Archive for residual feed intake

Unlock $700 Per Cow: The Rumen Microbiome Strategy That Fixes Hidden Feed Efficiency Losses.

$700/cow is hiding in your bunk. Weekend feed drift, DM swings, and sorting are quietly stealing it. Here’s the four-phase fix.

Sit at enough kitchen tables across dairy country, and you start hearing the same line in different accents.

“We’ve got good cows. The ration looks right on paper. But the milk just isn’t where it should be.”

You know that feeling. The ration balances, butterfat performance ought to be stronger, you’ve invested in genetics and decent forage… and the bulk tank still isn’t telling the story you’d expect.

What’s interesting here is that, in the last few years, some very solid research has started to put a name and a number on part of that gap: the rumen microbiome, and how stable—or unstable—we make it with day‑to‑day management, not just with what we put in the mixer.

A 2024 paper in Animal Microbiome, led by H.F. Monteiro at the University of California, Davis, used an AI‑based ensemble model on 454 genotyped Holsteins from commercial herds in the U.S. and Canada and found that the rumen microbiome alone accounted for about 36% of the variation in residual feed intake (RFI), even after diet composition and cow traits were accounted for. The authors described the microbiome as a “major driver” of feed efficiency, sitting alongside ration and genetics rather than behind them. That lines up with other work showing that when you follow Holstein cows across a full lactation, the composition of the rumen and lower‑gut microbiomes tracks closely with feed efficiency and production traits, and the prediction of efficient versus inefficient cows improves when microbiome data is added to diet and genetic information. 

On top of that, newer host–microbiome projects—such as the 2024 “host genome–microbiome networks” study on mid‑lactation Holsteins—are showing that parts of the core rumen microbiome are heritable and linked to both feed efficiency and methane output. In other words, the cow’s genome and her microbial passengers are working together to shape how she uses feed and what comes out the front of the tank and out the back as gas. 

So we’re not throwing out ration formulation or genetics. But the data suggests the microbiome is a third leg of the stool. And, as many of us have seen in the barn, those bugs are very sensitive to how consistent their world is. 

Looking at This Trend: What the Bugs Are Quietly Telling Us

What I’ve found, looking at this research alongside what producers are seeing on their own farms, is that microbiome‑first thinking mostly backs up what good cow people have been saying for years. It just gives those instincts a clearer scientific backbone. 

You probably know this already, but the rumen community isn’t one thing. Reviews of how the rumen microbiota shifts from the dry period into early lactation show a fairly consistent pattern: bacteria that specialize in rapidly fermentable carbohydrates tend to increase as starch and sugars rise, while classic fibrolytic species such as Fibrobacter and Ruminococcus are more sensitive to drops in rumen pH and rough dietary changes. When the feeding environment is steady—similar ration, predictable feeding and push‑up times, consistent dry matter—those different groups can settle into a balance that supports both butterfat performance and feed efficiency. When we keep changing the rules on them, the fast opportunists win more often, and the slower fiber‑digesters get pushed back. 

And as many of us have seen, that can show up as:

  • Butterfat levels are bouncing more than the diet changes would suggest
  • Fresh cows in the transition period that don’t ramp up on dry matter intake the way we’d expect based on the ration
  • More days where rumination, manure consistency, and overall cow behavior feel “off,” even though nothing obvious changed on paper

It’s worth noting that when you line up the science with on‑farm experience, three everyday management areas keep coming up as the main microbiome disrupters: feed timing and access, TMR dry matter, and particle size/sorting

Let’s walk through each one, because that’s where a lot of the opportunity is hiding.

Feed Timing and Access: The “Saturday Morning” Problem

Looking at this trend on real farms, feed timing and access are usually the first places where the microbiome story becomes very concrete.

In many Wisconsin freestall herds—and plenty of Ontario, New York, and Pennsylvania barns too—the weekday schedule on paper looks quite good. Feed at 6 a.m., push up several times in the next few hours, second feeding mid‑afternoon, a couple more push‑ups before night. Then Saturday and Sunday arrive. That 6 a.m. feeding quietly becomes 6:30 or 7:00, the early‑morning routine gets “flexible,” and late‑night push‑ups happen only if there’s time. I’ve noticed that pattern over and over, sitting in farm kitchens from the Midwest to the Northeast.

On larger Western dairies in California or Idaho, the pattern can be different, but the result is similar. You might have multiple feeding crews, and one crew is very tight on timing while another is a bit looser. To the cows—and to their microbes—that still feels like an irregular routine. 

Penn State’s “Benefits of Timely Feed Delivery and Push Ups,” written by extension educator Dr. Virginia Ishler and colleagues, brings together several studies that quantify what many of you have already felt. In their summary of work by Collings et al. and Matzke & Grant, cows that were restricted from feed for about ten hours—typically overnight—ate 3.5 pounds less dry matter per day and produced 7.9 pounds less milk per day than cows that had feed available throughout the night. A Dairy Herd article by Penn State educator Michal Lunak echoes those numbers and adds that herds routinely pushing feed up produced, on average, over eight pounds more milk than herds that didn’t. 

When feeding and push‑up practices were adjusted so that feed remained available from midnight to early morning and was pushed up more consistently, dry matter intake and milk yield increased, and cows spent more time both lying and eating. Penn State also highlights that bunk empty time should be kept under about three hours; beyond that, cows’ motivation to eat rises sharply, and they’re more prone to slug‑feeding when feed returns. 

From the microbial side, what’s happening is intuitive once you think about it. When cows go through long stretches with an empty bunk, they’re more likely to slug‑feed when the TMR finally arrives—packing in a big meal quickly. That dumps a heavy load of fermentable carbohydrate into the rumen all at once, causing rumen pH to drop more sharply and the slower fiber‑digesting microbes to get stressed or washed out. In herds that have taken the time to log feed delivery and push‑up times (some have done this with simple charts or camera snapshots), those longer gaps—especially on weekends—often match up with the days when butterfat drops and fat: protein ratios point toward subacute acidosis. 

There’s also a broader transition‑cow angle. Work on transition cow nutrition in North American herds has shown that more consistent routines around the dry and fresh periods—fewer abrupt diet changes, grouping, and environmental shocks—are associated with better metabolic profiles and stronger early lactation performance. Feeding schedule is one of the major “time cues” the cow’s system responds to. The microbes, even though they don’t have watches, are reacting to the same pattern. 

So one of the first microbiome‑friendly questions to ask is very simple: “How long are my cows actually going without feed they can reach?” Penn State emphasizes that bunks should not be empty for more than about three hours, and that more frequent push‑ups in the first hours after feeding are strongly associated with higher DMI and milk yield. The microbiome is one more good reason to take that seriously. 

TMR Dry Matter: The Quiet Thief in the Bunker

The second lever, TMR dry matter, is one of those things that quietly steals profit when no one’s looking.

Penn State’s “Total Mixed Rations for Dairy Cows,” by Dr. Virginia Ishler and the dairy nutrition team, spells out how changes in TMR dry matter affect what cows actually eat. When a TMR gets wetter but batch weights don’t change, cows fill up on volume but take in fewer kilograms of dry matter than the ration assumes they will. The bulletin shows farms where actual DMI drifts away from predicted intake as TMR moisture changes, and notes that herds that keep actual DMI within about 5% of expected intakes—and pay close attention to TMR accuracy—consistently achieve higher milk and more stable components than herds where DMI and TMR DM are rarely checked. 

Industry pieces on TMR moisture, including extension articles and dairy nutrition case reports, have shown that when TMR moisture comes in higher than expected, and no one adjusts, early‑lactation cows can lose several percent of their DMI and a few kilograms of milk per day until someone finally tests dry matter and corrects the ration. Many of you have lived that scenario: “Nothing changed… except we opened a new corner of the bunker or switched bags and didn’t test.” 

