WEBVTT

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Breaking free from the chains of the past Where

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truth moves faster than a Holstein calf No law

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waiting on some printed page We're charting new

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ground in the digital age From genomic codes

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to robot facts We cut through the noise, no hold

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them back not your daddy's dairy news tonight

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we're sparking Welcome back to the Bullbine Podcast,

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where we cut through dairy industry noise to

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get you the insights that actually matter for

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your operation. We're focused squarely on actionable

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intelligence for you, the producer. And today

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we're diving deep into a feature piece that's

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been generating some serious buzz across the

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industry. This topic has layers and some genuine

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surprises, actually, that are going to make smart

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farmers rethink how they've been approaching

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one of the most persistent and, frankly, costly

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problems on the dairy, calf health. We are talking

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about the long -awaited introduction of national

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genomic evaluations for calf resistance. That's

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right. Our mission today is, well, to decode

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the hype, the science, and yeah, the skepticism

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around CDCB's new genomic calf health evaluations.

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These are set to launch in April 2026, so still

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a ways off, but the discussion is happening now.

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We are asking the $3 ,500 calf question. Will

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this new technology actually save you money,

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or is it just another extensive layer on top

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of what should be basic management? We need to

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look at the cold, hard numbers from the devastating

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$350 ,000 annual economic hit that many operations

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are absorbing to the wildly variable $0 to $40

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,000 investment range this might require. Fascinating

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here, what really jumps out is that the answer

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depends entirely on the farm listening right

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now. It really does. This is not a universal

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fix. Not by a long shot. If your current management

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protocols are broken, if your colostrum management

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is, well... inconsistent or if your ventilation

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is poor the best genetics in the world won't

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save you a dime in fact you'll just waste thousands

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on testing seriously but if you're already doing

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a great job if you're an elite operator with

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mortality rates consistently below four percent

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this could be the free ticket to push you into

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truly unprecedented performance so we have to

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peel back these layers of expectation and yeah

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skepticism because the crucial details especially

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concerning data contribution and heritability

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they're definitely in the fine print Okay, let's

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unpack this then. Let's set the stage for why

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this is such a critical discussion right now.

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Yeah. We must start by defining our terms, just

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to be clear. We're talking about the Council

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on Dairy Cattle Breeding, or CDCB. That's the

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organization responsible for calculating and

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distributing genetic evaluations for dairy cattle

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in the United States. This is the official system

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we're discussing. Correct. And the context here

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is... Well, it's brutal. Let's be honest. We

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are talking about persistent, decades -long failure

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to fully solve a core problem. The industry,

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despite massive leaps in management, nutrition,

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and technology, still hasn't eliminated unacceptable

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levels of calf mortality. It's just a fact. If

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you look at the last comprehensive USDA survey

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back in 2014, okay, that's a while ago, but pair

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that with recent Canadian research, which is

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more current, we are still persistently losing

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about 6 % of our calves before they ever reach

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weaning. 6%. nationally. That is an enormous

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amount of loss potential and, frankly, capital.

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The 6%. And let's translate that 6 % into something

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tangible for the farmer listening right now,

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because the stakes here are... Astronomically

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high. Replacement heifers are not cheap anymore,

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are they? Not at all. The USDA market reports

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are consistently putting the value of a quality

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replacement heifer in the $3 ,000 to $4 ,000

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range, sometimes even higher depending on location

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and demand. So when you lose a calf, especially

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a promising heifer calf, you are literally watching

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several thousand dollars of future revenue and

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capital investment just evaporate. You're highlighting

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the catastrophic loss there, the direct hit.

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And for a typical 1 ,000 cow operation, that

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persistent 6 % loss turns into an annual financial

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hit that, when you really factor in everything,

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can easily exceed $350 ,000. That's why we have

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to pay attention to CDCB's upcoming launch. They

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are attempting to provide a tool, a genetic tool,

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that reduces those losses in the most cost -effective

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way possible by selecting for resistance before

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the calf is even born. And crucially, they're

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targeting the right culprits, right? The root

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cause. We know, based on research like Erie et

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al., in the Journal of Dairy Science back in

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2018, that scours or diarrhea and respiratory

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disease account for roughly 75 % of pre -weaning

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deaths, three quarters of them. So this new genomic

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evaluation, launching in April 2026, aims to

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breed genetic resistance specifically to those

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two most common causes of mortality. Okay, but

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here's where the controversy starts, though,

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and where we get into the meat of the bullvine

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skepticism, maybe? Is genetics truly the silver

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bullet, or is it merely going to amplify the

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success of farms that already have stellar, truly

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elite management? That cost variability we mentioned

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is absolutely key here. We're seeing entry cost

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estimates that range from $0, literally a free

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trait added to existing tests if you're already

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testing, to potentially $40 ,000 depending on

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the producer's current practices and whether

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they're already engaging in genomic testing through

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their Dairy Herd Improvement Association or DHI.

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Right. That disparity in entry costs, that zero

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versus 40 grand, coupled with the assumption

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of management excellence, that's the key challenge

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here, isn't it? It really is. If a producer is

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struggling with 8 % or 10 % mortality, their

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investment dollars need to go toward basic infrastructure

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and protocol improvement first. Things like...

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Colostrum. Better colostrum quality control,

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yeah. Yeah. Improved passive transfer rates,

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enhanced ventilation in the calf barn, rigorous

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hygiene protocols, basic blocking and tackling.

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You need to invest $50 ,000 in management fixes

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first, not $40 ,000 in genomic testing that will,

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at best, only offer marginal help against deeply

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flawed protocols. It just won't work. Exactly.

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But flip that coin, if you're already sitting

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at 3 % mortality, maybe 4%, and you've squeezed

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all the managerial efficiency you can out of

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your operation, that $0 price tag suddenly looks

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very, very attractive, doesn't it? Absolutely.

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This is a tool to potentially push you from 3

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% mortality down into that elite enviable 1 -2

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% zone. It's an efficiency amplifier for the

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already efficient. Okay. Now that we've set the

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context and defined the stakes, let's dive straight

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into the hard math, the economics of calf mortality

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and morbidity. This is where we break down exactly

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how that $350 ,000 annual hit is calculated.

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Okay. Let's run the numbers step by step. For

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that typical 1 ,000 cow dairy, right, you account

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for a 6 % pre -weaning mortality rate. Average?

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Maybe even a bit conservative for some. You're

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looking at losing approximately 54 calves every

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single year. 54 potential replacements or sale

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animals, gone. That's not just potential profit

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loss. That's raw capital loss. And when we calculate

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the direct loss, we factor in that replacement

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value. Let's use roughly $3 ,500 per potential

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heifer just as a working number. Right. That

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gives you a direct replacement value loss of

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around $189 ,000 annually. Poof. Gone. That's

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money that has to be spent to purchase a replacement

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or money lost from not selling a valuable animal

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you raised. Right. But that's just the direct

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loss, the obvious hit. We have to add what we

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call the sunk costs. And this is often forgotten,

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I think. Yeah, people overlook this. Before those

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54 calves died, you had already invested labor,

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feed, milk replacer, starter feed, bedding costs,

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and critically, vet costs and medication. That's

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another, what, $15 ,000 to $20 ,000 of investment

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just disappearing into thin air. You paid for

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the semen, you paid for the feed, the labor,

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the vet call, and you got absolutely zero return.

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Zip. And if you are using expensive hygienic

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semen, which many are these days, that sunk cost

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on the front end is even higher, right? Definitely.

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But here is the part that truly hits farmers

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harder, I think, because it's the hidden insidious

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cost. The one that keeps paying penalties long

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after the calf survives. We have to talk about

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morbidity, the calves that survive the scours

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or the respiratory infection, but are permanently

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damaged by the experience. Absolutely. And this

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is where the GILF research, specifically Winder

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et al. back in 2022, provides. the critical data

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point. It's really eye -opening. Sick survivors,

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those calves that made it past weaning and eventually

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entered the milting string, they produce over

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700 kilograms less milk in their first lactation

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compared to their counterparts who never got

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sick. That is permanent biological damage expressed

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as lost yield. It's baked in. 700 kilograms less

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milk. That's over 1 ,500 pounds. That's not a

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one -time dip. That is a ton of lost production

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capacity over her lifetime, isn't it? It is.

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And beyond that, those sick survivors are demonstrably

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less efficient in the herd. The research shows

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that they calve later. dragging down herd efficiency

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and increasing the unproductive days in the string.

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And crucially, they tend to leave the herd earlier,

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meaning their productive longevity is permanently

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hampered. They just don't last as long. They

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are less productive, less fertile, and less durable,

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basically. So when you synthesize all of this,

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you take the $189 ,000 direct replacement loss,

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the $20 ,000 in sunk costs, and then you factor

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in the permanent hit on first lactation performance

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and longevity from the dozens, maybe hundreds

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of sick survivors. That total annual hit easily

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exceeds $350 ,000 for that typical 1 ,000 cow

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operation. Easily. And remember, that calculation

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is based on a conservative 6 % mortality. If

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you're at 8 % or 10%, you're looking at half

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a million dollars walking out the door every

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year. Maybe more. That's the magnitude of the

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problem CDCB is trying to solve with a genomic

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solution. And we must acknowledge how this immense

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annual cost impacts operations differently. It's

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not the same for everyone. If you are a massive

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5 ,000 cow operation, you're looking at millions

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in annual loss from this. Millions. If you are

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a 300 cow operation, your annual loss might be

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closer to $100 ,000. Still a huge chunk of change,

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though. Absolutely. But the underlying necessity

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remains the same. The investment. whether it's

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in genomics or management, is only effective

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if the foundation is strong. The necessity of

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strong management is the non -negotiable prerequisite

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for this technology. We have to keep hammering

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this point. As we've stressed, genetics will

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not salvage broken protocols. It just won't.

