Data Management

From Good to Best: Record Keeping That Actually Gets Used

Wed, 04 Mar 2026 17:15:12 GMT

Most farms are not short on data. They are short on recorded, usable data.

Some have spreadsheets, some use software, some have their treatment sheets printed. But unless the numbers are prioritized, cleaned up and reviewed, they rarely change management decisions.

As Bethany Dado-Senn, calf and heifer technical specialist with Vita Plus, puts it: “The cows don’t lie. They’re trying to tell us all the time what is going on. But if we don’t have any way to measure their outputs and the results, then we can’t do anything about it.”

The real challenge is not collecting everything, but collecting what matters and building a system veterinarians and producers can sustain together.

A practical framework helps: prioritize, essentialize and systemize.

1. Prioritize: Decide What Actually Matters

Before adding another metric or installing another program, start with focus.

“We need to start small. We need to identify what our top priority is to start,” says Kelly Sporer, farm data consultant with Cornerstone Ag Management. “We can’t look at everything and say ‘We’re just going to start, we’re going to dive in, we’re going to do it all.’”

Trying to track everything almost guarantees that nothing will be done well.

Instead, identify the two or three numbers that will move the herd forward right now. Those priorities will differ by farm.

“Everything that we do, we have to think about that customer, that relationship or that client, and what they’re trying to do and where they are now,” Dado-Senn says.

For example, a veterinarian might help a producer prioritize:

Fresh cow losses
Calf morbidity trends
Pregnancy rate
Early-lactation culling

The goal is focus, not complexity. Once priorities are clear, progress becomes measurable.

2. Essentialize: Remove What Gets in the Way

“A lot of the dairies we work with are not collecting completely comprehensive data,” Sporer says.

Even when farms try to track data, the system often breaks down. When records are entered inconsistently, the story they tell can be misleading.

Dado-Senn recalls once reviewing records that appeared to show catastrophic losses: “I’ll look back and it’ll look like we had a mass die-off one month, but it was really just the one month they finally cleaned their records up.”

Incomplete data makes analysis nearly impossible.

“If we have incomplete and inaccurate data, I can do a whole lot for you as far as data analysis, but we can’t do very much in ways of recommendations or changing anything in management,” Dado-Senn says.

And if the data is never reviewed with the intention of making changes, motivation disappears.

“If nobody is looking at it, that data is completely useless to the farm that’s spending valuable time collecting it,” Sporer says.

Essentializing can help with this by removing friction.

That might involve:

Standardizing health event terms
Assigning one person responsible for data entry
Recording events the day they happen
Simplifying treatment sheets
Reviewing numbers regularly with the herd team

The veterinarian plays an important role here, helping define case definitions, treatment thresholds and consistent terminology so the records reflect real clinical events.

3. Systemize: Move From Good to Better to Best

Record keeping does not need to be perfect. The key is building a system that improves over time.

“This is not about being super robust. It’s about starting somewhere,” Sporer reminds.

GOOD — The basics are written down

Deaths recorded somewhere
Pregnancy checks entered
Treatments written down

BETTER — Records become organized

Health events recorded consistently
Reasons for death or culling included
Data entered into herd software

BEST — Data drives decisions

Trends reviewed by age or stage of lactation
Protocol changes evaluated before and after
Health and reproduction trends analyzed over time

The goal is simple: When management changes, records help answer one question: Did it work?

That is when records stop being paperwork and start becoming management tools.

Importantly, not every farm needs to operate at “best” immediately. Progress from inconsistent notes to reliable digital entry is already a major improvement.

5 Record-Keeping Mistakes That Make Data Useless

Collecting data takes time. But when records are incomplete or inconsistent, the information becomes nearly impossible to use.

Recording data months later
Events should be recorded as close to real time as possible.
Using vague or inconsistent health terms
Different staff describing the same condition differently makes data nearly impossible to analyze.
Collecting data no one reviews
Review meetings reinforce why records matter.
Incomplete herd inventory
Animals still listed in the system long after leaving the herd distort nearly every performance report.
Trying to track everything at once
Strong record systems develop step by step.

Partnership Turns Numbers Into Action

Record keeping works best when it is collaborative.

“When everybody is working toward the same goal, which is the success of the farm, there’s no room for pointing fingers,” Dado-Senn says.

The veterinarian–producer partnership is central to building that culture.

Veterinarians translate trends into management insights. Producers provide the operational context that explains what is happening in the barn.