From the microbiome’s point of view, those moisture swings do two things at once:

  • On wetter days, cows reach rumen fill sooner and don’t get the expected dry matter. Passage rate increases, long fiber particles spend less time in the rumen, and fiber‑digesting bacteria have less chance to colonize and break them down. 
  • On drier days, the same volume of TMR carries more dry matter and more fermentable energy, so the fermentation runs “hotter” and rumen pH can dip more sharply, again putting pressure on the fiber‑digesting community. 

What farmers are finding is that you don’t have to nail TMR dry matter at one exact number. But you do want to keep day‑to‑day changes in a reasonable band and adjust batch weights when moisture moves outside that band. Many Midwest and Northeast herds now do at least one or two TMR dry matter checks a week, more often when they start a new section of bunker or change forage sources, and they treat it as part of routine bunk and fresh cow management rather than just troubleshooting. 

The evidence suggests that habit alone can prevent many “mystery” weeks in which milk and components slip for reasons nobody can quite explain until someone dusts off the Koster tester. 

Particle Size and Sorting: Three Rations in One Bunk

The third piece is particle size and sorting—the classic “three rations in one bunk” problem that shows up on farms of all sizes.

After feeding a TMR, it’s common to walk the bunk an hour later and see a line of longer stems pushed out of the way while the finer material has been cleaned up. By early afternoon, cows are picking over what’s left, and what’s left doesn’t look much like the ration the nutritionist balanced. I’ve noticed that on everything from 80‑cow tiestalls to 4,000‑cow freestall barns.

The Penn State Particle Separator (PSPS) has become a standard tool for seeing what’s really happening. For many corn‑silage‑based rations, Penn State guidance suggests that only about 2–8% of the TMR should remain on the top sieve, roughly 30–50% on the next sieve, 10–20% on the 4 mm sieve, and no more than 30–40% in the bottom pan for high‑producing cows. Hoard’s articles on ration particle size have highlighted research showing that diets with overly long particles and high undigested NDF reduced DMI by 5–6 pounds per day, and that finer chopping and better PSPS distributions restored DMI and milk yield. 

When a TMR has too much long material on that top sieve, cows can sort around it. They end up eating a diet richer in starch and poorer in effective fiber than intended. Industry articles and extension pieces have repeatedly called out that gap between the “paper ration” and the “eaten ration” as a major driver of inconsistent butterfat performance and subacute rumen acidosis, even when the formulation itself looks sound. 

From a microbiome perspective, heavy sorting means you’re constantly pushing the rumen community toward the organisms that thrive on rapidly fermentable carbohydrates, while making life harder for the slower, fiber‑digesting bacteria that underpin fiber utilization and rumen health. 

What’s encouraging is that producers in very different environments—freestall barns in Ontario, tiestalls in Quebec, and dry lot systems in hot regions—have all reported improvements after making particle size checks and bunk observations a regular habit. Running the separator weekly for a period, adjusting chop length and mixing time, and watching what’s left at the bunk an hour after feeding are simple, practical tools that align very well with what the bugs seem to be asking for. 

Management GapWhat HappensMilk Loss per Cow/DayButterfat ImpactAnnual Cost per 1,000 Cows
10-Hour Overnight Feed RestrictionCows slug-feed; rumen pH crashes; fiber-digesting microbes washed out−7.9 lbs−0.4% (subacute acidosis)$1,153,600
TMR Dry Matter Drift (+2–3 points)Cows fill on volume but get fewer kg DM; passage rate increases; fiber digestion drops−3.5 to −5 lbs−0.2–0.3%$510,500–$728,750
Excessive Sorting (Long particles, fine refusal)Cows select around fiber, eating richer diet; slow fiber-digesters starved out−5 to −6 lbs−0.5–0.7% (fat:protein inversion)$728,750–$876,900
All Three Combined (Common State)Microbes destabilized; rumen environment chaotic; fresh cows struggle to ramp intake−14 to −16 lbs−1.0–1.5%$2,044,000–$2,332,000

What Farmers Are Finding: A Four‑Phase Plan That Fits Real Herds

So with all that on the table, the natural question is: how do you actually use this microbiome‑first lens on your own farm?

What I’ve noticed, talking with producers from Wisconsin, Ontario, the Northeast, and the West, is that the herds getting the most from this approach tend to move through four broad phases. They don’t always call them phases, but the progression shows up again and again, and it lines up nicely with what extension and research folks are seeing. 

Phase 1: Tighten Timing and Feed Access

Phase 1 is about getting honest about feed access.

A straightforward starting point looks like this:

  • For two weeks, write down when feed really hits each group and when it’s last pushed up at night. Don’t rely on memory. Include weekends and holidays. 
  • Look for recurring long gaps—especially overnight—where cows don’t have feed in front of them or can’t reach it.
  • Given your labor and layout, decide what’s realistic in terms of extra push‑ups, an automatic feed pusher, or improved hand‑offs between shifts to shorten those gaps.

Penn State’s work and related industry articles have shown that when cows move from long overnight feed restrictions to continuous access, dry matter intake and milk yield increase in ways that match the 3.5 lb DMI and 7.9 lb milk responses measured when feed is restricted versus available overnight. In a microbiome‑first mindset, you’re reducing the size and frequency of the shocks the microbial community has to deal with each day. 

Phase 2: Tune Up the Physical Ration

Once cows can depend on there being feed in front of them most of the time, Phase 2 is about what that feed looks like physically.

On farms where this has really moved the needle, Phase 2 typically includes:

  • Running the Penn State Particle Separator on the TMR weekly for a period and working with the nutritionist and forage team to adjust chop length, kernel processing, and mixing until the ration consistently falls into the recommended PSPS distributions for your forage mix.
  • Spending time at the bunk 45–60 minutes after feeding, especially in fresh and high pens, to see how much sorting is actually happening and what is left in front of the cows. 
  • Watching kernel processing scores for corn silage and keeping an eye on haylage or straw length to avoid overloading the top sieve and inviting sorting. 

The goal is a ration that’s chewable but not easily sorted. Research and field experience both show that when you hit that sweet spot, you see more consistent chewing, better saliva production, smoother manure, and more stable butterfat performance. 

Phase 3: Make Dry Matter Checking Routine

By the time herds get to Phase 3, many notice they’re not seeing as many “mystery” swings in milk and components. Phase 3 is about turning TMR dry matter checks into a standard part of bunk management.

In practical terms, that often means:

  • Testing TMR dry matter at set times each week—often early and late in the week. 
  • Logging those numbers so you and your nutritionist can track when moisture shifts as you move along the bunker or between forage sources.
  • Agreeing on a simple trigger—such as a two‑point or greater difference between actual and assumed TMR dry matter—that prompts ration adjustments rather than “wait and see.”

Penn State’s TMR bulletin and related herd‑level analyses suggest that farms with tighter control over TMR dry matter and loading accuracy see higher milk yield and more consistent components than those where dry matter is rarely checked. For the microbiome, this kind of consistency means fewer sudden jumps in fermentable load and a more predictable environment in which to work. 

Phase 4: Use Additives to Fine‑Tune, Not Patch

Only after those three pieces feel reasonably solid does it make sense to lean into live yeast, buffers, and other additives.

The research on live Saccharomyces cerevisiae in dairy cows brings several themes together:

  • In transition‑cow trials, such as those led by Marinho and colleagues, supplementing live yeast around calving improved postpartum dry matter intake and rumination, led to milder inflammatory and liver stress markers, and increased milk yield compared with unsupplemented cows on the same base ration. 
  • Reviews and industry summaries that pool results from multiple mid‑lactation trials often report milk yield gains in the range of 1–2 kilograms per day and more stable rumen pH when live yeast is added, particularly in herds with solid basic management. 
  • Under heat-stress conditions, especially in hot, dry regions, live yeast has been shown to help stabilize rumen pH and support production when combined with effective cooling and feeding strategies. 

