- Start Recording Your Herd
- EPD Movement: Change is Inevitable
- Improving Cow Herd Reproduction Via Genetics
Start Recording Your Herd
It is time for you to start recording your cattle to best analyze them for future breeding objectives. The ASA has very important guidelines that one must follow in order to properly record your animals.
Below are listed the criteria ASA uses to form contemporary groups. There are fixed criteria such as sex and weaning weight date, and also criteria that breeders set to help form proper contemporary groups such as herd ID, management code and pasture unit for weaning, feeding unit for yearling, and scan group for ultrasound. All criteria are used to form contemporary groups. For example, animals with different weaning weight dates will not be weaning contemporaries even if they have the same management code and pasture unit. Likewise, animals will not be weaning contemporaries if they have different management codes or pasture units even if they have the same weaning weight dates.
American Simmental Association criteria for forming contemporary groups:
At Birth, animals must
- Be weighed
- Have the same initial owner account
- Be reported at the same time, for interim EPD
- Have the same herd ID
- Be the same sex (females or males, steers are male at birth)
- Be a single birth
- Be born in the same calving season (Jan to Jun or Jul to Dec)
At Weaning, animals must
- Be in the same birth contemporary group
- Have the same weaning weight date
- Have the same management code
- Have the same pasture unit
- Be in the same age range at weaning (between 160–250 days)
At Yearling, animals must
- Be in the same weaning contemporary group
- Have the same yearling weight date
- Have the same feeding unit
- Be the same yearling sex (to handle bulls steered after weaning)
- Be in the same age range at yearling (between 330–440 days)
- There must be at least 60 days between weaning and yearling weight dates
For Ultrasound, animals must
- Be in the same weaning contemporary group
- Have the same scan date
- Have the same scan group designation
- Be the same sex when scanned (bulls are separated from steers)
- Be in the same age range when scanned (between 330–440 days)
You can hire a certified Ultrasound technician by searching http://ultrasoundbeef.com/Technicians.php
Wade Shafer, PhD
EPD Movement: Change is Inevitable
This tenet certainly holds true when it comes to genetic evaluation. By now, all of us are well aware that EPD inevitably change over time. Curiously, change, particularly more than moderate change, is often cited as a reason to discount the utility of our genetic evaluation system or to question breeder integrity. For the most part, these are unwarranted deductions. The fact of the matter is, EPD should change over time — in some cases dramatically.
There are two sources of change in EPD over time. First, change can be due to differences in the methodology of calculation. As the technology for calculating EPD improves, an upgrading of the system is warranted from time to time. Upgrades and resulting changes are justified because they improve the validity of our genetic evaluation system. We certainly saw this with the movement to the IGS Multi-breed Genetic Evaluation powered by BOLT. This upgrade caused changes in EPDs, in some cases substantial change; however, it greatly improved our system — certainly justifying the changes.
The second type of change, which represents the vast majority of change over time, is due to additional data being incorporated into the data set. To illustrate this change, it is helpful to consider the relationship between estimates and “true” values. Because we aren’t privy to animals’ true genetic values, we are required to estimate them through the use of phenotypic observations. As additional observations are collected from one evaluation to the next, EPD, on average, move closer to the true values they estimate. Change resulting from this “zeroing in” on true values results in more accurate EPD —certainly a good thing.
Though clearly beneficial, this change can be sizable as EPD move toward their true values. To demonstrate this fact, let’s look at the possible change (PC) statistic associated with each EPD. Possible change is the range ± an animal’s EPD that, 67 percent of the time, we expect the animal’s true genetic value to fall within. If we extend the range to 2 and 3 PC units ± an animal’s EPD, its true value is expected to fall within the range 95 and 99 percent of the time, respectively. With these percentages in mind, we can make some assumptions; first, in a group of 100 bulls, it is expected that 33 (100 - 67), 5 (100 – 95) and 1 (100 – 99) of them have true genetic values outside a 1, 2 and 3 PC unit range, respectively, from their EPD for a particular trait; second, when considering multiple traits, the number of instances in which true values fall outside PC ranges increases by a multiple of the number of traits. For example, if we consider 15 traits on our sample of 100 bulls, we expect 495 (15 x 33), 75 (15 x 5) and 15 (15 x 1) instances where sires have true values more than 1, 2 and 3 PC units, respectively, from their current EPD.
