There is something nostalgic about glass weigh jars in a parlor. I am not sure how many of these remain on dairy farms today, but seeing them makes me smile. The clean glass and the etched graduations showing the pounds of milk make me think of a Pyrex measuring cup in a kitchen.
At the same time, I am equally intrigued by a milking system with a wirelessly connected flow meter for each milking location with real-time measurements of not just the amount of milk but numerous other metrics and indications from components to cow health and reproduction. It is all so amazing, and it continues to drive home the point that the dairy cow might be the most “measured” species on Earth.
A head start
I have often considered the plethora of information that the dairy industry can measure, and this has been the case for more than a century. Among the first efforts in dairy record collection was in 1905 in Michigan. By 1927, the Dairy Herd Information Association (DHIA) was adopted and quickly spread across the country. For the remainder of the 20th century, state-run DHIA organizations were the primary place for gathering, summarizing, and reporting individual dairy cow performance. From this information, the dairy genetics industry was off to the races to help improve cows. And that they did.
A change in pace
What happened, though, as dairy farms started to grow? When farms had less than 100 cows on average, most of this information was measured on a per-cow basis. At some point after the turn of the century, and as herds became larger, some stopped the DHIA testing that had been up, to that point, the default for keeping dairy records. Not only did the various testing associations keep up with production information, but reproduction records, lifetime information, and other details were also in the database. When dairies started choosing not to test with their local DHIA and began to test with independent testing businesses, buying meters and testing themselves, or in some cases, not testing at all, something meaningful changed. These trends were very regionally specific and likely differed in certain areas.
What was lost with this evolution? I am sure a trained data scientist in dairy could elaborate on this more than me, but as a nutritionist, my first thought would be, “not much.” Many of the dairies I worked with early in my career didn’t have a strong genetic base, and we were working toward achieving volume for the farm or average per cow with less emphasis on milk for individual cows.
I am not saying that this was correct, but it is what happened. We traded interesting data points like percentage over 100 pounds of milk and lifetime production for things like milk-feed ratios and pounds of milk per freestall. Reproduction was still key, but there again, we looked at herd-level information such as percent pregnant by 150 days in milk (DIM) or pregnancy rate.
Data responsibility
Now, back to the glass weigh jars and the inline milk meters sending a plethora of data to the cloud. Technology has built the bridge between the essence of these two time-separated tools for measuring dairy performance. A modern dairy can now have the best of both worlds. We can keep all of the “big picture” skills we learned and once again start to look at each individual as an important data piece for tracking and decision-making.
Why do I care about all of this? It is simple. Early in my career, I had a mentor tell me that whoever owns the data, owns the relationship. This isn’t designed to elicit a contest between the nutritionist, breeding company, or the veterinary consultant, but it is to say that to be a complete field dairy nutritionist, you must be good at dairy data.
What about string samples?
There is, however, one other approach that has merit and may, in fact, as a nutritionist, be my favorite: string samples.
In the old days, groups of cows were commonly called strings. So, why do I advocate for string samples? First, we tend to group cows by stage of lactation, which mostly tracks with DIM. If a herd is fed any type of ration-group strategy, string or pen samples can be useful to gauge the success of a particular ration strategy that was applied only to a portion of the cows on a dairy.
When my grandfather managed a tie-stall dairy at the Alabama School of the Blind in the early 1940s, I bet he looked at udder pressure, milk weights, and how skinny a cow was and decided which individual needed a bit more feed. Now almost 100 years later, robot milkers are doing the same thing.
On a large dairy, where one pen of cows might contain a range of DIM no more than 50 days, we can also do the same thing. In this large group situation, you have a cohort of cows that can be considered one cow from a ration formulation perspective. Evaluating each specific ration is best accomplished by string samples as opposed to daily herd or tank averages.
Putting the data to use
There are ways to have sampling ports with impellers to be sure that the entire pen of cows contributes to the sample instead of the first few that come into the parlor. Some even put a hypodermic needle in a rubber gasket and babysit it on test day. In either case, I can get an average milk per cow, their pen’s average components, and milk urea nitrogen (MUN) levels. If the dairy has at least monthly milk weights per cow, we can apply those pen-level components to all the cows in that pen, and thus, each individual cow has their own milk weight with the pen’s milk details. It is not perfect, but much better than using herd or pen-level averages with bulk tank components.
As dairy feeding becomes increasingly complicated, we can use string samples to invest in improved ration strategies only for cows in early lactation that really need the boost. Likewise, we can consider actual intake levels and milk production results with solids corrected late lactation milk for tasks like calculating breakevens. As well, we can attempt careful cost savings approaches for pregnant and late lactation pens by following up to see if the savings boosted margins or if flow and components dropped to erase the cost reduction. The information is powerful for both biology and economics.
Information is power
It is true that paralysis from overanalysis is a risk. However, if done carefully, I think the more likely result for a well-managed dairy is the old saying that “information is power.” If the daily inline milk meters are not in your immediate future and you are not currently collecting string samples, give it a try for a few months and see what you learn. You could find that some type of monthly milk weight effort, a combination of daily creamery data, and quarterly string samples is the right balance.
Adding information like milk fatty acids and MUN can make the data even more interesting for evaluating feed cost investments like detailed fatty acid-balancing or simply investments in ration protein. In either case, more information from individual cows or pens of cows will likely be a tool for improving profitability. Don’t get lost in the numbers and don’t forget to go look at the cows. This combined approach will likely have the best results.
