EAST EARL, Pa. — Data collected through routine milk testing often sit quietly in the background of dairy operations, but when examined closely over time, they can reveal patterns that shape herd health, efficiency and long-term performance.

That theme emerged clearly during presentations by Angela Breneman of Penn State Extension and Dr. Asha Miles of Dairy Records Management Systems (DRMS) at the recent Lancaster DHIA annual meeting held at Shady Maple Smorgasbord in East Earl, Pennsylvania.

Although their topics ranged from milking routines and somatic cell counts to transition cow performance and genetic evaluations, both speakers returned to the same underlying point: consistent records that are interpreted in context expose hidden losses and identify opportunities that are easy to miss in day-to-day farm management.

Milk quality losses often go unnoticed

Breneman, a Penn State Extension dairy educator and Lancaster County dairy farmer, focused on the economic impact of somatic cell counts, emphasizing that mastitis losses are often underestimated when herds are not experiencing obvious clinical cases.

An SCC of 200,000 marks the threshold for infection, she said. Above that level, milk losses begin to accumulate. Based on industry data, first-lactation cows lose an average of 1.3 pounds of milk per day, while older cows lose 2.6 pounds per day. Over the course of a lactation, those losses can add up to significant lost revenue, even when bulk tank SCC remains below penalty levels.

“Producers often think, ‘We don’t have mastitis,’ because they’re not treating a lot of cows,” Breneman explained. “But the milk losses are still there.”

Using milk test records as a starting point, Breneman described Penn State Extension’s grant-funded milking quality audits, which combine on-farm observation with objective measurements of milk flow, vacuum stability, unit alignment and letdown timing.

The audits frequently uncover problems that are subtle but persistent, such as inconsistent teat dip coverage, delayed unit attachment, worn liners, inadequate prep time, or environmental conditions that expose teat ends to bacteria between milkings.

“These are usually not one big, obvious problem,” Breneman said. “They’re small details that happen twice a day, every day.”

She stressed that elevated SCC is rarely caused by a single factor. Instead, it reflects the interaction of milking routine, equipment performance, cow comfort and hygiene, making it difficult to diagnose without both structured evaluation and historical data.

Since 2022, Penn State Extension has conducted more than 60 milking evaluations across 16 counties and two states, affecting roughly 8000 cows. On average, participating herds reduced somatic cell counts by about 148,000, translating into improved milk yield, fewer discarded loads and better access to quality premiums.

Breneman estimated the total economic impact of those improvements at more than $700,000 across the farms that participated.

Feeding practices tied to milk quality

Breneman also highlighted Penn State’s feeding assessments, which pair milk records with forage quality, ration consistency, feed delivery and bunk management. Across evaluated herds, she said average milk production increased by about 3 pounds per cow, while somatic cell counts declined by roughly 100,000.

She noted that inconsistent feed mixing, sorting, or irregular feed push-ups can indirectly affect milk quality by increasing stress and suppressing immune function.

Although reproduction was not a primary focus of her presentation, Breneman said improvements in nutrition, cow comfort and milk quality often lead to better reproductive outcomes over time.

“Milk records let you see whether changes are actually making a difference,” she said. “They give you the long view.”

From records to broader insights

While Breneman focused on herd-level management, Miles took a wider view, showing how milk test data are aggregated and analyzed across thousands of herds.

As director of Dairy Records Management Systems, Miles oversees systems that process millions of test-day records contributed by DHI organizations nationwide. Her presentation illustrated how standardized data-sets become the foundation for tools that support both management decisions and genetic evaluations.

One example is the Fresh Cow Index (FCI), designed to evaluate how well cows perform during the transition period. FCI compares a cow’s first test-day milk to her expected performance, using more than two dozen factors including parity, genetics, gestation length and herd benchmarks.

Cows scoring above 100 exceed expectations, while those below 100 may warrant closer attention. Analysis of 1.5 million cows showed that animals with higher FCI scores had lower culling rates, reached higher peak milk, and produced more milk over their full lactation.

“This helps identify differences you wouldn’t see just by looking at milk pounds,” Miles said. “Two cows can give the same milk, but one had a much harder transition.”

Traits once considered subjective

Miles also discussed the development of U.S. milking speed genetic evaluations, a project that has relied on milk flow data collected from both parlors and automated milking systems.

Researchers analyzed records from more than 300 herds, 250,000 cows and 50 million milkings, standardizing data across 11 equipment manufacturers to create a reliable trait measured in pounds per minute.

Despite concerns that faster milking cows might have higher somatic cell counts, Miles said the data did not support that assumption. Milking speed showed a heritability of 42%, which was higher than milk yield or components, while showing no meaningful phenotypic relationship with SCC.

“That was a big concern going into the work,” she said. “The data showed it wasn’t the case.”

A shared takeaway

Although Breneman and Miles approached the data from different angles, both presentations underscored the same reality: milk test records gain value to the dairy producer when they are reviewed, questioned and applied over time.

From identifying hidden mastitis losses and refining milking routines to evaluating transition cow performance and developing new genetic traits, consistent records provide context that single observations cannot.

The presentations served as a reminder that while milk testing may feel routine, the information it generates continues to shape how dairy farms manage cows today and how the industry measures progress for the future.