WEBVTT

00:00:11.720 --> 00:00:15.060
Breaking free from the chains of the past Where

00:00:15.060 --> 00:00:18.820
truth moves faster than a Holstein calf No law

00:00:18.820 --> 00:00:21.660
waiting on some printed page We're charting new

00:00:21.660 --> 00:00:25.300
ground in the digital age From genomic codes

00:00:25.300 --> 00:00:29.120
to robot facts We cut through the noise, no hold

00:00:29.280 --> 00:00:32.460
them back not your daddy's dairy news tonight

00:00:32.460 --> 00:01:02.679
we're sparking Welcome to the Bullvine Podcast,

00:01:03.140 --> 00:01:05.359
where we dig deeper into the stories shaping

00:01:05.359 --> 00:01:08.439
dairy's future. I'm your host, and today we're

00:01:08.439 --> 00:01:10.439
tackling something that's been hiding in plain

00:01:10.439 --> 00:01:14.140
sight across the industry, the genetic gatekeeping

00:01:14.140 --> 00:01:16.700
that's quietly costing dairy producers millions

00:01:16.700 --> 00:01:19.920
while concentrating power in the hands of just

00:01:19.920 --> 00:01:23.299
four major companies. We're diving into genetic

00:01:23.299 --> 00:01:26.500
gatekeepers, the high -stakes gamble of dairy's

00:01:26.500 --> 00:01:29.920
elite bloodlines, an investigation that reveals

00:01:29.920 --> 00:01:33.579
how every 1 % increase in inbreeding is costing

00:01:33.579 --> 00:01:37.920
you $23 per cow, and most herds don't even know

00:01:37.920 --> 00:01:40.900
their levels. If you're a progressive producer

00:01:40.900 --> 00:01:43.239
who wants to understand what's really driving

00:01:43.239 --> 00:01:45.659
your breeding decisions and how to protect your

00:01:45.659 --> 00:01:48.739
herd's future profitability, this episode is

00:01:48.739 --> 00:01:52.140
essential listening. Let's dive in. Welcome to

00:01:52.140 --> 00:01:54.120
the Deep Dive, brought to you by The Bullvine.

00:01:54.340 --> 00:01:56.540
This is where we sift through the latest articles,

00:01:56.819 --> 00:01:59.019
research, and insights to bring you the knowledge

00:01:59.019 --> 00:02:01.239
that truly matters for dairy producers. And our

00:02:01.239 --> 00:02:03.500
goal really is always to cut through the noise,

00:02:03.659 --> 00:02:05.780
you know, to give you those essential nuggets

00:02:05.780 --> 00:02:08.520
of information presented with clarity and maybe

00:02:08.520 --> 00:02:10.759
a bit of a fresh perspective. Yeah, we want to

00:02:10.759 --> 00:02:12.800
arm you with the insights you need to make informed

00:02:12.800 --> 00:02:15.919
decisions for your operation. Absolutely. Today,

00:02:16.020 --> 00:02:18.830
we're embarking on a crucial deep dive. We're

00:02:18.830 --> 00:02:20.530
looking at a feature article from the Bullvine

00:02:20.530 --> 00:02:23.189
that's sparked a lot of discussion across the

00:02:23.189 --> 00:02:26.990
industry. Our mission is to dissect the incredible

00:02:26.990 --> 00:02:29.789
gains that genomic selection has brought to dairy.

00:02:29.949 --> 00:02:32.469
That part's undeniable. Right. Huge gains. But

00:02:32.469 --> 00:02:35.889
maybe more importantly, we need to uncover a

00:02:35.889 --> 00:02:39.650
less talked about yet equally profound consequence.

00:02:40.469 --> 00:02:44.430
A rapid and frankly accelerating rate of inbreeding.

00:02:44.530 --> 00:02:46.530
It's a huge paradox, isn't it? It really is.

00:02:46.919 --> 00:02:48.539
And we're here to unpack what we're calling the

00:02:48.539 --> 00:02:50.900
inbreeding tax. And it's a conversation that's

00:02:50.900 --> 00:02:53.960
more relevant now than ever. I mean, when genomics

00:02:53.960 --> 00:02:56.500
first hit the scene, the excitement was just

00:02:56.500 --> 00:02:59.960
palpable. You could feel it. And for good reason.

00:03:00.120 --> 00:03:03.659
It promised and honestly, it delivered unparalleled

00:03:03.659 --> 00:03:06.610
genetic progress. faster than anyone really thought

00:03:06.610 --> 00:03:09.289
possible. But, you know, as we've seen with so

00:03:09.289 --> 00:03:11.270
many revolutions, there are often these unintended

00:03:11.270 --> 00:03:13.770
side effects, these consequences that only really

00:03:13.770 --> 00:03:15.770
become clear with time. Right, so it's 2020,

00:03:15.990 --> 00:03:18.789
right? Exactly. So we'll be exploring the data,

00:03:18.870 --> 00:03:21.509
looking at the economic impact, and perhaps most

00:03:21.509 --> 00:03:24.409
importantly, what you, the producer listening,

00:03:24.669 --> 00:03:26.830
can actually do about it. That's right. We're

00:03:26.830 --> 00:03:29.729
going to dive headfirst into this powerful paradox.

00:03:30.370 --> 00:03:33.310
How the very thing that supercharged our herds

00:03:33.310 --> 00:03:38.240
might now be... quietly, almost silently, costing

00:03:38.240 --> 00:03:40.900
them. So let's start by setting the scene for

00:03:40.900 --> 00:03:44.719
those incredible games. Before, say, 2009, the

00:03:44.719 --> 00:03:47.060
dairy industry operated in a completely different

00:03:47.060 --> 00:03:49.620
genetic universe. For those perhaps newer to

00:03:49.620 --> 00:03:51.819
the industry, or maybe just as a reminder for

00:03:51.819 --> 00:03:55.620
everyone, what did that pre -genomic era, the

00:03:55.620 --> 00:03:58.520
one defined by progeny testing, truly look like

00:03:58.520 --> 00:04:00.939
on the ground? Well, it was a world that demanded

00:04:00.939 --> 00:04:04.759
immense patience. And significant capital investment.