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If your mortality rate is high, say consistently

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above 5%, you have fundamental issues. colostrum

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quality, hygiene, ventilation, something. You

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simply cannot expect a 2 .6 % genetic resistance

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trait to fix an 8 % management problem. It's

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mathematically impossible. This technology is

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designed to push excellent management into elite

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territory, but it will never patch poor fundamentals.

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Never. That brings us perfectly to the industry

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reality check then. Assuming a producer is doing

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a decent job managing well, let's transition

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into what exactly the CDCB evaluation is measuring

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and importantly, why the numbers we're seeing

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are confusing people, specifically around heritability.

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Yeah, it's critical to clarify the science here.

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We are dealing strictly with genetic predictions

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for resistance. That's all it is. We are looking

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at the calf's innate inherited ability to cope

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with or resist the pathogens that cause scours

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and respiratory disease. The data foundation

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is truly massive. Which is important to emphasize,

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isn't it? This wasn't a small study. No, huge.

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The CDCB researchers dug through records spanning

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the last decade and were able to analyze over

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200 ,000 diarrhea records and nearly 700 ,000

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respiratory disease records. 700 ,000. Yeah,

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the sheer volume of data tells you just how pervasive

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these problems are across the U .S. dairy landscape.

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It's everywhere. And this data gives us perspective

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on the populations being studied, too. Holsteins

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dominated, unsurprisingly, making up about 80

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% of the data. Jerseys accounted for about 17%.

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And the initial source material notes that Jersey

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calves in this data set showed slightly higher

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disease rates, 17 .8 % for scours and 23 .7 %

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for respiratory disease, compared to 13 .5 %

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and 14 .5 % for Holsteins in the same data set.

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Now, this isn't a critique of the breed, not

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at all. But it demonstrates the need for comprehensive

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data across all major breeds for accurate prediction

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modeling. We need representation. OK, now let's

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talk about the number that made a lot of people

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scratch their heads and, frankly, raise some

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skepticism. Heritability, often denoted as OSHORE.

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We're talking about the genetic contribution

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to the observed variation in the trait. How much

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is genetics versus everything else? And the resistance

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numbers are shockingly low. especially compared

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to the production traits we're used to selecting

00:12:54.669 --> 00:12:58.690
for, like milk yield. We're talking 2 .6 % heritability

00:12:58.690 --> 00:13:01.769
for diarrhea resistance and 2 .2 % for respiratory

00:13:01.769 --> 00:13:04.559
disease resistance. Tiny numbers. Yeah, that's

00:13:04.559 --> 00:13:06.399
the low heritability challenge. And this is where

00:13:06.399 --> 00:13:08.500
we have to pause and really explain why that

00:13:08.500 --> 00:13:10.639
number is what it is and why it's still potentially

00:13:10.639 --> 00:13:13.139
meaningful. If you're used to selecting for milk

00:13:13.139 --> 00:13:15.860
yield, which might be 30 % or 40 % heritable,

00:13:15.919 --> 00:13:18.259
maybe even higher for some components, then 2

00:13:18.259 --> 00:13:21.039
.6 % sounds like statistical noise, doesn't it?

00:13:21.100 --> 00:13:23.080
It sounds like you are fighting an absolutely

00:13:23.080 --> 00:13:25.639
massive uphill battle for a minimal genetic gain

00:13:25.639 --> 00:13:27.480
that will just be swamped by the environment.

00:13:27.720 --> 00:13:30.159
You're right to be skeptical, you know. Heritability

00:13:30.159 --> 00:13:32.659
tells you that only 2 .6 % of the variation in

00:13:32.659 --> 00:13:35.039
whether a calf gets scours or not is due to its

00:13:35.039 --> 00:13:38.960
genetics. That's it. The other 97 .4 % is due

00:13:38.960 --> 00:13:41.100
to the environment. Did it get enough high -quality

00:13:41.100 --> 00:13:43.700
colostrum quickly? Was the maternity pen clean?

00:13:43.919 --> 00:13:45.860
Was the ventilation system working right? What

00:13:45.860 --> 00:13:48.460
was the temperature outside? Was the milk replacer

00:13:48.460 --> 00:13:50.820
mixed correctly? All those factors, they dominate

00:13:50.820 --> 00:13:53.440
the expression of the trait. Right. So why is

00:13:53.440 --> 00:13:56.000
CDCB pushing this if the genetic contribution

00:13:56.000 --> 00:13:59.120
is so small? Why bother? This is where the sheer

00:13:59.120 --> 00:14:01.360
magnitude of the data comes into play. That's

00:14:01.360 --> 00:14:04.100
the key. Low heritability means you need a massive

00:14:04.100 --> 00:14:06.519
number of records to find that small, faint genetic

00:14:06.519 --> 00:14:08.820
signal in the overwhelming environmental noise.

00:14:09.279 --> 00:14:12.980
If CDCB had only analyzed, say, 5 ,000 records,

00:14:13.340 --> 00:14:16.379
2 .6 % heritability would be completely meaningless.

00:14:16.460 --> 00:14:18.679
You couldn't trust it. But because they analyzed

00:14:18.679 --> 00:14:21.240
200 ,000 and 700 ,000 records, the statistical

00:14:21.240 --> 00:14:24.139
power is there. They can reliably estimate that

00:14:24.139 --> 00:14:26.480
small genetic contribution, even if it's tiny.

00:14:26.799 --> 00:14:28.679
This allows us to deliver the bovine signature

00:14:28.679 --> 00:14:31.879
contrarian analysis here, moving past that initial

00:14:31.879 --> 00:14:35.220
skepticism about the low number. Dr. John Cole

00:14:35.220 --> 00:14:37.960
from CDCB basically reassured the industry, saying

00:14:37.960 --> 00:14:40.000
something like, don't worry too much about the

00:14:40.000 --> 00:14:41.940
lower hair ability. It's about getting started

00:14:41.940 --> 00:14:44.379
and making progress where we can. Why is that

00:14:44.379 --> 00:14:47.809
statement so crucial? Because calf health. unlike

00:14:47.809 --> 00:14:50.789
milk production, is intrinsically deeply bound

00:14:50.789 --> 00:14:53.830
to environmental factors. So making any reliably

00:14:53.830 --> 00:14:56.570
selectable genetic progress, even small progress,

00:14:56.850 --> 00:14:59.379
is a win, isn't it? it is but it means we must

00:14:59.379 --> 00:15:01.500
temper our expectations though we have to be

00:15:01.500 --> 00:15:03.639
realistic this isn't going to magically transform

00:15:03.639 --> 00:15:06.379
your calf barn overnight it's just not you are

00:15:06.379 --> 00:15:08.320
not going to eliminate scours in one generation

00:15:08.320 --> 00:15:10.600
using genetics alone forget it this is going

00:15:10.600 --> 00:15:13.139
to be gradual multi -generational progress slow

00:15:13.139 --> 00:15:16.039
and steady you might only chip away say 0 .5

00:15:16.039 --> 00:15:18.340
of your mortality rate per generation through

00:15:18.340 --> 00:15:21.039
genetics maybe less but when you compound that

00:15:21.039 --> 00:15:23.860
0 .5 reduction over five to ten years combined

00:15:23.860 --> 00:15:25.940
with parallel improvements in management you

00:15:25.940 --> 00:15:28.190
have to do both that really starts to add up

00:15:28.190 --> 00:15:29.850
to significant savings. It's an accumulation

00:15:29.850 --> 00:15:32.429
of marginal gains over time. OK, but what gets

00:15:32.429 --> 00:15:35.809
really interesting for me and potentially validates

00:15:35.809 --> 00:15:38.330
the entire effort, despite the low heritability,

00:15:38.570 --> 00:15:42.450
is the independence benefit. This is huge. The

00:15:42.450 --> 00:15:44.830
preliminary research shows that these new calf

00:15:44.830 --> 00:15:47.230
health traits appear to be genetically independent

00:15:47.230 --> 00:15:49.809
from everything else we select for production,

00:15:50.090 --> 00:15:53.549
fertility and longevity. The correlations are

00:15:53.549 --> 00:15:56.389
hovering near zero. That is massive. That's the

00:15:56.389 --> 00:15:58.669
point producers really need to latch on to. If

00:15:58.669 --> 00:16:01.350
that independence holds up long term, it means

00:16:01.350 --> 00:16:03.909
we might actually avoid the painful tradeoffs

00:16:03.909 --> 00:16:06.409
we've seen plague genetic selection efforts in

00:16:06.409 --> 00:16:09.070
the past. We need to clearly define that risk.

00:16:09.210 --> 00:16:11.710
We call that an antagonistic correlation. It's

00:16:11.710 --> 00:16:13.629
a breeder's nightmare. Yeah, let's use the analogy

00:16:13.629 --> 00:16:15.549
suggested in our notes to make this crystal clear

00:16:15.549 --> 00:16:17.710
for everyone listening. Think of it like balancing

00:16:17.710 --> 00:16:19.769
a high -performing race car, right? When the

00:16:19.769 --> 00:16:21.950
industry aggressively boosted the engine, selecting

00:16:21.950 --> 00:16:24.659
intensely for high milk yield, We often had to

00:16:24.659 --> 00:16:27.080
sacrifice the suspension, which was fertility

00:16:27.080 --> 00:16:29.480
and health. Remember those days? Oh, yeah. Big

00:16:29.480 --> 00:16:31.799
time. That was the classic antagonistic correlation.