Together they decide what to track, how to track it and when to review it.

Without that collaboration, records sit unused. With it, they guide decisions from calf to cow.

When farms prioritize what matters, remove unnecessary barriers and build simple systems together, record keeping moves from good to better to best. That’s when the numbers start working for the herd.

Monitoring Tools Can Strengthen Herd Health Programs

Wed, 12 Nov 2025 23:46:03 GMT

Early identification of health challenges is one of the most effective ways to reduce disease severity, minimize treatment costs and support long term herd productivity. While experienced caretakers have always relied on stockmanship and close observation, today’s operations can benefit from data beyond what we can consistently monitor with our own eyes. Monitoring tools provide an additional layer of insight, helping recognize subtle changes in behavior, intake or environment before clinical signs are obvious.

Dr. Tiago Tomazi, dairy technical services veterinarian with Merck Animal Health, shared his advice at the Dairy Cattle Reproductive Council annual meeting on how the dairy landscape has progressed, allowing us to use monitoring technologies and management practices for improved animal health.

“If we go back to the ‘80s, we can say that dairy farming was still dominated by small-and-medium-sized family farms, right? We used to call cows by name,” Tomazi says. “Then the ‘90s and 2000s came, and there was a marked acceleration in the growth of large scale operations. There was an explosion of studies, of investigations, and scientific investigation that helped a lot to bring us to the point we’re at nowadays as far as reproduction and production in dairy cows and cattle health.”

Following this growth in knowledge came the development of monitoring technologies to assist producers and veterinarians identify potential animal issues earlier.

That being said, data-driven decision making does not necessarily require advanced technology. In many herds, structured record keeping, routine scoring (such as body condition scoring, lameness scoring or feed bunk assessments), and consistent visual checks are foundational monitoring practices. The goal is to identify patterns and changes over time, not just isolated events.

Combine Technology with Strong Management Practices

When technology is added on top of strong stockmanship, the combination can be especially powerful.

“Point No. 1 is that monitoring systems are not a diagnostic tool,” Tomazi reminds. “But they help in identifying a cow that’s not having a good day.”

These activity monitoring technologies are not meant to tell you what ailment the cow has, but rather to alert you that a cow may be worth checking up on.

“Biology is not math,” Tomazi says. “While math uses formulas, numbers and calculations to get you the exact result, biology [has to] take into consideration a set of factors associated with the biology of the cow and the environment.”

While a variety of wearable tools exist for the earlier recognition of health changes, the interpretation of the data remains critical. Similar patterns can have multiple causes. For example, a decrease in rumination may reflect heat stress, early illness, social stress or rumen upset. Likewise, changes in activity could indicate estrus, discomfort or pain. Connecting these data points to clinical reasoning and management is key.

Application Evidence from Research

Data from these monitoring tools can be applied for health predictions in a number of scenarios.

Chabel and colleagues from the University of Florida evaluated of over 4,500 dairy cattle across three commercial dairy herds wearing automated monitoring devices designed for estrous detection. They found early postpartum estrous characteristics were valuable indicators of reproductive potential; they were able to identify cows at risk for suboptimal fertility. By combining these characteristics with other health, genetic and environmental data, they were able to improve the prediction of fertility outcomes and provide targeted reproductive management for the cattle.

A series of three studies by Stangaferro and colleagues at Cornell University investigated the use of automated monitoring devices for the identification of health issues in dairy cattle. They found health index scores (calculated using an algorithm using rumination and activity data) provided sensitive detection for cattle with metabolic and digestive disorders , reducing time to clinical diagnosis by approximately 2 days. However, this system was less sensitive for the detection of mastitis and metritis , being most effective for cattle with severe cases.

While these monitoring devices may not perfectly identify all health issues in your herd, they can certainly help identify some disease signs earlier than human observation alone. It’s also important to note that the implementation of these monitoring devices will be different from farm to farm.

“We have to take into consideration the level of disease protection you want at your farm as well as the feasibility and labor capabilities,” Tomazi says. “You can be very intensive, but if you don’t have the people to provide you with that intensity, then it makes everything harder. On the other hand, you can be less intensive and find that balance where you’re not going to compromise the reproduction or production during the location of those cows.”

Data Interpretation is just as Important

Training caretakers to recognize when and how to respond to monitoring data is just as important as selecting the technology. A monitoring system is only as good as the team interpreting it.