00:04:04.939 --> 00:04:07.699
A lot of money tied up. Right. To identify a

00:04:07.699 --> 00:04:10.520
truly superior sire back then, you basically

00:04:10.520 --> 00:04:12.800
embarked on what felt like a multi -year gamble.

00:04:13.139 --> 00:04:16.279
You'd breed a young, totally unproven bull to

00:04:16.279 --> 00:04:18.759
a large number of cows. A leap of faith, really.

00:04:18.920 --> 00:04:21.240
Absolutely. And then you'd wait and wait and

00:04:21.240 --> 00:04:24.379
wait some more. For years! Yeah, years. You'd

00:04:24.379 --> 00:04:26.360
wait for his daughters to be born, to grow up,

00:04:26.379 --> 00:04:29.540
to mature, and critically, to complete at least

00:04:29.540 --> 00:04:31.800
part of their first lactation. Only then could

00:04:31.800 --> 00:04:33.759
you get the actual data. Only then. After all

00:04:33.759 --> 00:04:35.959
that time, usually like five to seven years,

00:04:36.079 --> 00:04:38.579
could you finally collect and analyze their performance

00:04:38.579 --> 00:04:40.899
data. You know, measure their milk, their fat,

00:04:41.000 --> 00:04:43.920
their protein, check calving ease, all of that.

00:04:44.040 --> 00:04:47.120
Wow. Five to seven years. It was a slow, very

00:04:47.120 --> 00:04:49.420
methodical process and incredibly expensive,

00:04:49.600 --> 00:04:52.220
as you can imagine. So that sheer time commitment,

00:04:52.259 --> 00:04:55.779
it naturally limited how quickly anyone Bull's

00:04:55.779 --> 00:04:57.839
genetics could saturate the population, didn't

00:04:57.839 --> 00:05:00.420
it? Precisely. It was inefficient, sure, if you're

00:05:00.420 --> 00:05:02.439
looking purely at speed of progress. But it had

00:05:02.439 --> 00:05:05.120
a side effect. Yeah, it almost served as an inherent,

00:05:05.300 --> 00:05:08.199
albeit maybe a bit clunky, break on genetic concentration.

00:05:08.519 --> 00:05:11.459
It just sort of kept inbreeding somewhat in check

00:05:11.459 --> 00:05:13.399
by default. Because you couldn't flood the market

00:05:13.399 --> 00:05:16.939
instantly. Exactly. That multi -year wait to

00:05:16.939 --> 00:05:20.660
prove a sire naturally restricted how widely

00:05:20.660 --> 00:05:22.879
and quickly any single bull's genetics could

00:05:22.879 --> 00:05:26.259
spread. It was a bottleneck for sure, but maybe

00:05:26.259 --> 00:05:29.319
think of it as a slow and relatively wide one.

00:05:29.459 --> 00:05:31.199
Okay. Because you simply couldn't get enough

00:05:31.199 --> 00:05:33.939
semen from one top bull. out there fast enough

00:05:33.939 --> 00:05:36.779
to just dominate everything immediately. The

00:05:36.779 --> 00:05:39.959
system, just by its very nature, it kind of encouraged

00:05:39.959 --> 00:05:42.519
a broader spread of genetics, even if it wasn't

00:05:42.519 --> 00:05:44.360
the most efficient way to get rapid progress

00:05:44.360 --> 00:05:47.360
on paper. And then, boom, everything changed.

00:05:47.779 --> 00:05:50.920
You mentioned 2009 as this definitive inflection

00:05:50.920 --> 00:05:54.100
point. What were the specific scientific breakthroughs

00:05:54.100 --> 00:05:55.699
that all came together around that time, and

00:05:55.699 --> 00:05:57.519
what was their immediate, almost revolutionary

00:05:57.519 --> 00:06:01.319
impact? Yeah. 2009 really did redefine dairy

00:06:01.319 --> 00:06:03.699
genetics. And it wasn't just one single eureka

00:06:03.699 --> 00:06:07.360
moment, actually. It was more like a rapid convergence

00:06:07.360 --> 00:06:10.779
of incredible scientific and technological advancements

00:06:10.779 --> 00:06:13.800
that happened in the early to mid 2000s. Laying

00:06:13.800 --> 00:06:16.959
the groundwork. Exactly. First, back in 2004,

00:06:17.180 --> 00:06:19.610
we saw the sequencing of the bovine genome. That

00:06:19.610 --> 00:06:21.990
gave us the foundational genetic map, the blueprint,

00:06:22.230 --> 00:06:24.529
if you will. Yeah, the map. Then came the high

00:06:24.529 --> 00:06:26.850
-density single -nucleotide polymorphism chips.

00:06:26.889 --> 00:06:28.850
We just call them SNP chips. Right. These were

00:06:28.850 --> 00:06:30.610
the tools that allowed us to read that blueprint

00:06:30.610 --> 00:06:33.269
quickly and crucially cost -effectively. Think

00:06:33.269 --> 00:06:35.850
of them as incredibly advanced barcode scanners,

00:06:35.850 --> 00:06:38.610
but for DNA. Okay, that makes sense. Illumina,

00:06:38.670 --> 00:06:40.649
for instance, commercially released their Bovine

00:06:40.649 --> 00:06:44.589
SNP50B chip in December 2007. These chips let

00:06:44.589 --> 00:06:47.189
geneticists correlate specific DNA markers these

00:06:47.189 --> 00:06:49.920
SNPs with actual performance traits. So you could

00:06:49.920 --> 00:06:53.000
link the gene code to the milk production. Precisely.

00:06:53.279 --> 00:06:56.100
By comparing a young animal's genomic profile,

00:06:56.339 --> 00:06:59.420
its SNP results, to a large reference population

00:06:59.420 --> 00:07:02.519
that's animals with known pedigrees and actual

00:07:02.519 --> 00:07:05.259
performance data, the proven ones we could suddenly

00:07:05.259 --> 00:07:08.199
predict its genetic merit with remarkable accuracy.