00:16:32.500 --> 00:16:35.360
Gaining a point in milk yield often meant losing

00:16:35.360 --> 00:16:37.899
a point in reproductive efficiency or daughter

00:16:37.899 --> 00:16:40.360
pregnancy rate. It was a massive headache for

00:16:40.360 --> 00:16:44.019
decades. So if these calf health traits are truly

00:16:44.019 --> 00:16:46.600
independent, genetically speaking, it means we

00:16:46.600 --> 00:16:48.740
can select for healthier, more robust calves

00:16:48.740 --> 00:16:50.799
without having to sacrifice a point of milk,

00:16:50.860 --> 00:16:53.179
a day of reproductive efficiency, or a dolly

00:16:53.179 --> 00:16:55.379
of productive life. That's the dream, really.

00:16:55.500 --> 00:16:58.360
This becomes an additive benefit. We can stack

00:16:58.360 --> 00:17:00.529
genetic improvements. in calf health on top of

00:17:00.529 --> 00:17:02.970
existing gains in production and fertility rather

00:17:02.970 --> 00:17:05.410
than trading one for the other. And that makes

00:17:05.410 --> 00:17:07.750
the low heritability worthwhile because the cost

00:17:07.750 --> 00:17:10.150
of selection in terms of sacrificing other important

00:17:10.150 --> 00:17:12.750
traits is effectively zero if it holds true.

00:17:12.950 --> 00:17:15.170
That makes the investment calculus much more

00:17:15.170 --> 00:17:17.369
compelling, doesn't it? Yeah. Assuming that independence

00:17:17.369 --> 00:17:20.210
holds. Let's follow the money now. Because this

00:17:20.210 --> 00:17:22.470
is where the conversation gets incredibly nuanced

00:17:22.470 --> 00:17:25.369
for the individual producer. We established the

00:17:25.369 --> 00:17:29.069
$350 ,000 annual loss potential. Now, let's look

00:17:29.069 --> 00:17:31.630
at the investment required to tap into this genomic

00:17:31.630 --> 00:17:35.670
fix. It truly ranges from $0 to $40 ,000, and

00:17:35.670 --> 00:17:38.329
your current genomic status dictates your specific

00:17:38.329 --> 00:17:41.470
entry point. It really is the tale of two farms,

00:17:41.549 --> 00:17:43.849
maybe even three or four farms. Let's start with

00:17:43.849 --> 00:17:45.890
the cheapest option, the best case scenario,

00:17:46.150 --> 00:17:48.769
which we call the free ride. This applies if

00:17:48.769 --> 00:17:51.349
you are already genomic testing your herd, especially

00:17:51.349 --> 00:17:54.150
your heifers. Right. The zero cost applies because

00:17:54.150 --> 00:17:56.789
of international precedent. We've seen this happen

00:17:56.789 --> 00:17:59.369
in Canada, Australia, and other regions when

00:17:59.369 --> 00:18:01.289
new traits are added to the national evaluations.

00:18:01.869 --> 00:18:04.369
They're typically rolled into existing and historical

00:18:04.369 --> 00:18:06.930
genomic tests at no extra charge to the farmer.

00:18:07.009 --> 00:18:09.190
It's just part of the service update. If you've

00:18:09.190 --> 00:18:11.509
been testing your heifers for years, you could

00:18:11.509 --> 00:18:13.970
potentially have thousands of animals automatically

00:18:13.970 --> 00:18:16.349
evaluated for calf health resistance when this

00:18:16.349 --> 00:18:19.180
launches in 2020. without writing a new check.

00:18:19.339 --> 00:18:22.720
This is a huge bonus, a reward really, for those

00:18:22.720 --> 00:18:25.680
early adopters of genomics who already pay the

00:18:25.680 --> 00:18:28.279
cost of managing the data flow. That's the reward

00:18:28.279 --> 00:18:31.180
for past investment commitment to data. But let's

00:18:31.180 --> 00:18:33.880
look at the other extreme, the high cost. This

00:18:33.880 --> 00:18:35.779
is for the producer who has never genomic tested

00:18:35.779 --> 00:18:39.599
before and decides, OK, the $350 ,000 loss is

00:18:39.599 --> 00:18:42.359
too much. I need to go all in immediately. That's

00:18:42.359 --> 00:18:44.160
where the $40 ,000 figure comes from for a 1

00:18:44.160 --> 00:18:46.720
,000 cow dairy. It's a big number. If you decide

00:18:46.720 --> 00:18:49.299
to test the entire cow herd plus all replacement

00:18:49.299 --> 00:18:51.539
heifers, that's roughly 1 ,000 animals, maybe

00:18:51.539 --> 00:18:53.559
more depending on your replacement rate, at about

00:18:53.559 --> 00:18:56.319
$40 per test. That's the bulk of it right there.

00:18:56.519 --> 00:19:00.299
$40 a pop adds up fast. It does. And beyond the

00:19:00.299 --> 00:19:02.400
direct testing cost, you also have to factor

00:19:02.400 --> 00:19:05.660
in potential software upgrades to your herd management

00:19:05.660 --> 00:19:08.460
system to handle the data. The labor required

00:19:08.460 --> 00:19:11.000
to swab or take tissue samples, ship them, and

00:19:11.000 --> 00:19:13.559
accurately log all those results, the data management

00:19:13.559 --> 00:19:16.240
time, which isn't insignificant, and maybe a

00:19:16.240 --> 00:19:18.420
premium you might pay for genetically superior

00:19:18.420 --> 00:19:22.299
semen. Maybe $10 to $15 extra per unit to ensure

00:19:22.299 --> 00:19:24.180
you're using the highest -ranked bulls for this

00:19:24.180 --> 00:19:27.500
trait. $40 ,000 up front is a huge barrier to

00:19:27.500 --> 00:19:30.119
entry for many farms. Which is why I think the

00:19:30.119 --> 00:19:32.420
smart middle ground is what most currently untested

00:19:32.420 --> 00:19:34.579
producers will likely look at first, or should

00:19:34.579 --> 00:19:37.099
look at. If you've never tested, you don't necessarily

00:19:37.099 --> 00:19:39.960
need to test the entire 1 ,000 cow milking string

00:19:39.960 --> 00:19:43.099
right away. That's overkill to start. Start smaller,

00:19:43.319 --> 00:19:45.859
validate the technology on your own farm, and

00:19:45.859 --> 00:19:47.839
test only your replacement heifers. That's where

00:19:47.839 --> 00:19:49.279
the future genetics are. Anyway, that's maybe,

00:19:49.400 --> 00:19:52.420
what, 450 animals annually for a 1 ,000 cow herd?

00:19:52.500 --> 00:19:54.039
Something like that. Yeah, around that number.

00:19:54.099 --> 00:19:57.000
And that significantly reduces your testing cost,

00:19:57.220 --> 00:19:59.900
bringing it closer to $18 ,000 annually, not

00:19:59.900 --> 00:20:02.960
$40 ,000 up front. It's less than half the full

00:20:02.960 --> 00:20:05.660
herd cost. And you still gain crucial information

00:20:05.660 --> 00:20:08.299
for guiding your immediate sire selection and

00:20:08.299 --> 00:20:11.299
shaping the genetic future of your herd. Seems

00:20:11.299 --> 00:20:13.779
much more practical. But here's the crucial point

00:20:13.779 --> 00:20:15.980
for farm economics, the reality check maybe.

00:20:16.180 --> 00:20:20.160
Even if you spend $18 ,000, your first year returns

00:20:20.160 --> 00:20:24.180
are projected to be modest. Very modest. Maybe

00:20:24.180 --> 00:20:27.279
$12 ,000 to $15 ,000 in reduced mortality and

00:20:27.279 --> 00:20:30.480
morbidity if things go well. So not even breaking

00:20:30.480 --> 00:20:33.279
even year one. No, this is not a short -term

00:20:33.279 --> 00:20:35.259
cash injection that pays for itself in six months.

00:20:35.339 --> 00:20:38.200
It's a long play. Why is the return so slow then?

00:20:38.299 --> 00:20:39.720
Let's break that down. Well, think about it.

00:20:39.759 --> 00:20:42.079
You are making genetic decisions today, selecting

00:20:42.079 --> 00:20:44.779
bulls based on this new trait. But those decisions

00:20:44.779 --> 00:20:47.220
don't manifest as live calves for nine months.

00:20:47.539 --> 00:20:49.970
Gestation takes time. Right. And those calves

00:20:49.970 --> 00:20:51.930
don't enter the milking string and start generating

00:20:51.930 --> 00:20:54.369
revenue or avoiding costs for another 24 months

00:20:54.369 --> 00:20:56.809
after birth. Okay, gotcha. So you are not breaking

00:20:56.809 --> 00:21:00.009
even on that $18 ,000 investment until 24 to

00:21:00.009 --> 00:21:02.390
30 months after you start breeding for the trait.

00:21:02.509 --> 00:21:04.450
And that's if everything goes right and your

00:21:04.450 --> 00:21:06.609
management protocols are already top -notch.

00:21:06.630 --> 00:21:09.109
That requires patience and a long -term strategic

00:21:09.109 --> 00:21:12.109
view. Definitely not for the impatient. Exactly.