At the same time, extension and university reviews are clear that additives cannot overcome fundamental problems such as poor forage quality, erratic feeding schedules, or severe overcrowding. In many commercial herds, responses to yeast and buffers are variable, and benefits tend to be largest where the basics are already in decent shape. 

In a microbiome‑aware framework, that means treating additives as a way to fine‑tune a system that’s already working reasonably well, rather than as a band‑aid for underlying management issues.

Putting Numbers to the Four Phases: The Economics on a 1,000‑Cow Herd

So why is all this significant? Economics plays a big part in the story.

Imagine a 1,000‑cow freestall herd with:

  • Average production is around 38–39 kilograms (about 85 pounds) of milk
  • Butterfat at roughly 3.2% and protein just over 3.1%
  • Dry matter intake near 25 kilograms (55 pounds) per cow per day
  • Milk price is around $0.40 per kilogram, and feed cost is roughly $0.20 per kilogram of dry matter

Those numbers won’t fit every farm, but they’re realistic for many North American herds right now based on recent Hoard’s Dairyman economic analyses and regional milk price reports. 

If Phase 1—tightening feeding times and improving access—helps you realistically recover around 0.75–0.8 kilograms of milk per cow per day by eliminating long overnight feed gaps (a conservative figure compared to the 7.9 lb milk response Penn State reports when cows move from restricted to continuous night access), that’s roughly $0.30–0.35 per cow per day. Over a year and 1,000 cows, you’re looking at about $110,000–120,000 in additional milk revenue. 

If Phase 2—getting particle size and sorting under control—adds another 1.3–1.4 kilograms of milk per cow per day and nudges butterfat up a bit, that can easily translate into a couple of hundred thousand dollars a year in combined volume and component pay, depending on your milk pricing and how much room there was for improvement. That’s consistent with the kind of DMI and milk yield recoveries seen when rations shift from “too long and sorted” toward better PSPS targets and reduced excessively long particles. 

Phase 3—keeping TMR dry matter in line with regular checks and adjustments—might reasonably prevent a 0.5–0.6 kilogram per cow per day loss during those weeks when moisture shifts used to drag DMI and milk down quietly. Extension examples and field data show that even modest, unnoticed drops in DMI from dry matter changes can add up to tens of thousands of dollars per year on larger herds. 

Then, in Phase 4, if a well‑designed live yeast program on top of this more stable foundation adds another 0.7–0.8 kilograms of milk per cow per day in the pens you target—figures that fall within the 1–2 kg/day range often reported when live yeast is used in well‑managed herds—then after covering product cost you might realistically net on the order of $50,000 per year. 

Put those pieces together, and it’s not hard to model a total improvement on the order of $500,000–700,000 per year for a 1,000‑cow herd. On a per‑cow basis, that’s about $500–700. Early indications from extension economic estimates and field experience suggest that those kinds of gains are achievable in herds with significant room to tighten timing, dry matter control, and sorting—provided they treat this as a stepwise management project rather than a quick fix. 

Even if you only capture half of that modeled upside, you’re still talking about a six‑figure swing in annual income on a 1,000‑cow unit. That’s the kind of math that justifies taking a hard look at your feeding routine, DM checks, and PSPS readings.

Of course, if your feeding program is already very tight, your upside may be smaller. And if other bottlenecks like lameness, poor ventilation, water limitations, or chronic fresh cow problems are holding cows back, those will cap how much any microbiome‑focused approach can deliver until they’re addressed. 

Looking a bit further ahead, this development suggests that herds that get serious about microbiome‑aware management now may also be better positioned for future shifts in breeding goals and processor expectations—especially as more emphasis is placed on feed efficiency and methane in proofs, and as sustainability programs look more closely at emissions and feed conversion. 

How This Plays Out on Different Types of Farms

It’s also important to note that microbiome‑aware management doesn’t look the same in every system. The principles are the same; the levers change.

Smaller Family Herds

On a 120‑cow tie‑stall in Quebec or a 200‑cow freestall in Wisconsin, the total dollar amount won’t be as large as on a 1,000‑cow dairy, but the per‑cow impact can look very similar. Many of these farms have a key advantage: the people making decisions are the ones feeding cows and walking the alley every day, so they notice subtle changes quickly. 

The constraint is usually time. One person may be handling feeding, milking, fresh cow management, and fieldwork. On these operations, the most successful microbiome‑aware changes are often:

  • Keeping feed times reasonably consistent every day, including weekends
  • Adding a simple weekly TMR or key forage dry matter check, rather than trying to test constantly
  • Using the particle separator at least occasionally to see whether sorting might be part of why butterfat performance is more variable than expected

Additives like live yeast or buffers are often targeted at small groups—such as fresh cows during the transition period or high‑risk pens—where the return is easiest to see and monitor. 

Grazing and Seasonal Systems

In grazing and seasonal systems—such as many in Atlantic Canada, parts of the Northeast, Ireland, and New Zealand—the basic microbial principles remain the same, but the feeding context differs.

Instead of asking, “When does the TMR arrive?” the questions sound more like:

  • “How consistent are turnout times onto fresh pasture?”
  • “Are parlor concentrates or supplementary TMR fed at predictable times and rates?”
  • “Are we giving fresh cows enough time to adapt when moving from a winter ration to lush spring grass?”

Pasture‑based management guides and research reviews emphasize that consistent grazing rotations, careful pasture dry matter measurement, and smooth transitions between conserved feed and pasture are critical for avoiding digestive upsets and performance drops. In these systems, a microbiome‑aware approach often leads to more deliberate use of fiber sources or buffers alongside high‑sugar grass, and particular attention to fresh cow management so the rumen isn’t shocked by abrupt diet changes. 

Hot, Dry Regions and Dry Lot Systems

In hot, dry regions—such as parts of California, Arizona, and Texas—dry lot systems under high temperature‑humidity index conditions add heat stress to the rumen‑stability conversation. Research and field observations show that heat stress depresses intake, alters rumen fermentation (more acid load, lower pH), and can reduce fiber digestibility, making the rumen more fragile. 

On those dairies, producers who are thinking in microbiome terms often work on three fronts at once:

  • Feeding more of the ration during cooler times of day so cows actually feel like eating
  • Making sure shade, fans, and soakers are set up and managed so cows can stay comfortable enough to use the feed that’s in front of them
  • Using live yeast and buffers strategically, once cooling and feeding basics are in place, to help stabilize rumen pH and fermentation under heat stress

Industry sources have reported that, under those conditions, live yeast can provide a positive return when it’s part of a broader heat‑stress management package, not a stand‑alone solution. 

Farm TypeHerd SizeKey Implementation FocusPrimary Labor BarrierRealistic Annual Gain per CowTotal Herd Annual Gain
Tie-Stall Family120–200 cowsConsistent daily feeding times; weekly DM test; occasional PSPSSingle operator doing feeding + milking + fieldwork; weekends are tight$250–350 per cow$30,000–$70,000
Smaller Freestall300–500 cows2–3 week DM checks; PSPS quarterly; better push-up routine with existing crewHand-offs between shifts; weekend consistency$350–450 per cow$105,000–$225,000
Mid-Size Freestall800–1,200 cowsFull four-phase playbook; weekly DM; PSPS monthly; automatic feed pusher ROI positiveCrew discipline on timing; shift management$450–550 per cow$360,000–$660,000
Dry Lot & Hot Climate2,000–8,000 cowsPhase 1 (timing) + heat-stress additives; cooler-hour feeding; aggressive yeast useCooling infrastructure consistency; feed crew schedule discipline$300–400 per cow (capped by heat stress)$600,000–$3,200,000
Grazing/Seasonal80–300 cows (milk + calf)Pasture turnout timing consistency; transition management (winter→spring); forage DM variabilitySeasonal labor shifts; pasture readiness unpredictability$180–280 per cow$14,400–$84,000

Where Microbiome‑First Efforts Can Go Off Track

As promising as this way of thinking is, it’s not a magic wand. There are a few common ways it can go sideways.