00:07:08.519 --> 00:07:10.720
Long before it had any offspring. Long before

00:07:10.720 --> 00:07:12.839
it had any offspring or even really matured itself.

00:07:13.000 --> 00:07:15.259
It was a massive leap. So if I'm a producer listening,

00:07:15.459 --> 00:07:18.019
that means I no longer have to wait five, six,

00:07:18.060 --> 00:07:21.879
seven years for a proven bull. I can get a highly

00:07:21.879 --> 00:07:25.339
reliable prediction on a young bull, like, almost

00:07:25.339 --> 00:07:27.019
right away. Pretty much. It effectively made

00:07:27.019 --> 00:07:30.680
that whole long, expensive progeny testing process

00:07:30.680 --> 00:07:33.420
optional. That's a complete game changer. It

00:07:33.420 --> 00:07:36.279
was a seismic shift. And when the USDA released

00:07:36.279 --> 00:07:39.040
its first official genomic evaluations in January

00:07:39.040 --> 00:07:42.519
2009, it really felt like the dam breaking. The

00:07:42.519 --> 00:07:45.019
impact was immediate, transformative for the

00:07:45.019 --> 00:07:48.269
whole industry. How so? What changed first? The

00:07:48.269 --> 00:07:49.870
generation interval, that's the average time

00:07:49.870 --> 00:07:52.410
between generations. It just plummeted dramatically.

00:07:52.550 --> 00:07:54.149
We're talking about it shrinking from around

00:07:54.149 --> 00:07:57.069
seven years for those key sire -to -bull pathways.

00:07:57.470 --> 00:07:59.430
Seven years. Down to less than two and a half

00:07:59.430 --> 00:08:03.069
years. Wow. Cut by more than half. Easily. And

00:08:03.069 --> 00:08:05.649
selection accuracy on these young animals soared

00:08:05.649 --> 00:08:08.529
at the same time. Plus, the cost and time to

00:08:08.529 --> 00:08:11.050
identify the elite individuals, it just fell

00:08:11.050 --> 00:08:13.589
off a cliff. So cheaper, faster, more accurate.

00:08:13.829 --> 00:08:17.170
You got it. And producers and AI companies, naturally,

00:08:17.370 --> 00:08:20.189
they rapidly embrace these younger, high merit

00:08:20.189 --> 00:08:22.589
sires. Why wouldn't you? And the numbers really

00:08:22.589 --> 00:08:25.050
tell that story, don't they? By 2021, the market

00:08:25.050 --> 00:08:27.610
had completely flicked. Oh, absolutely. The statistics

00:08:27.610 --> 00:08:31.089
are, frankly, quite staggering. By 2021, an incredible

00:08:31.089 --> 00:08:34.750
71 percent, 71 percent of all AI breedings in

00:08:34.750 --> 00:08:37.429
U .S. dairy herds were to these genomic young

00:08:37.429 --> 00:08:40.009
sires. Bulls with no milking daughters of their

00:08:40.009 --> 00:08:42.500
own. None. Simply because the genomic prediction

00:08:42.500 --> 00:08:45.440
was seen as so reliable. That's just a profound

00:08:45.440 --> 00:08:47.720
testament to the industry's confidence in this

00:08:47.720 --> 00:08:50.960
new technology and its ability to deliver results.

00:08:51.279 --> 00:08:54.220
That 71 % figure really jumps out. It shows how

00:08:54.220 --> 00:08:57.279
quickly the industry adapted and, well, trusted

00:08:57.279 --> 00:08:59.320
this new paradigm. They jumped in with both feet.

00:08:59.669 --> 00:09:02.750
And the promise was indeed to quantify the game,

00:09:02.909 --> 00:09:05.309
right? We saw the numbers rocket up, particularly

00:09:05.309 --> 00:09:08.070
in net merit, NML, which is the main metric for

00:09:08.070 --> 00:09:10.850
genetic progress here in the U .S. What did those

00:09:10.850 --> 00:09:13.549
games actually look like in hard data? How much

00:09:13.549 --> 00:09:16.279
faster were we really moving? The gains were

00:09:16.279 --> 00:09:19.159
undeniable, no question, and pretty much exactly

00:09:19.159 --> 00:09:21.620
as promised, maybe even better in some areas.

00:09:21.919 --> 00:09:25.279
For Holstein -Sires, the average annual genetic

00:09:25.279 --> 00:09:28.259
gain in net merit, NMR, it jumped significantly.

00:09:28.259 --> 00:09:30.600
How significantly? Okay, so in the five years

00:09:30.600 --> 00:09:34.059
before widespread genomic use, roughly 2005 to

00:09:34.059 --> 00:09:37.100
2010, that gain was hovering around $40 per year.

00:09:37.389 --> 00:09:39.649
Give or take. OK, $40 a year progress. But in

00:09:39.649 --> 00:09:42.070
the post -genomic era, looking at, say, 2016

00:09:42.070 --> 00:09:44.710
to 2020, that rate more than doubled. It hit

00:09:44.710 --> 00:09:47.970
an average of $79 .20 per year. Double the speed.