00:21:12.210 --> 00:21:14.450
The models do suggest that the annual benefits

00:21:14.450 --> 00:21:17.990
only really start to shine, really start accumulating

00:21:17.990 --> 00:21:20.970
by year five. That's when you're milking a significant

00:21:20.970 --> 00:21:23.289
number of daughters from your highly ranked health

00:21:23.289 --> 00:21:26.150
bulls. By that time, projections hit annual recurring

00:21:26.150 --> 00:21:29.089
benefits of around $60 ,000 for that 1 ,000 cow

00:21:29.089 --> 00:21:31.829
herd. That's a significant return eventually.

00:21:32.269 --> 00:21:35.549
But, and it's a big but, that $60 ,000 payoff

00:21:35.549 --> 00:21:39.680
by year five Comes with the massive caveat we

00:21:39.680 --> 00:21:42.339
already stressed, right? Always. It assumes you

00:21:42.339 --> 00:21:44.440
are operating with solid, clean management protocols

00:21:44.440 --> 00:21:46.759
already, meaning your mortality is already down

00:21:46.759 --> 00:21:49.720
in the 3 -4 % range. For every percentage point

00:21:49.720 --> 00:21:51.819
higher your mortality is, say you are consistently

00:21:51.819 --> 00:21:54.579
at 7%, you should assume those financial projections

00:21:54.579 --> 00:21:56.539
are dramatically inflated for your situation.

00:21:56.920 --> 00:21:59.039
Because the genetic improvement simply won't

00:21:59.039 --> 00:22:01.460
stick, it won't express itself fully in a poor

00:22:01.460 --> 00:22:03.920
management environment. That's it. This technology

00:22:03.920 --> 00:22:07.160
amplifies existing efficiency. It does not create

00:22:07.160 --> 00:22:09.940
it from scratch. It's an elite tool for elite

00:22:09.940 --> 00:22:12.160
operations, or at least for those mid -level

00:22:12.160 --> 00:22:15.059
operations that are absolutely dedicated to fundamental

00:22:15.059 --> 00:22:17.559
management improvement first, get the basics

00:22:17.559 --> 00:22:20.180
right, then add the genetics, which leads us

00:22:20.180 --> 00:22:22.579
nicely into case studies and smart entry strategies

00:22:22.579 --> 00:22:24.700
because we need to talk about the existing competition

00:22:24.700 --> 00:22:28.019
in the field, the Zoetis factor. Yes, the Zoetis

00:22:28.019 --> 00:22:30.079
factor is critically important here because we

00:22:30.079 --> 00:22:32.539
are not waiting in a vacuum for CDCB's launch

00:22:32.539 --> 00:22:36.420
in April 2026. Yes. That's key. Zoetis has been

00:22:36.420 --> 00:22:38.519
offering proprietary wellness trade evaluations

00:22:38.519 --> 00:22:41.220
for many years, dating all the way back to 2016,

00:22:41.579 --> 00:22:43.799
I believe. That's right. They have a nearly decade

00:22:43.799 --> 00:22:46.380
-long head start in this specific space. Yeah,

00:22:46.539 --> 00:22:49.460
their system, which draws from hundreds of thousands

00:22:49.460 --> 00:22:52.579
of their own proprietary records, has been integrated

00:22:52.579 --> 00:22:56.480
into major AI stud catalogs for years now. Farmers

00:22:56.480 --> 00:22:58.720
using certain bull studs will already see Zoetis

00:22:58.720 --> 00:23:00.819
Wellness trait numbers. And it works reasonably

00:23:00.819 --> 00:23:03.000
well, particularly for those large operations,

00:23:03.140 --> 00:23:05.299
often in the upper Midwest and California maybe,

00:23:05.460 --> 00:23:07.779
that have been early adopters of their proprietary

00:23:07.779 --> 00:23:10.859
testing platforms like Clarified. So why bother

00:23:10.859 --> 00:23:13.720
with CDCB then if Zoetis is already integrated

00:23:13.720 --> 00:23:15.900
and seems to work? That's a fair question. It

00:23:15.900 --> 00:23:17.599
is. Well, there are several compelling reasons.

00:23:18.039 --> 00:23:21.039
First, CDCB is drawing from a broader national

00:23:21.039 --> 00:23:23.500
population through the DHI system. We're talking

00:23:23.500 --> 00:23:26.799
millions of genotypes potentially from over 15

00:23:26.799 --> 00:23:29.640
,000 herds across the country. That gives it

00:23:29.640 --> 00:23:32.460
potentially wider applicability, maybe more relevance

00:23:32.460 --> 00:23:35.460
to more diverse management systems. Second, the

00:23:35.460 --> 00:23:39.000
CDCB methodology is transparent, it's peer -reviewed,

00:23:39.000 --> 00:23:41.279
and it's standardized across the industry. Everyone

00:23:41.279 --> 00:23:44.039
gets the same calculation. Third, and this is

00:23:44.039 --> 00:23:46.440
big for many farms, if you're already participating

00:23:46.440 --> 00:23:49.220
in DHI testing, there's likely no premium pricing

00:23:49.220 --> 00:23:52.240
attached to the data retrieval for the CDCB evaluation.

00:23:52.740 --> 00:23:54.779
It's integrated into the national evaluation

00:23:54.779 --> 00:23:56.920
service you might already be paying for indirectly.

00:23:57.359 --> 00:23:59.339
Okay, but there's a discrepancy that producers

00:23:59.339 --> 00:24:01.920
absolutely must be aware of. The heritability

00:24:01.920 --> 00:24:04.000
numbers don't match up between the two systems.

00:24:04.039 --> 00:24:06.920
That's confusing. Zoetis reports about 4 .5 %

00:24:06.920 --> 00:24:09.420
heritability for scours resistance in their system,

00:24:09.480 --> 00:24:11.779
whereas the official CDCB number we discussed

00:24:11.779 --> 00:24:15.539
is 2 .6%. That is a significant difference in

00:24:15.539 --> 00:24:17.940
perceived genetic influence, almost double. It

00:24:17.940 --> 00:24:20.079
is puzzling, isn't it? The source material we

00:24:20.079 --> 00:24:22.180
looked at suggests this is likely due to different

00:24:22.180 --> 00:24:24.339
statistical approaches, maybe different ways

00:24:24.339 --> 00:24:27.079
they model the environmental effects. Or perhaps

00:24:27.079 --> 00:24:30.180
Zoetis is drawing from a population, those early

00:24:30.180 --> 00:24:32.700
adopter herds, that has managed environmental

00:24:32.700 --> 00:24:35.720
risk in a more standardized way already, thus

00:24:35.720 --> 00:24:37.880
allowing the genetic signal to be isolated more

00:24:37.880 --> 00:24:40.160
clearly in their data. Could be. It's not necessarily

00:24:40.160 --> 00:24:42.980
a contradiction, maybe, but it means they are

00:24:42.980 --> 00:24:44.920
looking at the trait through slightly different

00:24:44.920 --> 00:24:46.779
lenses, using different data pools and models.

00:24:47.150 --> 00:24:49.029
Practical implication for the farmer listening

00:24:49.029 --> 00:24:53.829
is this. Once CDCB launches in 2026, smart producers

00:24:53.829 --> 00:24:55.930
will compare the bull rankings between the two

00:24:55.930 --> 00:24:59.049
systems side by side. It's crucial. If they align,

00:24:59.289 --> 00:25:01.630
meaning bull A is ranked high for calf health

00:25:01.630 --> 00:25:04.910
by both Zoetis and CDCB Great, you have independent

00:25:04.910 --> 00:25:09.109
validation. More confidence. But if they diverge,

00:25:09.109 --> 00:25:11.230
where bull A is high on zoetis but just average

00:25:11.230 --> 00:25:14.069
on CDCB or vice versa, that's when you have to

00:25:14.069 --> 00:25:15.750
start scratching your head and figuring out which

00:25:15.750 --> 00:25:18.410
system predicts best for your specific barn environment,

00:25:18.589 --> 00:25:22.349
your management. This comparison is a free, powerful

00:25:22.349 --> 00:25:25.329
validation step you absolutely should take. Speaking

00:25:25.329 --> 00:25:28.049
of smart producers and practical steps, let's

00:25:28.049 --> 00:25:30.569
review those four practical strategies for entering

00:25:30.569 --> 00:25:33.450
the genomic calf health space, particularly for

00:25:33.450 --> 00:25:36.069
those farms who have never tested before. The

00:25:36.069 --> 00:25:39.109
goal here is risk management and on -farm validation.

00:25:39.490 --> 00:25:42.210
Okay. We covered option one, the free ride. If

00:25:42.210 --> 00:25:43.910
you're already testing, you're probably set.

00:25:44.029 --> 00:25:46.269
Just confirm it. For everyone else, we really

00:25:46.269 --> 00:25:49.109
advocate starting small and validating. So option

00:25:49.109 --> 00:25:51.730
two is the heifer -only testing. We talked about

00:25:51.730 --> 00:25:54.750
that $18 ,000 annual investment for a 1 ,000

00:25:54.750 --> 00:25:57.589
cow herd. Testing your 450 or so replacement

00:25:57.589 --> 00:26:00.069
heifers immediately gives you data on your future

00:26:00.069 --> 00:26:02.549
milking string and allows you to use those evaluations

00:26:02.549 --> 00:26:05.130
in your immediate breeding decisions. This is

00:26:05.130 --> 00:26:07.089
probably the recommended entry point for most

00:26:07.089 --> 00:26:09.569
solid mid -level farms with good management already

00:26:09.569 --> 00:26:11.809
in place. Then there's option three, the gradual

00:26:11.809 --> 00:26:14.509
build. This is excellent if you want to spread

00:26:14.509 --> 00:26:16.690
the cost out and truly validate the technology

00:26:16.690 --> 00:26:19.430
on your own farm before expanding significantly.