One is partial implementation. If a herd tightens up feeding times but leaves a very sortable ration unchanged, cows may simply eat more of the fast‑fermenting portion of the diet more consistently. In the short term, that can actually increase the risk of rumen acidosis rather than reduce it, which aligns with PSPS‑based research and field reports showing that excessively long particles encourage sorting. 

Another is overestimating labor capacity. On many family farms, it’s simply not realistic to add frequent night push‑ups and multiple TMR dry matter tests per week. Extension advisers often recommend starting with one or two high‑impact changes—like a weekly DM check and better weekend feeding consistency—that everyone believes can be sustained. 

A third is expecting additives to solve structural issues. In herds where forage quality is poor, dry cow and fresh cow housing are limiting, or stocking density is excessive, yeast and buffers might help at the margins, but they won’t turn the situation around on their own. Reviews of direct‑fed microbials and buffers emphasize that these tools complement, but cannot replace, sound ration formulation, forage management, and cow comfort. 

So while the microbiome lens is very useful, it’s healthiest to treat it as a way to prioritize and sharpen management decisions, not as a replacement for the fundamentals.

A Practical Starting Checklist

If we were wrapping this up over coffee in your farm office, here’s the simple checklist I’d leave on the table:

  • Log what really happens. For two weeks, write down actual feed delivery and push‑up times by group, including weekends and holidays. Let those numbers—not memory—show where the biggest gaps are. 
  • Watch the bunk after feeding. Stand at the bunk 45–60 minutes after a TMR delivery. What are cows doing? What’s left on the bunk? If you can borrow or buy a particle separator, run both fresh TMR and refusals at least once to see how much the ration changes between wagon and cow. 
  • Add one dry matter check to your week. Pick a day each week to test TMR dry matter and compare it to the value in your ration program. Talk with your nutritionist about adjusting when the difference becomes large enough to matter for DMI. 
  • Use pen‑level data as an early warning. Look at fat: protein ratios, rumination indices (if you have monitors), and manure scores by group. Treat changes there as early hints that the rumen—and the bugs—may not be as stable as you’d like. 
  • Put additives in their proper place. Once timing, TMR structure, and dry matter are under reasonable control, then sit down with your nutritionist to design a focused, time‑limited trial with yeast or buffers in specific pens, rather than making a blanket change and hoping for the best. 

The Bottom Line

At the end of the day, we’re not just feeding cows. We’re managing microbial ecosystems that live inside those cows and turn this season’s feed bill into next month’s milk cheque. 

What’s encouraging is that many of the things those microbes seem to like—steady routines, consistent dry matter, well‑structured rations, thoughtful fresh cow management—line up closely with what good producers have been working toward for a long time. The microbiome‑first perspective doesn’t throw any of that out. It simply connects the “why” and the “how much” in a way that helps you decide where your next management tweak should be, whether you’re milking 80 cows in a tie stall or 8,000 cows in a dry lot system. 

KEY TAKEAWAYS

  • The rumen microbiome drives 36% of feed efficiency—manage it or lose it. A 2024 AI study on 454 Holsteins found microbiome composition rivals genetics and diet in determining which cows convert feed to milk efficiently.
  • Three bunk-management gaps are quietly draining your tank. Weekend feed-time drift, unnoticed TMR dry matter shifts, and sortable rations cost pounds of DMI and milk every single day—often without any obvious ration change.
  • A 10-hour feed gap costs 3.5 lb DMI and 7.9 lb milk per cow per day. Penn State data shows that fixing overnight access alone can recover much of that loss. Bunks should never sit empty for more than three hours.
  • Additives can’t fix bad timing or a sortable ration. Follow the four-phase playbook: tighten feed delivery and push-ups first, tune particle size with the PSPS, make weekly DM checks routine, then use live yeast to fine-tune—not to patch.
  • The math: $500–700 per cow per year. Stack those four phases on a 1,000-cow herd, and you’re looking at $500,000–700,000 in recoverable margin. Even capturing half changes your year.

Executive Summary: 

If your ration looks right but the bulk tank keeps coming up short, this article explains why the missing piece may be your cows’ rumen microbiome—and how you manage the bunk around it. It starts with new AI‑based research showing the rumen microbiome accounts for roughly 36% of residual feed intake variation in Holsteins, then ties that directly to three daily levers you control: feed timing and access, TMR dry matter, and particle size/sorting. Using Penn State data, it quantifies how 10‑hour overnight feed gaps, unnoticed TMR moisture shifts, and highly sortable rations can quietly cost 3.5 lb of DMI and 7–8 lb of milk per cow per day—even in herds that think they’re “feeding well.” From there, it lays out a four‑phase, microbiome‑aware playbook: tighten feeding schedules and push‑ups, get the physical ration right with the PSPS, make routine DM checks part of bunk management, then use live yeast and buffers as fine‑tuning tools instead of expensive band‑aids. A realistic 1,000‑cow example shows how stacking those phases can unlock about $500–700 per cow per year—$500,000–700,000 across the herd—if you’re starting from the “common” level of drift in timing, DM, and sorting. Finally, the article shows how this approach scales from 80‑cow tiestalls to 8,000‑cow dry lot systems, with a simple checklist you can use to pick your first one or two changes and start turning microbiome theory into extra dollars on your milk cheque. ​

Complete references and supporting documentation are available upon request by contacting the editorial team at editor@thebullvine.com.

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Just found out our 90-lb cow loses $3/day while our 85-lb cow makes $10/day. The difference? 6kg of feed. This changes everything

Executive Summary: What if your highest-producing cows are actually costing you money? Feed efficiency technology deployed across 3,000 dairy farms proves it’s not just possible—it’s common. The numbers are stark: cows producing identical 100-pound milk yields show daily profit swings from -$7 to +$10, based solely on whether they consume 17kg or 23kg of feed. Ryzebol Dairy transformed this insight into action, breeding inefficient cows for beef ($700 premiums) while focusing genetics on the efficient third that actually drives profit. At $75-150K investment returning $470/cow annually, payback takes just 3-5 years. The industry is splitting fast between operations still chasing volume, and those chasing profit—and the profit-chasers are pulling away.

For nearly a century, dairy farming has operated on a simple equation: more milk per cow equals more profit.

But what farmers are discovering through new feed efficiency technology is turning that fundamental assumption on its head. The highest-producing cows in many herds are actually the least profitable—a revelation that’s prompting forward-thinking operations to reimagine their breeding, feeding, and culling strategies completely.

I recently had a fascinating conversation with Clare Alderink, general manager of Ryzebol Dairy’s 3,000-cow operation in Bailey, Michigan. When his farm implemented Afimilk’s feed efficiency estimation system, the data revealed something that challenged everything he thought he knew about his herd.

“There’s no way the service knew these cows were from the same farm, yet all those cows found themselves on the top of the list as the most feed efficient.”

All of his most feed-efficient animals traced back to one group of purchased Holsteins—cows that weren’t his top milk producers but were generating the highest profit per dollar of feed consumed.

The Hidden Economics That Traditional Metrics Miss

You know, what’s really striking when you dig into the economics is just how much variation exists between seemingly similar operations.

The folks at Vita Plus Corporation ran an analysis in 2024 examining 20 Midwestern herds—all shipping roughly 100 pounds of energy-corrected milk per cow daily. What they found should make every dairy farmer pause.

Income over feed cost ranged from less than $7 to greater than $10 per head per day.

