Expected Progeny Differences - A measure of the genetic potential of an animal as a parent. These represent the difference in the average performance of the progeny in units of a trait.
An EPD should be used in making comparisons of potential parent animals. If you are selecting for improved weaning weight and sire A has a WW EPD of 40 and sire B has a WW EPD of 70, then you should expect progeny of sire B to average 30 pounds heavier at weaning than the progeny of sire A.
One way to benchmark the genetic merit of one individual to the entire population is by using an EPD or $Index percentile rank. Animals that rank above the 50th percentile are considered to be above the breed average for a trait. Note: it's important to know which animals are included in the population used for the percentile ranking. ASA has four different populations established for the percentile rankings: Purebred Simmental, Fullblood, Simbrah, and Hybrid. The percentile ranking of a Purebred Simmental would compare that animal to all other Purebred Simmentals in ASA’s database. It would NOT compare it to SimAngus, for instance.
Traits that are economically relevant to your production system are the EPD you should be selecting for. If a vast majority of your customers sell calves at weaning and keep replacement females, the All Purpose Index ($API) combines all economically relevant traits into a weighted index to provide a balanced selection for your customers.
Weight measurements that are reported outside of the required age or weight ranges will not be accepted and thus no adjusted weight will be calculated.
- Weight must be between 30 and 160 pounds.
- Weight must be between 200 and 1200 pounds.
- Age must be between 160 and 250 days of age.
- Weight must be between 350 and 2,000 pounds.
- Age must be between 330 and 440 days.
- Weight must be reported at least 60 days after weaning.
Carcass ultrasound data are an appropriate substitute for actual harvest records, but since they are a correlated trait in the model, the improvement in carcass EPD accuracy can only go as high as the genetic correlation between carcass ultrasound and actual carcass. This is why the collection of actual harvest data is encouraged in tandem with carcass ultrasound.
Genomically enhanced EPD (GE-EPD) are significantly more accurate on young animals than standard EPD. The reason is because the genetic evaluation now has a new source of information to make genetic predictions: the animal’s own genome. The genome is host to a large number of known gene locations that are associated with a quantifiable effect for performance traits. We use these data to inform the genetic evaluation for improved prediction.
It is easy to see that there is a strong tendency for high-growth animals to be at the top of the heap for $TI, while it is not unusual to see low-growth animals rank highly for $API. Because of that, it may be logical to conclude that growth is treated differently between $API and $TI. What may be a surprise to many, however, is that growth is treated identically between the indexes. The economic value of growth in beef cattle production is realized through weight at weaning and harvest in cattle not retained for breeding. Since both indexes predict differences in profit in an integrated production system, the economic value of growth in slaughter cattle is factored into both $TI and $API. Further, since the same pricing grids are used, the value for a unit increase in growth in a sire’s slaughter offspring is the same for both indexes.
So, why does it appear that growth is treated differently between the indexes? Why does growth seem so important in $TI, while appearing to not be as important in $API? The reason lies in the fact $TI assumes that all of a sire’s offspring go to slaughter, while $API is predicated on retaining replacement females, i.e., only steers and cull heifers go to slaughter. This means that, though an increase in growth for a single slaughter calf has the identical impact between indexes, since all of a sire’s offspring go to slaughter under the $TI scenario, the collective impact of growth is magnified in $TI. Furthermore, since $API also factors in economic differences in cow herd traits, which tend to have more economic impact on the production system than growth (particularly stayability and cow herd intake), superiority in this area can easily make up for shortcomings in growth.
The assertion that index selection is akin to single trait selection is only true if the trait being referred to is profit. Unlike single-trait selection for biological traits, selection on $API and $TI simultaneously applies selection pressure to all traits that impact each phase of beef cattle production — cow calf, feedlot and packing plant. Because ASA’s indexes weight these traits in a mathematically optimal manner, selection on them maximizes genetic progress in the trait we all should be practicing single trait selection on — profit! A major distinction that sets ASA’s indexes apart from those published by some organizations is that they simultaneously account for all segments of beef cattle production. Our industry has been inundated with what I call “segmented” indexes — indexes geared toward specific and limited segments of the production cycle, e.g., weaning, feedlot, and grid. Though indexes of this nature may have utility for users who are interested in single phases of beef production, e.g., a feeder sourcing calves or a packer buying fat cattle, they should not be used to provide direction for a population. In fact, if used widely, “segmented” indexes can have unintended and undesirable consequences for a population.
Because ASA’s indexes consider all traits a sire will impact across the entire production system, they are “balanced” — this balance prevents selection on the indexes from taking our population down unintended and undesirable paths. It ensures that we will make optimal genetic progress in all traits that have an economic impact on beef cattle production.