00:26:19.809 --> 00:26:22.890
Maybe test just 100 to 150 animals per year,

00:26:23.069 --> 00:26:26.230
heifers preferably. That costs maybe $4 ,000

00:26:26.230 --> 00:26:29.490
to $6 ,000 annually, which is much more manageable

00:26:29.490 --> 00:26:32.490
financially than $18 ,000 or $40 ,000 all at

00:26:32.490 --> 00:26:35.759
once. Much easier cash flow. Exactly. If it works

00:26:35.759 --> 00:26:37.759
well on those small cohorts after a couple of

00:26:37.759 --> 00:26:40.400
years, then you can ramp up. If it doesn't show

00:26:40.400 --> 00:26:42.759
a benefit in your system, you minimize your financial

00:26:42.759 --> 00:26:45.539
loss. This is the most conservative risk -averse

00:26:45.539 --> 00:26:48.619
approach. And finally, option four, the bulls

00:26:48.619 --> 00:26:51.740
-only approach. Zero testing costs on the female

00:26:51.740 --> 00:26:54.859
side. You simply select sires based purely on

00:26:54.859 --> 00:26:57.440
their CDCB calf health evaluations when they

00:26:57.440 --> 00:27:00.059
launch, assuming the AI studs publish them clearly.

00:27:00.299 --> 00:27:02.779
While financially easy, this is definitely suboptimal

00:27:02.779 --> 00:27:04.660
for making rapid genetic progress, isn't it?

00:27:04.720 --> 00:27:06.440
Oh, for sure. You can bring in better genetics

00:27:06.440 --> 00:27:08.440
through the sires, but you won't know which of

00:27:08.440 --> 00:27:10.259
your cows are genetically better or worse for

00:27:10.259 --> 00:27:12.759
this trait. So you can't do targeted corrective

00:27:12.759 --> 00:27:15.059
mating to maximize the genetic gain because you

00:27:15.059 --> 00:27:17.339
lack data on the maternal side. You're flying

00:27:17.339 --> 00:27:19.740
half blind. Exactly. It's a cheap entry, yes,

00:27:19.920 --> 00:27:22.259
but it leaves a massive amount of potential gain

00:27:22.259 --> 00:27:25.160
on the table. The sweet spot, we'd argue, is

00:27:25.160 --> 00:27:28.140
probably that heifer -only testing to get the

00:27:28.140 --> 00:27:30.839
foundational data needed for both selection and,

00:27:30.920 --> 00:27:33.740
crucially, validation. Okay, let's move to future

00:27:33.740 --> 00:27:35.839
implications now, because this is the challenge

00:27:35.839 --> 00:27:38.500
that nobody in the industry really wants to talk

00:27:38.500 --> 00:27:41.079
about openly, and it's the critical issue of

00:27:41.079 --> 00:27:43.980
data contribution and the risk of selection bias.

00:27:44.970 --> 00:27:47.569
If we don't get this right, the national evaluation

00:27:47.569 --> 00:27:51.029
risks failing a large segment of the dairy industry,

00:27:51.210 --> 00:27:53.700
the very farms that might need it most. This

00:27:53.700 --> 00:27:55.660
is the most serious concern for me personally,

00:27:55.740 --> 00:27:58.059
because it directly impacts whether the prediction

00:27:58.059 --> 00:28:00.099
model is actually relevant to the local producer

00:28:00.099 --> 00:28:01.759
listening right now, wherever they might be.

00:28:02.019 --> 00:28:04.940
The Canadian research by Renault et al. in 2023,

00:28:05.220 --> 00:28:07.240
which is probably similar in the U .S., estimated

00:28:07.240 --> 00:28:10.259
that only about 12 % of dairy farms systematically

00:28:10.259 --> 00:28:14.119
record calf health data in a usable format. Only

00:28:14.119 --> 00:28:17.440
12%. Think about that. 88 % of farms are essentially

00:28:17.440 --> 00:28:19.819
invisible to the geneticists building these models.

00:28:19.980 --> 00:28:22.230
Their data isn't in the system. And who are those

00:28:22.230 --> 00:28:24.569
12 % that are contributing data? Let's be real.

00:28:24.730 --> 00:28:28.150
They're typically the large, often very well

00:28:28.150 --> 00:28:30.849
-managed operations. They have dedicated calf

00:28:30.849 --> 00:28:34.069
managers. They use sophisticated automated record

00:28:34.069 --> 00:28:38.109
systems like Dairy Comp 305 or PC Dart. And they

00:28:38.109 --> 00:28:40.589
probably already have lower than average mortality

00:28:40.589 --> 00:28:43.130
rates because they focus on it. This creates

00:28:43.130 --> 00:28:45.849
massive selection bias. The evaluation is built

00:28:45.849 --> 00:28:47.849
entirely on the data coming from the genetic

00:28:47.849 --> 00:28:50.450
and managerial elite. What's fascinating and

00:28:50.450 --> 00:28:52.430
worrying here is that the genetic evaluations

00:28:52.430 --> 00:28:55.410
end up being optimized naturally for farms that

00:28:55.410 --> 00:28:57.609
look exactly like the ones contributing the data.

00:28:57.869 --> 00:28:59.710
It makes sense statistically, but it's a problem

00:28:59.710 --> 00:29:02.329
practically. Let's break down the practical implication.

00:29:02.609 --> 00:29:05.309
If, say, 80 % of the contributed health data

00:29:05.309 --> 00:29:07.390
comes from large freestyle operations in Idaho

00:29:07.390 --> 00:29:09.910
or California, where calves are housed in individual

00:29:09.910 --> 00:29:12.269
hutches under specific, often very controlled,

00:29:12.390 --> 00:29:14.849
temperature protocols, then the prediction model

00:29:14.849 --> 00:29:17.150
is inherently designed to work best in that specific

00:29:17.150 --> 00:29:20.109
environmental context. Right. So if you're a

00:29:20.109 --> 00:29:22.450
smaller operation, With a completely different

00:29:22.450 --> 00:29:24.829
management style, say, a seasonal grazing operation

00:29:24.829 --> 00:29:28.069
up in Vermont's Northeast Kingdom, where calves

00:29:28.069 --> 00:29:30.150
might be raised in small groups on pasture with

00:29:30.150 --> 00:29:32.490
different pathogen exposure. Or a traditional

00:29:32.490 --> 00:29:35.609
tie stall barn in rural Pennsylvania with totally

00:29:35.609 --> 00:29:37.509
different ventilation dynamics and maybe older

00:29:37.509 --> 00:29:41.200
facilities. The genomic prediction for a particular

00:29:41.200 --> 00:29:44.319
bull based on data from Idaho freestalls might

00:29:44.319 --> 00:29:47.039
simply fail to translate into better calf health

00:29:47.039 --> 00:29:49.420
on your farm. It might not work. The genetic

00:29:49.420 --> 00:29:51.759
merit that works in a highly controlled, high

00:29:51.759 --> 00:29:54.339
input freestall environment might not express

00:29:54.339 --> 00:29:56.960
itself or might not even be beneficial in a lower

00:29:56.960 --> 00:29:59.480
input seasonal grazing environment because the

00:29:59.480 --> 00:30:01.980
environmental stressors, the challenges are fundamentally

00:30:01.980 --> 00:30:04.660
different. This raises the absolutely critical

00:30:04.660 --> 00:30:06.940
need for broader data sharing. It's essential.

00:30:07.259 --> 00:30:09.880
For the evaluations to become truly representative

00:30:09.880 --> 00:30:12.140
and locally validated across diverse systems,

00:30:12.359 --> 00:30:14.940
producers simply must actively authorize their

00:30:14.940 --> 00:30:17.960
dairy records processing center, the DRPC, to

00:30:17.960 --> 00:30:20.799
transmit their health data to CDCB. And they

00:30:20.799 --> 00:30:22.980
need to use a specific mechanism known as Format

00:30:22.980 --> 00:30:25.160
6 for health data. And we should probably define

00:30:25.160 --> 00:30:27.259
what those acronyms mean for the average producer

00:30:27.259 --> 00:30:30.309
listening because it can sound like jargon. The

00:30:30.309 --> 00:30:32.910
DRPC is the entity that processes your official

00:30:32.910 --> 00:30:35.470
DHI records. They're the keepers of that massive

00:30:35.470 --> 00:30:37.549
repository of data about your cows' production,

00:30:37.789 --> 00:30:41.190
reproduction, etc. Format 6 is simply the standardized

00:30:41.190 --> 00:30:43.769
digital format used to transmit specific animal

00:30:43.769 --> 00:30:47.130
health event codes, like a code for scours, often

00:30:47.130 --> 00:30:50.690
O1, or respiratory disease, O2, from your farm

00:30:50.690 --> 00:30:53.089
management software, like DairyComp, to the National

00:30:53.089 --> 00:30:57.210
Genetic Database at CDCB. It is the crucial digital