00:09:48.169 --> 00:09:50.490
Pretty much double the rate of progress for overall

00:09:50.490 --> 00:09:53.230
profitability index. And interestingly, for some

00:09:53.230 --> 00:09:55.210
specific fitness traits, things like fertility

00:09:55.210 --> 00:09:57.259
and health, which are just... incredibly important

00:09:57.259 --> 00:09:59.139
for your actual bottom line. Yeah, the things

00:09:59.139 --> 00:10:02.139
that keep cows in the herd. Exactly. The improvement

00:10:02.139 --> 00:10:04.820
there was even more dramatic. Right. We saw increases

00:10:04.820 --> 00:10:08.139
of 200 % to 300 % in the rate of gain for those

00:10:08.139 --> 00:10:10.240
traits. Two to three times faster improvement

00:10:10.240 --> 00:10:13.169
on health and fertility. Yeah. So... In terms

00:10:13.169 --> 00:10:15.409
of raw genetic progress, especially for those

00:10:15.409 --> 00:10:18.529
economically vital traits, genomics absolutely

00:10:18.529 --> 00:10:21.570
delivered. It truly did double our rate of progress,

00:10:21.750 --> 00:10:24.149
maybe even more in key areas. Those numbers are

00:10:24.149 --> 00:10:26.590
incredibly impressive. I mean, it's easy to see

00:10:26.590 --> 00:10:28.929
why producers and AI companies embraced it so

00:10:28.929 --> 00:10:31.250
wholeheartedly. Absolutely. But this brings us

00:10:31.250 --> 00:10:33.610
right back to that powerful paradox we introduced

00:10:33.610 --> 00:10:37.970
at the start. The unintended consequence. The

00:10:37.970 --> 00:10:40.789
acceleration of inbreeding. The other side of

00:10:40.789 --> 00:10:42.889
the coin. How exactly did the very mechanisms

00:10:42.889 --> 00:10:45.750
driving this amazing accelerated gain this incredible

00:10:45.750 --> 00:10:48.509
speed also create the perfect conditions for

00:10:48.509 --> 00:10:50.950
accelerated inbreeding? It's such a critical

00:10:50.950 --> 00:10:53.269
connection to grasp because they're intrinsically

00:10:53.269 --> 00:10:55.750
linked. The same tools that propelled us forward

00:10:55.750 --> 00:10:58.009
so quickly, those shortened generation intervals

00:10:58.009 --> 00:11:00.149
we talked about and the intensified selection

00:11:00.149 --> 00:11:02.470
pressure on just a handful of top ranking animals,

00:11:02.649 --> 00:11:05.470
they inadvertently created the perfect storm

00:11:05.470 --> 00:11:07.929
for accelerated inbreeding. how does that work

00:11:07.929 --> 00:11:10.929
exactly well when you can identify the best genetics

00:11:10.929 --> 00:11:13.730
so much faster and then use them so much more

00:11:13.730 --> 00:11:18.250
widely and quickly through ai you naturally almost

00:11:18.250 --> 00:11:20.909
inevitably concentrate those bloodlines across

00:11:20.909 --> 00:11:23.570
the entire population makes sense everyone wants

00:11:23.570 --> 00:11:27.149
the best right An inbreeding at its core is simply

00:11:27.149 --> 00:11:29.870
the mating of individuals who share common ancestors.

00:11:30.309 --> 00:11:33.490
This leads to increased homozygosity. Meaning

00:11:33.490 --> 00:11:36.570
more identical gene pairs. Exactly. Where an

00:11:36.570 --> 00:11:39.370
animal inherits identical gene pairs from both

00:11:39.370 --> 00:11:41.529
its mother and its father because they share

00:11:41.529 --> 00:11:44.450
ancestry. Now, selecting for desirable traits

00:11:44.450 --> 00:11:47.250
does increase homozygosity for those specific

00:11:47.250 --> 00:11:50.169
traits, which is good. That's the goal. But inbreeding

00:11:50.169 --> 00:11:52.590
increases homozygosity indiscriminately across

00:11:52.590 --> 00:11:55.620
the whole genome. And that raises the risk of

00:11:55.620 --> 00:11:57.899
expressing deleterious recessive alleles, those

00:11:57.899 --> 00:12:00.720
hidden genetic flaws that, when an animal gets

00:12:00.720 --> 00:12:03.399
two copies, can severely harm its health, fertility,

00:12:03.700 --> 00:12:06.240
or performance. And that's what we call inbreeding

00:12:06.240 --> 00:12:08.740
depression. That's inbreeding depression. It's

00:12:08.740 --> 00:12:11.759
the negative consequence of that increased homozygosity.

00:12:11.940 --> 00:12:14.879
So the genomic era didn't change the fundamental

00:12:14.879 --> 00:12:18.000
rules of genetics. It just, like, put them on

00:12:18.000 --> 00:12:20.070
fast forward. That's a great way to put it. It

00:12:20.070 --> 00:12:22.330
sounds like that slow and wide bottleneck of

00:12:22.330 --> 00:12:24.789
the old progeny testing days was replaced by

00:12:24.789 --> 00:12:27.629
something much faster and far, far narrower.

00:12:27.789 --> 00:12:29.909
Precisely. We found the fast lane all right,

00:12:29.990 --> 00:12:31.929
but it turned out to be a much more congested

00:12:31.929 --> 00:12:34.769
one, genetically speaking. Before genomics, like

00:12:34.769 --> 00:12:37.549
we said, the sheer time and cost involved naturally

00:12:37.549 --> 00:12:40.350
limited how quickly and widely any single bull's

00:12:40.350 --> 00:12:43.580
genetics could spread. But now... With genomic

00:12:43.580 --> 00:12:46.720
evaluations, the entire global industry can identify

00:12:46.720 --> 00:12:49.899
and converge around the same handful, literally

00:12:49.899 --> 00:12:53.340
the top five or 10 genomic young sires within

00:12:53.340 --> 00:12:56.320
a single evaluation run almost overnight. Wow.

00:12:56.639 --> 00:12:59.179
That's intense concentration. It is. And there's

00:12:59.179 --> 00:13:01.559
real world data. The Italian Holstein population

00:13:01.559 --> 00:13:04.440
provides a really clear, measurable case study.

00:13:04.840 --> 00:13:07.679
Researchers there observed a significant quantifiable

00:13:07.679 --> 00:13:10.120
increase in their annual rate of inbreeding immediately

00:13:10.120 --> 00:13:12.519
after genomic selection was widely introduced.

00:13:12.840 --> 00:13:14.720
So it wasn't theoretical. It actually happened.