Think about that $3.50 daily difference for a moment. On a 1,000-cow operation, we’re talking about over $1.2 million in margin opportunity annually. Money that’s essentially invisible if you’re only tracking milk production.

QUICK TAKE: THE EFFICIENCY GAP

Cow GroupDry Matter Intake (kg/day)Difference (kg/day)Cost Savings per Cow (lactation period)
Efficient17.306$700
Inefficient23.306$0

What’s interesting here is that we’re finally understanding the mechanism behind this variation through individual cow measurement. A study published in Frontiers in Genetics in 2024 evaluated genomic markers for residual feed intake in 2,538 US Holstein cows.

The differences they found between efficient and inefficient animals were eye-opening:

  • First-lactation cows? The most efficient animals consumed 17.30 kg of dry matter daily, while the least efficient needed 23.30 kg
  • Second-lactation cows showed an even wider gap, with efficient cows eating 20.40 kg versus 27.50 kg for inefficient animals

Now, here’s where it gets interesting for those of us looking at feed bills.

According to University of Wisconsin Extension data, feed costs in the Upper Midwest are averaging around $381 per ton of dry matter. That 6 kg daily difference? It represents roughly $700 per cow per lactation in feed cost variation between animals producing identical milk volumes.

Shane St. Cyr from Adirondack Farms in New York put it perfectly:

“You have the income half of the equation on most dairies. But without that expense equation, you’re really kind of flying blind.”

The Strategic Breeding Revolution: Beef-on-Dairy Meets Feed Efficiency

Perhaps the most dramatic shift I’m seeing—and I’ve been watching this space closely—is how farms are completely rethinking their breeding strategies once they have feed efficiency data in hand.

Instead of the old approach (trying to create replacement heifers from every cow that’ll stand still long enough to breed), operations are now using what’s essentially a three-tier system:

TOP 20-30% (HIGH EFFICIENCY):

  • Bred with sexed dairy semen
  • Create the next generation
  • Keep these genetics forever

MIDDLE 40-50%:

  • Conventional dairy semen
  • Backup replacement strategy
  • Flexible based on herd needs

BOTTOM 20-30% (LOW EFFICIENCY):

  • Bred exclusively with beef semen
  • Generate $350-700 premiums per calf
  • Transform losses into profit centers

The beef-on-dairy market has absolutely exploded in ways that, honestly, nobody saw coming five years ago.

Purina Animal Nutrition surveyed 500 dairy producers in 2024 and found that 80% are now receiving premiums for beef-on-dairy calves. Some crosses are fetching over $1,000 in tight cattle markets, particularly in Texas and the Central Plains.

Think about this for a minute:

  • Purebred dairy bull calf: $50-150 (if you’re lucky)
  • Many producers: Actually paying disposal costs
  • Same cow bred to beef: $500-850 per calf

The math here isn’t subtle, folks.

For Ryzebol Dairy, this strategic allocation based on feed efficiency data has completely transformed how they view their inefficient cows.

“I want that efficient cow to stay in my herd a long, long time,” Alderink explained. “Whereas the other inefficient cows I would want to use to make a beef calf because she’s a lower-value cow.”

What University Research Missed: The Power of Individual Variation

Here’s something that really drives home why on-farm measurement matters more than controlled research trials. Ryzebol’s experience with high oleic soybeans illustrates this perfectly.

The university studies—Penn State ran a trial with 48 Holstein cows in 2024, and Michigan State published similar work—showed that high-oleic soybeans improved energy-corrected milk and components. The improvements were significant, particularly for butterfat. Solid research. Peer-reviewed. Convincing stuff.

So Ryzebol implemented them herd-wide and saw improvements.

But then Alderink did something the research couldn’t do. He used individual cow feed efficiency data to dig deeper.

“Increasing the average doesn’t always tell the whole story. It may have made our best cows really efficient and done little for the low cows.”

What he discovered should make every nutritionist rethink how we apply research findings:

TOP 30% OF COWS:

  • Excellent milk and component response
  • Strong returns on premium ingredient cost
  • Worth every penny

MIDDLE 40%:

  • Marginal improvement
  • Barely justified the extra cost
  • Questionable economics

BOTTOM 30%:

  • Little to no benefit
  • Essentially throwing money away
  • Better off with standard ration

This insight—that research-validated improvements don’t apply equally to all animals—represents a fundamental shift in how we can optimize nutrition economics.

The Technology Landscape: Understanding What’s Real vs. What’s Promised

Let’s talk about what this technology actually does, because there’s plenty of confusion out there.

Afimilk’s feed efficiency service represents a breakthrough in estimating individual cow feed efficiency through collar sensor data. The system tracks eating time and rumination patterns, then combines this with milk production information to generate efficiency values for each animal.

You’re entering weekly dry matter intake data from your feeding software to calibrate the estimates. According to validation studies at UW-Madison, the correlation between the algorithm’s estimates and actual measured intake has proven strong enough for commercial application.

THE NUMBERS THAT MATTER:

InvestmentAnnual servicePayback periodROIBeef premiumFeed savings
$75,000-$150,000 (500 cows)$10,000-$25,0003-5 years$470/cow/year$350-700/calf$700/cow/lactation

Early adopters are reporting that the technology can deliver $470 per cow in annual profitability gains through better breeding and culling decisions.

On a 1,000-cow operation? That’s nearly half a million dollars in annual value.

Though I should note—and this is important—that’s assuming farms actually act on the data.

The Adoption Reality: Barriers Beyond Technology

Despite these clear economic benefits, several factors are creating real headwinds for adoption.

CAPITAL CONSTRAINTS We’re talking $75,000-$150,000 for basic sensor systems on 500 cows. Field data from early adopters suggests payback periods of 3-5 years. But that upfront investment? It’s tough when milk prices are volatile.

SYSTEM INTEGRATION Feed efficiency estimation needs to pull data from multiple sources:

  • Milk meters
  • Cow ID systems
  • Feeding software
  • Health records

According to Progressive Dairy’s 2024 tech adoption survey, approximately 70% of North American dairies have older equipment or mixed vendors. Additional integration costs that nobody mentions in the sales pitch.

PSYCHOLOGICAL RESISTANCE Here’s the barrier nobody wants to talk about. Kent Weisenberger from Vita Plus put it bluntly in a recent podcast:

“The technology works fine. Whether farmers will cull their favorite high-producing cow because she’s inefficient? That’s the real question.”

It’s worth noting that feed efficiency estimation isn’t a silver bullet for every situation. Grazing-based operations or farms with highly variable feed quality from homegrown forages might find the economics less compelling.

Environmental Benefits: The Profit-Sustainability Alignment

What I find particularly interesting about feed efficiency selection is how environmental benefits just naturally emerge from economic optimization.

You’re not trying to save the planet—you’re trying to make money—but the planet benefits anyway.

Research from Wageningen University in 2024 found that methane production varies by approximately 25% within herds due to genetic factors. The correlation between feed efficiency and methane reduction is strongly positive.

Since April 2023, Canada has been implementing national genetic evaluations for methane emissions through Lactanet. They’re projecting 20-30% reductions in breeding alone by 2050.

The Council on Dairy Cattle Breeding calculates that genomic selection for feed efficiency has already delivered $70 per cow per year in additional value—before accounting for any environmental benefits or carbon credits.

The key point? You don’t need expensive additives. Simply breeding from more efficient animals reduces methane automatically at zero additional cost.

Looking Ahead: The Industry Transformation

Here’s where things get really interesting for the bigger picture.

If enough operations start breeding away from high-volume, low-efficiency genetics, it fundamentally challenges what the breeding industry has been selling for decades.

VikingGenetics launched their Feed Efficiency 3.0 program earlier this year, explicitly prioritizing efficiency over raw production. Meanwhile, established players like Semex and Alta have scrambled to launch “sustainable genetics” programs.

