The author is an associate professor of dairy cattle genetics at Penn State University.

Two similarly themed projects conducted by different research teams were presented at this year’s American Dairy Science Association annual meeting in June. The meeting format was, as you might expect, quite a bit different than most years but still facilitated an exchange of research results and ideas.

There are two methods that could help generate genetic evaluations for lifetime economic merit. We could directly determine economic merit by tracking income and expenses for all cows. Keeping track of lifetime income and expenses for many thousands of individual cows is a daunting challenge. We can determine lifetime yield with reasonable accuracy for many cows, but we do not have a national database of body weights, cull prices, or individual cow feed costs.

The second alternative — and the option that is used almost exclusively — is indirect. We determine genetic merit for the individual traits related to profitability and then combine all the traits together into a single measure of lifetime economic merit.

In principle, using the indirect method for estimating total lifetime profitability is sound so long as all the traits that influence profitability are considered and the economic weights are properly determined. We still need to examine how well economic merit indexes predict actual profitability to validate the index and to find opportunities to improve and refine the indexes.

Profitability by quartile

In the first project, scientists from Zoetis and the University of Pennsylvania evaluated the effectiveness of the Dairy Wellness Profit (DWP$) index. Cows born in 2011 were used for the analysis to allow a sufficient time for each cow’s total lifetime profit to be realized. They determined revenue as the value of the cow’s milk, beef, and calves. With this calculated, researchers deducted expenses that included the feed costs associated with milk production, breeding costs, disease costs, and heifer rearing expenses.

The scientists split the cows into four equally sized groups, or quartiles, based on their 2012 DWP$; the differences in performance among the groups are shown in Table 1. Results clearly favored the highest quartile.

The highest quartile cows produced nearly 20,000 pounds more energy-corrected milk over their lifetimes than those in the bottom quartile. Much of this was because they lived longer — they spent 202 more days in the lactating herd. The best cows also had lower health expenses and less death loss. Over their lifetimes, the highest quartile generated $811 more profit than cows in the bottom quartile.

The second study was a joint effort between scientists from the University of Florida and Penn State University. We collected daily milk production and body weights from 2000 through 2017 at Penn State, which determined daily milk income and feed expenses.

We also considered health and breeding expenses to derive two measures of lifetime profitability: actual lifetime profitability and standardized lifetime profitability. The difference between the two measures is that profit per day was extrapolated to 2.78 lactations for standardized profitability, which is the herd life assumed by the Lifetime Net Merit (NM$) formula. We will discuss the implications of actual versus standardized results a bit later.

Our measure of genetic merit was the cow’s direct genomic value (DGV) for NM$. A DGV is the portion of a cow’s genomic predicted transmitting ability (PTA) that is determined exclusively by her DNA markers. We use this measure because it is independent of the cow’s own performance, which needed to be eliminated as a source of bias. The Zoetis study also took care to eliminate such bias but with a different approach — they used DWP$ early in life and before animals began producing milk.

The results from our study are reported in Table 2. I have expressed the results as the change in profitability we observed for every $1 of expected gain in profit. As you can see, we observed an 80-cent improvement on the actual basis and $1.07 for the standardized basis. Keep in mind that this result is for a single herd, but the main conclusion is comforting. Those animals expected to be more profitable were, in fact, more profitable.

The difference in actual versus standardized profitability is an interesting one — the results suggest that NM$ was a more accurate predictor of standardized profit than actual profit. My interpretation of that result is that we at Penn State should have done one of the following to take full advantage of the genetic merit of our cows:

  1. We should have raised fewer heifers and allowed our cows to express more of their lifetime potential.

  2. We should have placed more emphasis on yield traits and less on productive life since cows did not remain long enough to fully realize their potential.

I expect that many herds would come to the same conclusion if they looked at data from the last decade, and breeding cows with beef semen is one mechanism we are using to reduce a heifer oversupply.

We also calculated that NM$ explained 6% of the variation among cows in actual lifetime profitability and 12% of the variation in standardized profitability. This means that, while we saw an average improvement in profitability that mirrored expectation, there are a lot of cows that are more profitable or less profitable than predicted. That is true of all traits, but it might be even more true for measures of profitability as milk prices, feed prices, beef prices, and other economic factors fluctuate over time.

More to learn

Do these studies mean that, if you could raise NM$ of your cows by an average of $100 tomorrow, your herd profitability would improve by $100 per cow? There are two reasons I do not think we can reach that conclusion.

First, your competitors’ herds are also going to make strides in genetic merit. If every herd becomes more efficient, prices received effectively decline relative to the rate of inflation and no one really becomes more profitable. Genetic improvement can help you maintain an efficiency edge, but it is not a guarantee of profitability.

Second, just as you are in a competition with other businesses, the cows in your herd are in a competition of sorts. If you take the “top dog” out of your herd, the cow that is second in line will move to the top spot. Her genetics will not be any different, but might her performance improve? I’m not sure we know the answer to that question.

The bottom line from both studies is that cows with higher genetic merit for economic selection indexes were more profitable than cows with low genetic merit. We can have confidence that our multiple-trait economic selection indexes are robust indicators of lifetime profitability.