00:30:57.210 --> 00:30:59.829
bridge between your specific farm data and the

00:30:59.829 --> 00:31:02.410
national CDCB model. Without it, your farm's

00:31:02.410 --> 00:31:04.509
experience doesn't count. And we know, based

00:31:04.509 --> 00:31:06.829
on conversations with DRPC folks and industry

00:31:06.829 --> 00:31:09.630
insiders, that participation in Format 6 data

00:31:09.630 --> 00:31:12.569
sharing is currently, well, lower than ideal

00:31:12.569 --> 00:31:14.450
is the polite way to put it. Yeah, very low in

00:31:14.450 --> 00:31:16.769
some areas. It's a vicious cycle, isn't it? If

00:31:16.769 --> 00:31:18.730
you're a smaller farm or have a unique system,

00:31:18.970 --> 00:31:21.049
you desperately need the data to be validated

00:31:21.049 --> 00:31:22.890
in your local environment so the predictions

00:31:22.890 --> 00:31:25.599
work for you. But if you don't contribute your

00:31:25.599 --> 00:31:27.859
data, the system will never include your environment

00:31:27.859 --> 00:31:30.940
in the validation pool, and the bias just persists

00:31:30.940 --> 00:31:33.640
or gets worse. And this risk of selection bias

00:31:33.640 --> 00:31:36.279
also ties into a wider industry gap we've unfortunately

00:31:36.279 --> 00:31:38.720
seen develop since genomic selection really took

00:31:38.720 --> 00:31:41.720
off back in 2009. Research consistently shows

00:31:41.720 --> 00:31:45.019
that large herds, those over 500 cows, have progressed

00:31:45.019 --> 00:31:47.619
genetically much faster than small herds, those

00:31:47.619 --> 00:31:50.680
under 100 cows. The genetic merit gap between

00:31:50.680 --> 00:31:53.019
large and small herds has widened significantly.

00:31:53.640 --> 00:31:56.539
not narrowed over the last 15 years. And this

00:31:56.539 --> 00:31:58.980
new calf health trait, as important as it is,

00:31:59.059 --> 00:32:02.420
risks widening that gap even further. Why? Because

00:32:02.420 --> 00:32:04.579
successful implementation requires two things

00:32:04.579 --> 00:32:07.400
that aren't equally distributed. First, detailed,

00:32:07.519 --> 00:32:09.700
rigorous, consistent record keeping, which takes

00:32:09.700 --> 00:32:12.519
time and often technology. And second, often

00:32:12.519 --> 00:32:15.160
that initial investment, that $18 ,000 maybe

00:32:15.160 --> 00:32:17.119
for heifer testing, which is a bigger hurdle

00:32:17.119 --> 00:32:19.700
for smaller operations. The massive specialized

00:32:19.700 --> 00:32:22.140
calf raising facilities in places like the central

00:32:22.140 --> 00:32:24.680
sands of Wisconsin or the huge automated dairies

00:32:24.680 --> 00:32:28.740
down in Texas are simply better positions structurally

00:32:28.740 --> 00:32:30.920
and financially to benefit more rapidly from

00:32:30.920 --> 00:32:34.039
this. than, say, the traditional 80 -cow mixed

00:32:34.039 --> 00:32:36.299
farms in Michigan or Ohio. Right. It's those

00:32:36.299 --> 00:32:39.259
mid -sized operations, maybe the 300 to 800 -cow

00:32:39.259 --> 00:32:41.900
herds. They're the ones who truly have to wrestle

00:32:41.900 --> 00:32:44.000
with this decision the hardest, I think. They

00:32:44.000 --> 00:32:46.220
have significant potential losses, but they also

00:32:46.220 --> 00:32:49.599
face that real risk. Is the $18 ,000 investment

00:32:49.599 --> 00:32:52.579
worth the risk of selection bias if my specific

00:32:52.579 --> 00:32:54.599
environment isn't well represented in the data

00:32:54.599 --> 00:32:56.819
model yet? They need to be extremely cautious,

00:32:57.099 --> 00:32:59.240
ask the right questions, and they absolutely

00:32:59.240 --> 00:33:05.559
need to implement rigorous... That transition

00:33:05.559 --> 00:33:08.019
leads us perfectly into our contrary intake section.

00:33:08.200 --> 00:33:10.579
How exactly do you know if this technology is

00:33:10.579 --> 00:33:12.400
actually working for your specific farm, given

00:33:12.400 --> 00:33:14.599
all this potential for data bias and the fundamentally

00:33:14.599 --> 00:33:17.960
low heritability we discussed? The answer, bluntly,

00:33:18.000 --> 00:33:20.880
is that the technology requires obsessive record

00:33:20.880 --> 00:33:22.990
keeping, and we mean that literally. No cutting

00:33:22.990 --> 00:33:25.049
corners. That's the first hurdle, and it's a

00:33:25.049 --> 00:33:28.130
big one for many farms. If you want to use genetics

00:33:28.130 --> 00:33:30.869
to drive improvement in a low heritability trait

00:33:30.869 --> 00:33:33.710
like this, you need a quantifiable baseline,

00:33:34.150 --> 00:33:36.750
period. You cannot manage what you don't measure.

00:33:37.269 --> 00:33:39.190
It's an old saying, but it's absolutely true

00:33:39.190 --> 00:33:41.529
here. That means you must document every health

00:33:41.529 --> 00:33:43.849
event consistently, not just when a calf dies,

00:33:44.230 --> 00:33:47.190
but every single time you treat for scours, every

00:33:47.190 --> 00:33:49.569
diagnosed case of pneumonia, every time you use

00:33:49.569 --> 00:33:52.519
an antibiotic for a calf issue. Log it all. And

00:33:52.519 --> 00:33:54.400
crucially, you must also document the healthy

00:33:54.400 --> 00:33:57.099
calves accurately to create a necessary control

00:33:57.099 --> 00:34:00.119
baseline for comparison. You need denominators,

00:34:00.140 --> 00:34:02.779
not just numerators. If you can't consistently

00:34:02.779 --> 00:34:05.920
record everything accurately, you'll never be

00:34:05.920 --> 00:34:08.260
able to reliably validate whether the genetics

00:34:08.260 --> 00:34:10.059
are actually making a difference on your specific

00:34:10.059 --> 00:34:12.679
operation. You'll just be guessing. OK, so assuming

00:34:12.679 --> 00:34:14.599
you can commit to that level of record keeping,

00:34:14.780 --> 00:34:17.619
we need concrete checkpoints. Let's outline the

00:34:17.619 --> 00:34:20.039
necessary validation timeline that every farmer

00:34:20.039 --> 00:34:22.130
should adopt if they decide to invest. time or

00:34:22.130 --> 00:34:24.469
money in this technology, starting from the April

00:34:24.469 --> 00:34:27.570
2026 launch. Right. The first critical checkpoint

00:34:27.570 --> 00:34:30.210
comes around 12 to 18 months after you start

00:34:30.210 --> 00:34:33.570
consistently using the top health sires. So we're

00:34:33.570 --> 00:34:36.730
looking at late 2027 or early 2028, depending

00:34:36.730 --> 00:34:38.610
on your calving interval and when you adopt it.

00:34:38.690 --> 00:34:40.630
This is when you'll have enough calves born from

00:34:40.630 --> 00:34:42.889
the genetically superior bulls to make a fair

00:34:42.889 --> 00:34:44.690
comparison against the calves born during the

00:34:44.690 --> 00:34:47.469
same period from your average sires or your historical

00:34:47.469 --> 00:34:49.840
baseline if your records are good. What is the

00:34:49.840 --> 00:34:52.039
target you should be looking for then? What's

00:34:52.039 --> 00:34:54.519
a realistic expectation for improvement given

00:34:54.519 --> 00:34:57.760
the low 2 -3 % heritability? You should be aiming

00:34:57.760 --> 00:34:59.840
to see the calves from your top genetic health

00:34:59.840 --> 00:35:03.460
sires, showing maybe 20 % to 30 % lower disease

00:35:03.460 --> 00:35:05.739
incidence rates compared to the calves from your

00:35:05.739 --> 00:35:08.599
average sires. Note we set the target relatively

00:35:08.599 --> 00:35:11.699
high, a 20 -30 % reduction in... incidents, not

00:35:11.699 --> 00:35:13.699
overall mortality, because of the huge economic

00:35:13.699 --> 00:35:16.519
value of avoiding morbidity, the sick but survived

00:35:16.519 --> 00:35:19.000
calves we talked about. Reducing treatments saves

00:35:19.000 --> 00:35:21.860
money directly. If you run that comparison on

00:35:21.860 --> 00:35:24.079
your own data, and there's no measurable difference

00:35:24.079 --> 00:35:26.940
in scours or respiratory incidents, if the rates

00:35:26.940 --> 00:35:28.800
are identical between the high genetic group

00:35:28.800 --> 00:35:31.159
and the average group, then the prediction model

00:35:31.159 --> 00:35:33.860
is likely not working effectively for your specific

00:35:33.860 --> 00:35:36.019
barn environment, and you need to adjust your

00:35:36.019 --> 00:35:38.460
breeding strategy immediately. Don't keep throwing

00:35:38.460 --> 00:35:41.329
money at it. And we also need to watch for those

00:35:41.329 --> 00:35:43.969
unexpected negative correlations at this 12 to

00:35:43.969 --> 00:35:46.389
18 month checkpoint, right? Yeah. The unintended

00:35:46.389 --> 00:35:49.050
consequences. Absolutely critical. Are those

00:35:49.050 --> 00:35:51.289
genetically healthier calves perhaps growing

00:35:51.289 --> 00:35:53.429
slower than expected? Are their birth weights

00:35:53.429 --> 00:35:55.730
creeping up too high, leading to more calving

00:35:55.730 --> 00:35:58.309
difficulty for the heifers? Genomic selection,

00:35:58.690 --> 00:36:01.349
if applied narrowly or incorrectly, can create

00:36:01.349 --> 00:36:03.469
new problems just as easily as it solves old

00:36:03.469 --> 00:36:06.269
ones, particularly if the selection index isn't

00:36:06.269 --> 00:36:08.889
balanced or if the trait is linked to unin...