The uncomfortable truth? While high producers generally dilute maintenance costs effectively (gross feed efficiency), metabolic efficiency—measured as Residual Feed Intake—is a distinct genetic trait. You can have a high producer that’s metabolically inefficient, or a moderate producer that’s exceptionally efficient at the cellular level.

For 40 years, the breeding industry chose production over efficiency. With feed accounting for 50-75% of operating costs, according to USDA data, the math increasingly favors a more nuanced approach.

THE BULLVINE BOTTOM LINE: Your Monday Morning Action List

IMMEDIATE ACTIONS (THIS WEEK):
□ Calculate your current income over feed cost variance between top and bottom cows
□ Call your nutritionist—ask if they’ll support data-driven feeding changes
□ Visit a farm already using the technology (find one in your area)

EVALUATION PHASE (NEXT 30 DAYS):
□ Get quotes from 3 vendors for feed efficiency estimation systems
□ Run your herd’s numbers: What’s your potential at $470/cow/year?
□ Talk to your banker about financing options (3-5 year payback)

DECISION CHECKPOINT:
□ Can you afford to wait while neighbors gain $700/cow/lactation advantage?
□ Will you act on uncomfortable data about favorite cows?
□ Are you ready to challenge 40 years of production-first thinking?

The technology exists. The economics are proven. The only question: Will you act before your neighbors do?

As Alderink reflects: “I think we are just scratching the surface on all this, but it is taking us down a path where we can really start to look at these things because we have something to measure it.”

That ability to see which cows convert feed efficiently—versus which simply produce milk—represents the difference between optimizing for volume and optimizing for profit.

In today’s margin environment, that distinction increasingly determines which operations thrive and which struggle to survive.

Your move.

Key Takeaways:

  • The $700 Discovery: Efficient cows (17kg DMI) and inefficient cows (23kg DMI) produce identical milk but differ by $700/lactation in profit—measure to know which you have
  • Transform Your Breeding: Feed data creates three profit tiers → Top 30% get premium genetics | Bottom 30% produce beef calves ($350-700 each) | Middle 40% flex by needs
  • Precision Feeding Pays: Individual response data shows premium feed additives only benefit ~30% of cows—saving $200+/cow by removing non-responders from expensive rations
  • Competitive Clock Ticking: 3,000 early adopters gaining $470/cow annually are building herds 10-15% more efficient by 2030—each month you wait widens the gap

Complete references and supporting documentation are available upon request by contacting the editorial team at editor@thebullvine.com.

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Cracking the Code: Behavioral Traits and Feed Efficiency

Uncover the hidden potential of Holstein cows’ behaviors for enhancing feed efficiency. Are you set to amplify dairy profits by delving into these genetic revelations?

Picture this: every bite your cow takes could boost profits or quietly nibble away at them. Feed efficiency, crucial in dairy farming, accounts for a staggering 54% of total milk production costs in the U.S. as of 2022 (USDA ERS, 2023). Like a car’s fuel efficiency, feed efficiency maximizes milk production per pound of feed, directly impacting profitability. Traditionally measured by Residual Feed Intake (RFI), it requires costly and labor-intensive individual feed intake tracking. But did you know hidden wisdom lies in your Holsteins’ daily routines? Their behaviors—captured through sensors monitoring rumination, downtime, and activity levels—offer incredible insights into feed efficiency, potentially saving resources without the hefty costs. Rumination time indicates efficient feed processing, lying time shows energy conservation, and steps reflect exertion, giving a cost-effective glimpse into feed efficiency.

Exploring Cow Behavior: A New Path to Understanding Productivity 

Let’s dive into the fascinating study that explores the genetic ties between behavioral traits and feed efficiency in lactating Holstein cows. Imagine observing what makes a cow more productive by observing its everyday habits. That’s what researchers aimed to uncover here. They looked at how cows spent their days—ruminating, lying down, and moving about—to see how those activities tied back to how efficiently cows used to feed.  Published in the Journal of Dairy Science:  Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows.

This was no ordinary study. It involved two major research stations, tapping into the knowledge of the University of Wisconsin-Madison and the University of Florida. Researchers gathered a wealth of data at each site using the latest animal monitoring technology. From fancy ear tags to trackers counting each step, they banked on the latest gadgets to give each cow its behavior profile and feed efficiency. The data was then analyzed using statistical methods to identify genetic correlations and potential applications for improving feed efficiency on dairy farms. 

Here’s a big part of what they did: They harnessed thousands of daily records about how many steps cows took, how long they spent ruminating (cow-speak for chewing their cud), and how much downtime they logged lying around. Then, they matched those with how well the cows converted feed into milk. This process helps pinpoint whether genetics have a hand in which cows become efficient producers. By breaking it down to basics like rumination time and activity levels, they hoped to draw links to feed efficiency without the usual heavy lifting of manually tracking each cow’s feed intake. This research can be applied to your farm using similar monitoring technology to track your cows’ behavior and feed efficiency.

Unlocking Feed Efficiency: The Genetic Link Between Cow Behaviors and Productivity

Understanding the intricate genetic connections between behavioral traits and feed efficiency gives us insightful information into dairy cattle production. Specifically, rumination time, lying time, and activity levels play significant roles. Rumination time is strongly correlated with higher dry matter intake (DMI) and residual feed intake (RFI), implying that cows with higher consumption tend to ruminate more and are generally less efficient. Meanwhile, longer lying times show a negative genetic correlation with RFI, suggesting that cows resting more are more efficient overall. 

From a genetic selection perspective, these behavioral traits exhibit varying heritability and repeatability, which are crucial for breeding decisions. Rumination and activity traits have moderate heritability, approximately 0.19, whereas lying time shows a slightly higher heritability, 0.37. These traits are not only genetically transferrable but also display high repeatability across different timeframes, indicating their potential for consistent genetic selection. Lying time stands out with a repeatability estimate ranging up to 0.84 when aggregated weekly, emphasizing its reliability as a selection criterion. 

Predicting feed efficiency using these traits is beneficial as commercially available wearable sensors easily record them. This technology supports the identification and selection of genetically efficient cows. It promotes healthier and more cost-effective dairy farm operations. Transitioning from traditional to sensor-based monitoring systems provides farmers practical tools to enhance herd productivity while leveraging genetic insights for sustained improvement. 

Delving into the Genetic Connections Between Cow Behaviors and Feed Efficiency

When we talk about cow behavior, we’re delving into a treasure trove of insights that can inform us about their efficiency in feed conversion. One standout finding from recent studies is the positive genetic correlation between rumination time and dry matter intake (DMI). In numerical terms, this correlation sits at a robust 0.47 ± 0.17. What does this tell us? Simply put, cows that spend more time ruminating tend to consume more, which might make them seem less efficient in terms of residual feed intake (RFI). Isn’t it fascinating to consider how chewing could unveil so much about a cow’s intake patterns? 

On the other hand, lying time paints a different picture. There’s a negative genetic correlation, with RFI hovering at -0.27 ± 0.11. This genetic wisdom suggests that our bovine friends who enjoy more downtime are more efficient. It makes you wonder: How might a cow’s leisure time hint at its overall efficiency? 

These behavioral gems potentially allow us to streamline farm operations. By monitoring cows’ rumination and lying times through wearable sensors, farmers can gradually identify superstars who convert feed more efficiently without the nitty-gritty of tracking every nibble they take. This saves time and labor and provides a more comprehensive understanding of each cow’s productivity, leading to more informed breeding and management decisions. 

Time to Transform Your Herd: Are We Overlooking the Quiet Achievers? 

Imagine pinpointing which cows in your herd are top producers and efficient eaters. Thanks to advancements in sensor-based data collection technologies, this is now possible! For those contemplating adding a layer of tech to their herd management, sensors can revolutionize how they select and breed Holstein cows. 

