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Copyright © 2015
Angus Journal

Advantages of AI vs. Natural Service

AI offers advantages in accuracy of selection, selection intensity and genetic diversity.

STAUNTON, Va. (Oct. 16, 2013) — Artificial insemination (AI) gives breeders many advantages in terms of management, economics and genetic improvement, said Scott Greiner, professor and beef cattle extension specialist with Virginia Tech. He spoke to more than 170 attendees at the 2013 Applied Reproductive Strategies in Beef Cattle (ARSBC) Symposium in Staunton, Va., Oct. 15-16.

Sandy Johnson

One of the big reasons to AI is the opportunity to use proven, high-accuracy bulls, said Scott Greiner, describing accuracy as the correlation, or relationship, between the true breeding value of a bull and what his estimated breeding value is.

“There are lots of reasons to AI, and we’ve spent this conference … talking about those benefits,” Greiner said. AI and estrous synchronization help enhance reproductive efficiency, jump-start non-cycling cows to start cycling, shorten breeding and calving seasons, increase the average age of calves and their uniformity at sale time, increase pay weights, improve management of cows and calves, and improve the herd’s genetics, said Greiner.

“As we look at making genetic change in beef cattle, there’s really four critical areas,” said Greiner, pointing to accuracy of selection, selection intensity, genetic variation in the trait and generation interval. “How quickly we can change genetics and how effectively we can do that are influenced by these things.

One of the big reasons to AI is the opportunity to use proven, high-accuracy bulls, said Greiner, describing accuracy as the correlation, or relationship, between the true breeding value of a bull and what his estimated breeding value is.

“In a perfect world, that relationship is 1,” he explained. “That means with 100% confidence, or correlation of 1, that we truly know what that bull’s breeding value is. That never happens. We can approach that, but we never know with 100% confidence.”

On the other end of the spectrum is 0, which Greiner called equivalent to randomly picking a number and putting it down on paper. Yearling bulls with a performance pedigree generally have accuracies of 0.05-0.30. AI sires that have been widely used in numerous herds may have accuracies of 0.9 and greater.

Greiner explained that the industry deals in Beef Improvement Federation (BIF) accuracies, which are more conservative than true accuracies (see Slide 7 in PowerPoint) and should give us greater confidence in the predictive power of the EPD.

Accuracies have value in showing us how much an individual’s values might change with more data added from additional progeny and additional herds, Greiner noted. Breed associations publish possible change values that can be used to establish a range in which the true EPD would fall (see Slide 8).

Greiner said he finds it useful to plot that possible change on the percentile table as depicted in Fig. 1 (see Slide 9). The green circle encloses the range in which the proven bull’s probable true birth weight EPD would be expected to occur two-thirds of the time. The red box indicates the range in which the lower-accuracy bull’s probable true accuracy would be expected to fall. Whether he changes favorably or unfavorably, the high-accuracy sire is going to be a calving-ease sire. Not so for the unproven bull.

“Keep in mind that from a statistical standpoint the chances are equal that the young bull will get better in terms of calving ease and lower birth weight,” he emphasized. “That chance is equal to him getting poorer from the context of being a heavier-birth-weight bull.” There is also a chance that he would fall out of that 67% confidence-interval box.

Greiner said accuracy is influenced by data — both the quantity and the quality of data submitted, including pedigree, individual performance and progeny data; heritability; and genomics.

Greiner shared a table (see Slide 12) showing how the number of progeny records influence accuracy for traits of low, moderate and high heritability. As heritability goes up, fewer progeny are needed to achieve higher accuracy levels.

Genomics are now being applied in several breeds to enhance EPDs, Greiner noted. Genomic results are incorporated into EPDs as a correlated trait through national cattle evaluation, adding information and enhancing the accuracy of the EPDs cattlemen are already using.

“How much the genomic result impacts accuracy is dependent on several factors,” said Greiner. “One of those is how much of the genetic variation does that genomic test explain in the trait itself.” The more variation it explains, the larger its influence on accuracy.

Using examples from Angus, Greiner said most of the traits for which there are genomic tests explain between 35% and 49% of the genetic variation of the trait. That means that for most traits, genomic results would be similar to having eight-20 progeny records (depending on the trait). That can increase the confidence level in buying unproven yearling bulls.

Regarding selection intensity, Greiner noted that a significant number of proven sires are superior to breed average for multiple traits. These proven bulls can be used as AI sires to provide genetic reach with confidence and predictability.

AI also helps manage genetic antagonisms, Greiner said. Several traits are antagonistic to each other, like calving ease and growth, growth and mature size, marbling and carcass fat, and marbling and ribeye area. Calling them “curve benders,” he noted that several proven sires have favorable combinations of these antagonistic traits.”

Some producers worry that AI will reduce genetic variation, but Greiner offered a different view. AI gives the opportunity to select multiple sires of differing pedigrees but similar genetic merit, so pedigrees can be diverse without compromising uniformity in genetic quality. Additionally, he noted that AI helps a crossbreeding program by requiring fewer bulls and fewer breeding pastures.

Additional benefits include simplification of natural-service sire selection in AI herds. For instance, if a maternal sire is used to AI heifers, a higher-birth-weight-EPD, terminal sire can be purchased as a cleanup bull, generally at a lower price point.

The advantage of AI calves being born earlier in the season, in a tighter group with better genetics interact to add value to AI-sired calves, noted Greiner, sharing documented added value shared by commercial cattlemen Tim Sutphin (at the 2010 ARSBC Symposium) and Terry Slusher (earlier during this symposium).

In terms of non-EPD traits such as udder scores and other phenotypic traits, the AI companies have in place systems to help evaluate and rate bulls.

In conclusion, Greiner noted, “Every great proven bull was once a young, unproven bull — every single one of them. We need to keep that in mind and certainly there’s a need to test those young, exciting bulls, get them proven and then put them to work.”

Greiner spoke during Wednesday’s ARSBC session focused on genetic and management tools to get the most from reproductive efforts. Visit the Newsroom at to listen to his presentation and to view his PowerPoint slides and proceedings paper. This comprehensive coverage of the symposium is compiled by the Angus Journal editorial team. The site is made possible through sponsorship by the Beef Reproduction Task Force.

Editor's Note: This article was written under contract or by staff of the Angus Journal. To request reprint permission and guidelines, contact Shauna Rose Hermel, editor, at 816-383-5270.