00:36:08.940 --> 00:36:11.099
intended metabolic pathways we don't fully understand

00:36:11.099 --> 00:36:15.679
yet. Watch closely. Okay, the second checkpoint

00:36:15.679 --> 00:36:18.460
is around 24 to 30 months after implementation.

00:36:18.880 --> 00:36:21.219
This is your financial check. Time to look at

00:36:21.219 --> 00:36:23.480
the dollars and cents. You should be approaching

00:36:23.480 --> 00:36:25.139
that break -even point we discussed earlier,

00:36:25.340 --> 00:36:27.219
especially if you made an initial investment

00:36:27.219 --> 00:36:30.639
like the $18k heifer testing. You need to calculate

00:36:30.639 --> 00:36:33.699
not just potentially reduced mortality which

00:36:33.699 --> 00:36:35.599
is hard to prove statistically in the short term,

00:36:35.699 --> 00:36:38.199
but more tangibly, reduce treatment costs and

00:36:38.199 --> 00:36:40.460
reduce labor costs associated with sick calves.

00:36:40.719 --> 00:36:43.599
Are you buying less medicine? Spending less time

00:36:43.599 --> 00:36:45.719
treating? Unmeasurable savings. If you're still

00:36:45.719 --> 00:36:47.900
deep in the red financially after 30 months,

00:36:48.059 --> 00:36:50.320
it might be time to seriously consider pulling

00:36:50.320 --> 00:36:52.739
the plug on the specific genomic investment strategy

00:36:52.739 --> 00:36:55.239
and redirecting that capital back into core management

00:36:55.239 --> 00:36:57.860
fixes or other technologies with faster ROI.

00:36:58.320 --> 00:37:00.860
The return must be demonstrable within about

00:37:00.860 --> 00:37:03.989
30 months. and the ultimate definitive test comes

00:37:03.989 --> 00:37:07.730
later around 36 to 42 months that's three to

00:37:07.730 --> 00:37:10.070
three and a half years out this is when those

00:37:10.070 --> 00:37:12.309
first heifers conceived using your high health

00:37:12.309 --> 00:37:15.610
sires actually enter the milking string this

00:37:15.610 --> 00:37:17.750
is when you check their actual performance production

00:37:17.750 --> 00:37:20.070
and fertility especially against their herd mates

00:37:20.070 --> 00:37:22.250
born from average sires during the same period

00:37:22.730 --> 00:37:25.510
Exactly. If their production is significantly

00:37:25.510 --> 00:37:28.369
below their genetic predictions, or if their

00:37:28.369 --> 00:37:31.530
fertility is tanking, if they're failing to breed

00:37:31.530 --> 00:37:33.590
back on time compared to their contemporaries,

00:37:33.670 --> 00:37:36.150
then you're seeing those dreaded antagonistic

00:37:36.150 --> 00:37:38.469
correlations emerge in your herd. That's proof.

00:37:38.710 --> 00:37:40.710
That means the genetic improvement you thought

00:37:40.710 --> 00:37:42.750
you were getting in calf health came at a hidden,

00:37:42.889 --> 00:37:45.650
economically damaging cost in another, potentially

00:37:45.650 --> 00:37:47.909
more valuable trait like milk or reproduction.

00:37:48.530 --> 00:37:51.050
If you see that pattern emerge, you must aggressively

00:37:51.050 --> 00:37:53.739
adjust your sire selection index or your emphasis

00:37:53.739 --> 00:37:55.920
on this trait because the promised independence

00:37:55.920 --> 00:37:58.780
benefit failed to hold true in your specific

00:37:58.780 --> 00:38:01.000
herd environment or with the bulls you chose.

00:38:01.179 --> 00:38:04.139
So given all these risks, the low heritability,

00:38:04.440 --> 00:38:07.539
the data bias, the need for obsessive records,

00:38:07.639 --> 00:38:10.539
a long validation timeline, the potential negative

00:38:10.539 --> 00:38:13.460
correlations, the practical implementation strategy

00:38:13.460 --> 00:38:16.059
must be fundamentally conservative, shouldn't

00:38:16.059 --> 00:38:18.139
it? You don't just jump in feet first. Absolutely

00:38:18.139 --> 00:38:21.380
not. That would be foolish. When April 2026 rolls

00:38:21.380 --> 00:38:23.139
around and these evaluations become available,

00:38:23.420 --> 00:38:26.659
our advice would be use the top -ranked calf

00:38:26.659 --> 00:38:30.079
health sires for maybe only 20 % to 30 % of your

00:38:30.079 --> 00:38:33.019
breedings initially, not 100%. That's enough

00:38:33.019 --> 00:38:35.659
to establish a good, statistically relevant validation

00:38:35.659 --> 00:38:38.460
cohort on your farm without betting the entire

00:38:38.460 --> 00:38:40.940
future genetic base of your herd on an unproven

00:38:40.940 --> 00:38:43.139
concept in your specific environment. Furthermore,

00:38:43.300 --> 00:38:46.199
a crucial point, stick with proven high -reliability

00:38:46.199 --> 00:38:48.360
bulls, especially at first. Good point. This

00:38:48.360 --> 00:38:50.840
is not the time to gamble on unproven young sires

00:38:50.840 --> 00:38:53.480
with low reliability for this new low heritability

00:38:53.480 --> 00:38:55.619
trait. We need reliability estimates well above

00:38:55.619 --> 00:38:58.179
50%, preferably higher, for this new trait to

00:38:58.179 --> 00:38:59.800
have even moderate confidence in the prediction

00:38:59.800 --> 00:39:02.920
actually working. Use proven sires. Okay. Before

00:39:02.920 --> 00:39:05.659
any producer commits to the $18 ,000 or $40 ,000

00:39:05.659 --> 00:39:07.780
investment, Or frankly, even if they're getting

00:39:07.780 --> 00:39:10.219
the evaluations for free as part of their existing

00:39:10.219 --> 00:39:14.159
testing, they must ask their dairy records processing

00:39:14.159 --> 00:39:16.880
center, their DRPC, these three key questions.

00:39:17.019 --> 00:39:20.659
Get answers before 2026. Question one, and this

00:39:20.659 --> 00:39:23.320
addresses the selection bias risk directly. Ask

00:39:23.320 --> 00:39:25.699
them, what percentage of herds in our specific

00:39:25.699 --> 00:39:28.079
region are actually contributing calf health

00:39:28.079 --> 00:39:31.440
data using Format 6 right now? Be specific about

00:39:31.440 --> 00:39:33.539
your region. If that number is really low, say

00:39:33.539 --> 00:39:36.920
below 20 % or even 30 % in your area, you should

00:39:36.920 --> 00:39:38.860
have major skepticism about whether the national

00:39:38.860 --> 00:39:40.960
prediction is robust and relevant for your local

00:39:40.960 --> 00:39:42.800
climate, housing, and management environment.

00:39:43.239 --> 00:39:45.420
If very few local farms are contributing, the

00:39:45.420 --> 00:39:47.199
prediction might be essentially irrelevant to

00:39:47.199 --> 00:39:49.949
you, statistically speaking. Good question. Question

00:39:49.949 --> 00:39:52.630
two, ask them, can you show me a side -by -side

00:39:52.630 --> 00:39:55.630
comparison of the upcoming CDCB rankings and

00:39:55.630 --> 00:39:58.349
the current Zoetis Wellness rankings for the

00:39:58.349 --> 00:40:00.230
bulls we are currently using or considering?