First, wearable sensors—like SMARTBOW ear tags used in recent studies—can provide continuous data on cow behavior, such as rumination time, lying time, and activity levels. You can identify genetic patterns that correlate with feed efficiency by understanding these behaviors. This means selecting cows that lie more and walk less, as they are more efficient producers. 

Beyond selection, these sensors offer multiple advantages in everyday management. They can alert you to changes in a cow’s behavior that might indicate health issues, allowing for early intervention. This proactive approach boosts cow welfare and can save significant costs for treating late-diagnosed health problems. 

Additionally, these real-time insights can enhance reproductive management. Sensors help pinpoint the perfect estrus detection, improving the timing of insemination and increasing success rates—every dairy farmer’s dream. With each chosen selection, you’re not just reducing reproductive waste; you’re enhancing the genetic lineage of your herd. 

The benefits of sensor technology extend to data-driven decision-making regarding feed adjustments. With precise intake and behavior data, farmers can tweak diets to match each cow’s nutritional needs, potentially skyrocketing productivity and reducing feed costs—a win-win! 

While the initial investment in wearable technology might seem significant, consider it an asset purchase rather than a liability. These devices pay for themselves through improved herd management, production rates, and more innovative breeding selections. So, ask yourself: Is it time to embrace Tech in your dairy operation? We think the ROI will echo with each moo of approval. 

The Bottom Line

The genetic interplay between behavioral traits like rumination time, lying time, and activity and feed efficiency is an intriguing research topic and a practical opportunity for the dairy industry. As we’ve uncovered, more efficient cows generally spend more time lying down—a simple indication that precision and efficiency can be quietly monitored through actions we might have previously overlooked. 

Behavioral traits are emerging as feasible proxies for assessing feed efficiency. They are already accessible through wearable technology. Behavioral traits offer a promising pathway to optimizing productivity without requiring intensive manual data collection. This presents a significant advancement for dairy farmers aiming to streamline operations and improve herd performance. 

But what does this mean for you? Whether you work directly on a dairy farm or serve the industry in another capacity, consider integrating these insights into your decision-making processes. Invest in the right technologies, monitor the right behaviors, and select cows with these traits to improve your herd’s economic outcomes. 

Don’t just take our word for it—try implementing these strategies and observe the results. We want to hear from you! Share your experiences and thoughts on how these findings could reshape your approach to herd management. Comment below, or start a conversation by sharing this article with your network. If you’re already using these wearable technologies, what changes have you noticed in your herd’s efficiency? 

Key Takeaways:

  • Behavioral traits like rumination time, lying time, and activity are heritable in lactating Holstein cows.
  • Rumination time shows a positive genetic correlation with dry matter intake (DMI) and residual feed intake (RFI), reflecting its potential as a proxy for feed efficiency.
  • more efficient Cows tend to spend more time lying down, which is linked to lower RFI.
  • Highly active cows, as measured by the number of steps per day, often demonstrate less efficiency due to higher energy expenditure.
  • Using wearable sensors can facilitate easy and practical data collection of behavioral traits on commercial farms.
  • Selection of cows based on these behavioral traits can improve feed efficiency without costly individual feed intake measurements.
  • This study highlights the potential of sensor-based behavioral monitoring to enhance dairy cow productivity and management.

Summary:

Welcome to the fascinating world of dairy cow genetics and behavioral traits! Imagine unlocking a new level of feed efficiency in your Holstein herd by understanding milk production or size and how your cows behave—how they rest, eat, and move. This intriguing study reveals that behaviors like lying time and activity are heritable and inversely related to feed efficiency, suggesting that the most relaxed cows might be the most efficient. Feed expenses account for a whopping 54% of U.S. milk production costs, and understanding this can bolster profitability. Researchers using wearable sensors have uncovered genetic links between behavioral traits and feed efficiency, showing cows with higher dry matter intake (DMI) and residual feed intake (RFI) tend to ruminate more, appearing less efficient overall. In contrast, more resting correlates with better efficiency. Predicting feed efficiency through these traits, quickly recorded by sensors, offers practical tools for enhancing productivity and sustaining improvements in dairy operations.

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Unlocking the Secrets of Dry Matter Intake in US Holstein Cows: The Genomic and Phenotypic Influence on Milk Components and Body Weight

Uncover the potential of genomic and phenotypic insights to enhance dry matter intake management in US Holstein cows, ultimately boosting milk production and body weight management. Intrigued by the possibilities?

In the context of dairy farming, ‘dry matter intake’ (DMI) is not just a term for veterinarians and nutritionists. It’s a crucial factor for US Holstein cows, the key players in milk production. The efficiency of these cows is directly linked to what they eat, how much they eat, and how effectively they convert that intake into milk and robust health. Therefore, understanding DMI is not just important for maximizing farm potential, but it’s also the key to connecting feed efficiency, milk production, and overall animal welfare

“Optimizing dry matter intake is crucial for enhancing milk yield and ensuring cow health. It’s the linchpin of dairy farm efficiency.” 

This article explores the genomic and phenotypic impacts of DMI, highlighting its role in milk production and body weight management. Using data from 8,513 lactations of 6,621 Holstein cows, we’ll examine: 

  • The link between DMI and milk components like fat and protein.
  • How body size traits affect DMI.
  • The impact on breeding programs aiming for better feed efficiency and productivity.

Join us as we dive into these dynamics and discover strategies to boost profitability and sustainability in dairy farming.

Unveiling the Genomic and Phenotypic Dynamics of Dry Matter Intake in Holstein Cows 

Understanding dry matter intake (DMI) in Holstein cows is crucial for nutrition management and breeding programs. Large data sets have revolutionized this research, allowing precise estimation of feed requirements for milk production and body maintenance. These datasets provide a strong foundation for refining predictive models. 

Two main approaches are used to evaluate DMI: phenotypic and genetic regressions. Phenotypic regressions use visible traits and help dairy farmers adjust feeding strategies based on real-time data for milk yield, fat, and protein content. This is vital for optimizing feed efficiency and maintaining herd health. 

Genetic regressions, on the other hand, examine the genetic factors influencing DMI. These are especially useful in breeding programs that aim to enhance important traits through selective breeding. Genetic evaluations guide breeding decisions that promote traits like higher milk yield, better milk quality, and improved feed efficiency. 

The difference between phenotypic and genetic regressions highlights the distinct goals of nutrition management and genetic improvement. Phenotypic data meets immediate needs, while genetic data fosters long-term improvements. Combining both approaches enhances current and future herd performance. 

These advancements in genomic tools and statistical models, such as BostaurusUMD3.1.1 for genomic evaluations, underscore the collaborative effort to advance DMI research. This collective endeavor aims to optimize productivity and sustainability in dairy farming, a goal we all share in the scientific community.

An Unprecedented Dive into Dry Matter Intake Through Genomic and Phenotypic Lenses 

This study makes a unique contribution to the field of dairy farming and genetics by analyzing DMI using a large dataset from 8,513 lactations across 6,621 Holstein cows. By integrating phenotypic and genomic views, we were able to provide a detailed look at DMI through sophisticated mixed models. These models included variables like days in milk, age parity, trial dates, management groups, and body weight changes during 28—and 42-day feeding trials in mid-lactation, ensuring accuracy in the results. 

Based on observable traits, phenotypic regressions gave practical insights for nutritional management. In contrast, genomic regressions, grounded in genetic data, offered deeper insights crucial for breeding programs. Both evaluation types provided a comprehensive understanding of feed efficiency and milk production potential, aiding in better selection and breeding strategies.