To add further perspective, let’s take a bull calf with a 60 YW EPD and a corresponding 0.30 accuracy. The PC range for this calf’s EPD is ± 18. If he turns into an AI sire, eventually developing a YW accuracy of 0.99 (i.e., his EPD is essentially his true genetic value), there is a 67, 95 and 99 percent chance that his 0.99 accuracy EPD will fall between 78-42 (± 1 PC unit), 96-24 (± 2 PC units) and 114-6 (± 3 PC units), respectively. As you can see, it would be fairly common (33 percent of the time) for the calf to end up with an EPD over 78 or under 42, a result that would fairly categorize him as either a high- or low-growth bull. Furthermore, it wouldn’t be that extraordinary (1 percent of the time) for this middle-of-the-road YW calf to end up being on the very extreme ends of the spectrum (over 114 or under 6). If we expand the array of traits to 15 for this calf, it would hardly be remarkable for one of his 0.99 accuracy EPDs to end up 3 PC units from where he started; it should happen 15 percent of the time.
What does all this mean? From my vantage point, this puts into perspective the fact estimates are going to change — in some cases, dramatically (e.g., beyond 3 PC units). Furthermore, through PC, we are told “upfront” about the range of change to anticipate. Therefore, when a sire moves dramatically, rather than discount our genetic evaluation system or assume there were faulty data submitted on him, we should be more accepting of it — knowing that it is expected to occur at a predicted frequency.
Improving Cow Herd Reproduction Via Genetics
by Wade Shafer, PhD, ASA executive vice president
Editor’s note: This article was originally published in the March 2008 issue of SimTalk written by Wade Shafer, PhD. Drs. Lauren Hyde and Jackie Atkins provided updates for reprint.
A beef cow’s job is not an easy one. She is expected to conceive at slightly over one year of age, to calve by the time she is two, and rebreed shortly after that while weaning a healthy, viable calf. Furthermore, we demand that she consistently repeats this cycle for the rest of her life — one stumble and, hasta la vista, baby!
To be sure, producers are best served when the cow successfully performs her task for many years, as the longer her productive life, the more profitable she is to the enterprise. Is there anything that can be done to help her out? Certainly, there are environmental factors we can manage that will give her a leg up. For example, by providing adequate nutrition and a proper vaccination regimen and mating her to easy-calving sires (particularly when she is young), we increase the odds of her success. While a cow’s environment has a substantial impact on her reproductive performance, her genetic makeup can too. This article explores the genetics of female reproduction and offers suggestions on how to improve the reproductive performance of your cow herd via genetics.
The obvious place to start a discussion about the genetics of female reproduction is the factor that far and away has the greatest effect on it: crossbreeding. It has long been recognized that crossbreeding enhances virtually all aspects of reproductive performance. Studies too numerous to list here have established the reproductive superiority of crossbred over straightbred cows.
In one of an abundance of studies with similar findings, scientists at the Meat Animal Research Center (MARC) concluded that two-breed rotational cross cows produced 20% more calves over their lifetime than straightbreds due to the favorable impact of heterosis on dam fertility/longevity and calf survivability brought about by the improved calving and mothering ability of the dam (Cundiff et al., 1992). Furthermore, they estimated that when mated to a bull of another breed, the two-breed cross cows would wean 36% more weight over their lifespan than straightbred cows raising straightbred calves. The dramatic increase is attributable to the positive influence of heterosis on reproduction and production in the dam as well as increased growth and survivability in their calves.
Given the overwhelming evidence of the crossbred cow’s reproductive supremacy and the fact that reproduction is a major piece of the profitability puzzle (by most accounts exceeding all other functions by a wide margin of relative importance), it is difficult to conceive of a situation where a commercial enterprise would not benefit financially from a crossbred cow herd.
Are we implying that selecting animals within a breed for reproductive performance is not a worthwhile endeavor? No! Reproductive progress can be made via selection (which we will address later); however, it would take years of intense selection within a breed to yield the kind of improvement that can be achieved in one fell swoop by simply crossbreeding.
Therefore, crossbreeding makes a logical cornerstone in any effort to enhance cow herd reproductive performance. With crossbreeding as the foundation, the selection of superior animals of multiple breeds as inputs to the crossbreeding system can be considered a supplemental means of further boosting reproductive function; however, identifying reproductively superior animals has its challenges, as we will explain.