00:40:00.449 --> 00:40:02.630
This tells you whether the two biggest players

00:40:02.630 --> 00:40:04.909
in the space agree or disagree on the genetic

00:40:04.909 --> 00:40:08.429
merit for health for specific bulls. Significant

00:40:08.429 --> 00:40:10.690
disagreement requires extreme caution and further

00:40:10.690 --> 00:40:13.030
investigation. And question three, the practical

00:40:13.030 --> 00:40:15.929
one. What is the actual process, the steps involved,

00:40:16.230 --> 00:40:17.989
and the potential cost for setting up format

00:40:17.989 --> 00:40:20.590
six health data transmission from our specific

00:40:20.590 --> 00:40:23.909
herd management software? If you use a system

00:40:23.909 --> 00:40:26.230
like Dairy Comp 305, it might require different

00:40:26.230 --> 00:40:28.210
modules, different setup costs, or different

00:40:28.210 --> 00:40:30.449
technician visits than if you use PC DART or

00:40:30.449 --> 00:40:33.230
another system. Know your potential upgrade path

00:40:33.230 --> 00:40:35.630
and labor costs up front to ensure you can provide

00:40:35.630 --> 00:40:37.769
the consistent, accurate data needed for your

00:40:37.769 --> 00:40:40.469
own validation efforts down the road. And here's

00:40:40.469 --> 00:40:42.510
the bonus question, maybe the most important

00:40:42.510 --> 00:40:45.289
one for those already testing. Get it in writing,

00:40:45.349 --> 00:40:48.940
if possible, from your DRPC or DHI center. If

00:40:48.940 --> 00:40:51.119
I'm already genomic testing my heifers, will

00:40:51.119 --> 00:40:53.800
my historical tests automatically receive these

00:40:53.800 --> 00:40:57.739
new CDCB calf health evaluations in April 2026

00:40:57.739 --> 00:41:01.539
at no extra charge based on expected international

00:41:01.539 --> 00:41:04.519
precedent? Yeah, nail that down. Get that assurance

00:41:04.519 --> 00:41:06.719
now so you know if you are indeed getting the

00:41:06.719 --> 00:41:09.639
free ride or if there might be unexpected fees

00:41:09.639 --> 00:41:12.219
associated with accessing the new data on old

00:41:12.219 --> 00:41:14.639
samples. Okay, that sets up our actionable insights

00:41:14.639 --> 00:41:17.849
segment perfectly. Covered a ton of ground. are

00:41:17.849 --> 00:41:19.489
listening, just finished morning chores. They're

00:41:19.489 --> 00:41:21.110
driving to the feed store, maybe listening to

00:41:21.110 --> 00:41:23.210
this. What are the three things they need to

00:41:23.210 --> 00:41:25.730
take action on from this entire discussion? Immediate,

00:41:25.730 --> 00:41:28.809
medium, and long -term actions. Okay. Immediate

00:41:28.809 --> 00:41:30.710
action. And this is for this week, right now.

00:41:31.030 --> 00:41:33.909
Perform an honest, rigorous, data -driven assessment

00:41:33.909 --> 00:41:36.449
of your current pre -weaning mortality rate.

00:41:36.670 --> 00:41:39.909
Be brutally honest. If it's consistently over

00:41:39.909 --> 00:41:43.230
5%, your investment focus, your time and money

00:41:43.230 --> 00:41:45.829
must be only on management fixes first. Full

00:41:45.829 --> 00:41:49.519
stop. Colostrum quality in delivery. Ventilation,

00:41:49.739 --> 00:41:52.639
sanitation protocols, maybe staffing or training.

00:41:53.079 --> 00:41:55.800
Genetics are wasted money if you're above 5%.

00:41:55.800 --> 00:41:58.820
Stop looking at the bull catalog and start looking

00:41:58.820 --> 00:42:01.300
critically at your calf barn protocols and environment.

00:42:01.869 --> 00:42:04.070
Second immediate action, and this is for everyone

00:42:04.070 --> 00:42:06.010
listening, regardless of your current mortality

00:42:06.010 --> 00:42:08.469
rate. Call your dairy records processing center

00:42:08.469 --> 00:42:11.889
today or this week. Authorize Format 6 data transmission

00:42:11.889 --> 00:42:13.630
for calf health records. If you haven't already,

00:42:13.829 --> 00:42:16.130
find out how to do it. You need to start contributing

00:42:16.130 --> 00:42:18.150
to the national data pool to help improve its

00:42:18.150 --> 00:42:20.250
accuracy and relevance for everyone, including

00:42:20.250 --> 00:42:22.530
yourself eventually. And you need to ensure your

00:42:22.530 --> 00:42:24.409
own data systems are ready for the transition

00:42:24.409 --> 00:42:27.170
if you plan to use these evaluations. This step

00:42:27.170 --> 00:42:29.730
costs nothing but time and is vital for the industry's

00:42:29.730 --> 00:42:31.849
future success with this. Okay, medium -term

00:42:31.849 --> 00:42:34.130
strategy. Looking out over the next three to

00:42:34.130 --> 00:42:36.369
six months, maybe planning for next year's budget

00:42:36.369 --> 00:42:38.969
cycle. If your mortality is already in that sweet

00:42:38.969 --> 00:42:42.469
spot, say 3 % to 4 % consistently, and you are

00:42:42.469 --> 00:42:45.530
not currently genomic testing, you need to seriously

00:42:45.530 --> 00:42:48.349
consider budgeting for that $18 ,000 heifer -only

00:42:48.349 --> 00:42:50.550
testing option. Think of it as your validation

00:42:50.550 --> 00:42:52.969
ticket, your entry fee to see if this works for

00:42:52.969 --> 00:42:56.190
you. At the same time, starting now, establish

00:42:56.190 --> 00:42:58.650
those consistent, detailed, obsessive record

00:42:58.650 --> 00:43:00.610
-keeping protocols for all calf health events

00:43:00.610 --> 00:43:03.510
immediately. Include healthy calves as controls.

00:43:03.769 --> 00:43:06.130
You need a clean, consistent baseline before

00:43:06.130 --> 00:43:08.849
the evaluations even launch. And the long -term

00:43:08.849 --> 00:43:11.010
positioning, looking ahead to the April 2026

00:43:11.010 --> 00:43:13.389
launch and the years beyond. When the evaluations

00:43:13.389 --> 00:43:15.969
finally drop, integrate them cautiously and strategically.

00:43:16.510 --> 00:43:19.650
Don't go all in. Only use top -ranked calf health

00:43:19.650 --> 00:43:22.469
sires for maybe 20 % to 30 % of your breedings

00:43:22.469 --> 00:43:24.889
initially. And as we said, always stick to sires

00:43:24.889 --> 00:43:27.489
with high reliability, preferably above 50 %

00:43:27.489 --> 00:43:29.130
or even higher for this trait, especially in

00:43:29.130 --> 00:43:31.289
the first couple of years. Select defensively.

00:43:31.329 --> 00:43:33.849
And maybe most importantly in the long term,

00:43:33.949 --> 00:43:36.710
commit mentally and operationally to that 18

00:43:36.710 --> 00:43:39.570
-month and 36 -month validation checkpoint schedule

00:43:39.570 --> 00:43:42.389
we outlined. You have to track your own results.

00:43:42.610 --> 00:43:45.579
You must know. based on data from your own farm,

00:43:45.679 --> 00:43:48.039
whether the technology is actually working in

00:43:48.039 --> 00:43:50.179
your environment and whether you are seeing that

00:43:50.179 --> 00:43:53.179
targeted 20 -30 % reduction in disease incidence

00:43:53.179 --> 00:43:55.940
before you expand its use further. If you don't

00:43:55.940 --> 00:43:58.639
validate rigorously, you risk throwing thousands

00:43:58.639 --> 00:44:00.940
of dollars year after year at a technology that

00:44:00.940 --> 00:44:03.039
might be biased against your specific farm environment

00:44:03.039 --> 00:44:06.179
or worse, one that may be introducing antagonistic

00:44:06.179 --> 00:44:08.320
correlations you didn't anticipate and are hurting

00:44:08.320 --> 00:44:11.159
your bottom line elsewhere. This has been a massive

00:44:11.159 --> 00:44:13.940
deep dive, hasn't it, into what is arguably the

00:44:13.940 --> 00:44:16.699
most economically impactful new genomic trait

00:44:16.699 --> 00:44:18.900
we've seen introduced since maybe longevity or

00:44:18.900 --> 00:44:21.059
fertility traits came online a decade or more

00:44:21.059 --> 00:44:24.300
ago. It tackles that potential $350 ,000 problem

00:44:24.300 --> 00:44:26.960
head on, which is huge. The technology is real.

00:44:27.059 --> 00:44:30.179
The science seems sound. And the potential $60

00:44:30.179 --> 00:44:33.280
,000 annual benefit by year five is certainly

00:44:33.280 --> 00:44:36.650
compelling for well -managed herds. But its success

00:44:36.650 --> 00:44:39.550
hinges entirely on matching the tool to the specific

00:44:39.550 --> 00:44:42.489
farm situation and, critically, having those

00:44:42.489 --> 00:44:45.829
robust management basics locked down first. It's

00:44:45.829 --> 00:44:48.030
not magic. If you have questions about this,

00:44:48.130 --> 00:44:50.289
or if you want to share your validation journey

00:44:50.289 --> 00:44:53.329
once these traits launch in 2026, send your feedback

00:44:53.329 --> 00:44:55.969
to us at editorial at thebullvine .com. We'd

00:44:55.969 --> 00:44:57.889
love to hear producer experiences. And seriously,

00:44:58.130 --> 00:45:00.929
subscribe wherever you get your podcasts. We're

00:45:00.929 --> 00:45:02.909
releasing episodes twice weekly now covering

00:45:02.909 --> 00:45:06.329
timely topics. Head over to www .thebullvine

00:45:06.329 --> 00:45:08.750
.com for more analysis that challenges the status

00:45:08.750 --> 00:45:11.469
quo and provides actionable advice based on the

00:45:11.469 --> 00:45:13.989
hard numbers, not the hype. And trust me, you

00:45:13.989 --> 00:45:15.369
don't want to miss what we've got coming next

00:45:15.369 --> 00:45:17.409
week. We'll be talking about the structural changes

00:45:17.409 --> 00:45:19.949
affecting US market dairy contracts and how they

00:45:19.949 --> 00:45:21.969
might impact your forward pricing strategy heading

00:45:21.969 --> 00:45:24.210
into next year. We'll be breaking down the forces

00:45:24.210 --> 00:45:26.630
shaping your milk check next time. Thanks for

00:45:26.630 --> 00:45:28.349
joining us for another deep dive from the Bullvine

00:45:28.349 --> 00:45:29.769
podcast. We'll see you next time.