Using genomics takes guesswork out of selecting for feed efficiency

The potential to gain is all hidden under the skin, says a feedlot researcher.

Despite all the talk about genomics, many in the cattle industry are confused by what it all means.

But the bottom line is pretty simple, says William Torres, researcher at Cattleland Feedyards in Strathmore.

“Cattle all gain different — you don’t know what is underneath the skin,” Torres told attendees at the recent Livestock Gentec conference.

“All you’re doing is guessing where they are going to be 150 or 200 days from now. If you use genomics, you’re taking the guessing game out of the equation. You’re going to more accurately manage and predict how the cattle are going to perform.”

Torres is bull evaluator, cattle manager, and researcher with Cattleland Feedyards, operated by the Gregory family. Its for-profit research facility is home to North America’s largest bull test centre and Torres has managed about 38 contract research projects involving about 65,000 head since joining the company in 2008.

Much of that research involves genomics and residual feed intake, also known as net feed efficiency.

“It is defined as the difference between an animal’s actual feed intake and what is expected for feed requirements — you know, what you think it’s going to eat and what it actually ate,” he said.

Since feed efficiency is a heritable trait, genomics can be used to select better animals.

“If you point out to producers the difference in amount of feed consumed and then you start translating that to dollars, that’s when you start getting people’s attention,” he said.

Tracking consumption

At Cattleland, researchers use the Alberta-developed Grow Safe System, which uses a second electronic identification tag to record feeding data. Since only one animal can eat from a bunk at a time, the system tracks both how much each one eats and how often they come to feed.

Although Cattleland has eight pens with 40 nodes, each test takes about 100 days and producers must book a year in advance to get a cow into the facility.

On the genomics side, Cattleland works closely with Saskatoon’s Quantum Genetics to track leptin production in cattle. The hormone, present in fat tissue, acts on the brain to regulate food intake and body weight. In cattle, there are three leptin variants: CC, CT and TT. Cows with the CC version are lean animals that have low body weight, TTs are the ones that put on fat and are more desirable, and those with CT are in between. The difference is huge — Cattleland has found TT animals will reach the same end point about 45 days sooner than CC animals.

“That’s 45 days less feed and 45 days less on earth,” said Torres. “So less management, less manure, and less headaches because I can get them to market faster.

“The other thing is that we know what the yield is going to be and we can predict what the grade is going to be. So we were able to work out better prices with the packing plant and we got paid a little bit more and then passed that back on to our producers, who were bringing us these animals.”

The leptin trait affects weaning weight, milk production, accumulation of back fat, yield grade, quality grade, and feed intake. TT animals have more back fat at the end of spring and prior to weaning, which relates back to a good body score condition and fertility.

“We use what is called a Q-sort system designed for feedlots,” Torres said.

Cattleland staff also measure back fat while using the system, and also consider the age and sex of the cattle as well as the hide colour. The system sorts the animals into four different groups. Animals in each group are given different feeding regimes and marketing dates.

“This increases the carcass value and we know how to feed the cattle more efficiently,” said Torres.

About the author


Alexis Kienlen lives in Edmonton and has been writing for the Glacier FarmMedia publication, the Alberta Farmer Express, since 2008. Originally from Saskatoon, Alexis is also the author of two collections of poetry, a biography, and a novel called "Mad Cow."



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