genetic improvement strategies.

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e.g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herdtest-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM. Abbreviation key: BV = breeding value, HTD = herd test day, HTY = herd test year, HTMp = herd test month period, HTDr = random HTD, PE = prediction error, PEV = PE variance, TDM = test-day model.

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