Body condition scores (BCS) have long been a useful tool for evaluating a cow’s status and supporting decisions around nutrition, reproduction and health. Yet their usefulness depends on having reliable information at the right time. Manual scoring makes that difficult because it takes time, requires trained observers and introduces subjectivity.
For Dr. Joaquin Azocar, veterinarian and commercial portfolio manager for digital solutions at DeLaval, the challenge is not recognizing the value of body condition. It is obtaining the measurement frequently enough to make it actionable.
“The thing is that body condition assessment is very well known as very important information and data to have to know the status of our cows,” Azocar says. “You have many implications and many decisions that you can make about feeding, reproduction, health, many things.”
Automated systems change the frequency at which that information can be collected, generating a daily record for individual cows rather than relying on occasional manual assessments. That creates an opportunity to think about BCS differently, moving beyond the individual score toward the pattern that develops over time.
From a Number to a Trajectory
Traditional BCS provides a snapshot at a particular point in lactation. A cow may be scored around calving, at peak lactation, at breeding or before dry-off. Those measurements help establish whether her condition is appropriate at a particular stage of production, but continuous measurements allow veterinarians to follow the changes between those snapshots.
“When you start looking daily, when you look at each trajectory, then you will see if the cow is gaining or losing condition,” Azocar explains.
The clinical value is in the context that develops around those measurements. A cow’s condition at calving, for example, can be evaluated alongside what happens during early lactation and subsequent recovery. Instead of simply determining whether her score falls within an acceptable range at a particular checkpoint, you can examine whether her progression fits the pattern expected for her stage of production.
That distinction is particularly relevant after calving, when changes in body condition are expected.
“It is normal that cows lose condition after calving, for example. That is normal and is expected,” Azocar says. “But how much would you allow it to drop before you decide that something is not right with the cow? Or for how long would it be normal for her to continue in negative energy balance before she’s recovering?”
Those questions point toward a more individualized approach to BCS, in which the magnitude and duration of a change is considered rather than treating any loss of condition as inherently problematic.
Finding Cows That Don’t Fit the Pattern
Continuous measurements can also change how you look across a herd. Instead of manually evaluating a limited number of cows at selected points, the data can be used to identify animals whose condition is outside the pattern established for the herd or a particular group. Azocar encourages using the data to establish what is typical and then find the cows that deviate from it.
That creates a triage function for BCS data. An outlier does not automatically identify a disease or management problem; it identifies a cow that may warrant a closer look. The same concept can be applied beyond the individual animal. If a pattern emerges across a pen, the veterinarian and producer can investigate whether nutrition, transition management or another aspect of herd management could be contributing. The value, then, is not necessarily that automated BCS diagnoses a problem. It can help determine where to look next.
Looking Backward Can Be Just as Useful
Continuous BCS data may also provide information after a problem has already emerged. Body condition is not necessarily an early indicator of every health or management problem. A change visible today may have its roots in an event that occurred weeks earlier.
“Sometimes body condition actually reflects something that has already happened,” Azocar says. “When you look back on some cows that are having current body condition problems, you find that something happened to her a few weeks ago.”
That makes the historical record useful during an investigation. Instead of relying solely on what a cow looks like when she is examined, you may be able to look at how her condition changed leading up to that point and consider that history alongside other records. Continuous BCS does not necessarily predict every problem, but it can add another layer to understanding what happened.
Automation Changes the Veterinarian’s Role
If the technology handles the repeated measurement, the veterinarian’s time can shift toward interpretation.
“Instead of having to do the manual work, use the data to use your brain and your knowledge,” Azocar says.
That could mean spending less time collecting scores and more time investigating why a particular cow is behaving differently from the expected pattern. The veterinarian still has to put the information into context, considering lactation stage, parity, production and what else is happening with the animal. This is where continuous data becomes most useful: not as a replacement for observation and clinical assessment, but as another source of information that can help focus those efforts.
BCS is One Piece of a Larger Picture
Body condition becomes more useful when considered alongside other measurements. Modern dairy operations may have information on milk production, activity, rumination, eating time and other aspects of cow behavior and performance. The challenge is turning those separate measurements into a coherent picture.
“Put all this together in an easy way, then show me the cow I need to give attention to and tell me why,” Azocar says.
As farms collect more data, analytical tools can help bring meaningful patterns into focus and prioritize cows that warrant attention. That allows veterinarians to spend less time sorting through individual data points and more time applying clinical judgment to determine what those patterns mean for the cow and herd.
Starting With the Basics
When working with a farm that has just begun collecting automated BCS data, Azocar recommends starting with the herd’s overall energy status before trying to extract every possible insight from the system. The first step is to understand what is happening within individual pens and identify cows that fall outside the expected range. From there, BCS can be considered alongside production, parity and other information to determine whether the pieces fit together.
The approach becomes more valuable as historical data accumulates.
“Over time, historical data starts building up,” Azocar explains. “I will start giving more attention to energy balance. Seeing how much condition my cows lose in a lactation and how long it takes them to recover condition.”
That history can establish a farm’s own patterns and allow the comparison of how different groups move through lactation and evaluate whether first-lactation cows are following the same trajectory as older cows. Instead of relying only on generalized expectations, the farm can begin building a picture of what its own cows typically do. Over time, that can make deviations more meaningful.
From Describing the Past to Predicting the Future
“Today, most of the technologies out there will help us to know what happened and maybe why that happened,” Azocar says of continuous BCS scoring and other data. “The next steps will be further integration on data to predict what’s going on, what do we have next.”
That could eventually move precision dairy technology beyond identifying cows that need attention and toward helping producers evaluate potential outcomes of different management decisions. The industry is not there yet, and one obstacle is that data from different technologies and manufacturers often remain separated.
“We have many companies, many manufacturers, many different pieces of technology. They’re all completely disconnected from each other,” Azocar says. “What our dairies need is to integrate the data to make it easy to use.”
That may be the larger challenge facing automated BCS and precision dairy technology more broadly. The industry is no longer limited by a lack of information. It is increasingly challenged by how to turn that information into something useful.
For body condition scoring, the advancement may have started with the ability to measure more frequently and consistently. The bigger opportunity is what veterinarians can do with that history.
A single BCS can describe a cow’s condition at one moment. A series of scores can show where she has been, where she is going and whether that trajectory fits the expected pattern. That turns body condition from a periodic measurement into something closer to a body condition story.
The technology may be able to collect that story, but interpreting what it means for the cow and the herd remains a job for the veterinarian.
To hear more from Dr. Azocar, as well as the perspective of Herd-i US CEO Nicole Mozeliak, on the benefits of continuous body condition scoring, tune in to the latest episode of “The Bovine Vet Podcast":