00:13:14.960 --> 00:13:17.120
It actually happened and they measured it. The

00:13:17.120 --> 00:13:20.659
system effectively became architected for convergence,

00:13:20.899 --> 00:13:24.639
not necessarily for diversity. Its primary objective

00:13:24.639 --> 00:13:29.879
was laser focused on maximizing progress as measured

00:13:29.879 --> 00:13:32.980
by a single standardized definition of economic

00:13:32.980 --> 00:13:35.399
merit like net. merit here in the U .S. Right.

00:13:35.559 --> 00:13:38.500
Maximize NAO8. Exactly. There wasn't an equally

00:13:38.500 --> 00:13:41.639
weighted built -in incentive for actively maintaining

00:13:41.639 --> 00:13:44.759
genetic diversity alongside that push for gain.

00:13:44.840 --> 00:13:47.480
It really became all about climbing that leaderboard.

00:13:47.620 --> 00:13:50.779
That structural design. Focusing on maximizing

00:13:50.779 --> 00:13:53.000
just one metric, it makes complete sense from

00:13:53.000 --> 00:13:56.039
a purely business perspective maybe, but it clearly

00:13:56.039 --> 00:13:58.100
has this critical side effect. Absolutely. And

00:13:58.100 --> 00:14:00.440
this brings us to what you've termed the inbreeding

00:14:00.440 --> 00:14:04.179
tax. Quantifying the real economic drag on profitability

00:14:04.179 --> 00:14:06.600
that dairy producers are facing, maybe without

00:14:06.600 --> 00:14:08.360
even realizing it. What are we talking about

00:14:08.360 --> 00:14:10.379
here in terms of actual dollars walking out the

00:14:10.379 --> 00:14:12.409
barn door? This is really where it hits home

00:14:12.409 --> 00:14:14.570
for producers, because it's not some abstract

00:14:14.570 --> 00:14:16.809
concept. It's a constant, persistent drain on

00:14:16.809 --> 00:14:19.269
the bottom line. The core metric, which has been

00:14:19.269 --> 00:14:21.529
consistently revealed through decades of research,

00:14:21.590 --> 00:14:24.389
actually, it's not new then. The effect isn't

00:14:24.389 --> 00:14:27.149
new, no. It's the rate that's accelerated. The

00:14:27.149 --> 00:14:30.590
research shows approximately $23 to maybe $24

00:14:30.590 --> 00:14:34.549
to maybe 24 cents per cow. in lost lifetime profit

00:14:34.549 --> 00:14:39.129
occurs for every single 1 % increase in an animal's

00:14:39.129 --> 00:14:42.070
inbreeding coefficient. 23 bucks per cow per

00:14:42.070 --> 00:14:45.049
1 % inbreeding. Roughly, yeah. And that's lifetime

00:14:45.049 --> 00:14:48.250
profit lost. It's not a one -off cost. It's consistent,

00:14:48.450 --> 00:14:51.549
it's cumulative, and it persistently drains profitability

00:14:51.549 --> 00:14:54.470
throughout a cow's entire productive life. It's

00:14:54.470 --> 00:14:56.669
like paying an invisible toll for every single

00:14:56.669 --> 00:14:59.700
cow. every year. That's a perfect analogy. An

00:14:59.700 --> 00:15:01.799
invisible toll. So for a producer listening right

00:15:01.799 --> 00:15:03.940
now, this translates into real money that's just

00:15:03.940 --> 00:15:07.389
gone. Year after year. Can you give us some compelling

00:15:07.389 --> 00:15:09.269
concrete examples? What does this inbreeding

00:15:09.269 --> 00:15:11.230
tax look like for a typical dairy operation?

00:15:11.350 --> 00:15:12.850
What kind of financial hit are we talking about?

00:15:13.009 --> 00:15:15.230
Absolutely. Let's make it real. Take a 300 cow

00:15:15.230 --> 00:15:18.450
herd. If that herd gradually increases its average

00:15:18.450 --> 00:15:21.129
inbreeding from, say, a baseline of 5 % up to

00:15:21.129 --> 00:15:23.429
12 % over a generation, that's a 7 % increase,

00:15:23.629 --> 00:15:25.269
which is happening in many places. Okay, 7 %

00:15:25.269 --> 00:15:27.490
increase. That herd is looking at a lifetime

00:15:27.490 --> 00:15:30.690
loss accumulated across all those cows of approximately

00:15:30.690 --> 00:15:35.370
$48 ,300. Wow. Nearly 50 grand vanished. Yeah.

00:15:35.509 --> 00:15:38.149
Now scale it up. Imagine a larger operation,

00:15:38.350 --> 00:15:41.950
maybe a 500 cow herd. If that herd averages 12

00:15:41.950 --> 00:15:45.070
% inbreeding compared to a similar herd that

00:15:45.070 --> 00:15:47.669
maybe managed to maintain, say, 5 % inbreeding.

00:15:47.710 --> 00:15:50.610
Okay. That operation is bleeding roughly $80

00:15:50.610 --> 00:15:55.240
,500 annually. $80 ,000 a year. Roughly. It's

00:15:55.240 --> 00:15:57.659
the silent tax, as you called it, that just compounds

00:15:57.659 --> 00:16:00.659
quietly year after year across every single animal

00:16:00.659 --> 00:16:03.159
in the herd. It erodes margins without ever sending

00:16:03.159 --> 00:16:05.460
you a bill you can easily itemize. And it's not

00:16:05.460 --> 00:16:07.860
just hitting one specific area, is it? This inbreeding

00:16:07.860 --> 00:16:09.740
tax isn't a single straightforward deduction.

00:16:09.919 --> 00:16:12.679
It kind of deconstructs across several key traits

00:16:12.679 --> 00:16:15.360
that are absolutely fundamental to a cow's performance

00:16:15.360 --> 00:16:17.519
and profitability. Where are these costs hitting

00:16:17.519 --> 00:16:20.399
hardest? It's truly multifaceted. It impacts

00:16:20.399 --> 00:16:22.740
nearly every aspect of a cow's performance, unfortunately.