While the benefits can be significant, monitoring tools also introduce challenges. The amount of data generated can be overwhelming. Time and training is required to get the most out of your investment and use the data effectively. This provides a great opportunity for veterinarians and producers to work together in prioritizing the metrics that are the most actionable and will make the most impact on the operation’s herd health goals.

The Future with Monitoring Tools

Looking ahead, predictive models and machine learning based alerts are likely to play an increasing role in herd health programs. With these systems will come the need for increased expertise on interpretation, contextual decision making and ongoing evaluation of how monitoring tools fit into preventative care strategies.

“I think integration is what we are walking toward,” Tomazi says. “The integration of these technologies with biological factors and management factors all together will help us make decisions considering the specific scenario at each dairy farm.”

Ultimately, monitoring tools are most effective when combined with strong stockmanship and veterinary oversight. When data and observation inform each other, disease can be detected earlier, interventions become more targeted and herds can become healthier overall.

New Dairy Cattle Nutrition Model Coming From Cornell Soon

Sat, 27 Jan 2024 15:39:21 GMT

For more than 40 years, the Cornell Net Carbohydrate and Protein System (CNCPS) has been both an academic and commercially applied nutrition model to predict requirements, feed utilization, animal performance and nutrient excretion for dairy and beef cattle.

The CNCPS is a “living model,” and is routinely updated using accumulated knowledge about feed composition, digestion, and metabolism in supplying nutrients to meet requirements. For the past 13 years, Cornell researchers have been developing the latest, highly anticipated CNCPS version 7, which will be an update to the current version, 6.5.5.

Dr. Mike Van Amburgh, Professor of Animal Science at Cornell, has been leading the team of researchers who have been steadfastly working toward the launch of CNCPSv7. Van Amburgh expects the upgraded model will be commercially available in the second half of 2024. Among the highlights of the update are:

A “mouth-to-rectum” model. Version 7 will feature a “revised dynamic structure” that accounts for segmented digestion dynamics in the rumen, abomasum, small intestine, and large intestine.
A nitrogen-based model. “We would like to eliminate the use of the term ‘crude protein,’” stated Van Amburgh. “Crude protein as a percentage can be quantified, but each amino acid has its own nitrogen content.” He said using grams of nitrogen per Mcal of metabolizable energy allows for the delineation of each essential amino acid’s nitrogen contribution, versus less-precise crude protein percentage.
A new model constructed with the goal of improving predictions of rumen function; aNDFom digestibility; essential amino acid supply and their individual behavior and contributions; and nitrogen supply.
New estimates of amino acid requirements on an energy basis along with a full urea and nitrogen recycling sub model to improve nitrogen use efficiency.
New accommodation of dairy cow feed intake patterns.
A new estimation of the contribution of protozoa to rumen microbial flow.
A complete lifecycle model that includes requirements and ration structure for neonatal calves through weaning; followed by heifer development; and finally, the lactating cow.

Van Amburgh said as Cornell researchers developed the model update, incorporation of protozoa growth and yield; endogenous protein supply and digestibility; recycled urea nitrogen; and intestinal digestibility all together provided new insights into amino acid supply and nitrogen efficiency.

However, despite all of the discoveries of essential amino acid (EAA) behavior and dietary contributions, Van Amburgh cautioned that diets still cannot be formulated on EAA balancing alone. “Our updated knowledge of EAAs is exciting, but we cannot forget about the non-essential amino acids,” he advised. “Even though they are ‘non-essential,’ they are still required in the cow’s diet, and for that reason, we still need to formulate on the basis of metabolizable protein.”

The Cornell researchers are developing the new CNCPS version with a triad of objectives: (1) improve animal health and productivity; (2) reduce resource use; and (3) protect the environment.

When the model is launched, Van Amburgh said a written manual will also be published featuring case studies to walk users through the model’s capabilities, and what to do with the information it provides. You can hear Van Amburgh and his colleagues describe and discuss CNCPSv7 on a webinar sponsored by Balchem; at a presentation from the 2023 American Dairy Science Association Annual Conference; and on a webinar sponsored by Adisseo.

For more on nutrition, read:

Managing 8,000 Cows with Activity Monitors at Del Rio Dairy

Tue, 02 Jan 2024 16:45:29 GMT

A big question on the minds of many decision makers on the dairy is this: Should one invest more heavily in hiring highly skilled cow people or in adopting technology that can perform tasks that good cow people could do?

