Through the years, the dairy industry has used many terms to describe milk production. It is, after all, the primary product we produce, but measuring it in a meaningful way has been an age-old topic. Over the decades, we have moved from pounds per cow to items like start-up milk, peak milk, rolling herd average (RHA), management level milk, ISO days in milk (DIM) corrected milk, fat-corrected milk (FCM), energy-corrected milk (ECM), money-corrected milk (MCM), and the list goes on.
Most recently, there has been a perplexing trend of simply adding pounds of protein and fat together and discussing that. I even remember when my dairy friends from California talked in terms of gallons.
I first recall seeing shortcomings in these various approaches when discussing peak milk a couple of decades ago. As an industry, we know about lag in dairy data: we measure things that each individual cow may have achieved in their contribution to the average many months ago.
Of course, moving from simple pounds of liquid milk to a solids-corrected milk of some sort was critical. It wasn’t widely accepted by producers until it became closely associated with the way they were paid.
Big-picture thinking
Now, ECM seems to be the most accepted term used. It is a good one, but as a tank- or herd-level metric, it still falls short of describing what is really going on for a particular dairy at a particular time.
One of the most common places I still see straight milk pounds used instead of using ECM is in things like start-up milk or break-even milk for culling or early dry-off decisions. One reason is that tank averages are easy to convert to a solids-corrected value since the butterfat and protein percent of that milk is easily accessible. Components from individual late-lactation cows or the fresh pen is harder to get. But what do we miss by not solids-correcting those key management metrics? I would say a lot, especially since both of these examples can have fat and protein levels much higher than the bulk tank report from the creamery.
So, I suggest for the big picture, we settle on ECM as the most common industry standard. It is truly a biological measurement, but since it includes the info needed to make the calculation, you can easily use the payment approach for any dairy and quickly predict income per cow. However, when getting into the weeds on why a dairy has a particular herd-level ECM, I suggest going straight to ECM cohort milk.
The word cohort is not commonly used by most people. I think some might think of education or health care when hearing the term. They may have heard it used to describe a group of students at a school who are a subset of the total student body. As a medical topic, it might be used to describe in what group of people a medical study was relevant. Other uses might be a peer group or subset as opposed to an entire population. How does this concept help in measuring milk at a dairy?
Why cohort milk?
There are three main reasons why cohort milk matters when digging into milk production trends. First is the shape of a normal lactation curve. We all know that comparing a cow at 60 DIM is meaningless when compared to a cow at 260 DIM. The shape of a standard lactation curve explains this. Another note on this would be the fact that the normal lactation curve we could all draw is probably not energy-corrected. A true ECM curve would be much flatter due to a lower percentage of solids near the peak and a higher percentage near the end of lactation.
The second example of needing cohort milk is the undeniable seasonality of milk production. If you were comparing start-up or peak milk production between cows that were between 50 and 80 DIM in March versus those in that same DIM range during August, you would not consider that a legitimate comparison.
The third one is very simple. Lactation cohorts (LACT) allow for the understanding that as cows progress through their first, second, third, and beyond lactations, we have different expectations for milk production potential. What solves these problems of the risk of making a bad comparison? Putting cows in LACT cohorts before making those comparisons.
Let’s look at an example. If I really want to know how a dairy is doing for production, I first want to know the ECM of cows with LACT greater than 2 between 60 and 90 DIM. Then, to see potential seasonal impacts on production, I might look at a cohort of these in this DIM range in April and August.
Taking this a step further, if the data was readily available, we could consider these two different cohorts of “peaking DIM cows” by year to see if the herd is progressing over time. Using this same approach for LACT=1 animals in a year-over-year fashion would be a great way to see how the herd is making genetic progress.
From concept to comparison
To broaden the example of making use of cohort groups between dairies, we can use the same concept to make sure that comparing Dairy A to Dairy B makes sense. Using this same DIM cohort between multiple dairies would be a great way to assess things like genetics, management, facilities, and nutrition. If the dairies being compared are not in the same geographical region, a regional cohort would be helpful. Comparing a dairy in Florida to one in Wisconsin in August might lead to incorrect conclusions.
The concept is not difficult and is likely intuitive in the way we compare things. We wouldn’t ever compare basketball scoring in a fifth grade league to the same metric in the NBA. We also would know not to compare peak milk for Lactation=1 animals in Florida to Lactation=3 animals in Wisconsin in August.
Taking this same concept a step deeper to be sure we are making good comparisons within one dairy over time, or across multiple dairies at any given time, will keep things on an even playing field. In this way, we will be sure that the comparisons we are making are legitimate and can be used to find opportunities to help cows be more successful.