00:16:23.139 --> 00:16:25.129
Let's break it down. For production traits, we

00:16:25.129 --> 00:16:27.970
consistently see a reduction in milk, fat, and

00:16:27.970 --> 00:16:30.509
protein yield. Less in the tank. Exactly. Research

00:16:30.509 --> 00:16:33.269
indicates a 1 % increase in inbreeding can lead

00:16:33.269 --> 00:16:36.590
to a decrease of somewhere between 66 .5 to maybe

00:16:36.590 --> 00:16:39.870
82 pounds of milk, about 2 .5 pounds of fat,

00:16:39.990 --> 00:16:43.870
and 2 .0 pounds of protein. Per lactation. Per

00:16:43.870 --> 00:16:46.269
lactation. Per lactation. And over a cow's lifetime,

00:16:46.509 --> 00:16:49.070
that adds up. We're talking a significant loss

00:16:49.070 --> 00:16:52.590
of around 790 pounds of lifetime milk just from

00:16:52.590 --> 00:16:54.970
that 1 % increase. Wow. Then there's fertility,

00:16:55.169 --> 00:16:57.149
right? That's huge. Absolutely. Fertility and

00:16:57.149 --> 00:16:59.250
reproduction is a cornerstone of dairy profitability.

00:16:59.590 --> 00:17:02.149
Everyone knows that. And inbreeding demonstrably

00:17:02.149 --> 00:17:03.750
decreases reproductive performance estimates

00:17:03.750 --> 00:17:07.230
range from 3 % to 5 % drop per 1 % inbreeding.

00:17:07.349 --> 00:17:09.009
That leads to longer calving intervals. Yep,

00:17:09.029 --> 00:17:12.109
about 0 .26 days longer calving interval per

00:17:12.109 --> 00:17:15.150
1 % inbreeding. And it means requiring more services

00:17:15.150 --> 00:17:17.250
per conception. More straws, more time, more

00:17:17.250 --> 00:17:19.589
hassle. Exactly. These are direct heads to your

00:17:19.589 --> 00:17:21.950
bottom line. More time, more semen, more labor

00:17:21.950 --> 00:17:24.369
tied up just trying to get cows pregnant. So

00:17:24.369 --> 00:17:27.670
we're talking less milk, harder breeding, longer

00:17:27.670 --> 00:17:31.170
open periods. And I assume it also impacts the

00:17:31.170 --> 00:17:33.730
long -term health and, critically, the longevity

00:17:33.730 --> 00:17:36.309
of the herd. It cuts short a cow's productive

00:17:36.309 --> 00:17:38.410
life, which is where they really start paying

00:17:38.410 --> 00:17:40.890
you back. Precisely. You hit the nail on the

00:17:40.890 --> 00:17:44.329
head. For longevity and health, each 1 % increase

00:17:44.329 --> 00:17:46.549
in inbreeding can shorten a cow's productive

00:17:46.549 --> 00:17:49.650
life by anywhere from 6 to 13 days. You're gone.

00:17:50.140 --> 00:17:53.059
Just gone. And this translates directly to higher

00:17:53.059 --> 00:17:55.839
involuntary culling rates, cows leaving the herd

00:17:55.839 --> 00:17:58.400
before you want them to, often for health or

00:17:58.400 --> 00:18:00.440
reproductive reasons linked back to this. That

00:18:00.440 --> 00:18:03.039
means higher replacement costs. Exactly. Increased

00:18:03.039 --> 00:18:05.279
replacement costs because you need to raise or

00:18:05.279 --> 00:18:07.599
buy more heifers just to maintain herd size.

00:18:07.859 --> 00:18:10.019
And you suffer that loss of revenue from those

00:18:10.019 --> 00:18:12.160
mature, high -producing cows who should be your

00:18:12.160 --> 00:18:14.480
most efficient milkers. But they're leaving the

00:18:14.480 --> 00:18:16.660
herd prematurely. You're basically losing your

00:18:16.660 --> 00:18:19.619
prime production years from those animals. I

00:18:19.619 --> 00:18:21.960
guess unsettling about this inbreeding tax is

00:18:21.960 --> 00:18:24.980
how you describe it as often going unrecognized.

00:18:25.039 --> 00:18:27.500
It kind of masquerades as other more visible

00:18:27.500 --> 00:18:30.400
farm level problems. That's what makes it so

00:18:30.400 --> 00:18:33.440
insidious. It's truly the silent tax, something

00:18:33.440 --> 00:18:35.759
that's always there, but almost never gets explicitly

00:18:35.759 --> 00:18:38.259
identified on the farm balance sheet. How does

00:18:38.259 --> 00:18:41.579
that happen? Well, producers rarely, if ever,

00:18:41.640 --> 00:18:44.980
receive an itemized bill that says loss due to

00:18:44.980 --> 00:18:48.029
inbreeding. x dollars instead they see symptoms

00:18:48.029 --> 00:18:51.170
maybe a few more open cows than expected this

00:18:51.170 --> 00:18:54.430
year or a slight but consistent increase in calling

00:18:54.430 --> 00:18:57.069
for reproductive failure or maybe milk production

00:18:57.069 --> 00:18:59.769
that consistently falls just a bit short of projections

00:18:59.769 --> 00:19:01.990
even though they feel they're doing everything

00:19:01.990 --> 00:19:04.250
right with nutrition and management so you blame

00:19:04.250 --> 00:19:06.849
the feed or the weather or something else exactly

00:19:06.849 --> 00:19:09.650
these issues are logically but often incorrectly

00:19:09.650 --> 00:19:12.130
attributed to more visible factors nutrition

00:19:12.130 --> 00:19:14.869
programs heat stress protocols maybe even the

00:19:14.869 --> 00:19:16.799
labor forces compliance with milking or breeding

00:19:16.799 --> 00:19:20.000
protocols. So the financial drag from inbreeding

00:19:20.000 --> 00:19:22.440
is systematically misdiagnosed at the operational

00:19:22.440 --> 00:19:24.880
level. Which prevents any targeted intervention.

