Sometimes, getting to simple can be a very complicated process.

Take, as an example, Cargill Cattle Feeders. In mid-2007, the cattle-feeding company began using DNA markers for marbling in their sorting criteria for feeder cattle.

Simple, right? Well, yes and no. Just ask Bill Kolath.

Kolath, a nutritionist and research scientist with Cargill Cattle Feeders in Dalhart, TX, oversees the company's use of DNA marker-assisted selection to sort cattle. It's a joint development effort with MMI Genomics, Calverton, MD, that they started in late 2006 after finishing an extensive validation and development process that began around nine years ago. By 2007, they were testing the majority of the cattle fed in all five of their feedyards. By mid-2008, they were DNA testing 100% of the cattle in their four yards in Texas and Kansas and using the data to help sort cattle into four different outcome groups.

“It's been a learning experience to get the software where it is today,” Kolath says. “The decision is complex, but from a computing standpoint it's pretty simple.”

How it works

Newly arrived cattle are processed no differently than at any other feedyard, with a few notable exceptions. In addition to being dewormed, vaccinated, implanted and run through other standard processing procedures, the calves are individually weighed, a DNA sample is taken and each animal gets an EID tag. The animal's weight and DNA results are then linked to its individual identification number.

What doesn't happen at initial processing is any sorting. The calves are penned in chute-run fashion, usually as they come off the truck.

Where the big dance happens is at re-implant time which, based on the incoming weight of the cattle, is anywhere from 60 to 90 days after arrival. The incoming weight of the animal and its genetic potential for marbling, based on the DNA marker data, are already stored in the EID database. These data are combined with ultrasound data taken at the chute.

It all happens in real time, quickly and simply. As an animal enters the chute, a crew member reads the EID tag while another does the ultrasounding. A computer crunches the information and within seconds, a screen pops up that tells the crew into which of four outcome groups to sort the calf and what implant strategy to use — no implant, a mild dose or an aggressive approach.

The outcome groups are determined by the desired carcass endpoint, which is largely determined by days on feed. Group 1 is early-maturing animals that need fewer days on feed to reach the feedyard's carcass endpoint goals. Group 2 is average-type cattle. Group 3 is large-framed, late-maturing animals that need more time at the feedbunk, and Group 4 is genetically superior animals in terms of their ability to marble.

From there, the cattle are managed on a pen basis just like at any other feedyard. However, the data also help determine pen management decisions, including projected harvest dates and whether or not to use a beta agonist. “We haven't gone this far, but we could implement different rations to the different groups to maximize production efficiency,” Kolath says. That may yet come, as Cargill continues to look to technology to help it squeeze efficiency out of the feeding process.

What doesn't go into the equation is breed composition and frame score. “The measurements, along with the genetic information, won't tell us breed composition,” Kolath says. “But it will tell us how to manage that animal. The data drive the decision-making process.”

And having both the genetic data, as indicted by the DNA marker score, and an environmental measurement provided by ultrasound is necessary. “They go hand in hand,” he says. “You can have the best genetics in an animal, but if something in its history has messed it up, it's never going to get to that genetic potential.”

And in a bottom-line business like cattle feeding, genetic potential isn't as important as economic potential. Knowing the genetics, however, helps Cargill decide how to best take advantage of the economic potential.

“What we're really after is being able to optimize the capability of that animal. The animal may be able to meet a certain genetic potential, but from a production efficiency standpoint, it may not make sense for us to get there. So really, it's the economic capability of that animal.”

Combining genetic information with other measurable data simply allows Cargill Cattle Feeders to determine how best to manage each animal, grouped into outcome groups, to best optimize its economic potential. Kolath says almost any animal can perform in their management system.

Using as an example a large-framed, late-maturing animal with little genetic potential to grade Choice, he says while those animals won't work on a Choice grid, they can still work, assuming they can be identified. “Those cattle, in this management system, can perform. We can manage them accordingly and have some value at the end of the day. They're not necessarily a bad animal to have if you can manage them effectively.”

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