Balancing Nutritional Demands: Insights from Phenotypic and Genomic Regressions 

The phenotypic regressions of Dry Matter Intake (DMI) on milk, fat, and protein revealed specific coefficients that underscore the intricate balance required in nutrition management. For milk, the coefficient was modest (0.014 ± 0.006), indicating a relatively low increase in DMI per unit increase in milk production. Conversely, fat (3.06 ± 0.01) and protein (4.79 ± 0.25) showed more substantial coefficients, demonstrating that increases in these components significantly elevate the DMI requirements. These results suggest that nutritional plans must be meticulously tailored, focusing more on the feed requirements for fat and protein production to ensure optimal energy balance and animal health

When we compare these findings to the corresponding genomic regressions, we observe stark contrasts. Genomic regressions yielded higher coefficients across all components: milk (0.08 ± 0.03), fat (11.30 ± 0.47), and protein (9.35 ± 0.87). This difference implies that genetic potential is more dominant in determining feed efficiency than phenotypic observations alone. Simply put, cows with higher genetic predispositions for milk components require substantially more feed, reflecting their superior production capabilities. 

These discrepancies highlight an essential consideration for breeding programs. While phenotypic data provide valuable insights into immediate nutritional needs, genomic data offer a more comprehensive forecast for long-term feed efficiency and production potential. Consequently, integrating these genomic insights into breeding strategies can drive advancements in producing more feed-efficient cows, aligning with evolving economic and environmental objectives.

The ECM Formula: Unveiling the Energy Dynamics in Dairy Production 

The ECM formula is vital for measuring milk’s energy content by considering its fat, protein, and lactose components. This standardization allows for fair comparisons across various milk types. Our study uses the ECM formula to reveal the energy needs of different milk components, shedding light on the nutritional and economic facets of dairy farming. 

Regarding DMI for fat and protein, phenotypic and genomic regressions show significant differences. Phenotypic regressions suggest protein production needs 56% more DMI than fat. Genomic regressions show a smaller gap, with protein needing 21% more DMI than fat. Sire genomic regressions add complexity, indicating fat requires 35% more DMI than protein. These differences highlight the challenge of converting genetic data into practical feed efficiency. 

These findings have profound implications for feed cost management. Increased DMI for any milk component escalates feed expenses, a critical consideration for farmers aiming to enhance profitability. However, breeders can leverage genomic data to select cows with lower residual feed intake that still yield ample milk, fat, and protein. This strategic approach enhances the economic viability of dairy operations, fostering more efficient and sustainable feeding practicesthat benefit both producers and consumers.

Sustaining Holstein Vigor: The Role of Body Weight and Maintenance 

Examining annual maintenance needs in Holstein cows through phenotypic, genomic, and sire genomic regressions unveils notable consistency. Estimates, expressed in kilograms of dry matter intake (DMI) per kilogram of body weight per lactation, show phenotypic regression at 5.9 ± 0.14, genomic regression at 5.8 ± 0.31, and sire genomic regression, adjusted by two, at 5.3 ± 0.55. These are higher than those from the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) using Net Energy for Lactation (NEL) equations. 

Discrepancies arise because NASEM’s general equations overlook individual genetic and environmental nuances. Genomic data offer a more dynamic and specific view, capturing intricate biological interactions. Modern genomic evaluations, encompassing various genetic traits, provide a clearer picture of maintenance needs, suggesting earlier models may underestimate the metabolic demands of high-yield dairy cows

This analysis highlights the need to blend genomic insights with phenotypic data to grasp maintenance requirements reliably. By refining models with the latest genetic data, the dairy industry can enhance nutrition plans, improving animal welfare and productivity.

Decoding Dairy Efficiency: The Interplay of Type Traits and Body Weight Composite

Exploring multiple regressions on genomic evaluations for the body weight composite (BWC) traits, we find that strength stands out. It’s the best predictor of body weight and Dry Matter Intake (DMI), confirming its crucial role in the current BWC formula. 

Other traits seem less significant in predicting DMI. This suggests that breeding programs enhance strength to improve body weight and feed efficiency. Prioritizing strength can balance robust body weight with better feed utilization. 

Breeders can build more productive and cost-effective Holstein herds by selecting for strength. This aligns to improve profitability through more brilliant breeding and makes a strong case for ongoing genomic research in dairy production.

Optimizing Genetic Gains: The Evolution of the Net Merit Formula 

The 2021 revision of the Net Merit formula marked a pivotal shift towards improving the economic efficiency of breeding programs. Integrating recent findings on dry matter intake (DMI) and other traits, the formula better aligns with the complex relationships among milk production components, body size, and feed efficiency. 

The updated formula prioritizes more miniature cows with traits like harmful residual feed intake and higher milk, fat, and protein yields. This strategic approach promotes cows that produce more milk and enhance feed efficiency, reducing operational costs and boosting profitability. By incorporating genomic and phenotypic data, the Net Merit formula advances precision breeding, considering the economic impact of each trait and supporting a sustainable dairy industry. 

This revision synchronizes breeding goals with economic benefits, encouraging the development of cows that excel in productivity while minimizing feed costs. It highlights the vital link between genetic research and practical breeding strategies, solidifying the Net Merit formula’s essential role in modern dairy farming.

The Bottom Line

The exploration of dry matter intake (DMI) in US Holstein cows through both genomic and phenotypic lenses has unveiled crucial insights into the nutritional and economic dynamics of dairy farming. The study revealed that genomic regressions provide a more accurate estimate of feed required for individual milk components or body maintenance than phenotypic regressions. Furthermore, the energy-corrected milk (ECM) formula highlighted that fat production demands significantly higher DMI than protein production, establishing a clear difference in nutrient requirements based on milk composition. 

One of the pivotal findings emphasizes the significant benefits of selecting more miniature cows with harmful residual feed intake (RFI). These cows require less feed and exhibit an enhanced production of milk, fat, and protein, thereby improving overall farm profitability. This aligns with the revised Net Merit formula, which aims to optimize genetic traits for economic efficiency. 

The implications for breeding programs are profound. Adopting strategies that prioritize genomic evaluations can lead to more efficient feed utilization and better economic outcomes. This study suggests that future research should delve deeper into the genetic mechanisms underlying RFI and explore the long-term impacts on herd health and productivity. Additionally, there’s potential for these findings to inform genetic selection criteria in dairy breeding programs globally, enhancing the sustainability and profitability of the dairy industry.

Key Takeaways:

  • Large datasets allow precise estimation of feed required for individual milk components and body maintenance.
  • Genetic regressions are more impactful for breeding programs than phenotypic regressions, which are more useful for nutrition management.
  • Fat production requires significantly more DMI than protein production when analyzed through the energy-corrected milk (ECM) formula.
  • Phenotypic regressions underestimate the DMI compared to genetic regressions.
  • Annual maintenance DMI for body weight is slightly underestimated in phenotypic regressions compared to genomic estimations.
  • Strength is the type trait most strongly associated with body weight and DMI, as highlighted by the revised body weight composite (BWC) formula.
  • To enhance profitability, breeding programs should focus on selecting smaller cows with negative residual feed intake that are high producers of milk, fat, and protein.
  • The Net Merit formula has been updated to reflect these insights, aiming for an economically optimal genetic selection response.

Summary: A study analyzing dry matter intake (DMI) in US Holstein cows found that understanding DMI is crucial for maximizing farm potential and connecting feed efficiency, milk production, and animal welfare. The study used data from 8,513 lactations of 6,621 Holstein cows and genetic regressions to analyze DMI. Phenotypic regressions used visible traits to adjust feeding strategies based on real-time data for milk yield, fat, and protein content. Genetic regressions examined genetic factors influencing DMI, useful in selective breeding programs. Results suggest that nutritional plans must be meticulously tailored, focusing on feed requirements for fat and protein production to ensure optimal energy balance and animal health. Genomic insights can drive advancements in producing feed-efficient cows, aligning with economic and environmental objectives. The Energy-Correlated Milk (ECM) formula is a crucial tool for measuring milk’s energy content, revealing significant differences in DMI for fat and protein.

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