Because the assessment of a cow’s reproductive performance is generally determined later in her life, it seems logical to look for early indicators to hasten the process. For example, it is a commonly held belief that females with a propensity toward fatness will excel reproductively.
Though research has shown that increased fatness, to a point, is strongly and favorably associated with reproductive performance on a phenotypic scale, the few attempts to assess the relationship on a genetic level shows an unfavorable, though weak, relationship. Using data from the Red Angus Association of America (RAAA), researchers at Colorado State University (CSU; Beckman et al., 2006) derived genetic correlations ranging from -.12 to -.22 between body condition at various ages and Stayability (by industry convention, the probability of a cow remaining in the herd through six years of age). At the American Simmental Association (ASA), we have found a correlation of -.19 between an animal’s genetic propensity for backfat in the feedlot and their inherent Stayability. We (ASA) have also calculated a -.11 genetic correlation between backfat and heifer pregnancy (the likelihood of a heifer being pregnant at the end of the breeding season) using RAAA data.
Admittedly, these unfavorable correlations between fatness and reproduction may seem illogical. We have all seen a higher proportion of thin cows open at pregnancy test time. Keep in mind, however, that the aforementioned correlations are genetic correlations. The relationships we actually observe (i.e., phenotypic correlations) are influenced by a combination of underlying environmental and genetic relationships. There is little question that females within a herd lucky enough to experience an environment for increased body condition (e.g., extra energy intake) are likely to have better reproductive performance than their herd mates. Furthermore, this strong and positive environmental relationship between fat and reproduction apparently overwhelms what appears to be a slightly negative genetic relationship — yielding the strong, favorable phenotypic relationship we typically observe.
Frankly, there is not enough evidence about the genetic relationship between fatness and reproductive function to make recommendations based on it at this time; however, though it may fly in the face of conventional wisdom, it appears that selecting “easy-fleshing” genotypes will not gain us ground reproductively.
Scrotal circumference has been considered as a predictor of female reproductive performance. Though the preponderance of evidence indicates a strong to a moderately favorable relationship between scrotal circumference and age at puberty in related females, research is less clear on the relationship between scrotal circumference and subsequent measures of reproduction. In a study based on a large population involving several breeds at the MARC, Martinez-Velazquez et al. (2003) found a slightly unfavorable (.15) relationship between scrotal circumference and age at first calving and no relationship between scrotal circumference and first pregnancy, first calving, and first weaning rates. Their conclusion was that selection on scrotal circumference would not be effective in improving female reproduction. These findings are in agreement with some studies and contradicted by others. For those interested, Martinez-Velazquez et al. (2003) provides an excellent literature review on the subject. Given the conflicting evidence, it may not be advisable to base selection decisions on scrotal circumference with the intent of enhancing maternal reproduction.
As for other traits that may be related to reproductive function, Rogers et al. (2004) found that increased levels of milk EPD increased the risk of females being culled. This finding is consistent with ASA data showing an unfavorable (-.15) genetic correlation between milk and Stayability. Other ASA genetic correlations of note are -.26, .40, and -.19 between Stayability and mature weight, maternal calving ease, and marbling, respectively. Based on these findings, we would expect females that are inherently lower milking, smaller at maturity, easier calving, and less marbled to stay in the herd longer; however, none of these relationships is strong enough to make a sizable impact on Stayability by selecting for them. Furthermore, other than mature weight, because of its strong relationship to early growth, determining the genetic level of a young heifer for these traits by simply observing them (which is what most commercial producers are limited to) is not possible. Therefore, a different tactic will be required if we wish to improve reproductive performance via selection. Namely, select for it directly — which, as we will point out, is not a trivial task.
A well-entrenched view of both commercial and seedstock producers is that the “cows left standing” after culling on the components of reproduction (e.g., pregnancy status and calf loss) are genetically superior. By extension, it is presumed that a great deal of progress in reproduction is made through rigorous culling and the retention of heifers out of dams making it to advanced ages. Though this may seem like a reasonable deduction, it is generally not the case.
Unfortunately, little genetic headway is made by simply culling cows that do not achieve reproductive thresholds. This may seem counterintuitive. Why wouldn’t getting rid of the offenders improve your genetics for reproduction? The main reason lies in the fact that measures of reproduction tend to be lowly heritable (estimates typically run between 5–20%). And, with lowly heritable traits, an animal’s own performance is not a good indicator of its genetic level for the trait. Therefore, many open culls may be genetically above average or even superior for reproduction. By the same token, several cows kept because they are bred may be genetically inferior for it — certainly not an outcome that will yield much improvement.