00:19:25.339 --> 00:19:28.180
Precisely. It's like having a slow, persistent

00:19:28.180 --> 00:19:30.279
leak in your pipeline that you just can't quite

00:19:30.279 --> 00:19:33.000
pinpoint. It's constantly eroding your efficiency,

00:19:33.339 --> 00:19:35.480
but you're busy fixing other things you can actually

00:19:35.480 --> 00:19:37.960
see. And some recent Italian research really

00:19:37.960 --> 00:19:40.779
underscores just how much we might be underestimating

00:19:40.779 --> 00:19:43.519
the actual damage. It suggests that silent tacks

00:19:43.519 --> 00:19:45.779
might be even more impactful than we previously

00:19:45.779 --> 00:19:48.359
thought. That's a crucial point, and it highlights

00:19:48.359 --> 00:19:50.539
the limitations of the older ways we used to

00:19:50.539 --> 00:19:53.680
measure this. A 2023 Italian study demonstrated

00:19:53.680 --> 00:19:56.740
something really stark. When they used genomic

00:19:56.740 --> 00:19:59.819
data, the actual DNA, to assess inbreeding, it

00:19:59.819 --> 00:20:02.779
revealed milk reductions of about 61 kilograms

00:20:02.779 --> 00:20:06.099
per 1 % inbreeding. Okay, 61 kilos using genomics.

00:20:06.240 --> 00:20:08.809
Right. But that was significantly worse than

00:20:08.809 --> 00:20:11.329
the 44 kilograms that was predicted by just using

00:20:11.329 --> 00:20:13.589
traditional pedigree data for the same animals.

00:20:13.690 --> 00:20:15.630
Wow, that's a big difference. It's a huge difference.

00:20:15.769 --> 00:20:18.150
It confirms that traditional pedigree -based

00:20:18.150 --> 00:20:20.690
methods often underestimate the true genomic

00:20:20.690 --> 00:20:23.769
inbreeding by 40 % or even more in some cases.

00:20:23.910 --> 00:20:26.539
So the economic cost is actually higher. Exactly.

00:20:26.640 --> 00:20:29.420
It means the true economic costs we're talking

00:20:29.420 --> 00:20:32.019
about, the actual inbreeding tax you're paying

00:20:32.019 --> 00:20:34.940
on farm, is likely even greater than previously

00:20:34.940 --> 00:20:38.039
thought based on older pedigree estimates. We

00:20:38.039 --> 00:20:39.720
were essentially, like you said before, flying

00:20:39.720 --> 00:20:41.799
with a faulty altimeter, thinking the problem

00:20:41.799 --> 00:20:44.319
was less severe than it actually was. That's

00:20:44.319 --> 00:20:46.700
a truly sobering thought. It means the problem

00:20:46.700 --> 00:20:50.400
is not just hidden, but potentially much deeper,

00:20:50.400 --> 00:20:53.680
much more costly than we realized. Okay, let's

00:20:53.680 --> 00:20:55.579
try and unpack this further. We've talked about

00:20:55.579 --> 00:20:57.460
the genetic and economic impacts right there

00:20:57.460 --> 00:20:59.359
on the farm, but let's shift focus a bit to the

00:20:59.359 --> 00:21:03.299
broader industry structure. Because despite what

00:21:03.299 --> 00:21:05.759
often appears to be a wealth of choices for producers,

00:21:06.119 --> 00:21:08.940
lots of bulls in the catalogs, genetic diversity

00:21:08.940 --> 00:21:11.799
seems to be shrinking overall. This brings us

00:21:11.799 --> 00:21:13.640
to what the article calls the architecture of

00:21:13.640 --> 00:21:16.690
concentration. The market dynamics and what you

00:21:16.690 --> 00:21:19.549
refer to as the elite sire funnel. Yeah. And

00:21:19.549 --> 00:21:21.410
what's truly fascinating, maybe even a little

00:21:21.410 --> 00:21:24.029
ironic here, is how individual, perfectly rational

00:21:24.029 --> 00:21:27.009
decisions made by producers like you, when aggregated

00:21:27.009 --> 00:21:29.230
across an entire industry, can inadvertently

00:21:29.230 --> 00:21:31.549
lead to a collective long term detriment for

00:21:31.549 --> 00:21:34.529
the breed itself. How so? The elite sire funnel

00:21:34.529 --> 00:21:37.930
is this really powerful mechanism where everyone.

00:21:38.680 --> 00:21:41.180
quite understandably, wants the best genetics

00:21:41.180 --> 00:21:43.380
available, right? The highest index bulls. Of

00:21:43.380 --> 00:21:46.059
course. You want progress. Exactly. But that

00:21:46.059 --> 00:21:48.559
collective desire, especially when it's combined

00:21:48.559 --> 00:21:51.660
with current market dynamics, can inadvertently

00:21:51.660 --> 00:21:54.460
create this profound concentration problem. It

00:21:54.460 --> 00:21:57.579
just pushes us all towards the same narrow genetic

00:21:57.579 --> 00:22:00.660
bottleneck. So let's start by laying out that

00:22:00.660 --> 00:22:03.779
highly concentrated industry landscape. How big

00:22:03.779 --> 00:22:06.680
is this global animal genetics market? And who

00:22:06.680 --> 00:22:08.680
are the key players that really dominate it,

00:22:08.720 --> 00:22:11.200
especially in dairy? Well, the global animal

00:22:11.200 --> 00:22:13.880
genetics market is a significant economic engine.

00:22:13.940 --> 00:22:17.079
It's valued at over $6 billion annually, and

00:22:17.079 --> 00:22:19.720
it continues to grow pretty steadily. North America

00:22:19.720 --> 00:22:22.200
is a major player in that, commanding over, say,

00:22:22.319 --> 00:22:25.720
31%, 32 % of that global market share. And dairy

00:22:25.720 --> 00:22:28.119
is a big part of that. Oh, yeah. The bovine segment

00:22:28.119 --> 00:22:30.559
dairy and beef is the largest and most valuable

00:22:30.559 --> 00:22:32.859
component within that market. But what's truly

00:22:32.859 --> 00:22:34.799
striking, especially for you listening as a dairy

00:22:34.799 --> 00:22:37.359
producer, is the intense concentration on the

00:22:37.359 --> 00:22:40.180
supply side. Meaning who sells the semen. Exactly.