On the Uplevel Dairy Podcast , Nathan Moroney from Del Rio Dairy explained how leveraging technology has revolutionized cow flow, cow health and efficiency, while allowing his best cow people to focus their time for the greatest impacts.

Streamlining Cow Flow and Workflow

Del Rio Dairy, located in Friona, Texas, has experienced significant growth over the years, expanding their herd from around 3,000 cows to over 8,000. With three parlors on-site, including a recently built 90-stall rotary, the management team faced the challenge of managing a larger herd efficiently. To tackle this obstacle, they transitioned from capturing basic data from milk meters to implementing sort gates to adopting real-time rumination and activity monitoring using GEA Cow Scout activity monitoring collars.

“So we’re capturing not only that ID, not only the activity information, but we’re getting rumination and feeding time data,” Moroney says.

The implementation of these collars allowed the Del Rio Dairy team to focus their time and attention on cows that truly needed it. By monitoring rumination, feeding time, and activity data, the team members could identify cows at risk and intervene before the situation escalated. As a result, they have been able to reduce the number of cows in the hospital pen by a staggering 30%.

Additionally, Moroney sees the benefits to not only efficiency, but also animal welfare, saying, “There’s also a lot of value in not having to just lock up a cow that doesn’t need to be locked up.”

Don’t Skip the Close-Up Heifers or Dry Cows

Del Rio Dairy is continuously exploring new ways to enhance their management practices through technology. They have expanded the use of collars beyond the milking herd to include pre-fresh heifers, and cows maintain their collars all through the dry period. This gives the team access to the baseline data that can be collected for each animal before they enter stressful periods at calving and into the first few weeks post-calving. With this information in hand, deviations to a heifer or cow’s normal patterns can be identified and responded to promptly.

Enhancing Efficiency and Time Management

Besides improving cow health, the adoption of technology at Del Rio Dairy has had a transformative impact on the farm’s overall efficiency and time management. By leveraging real-time data from the collars, team members can focus their efforts and prioritize tasks more effectively. The collars allow them to isolate individuals in need of attention, minimizing the need for lockups or unnecessary examinations. This streamlined workflow not only saves time but also enhances the well-being of the cows by reducing stress and handling.

Employees are less stressed as well. Moroney says that no one looks overwhelmed. Cow flow and workflow are steady, and they don’t have to stop the deck or cause a backlog on the rotary.

For Moroney, having this dashboard of data at the fingertips of his crew at Del Rio Dairy has allowed them to streamline cow flow and workflow. It’s been good for the cows, the people, and the bottom line of the business.

Listen to the full episode of the Uplevel Dairy Podcast featuring Nathan Moroney here:

A New Kind of “A.I.” for Dairies

Fri, 09 Dec 2022 19:11:22 GMT

Move over, artificial insemination. There’s a new “A.I.” in town, and it’s more intelligent than you.

A graduate student at the University of California-Davis has developed a customizable artificial intelligence (A.I.) platform that analyzes cow behavior data to help farmers anticipate changes and problems in their herds, and choose the right management interventions to address them.

Animal biology PhD candidate Catie McVey said she developed the platform, called DairyFit , utilizing data that already is being captured from sources like activity monitors, ear tags, and other precision dairy technologies that document behaviors like eating, chewing cud, walking, and resting time.

“DairyFit helps to pull out patterns hiding in big, messy data streams,” said McVey. “Then it lets the farmer decide…I want to empower farmers with data, not replace them.”

An interesting example of the power of DairyFit emerged in the analysis of sick-cow behavior. McVey and her team anticipated that sick cows would enter pens last, with their healthier herd mates leading in the front. Instead, they found that healthy cows led from the front and chased from the back, sandwiching the sick cows in the middle of the pen to keep the herd together.

Earlier this year, McVey’s research earned the “Animal Health + Industry” award as part of the Big Bang! Business Competition at UC-Davis. She currently is testing DairyFit with a group of farmers, with the goal of converting it to a smartphone app.

The platform can be applied to herds of all sizes and is designed to by highly affordable, opening up the world of big data analysis to small farmers. “We should be giving them tools to engage with their data,” McVey declared. “I’m never going to make an algorithm that knows better than the farmer. And I don’t want to live in a world anymore where the algorithms that monitor my online shopping are more sophisticated than the ones that keep my cows alive.”