So, how do we directly select for reproduction? Because a cow’s reproductive performance is expressed later in life, and even then it only provides a very cloudy picture of her genetic merit, are we relegated to making little to no selection progress for reproduction? Heck, no! We can clear the clouds with reproductive EPD.
Though EPD always provide the best estimate of an animal’s genetic merit, they are especially valuable when applied to low-heritability traits. This is because, when an animal’s own record is a poor indicator of its genetic makeup, gathering information on its relatives is the only means we currently have of getting a clear picture of the animal.
You may ask yourself, “If an animal’s own performance does not tell us much, what can be gained by records on its relatives?” It is not that a single relative record brings much to the mix (obviously it adds even less than the animal’s own record); it is that there is strength in numbers — an animal can have many relatives with records, but only one record on itself. Through the use of EPD, we utilize information on all of an animal’s relatives and, in doing so, chip away at the cloud with each record that flows in.
With a low-heritability trait expressed later in life, like reproductive function, the cloud clears slowly — but it will clear. In fact, if an animal has enough progeny records, we can see its genetic merit for reproduction as clear as a bell.
Fortunately, the seedstock industry now has EPD that are, for the most part, direct measures of reproductive function: Stayability (STAY) and heifer pregnancy (HP). Researchers at CSU developed STAY (Snelling et al., 1995) and HP (Doyle et al., 2000) EPD, and the RAAA implemented them into the association’s national cattle evaluation a few years later. Since its development, STAY has undergone several revisions. Most recently, the ASA released the industry’s first multi-breed STAY evaluation, which incorporated genomic data in a single-step random regression model.
Though STAY and HP have potential shortcomings (e.g., seedstock breeders’ culling practices are probably not in step with the commercial industry’s, and breed association culling records tend to be sketchy), they are the most effective selection tools available for improving reproductive function. What’s more, based on computer simulation efforts by retired USDA scientist M.D. MacNeil, the economic impact of Stayability when selecting a sire for female replacement is nearly twice that of the next closest trait, while the relative importance of heifer pregnancy is on par with the most important carcass or growth traits (personal communication) — so these reproductive EPD certainly warrant a great deal of attention in the selection process.
Most commercial producers do not have the luxury of using STAY or HP EPD to select replacement females; however, if you select sires with superior EPD in these areas, the reproductive function of your cow herd is likely to improve over time. Given their relationship to Stayability, you may also gain some reproductive ground by selecting sires with lower milk, smaller mature size, and better maternal calving ease EPD. Another option to consider for commercial producers is the commercial option of the American Simmental’s Total Herd Enrollment. The commercial option predicts EPD on commercial females and coupled with the Cow Herd DNA Roundup provides genomically enhanced EPD to commercial females.
In closing, we must reiterate that crossbreeding needs to be at the center of any effort to improve the reproductive function of your cow herd. The dramatic impact of heterosis on reproductive performance is crystal clear — no herd should be without it! Though reproductive improvement through selection is possible, it is generally limited to utilizing reproductive EPD when selecting your herdsires. By combining crossbreeding with the selection of superior sires you will position your enterprise to excel in the most vital area of beef cattle production: cow herd reproduction.
Beckman, D. W., S. E. Speidel, B. W. Brigham, D. J. Garrick, and R. M. Enns. 2006. Genetic parameters for stayability and body condition score in beef females.
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Doyle, S. P., Golden, B. L., Green, R. D., and J. S. Brinks. 2000. Additive genetic parameter estimates for heifer pregnancy and subsequent reproduction in Angus females. Journal of Animal Science. 78:2091-2098.
Martinez-Velazquez G., K. E. Gregory, G. L. Bennett and L. D.
Van Vleck. 2003. Genetic relationships between scrotal circumference and female reproductive traits. Journal of Animal Science. 81:395-401.
Rogers, P. L., Gaskins, C. T., Johnson, K. A., and M. D. MacNeil. 2004. Evaluating longevity of composite beef females using survival analysis techniques. Journal of Animal Science.
Snelling, W. M, Golden, B. L., and R. M. Bourdon. 1995. Within-herd genetic analyses of stayability of beef females. Journal of Animal Science. 73:993-1001.