00:22:40.180 --> 00:22:42.220
We're talking about just a handful of key players

00:22:42.220 --> 00:22:44.119
that control the vast majority of the market.

00:22:44.480 --> 00:22:47.380
Companies like ST Genetics, Genus Platte, which

00:22:47.380 --> 00:22:50.880
owns ABS Global Cemex, and URUS Group LP. Those

00:22:50.880 --> 00:22:52.500
are the most prominent names that come up again

00:22:52.500 --> 00:22:54.640
and again. And you have some concrete data that

00:22:54.640 --> 00:22:56.980
really illustrates this dominance, don't you?

00:22:57.079 --> 00:22:59.039
Yeah. Especially when we look at the very top

00:22:59.039 --> 00:23:02.750
tier of genetics, the elite bulls. everyone wants

00:23:02.750 --> 00:23:05.509
absolutely it's quite stark when you dig into

00:23:05.509 --> 00:23:08.369
it if you examine the genomic net merit sire

00:23:08.369 --> 00:23:10.970
share meaning which companies bulls are topping

00:23:10.970 --> 00:23:13.849
the lists and being used most often as of say

00:23:13.849 --> 00:23:18.009
april 2025 data st genetics commands a staggering

00:23:18.009 --> 00:23:21.890
53 .5 percent of that market share Just one company.

00:23:21.990 --> 00:23:24.190
Over half the elite market. Over half. And then

00:23:24.190 --> 00:23:26.289
if you add in Select, Sires, and Cimex to that,

00:23:26.390 --> 00:23:28.890
you're looking at roughly 85 % of the elite genetics

00:23:28.890 --> 00:23:31.309
market in North America being controlled by just

00:23:31.309 --> 00:23:34.799
those three companies. Wow. 85 % from three players.

00:23:34.819 --> 00:23:36.859
Yeah. So this isn't just about general market

00:23:36.859 --> 00:23:39.079
share. It's a very strong signal that the elite

00:23:39.079 --> 00:23:41.000
Cyrus stream, the one everyone is chasing for

00:23:41.000 --> 00:23:43.240
maximum genetic gain, effectively runs through

00:23:43.240 --> 00:23:45.799
just a few very specific gates. So it's not just

00:23:45.799 --> 00:23:48.599
the market structure itself. It's how incentives

00:23:48.599 --> 00:23:51.319
align within that structure that leads these

00:23:51.319 --> 00:23:54.779
individual, perfectly rational choices to drive

00:23:54.779 --> 00:23:58.259
this collective risk. How does value -based pricing,

00:23:58.359 --> 00:24:00.539
for instance, play directly into this funnel

00:24:00.539 --> 00:24:03.410
effect? It's an incredibly powerful, almost magnetic

00:24:03.410 --> 00:24:06.650
mechanism. Value -based pricing directly ties

00:24:06.650 --> 00:24:09.009
the cost of the semen straw to a bull's index

00:24:09.009 --> 00:24:11.950
ranking, whether that's net merit, NM1, or TPI,

00:24:12.130 --> 00:24:14.029
or whatever index is popular. The higher the

00:24:14.029 --> 00:24:16.470
rank, the higher the price. Generally, yes. And

00:24:16.470 --> 00:24:18.650
this creates an undeniable economic pressure.

00:24:18.990 --> 00:24:21.029
Producers acting rationally to maximize their

00:24:21.029 --> 00:24:23.490
own herd's profitability are naturally drawn

00:24:23.490 --> 00:24:25.589
to those top -ranking bulls on the leaderboard.

00:24:25.809 --> 00:24:27.910
I mean, why wouldn't you want the best performing

00:24:27.910 --> 00:24:30.109
genetics for your herd, right? Makes perfect

00:24:30.109 --> 00:24:33.059
sense on paper. But this collective gravitation

00:24:33.059 --> 00:24:36.160
towards the same few top sires inevitably leads

00:24:36.160 --> 00:24:39.279
to repeated use of the same genetic lines. And

00:24:39.279 --> 00:24:42.140
as an unavoidable side effect, that tightens

00:24:42.140 --> 00:24:44.559
pedigree overlap across the entire industry.

00:24:44.759 --> 00:24:47.609
It's not... a malicious act by anyone. It's just

00:24:47.609 --> 00:24:50.150
rational actors making what they perceive as

00:24:50.150 --> 00:24:52.410
the smartest, most immediate economic decision

00:24:52.410 --> 00:24:55.410
for their own farm. And beyond just the pricing,

00:24:55.569 --> 00:24:58.529
there are also these exclusive contracts and

00:24:58.529 --> 00:25:01.710
nucleus herds that further concentrate the most.

00:25:02.190 --> 00:25:04.690
sought after genetics, effectively keeping them

00:25:04.690 --> 00:25:06.589
within a very tight circle, right? That's another

00:25:06.589 --> 00:25:08.950
crucial layer to this concentration puzzle. Things

00:25:08.950 --> 00:25:12.369
like early access agreements, VIP semen contracts,

00:25:12.730 --> 00:25:15.470
first option rights on progeny. These are pretty

00:25:15.470 --> 00:25:17.549
common business practices among the major AI

00:25:17.549 --> 00:25:19.670
companies. So they lock up the best stuff early.

00:25:19.890 --> 00:25:22.349
Essentially, yes. These arrangements effectively

00:25:22.349 --> 00:25:25.670
keep the very best, newest genetics within their

00:25:25.670 --> 00:25:28.609
internal pipelines, primarily for those companies'

00:25:28.750 --> 00:25:31.130
own nucleus herds. These are the elite herds

00:25:31.130 --> 00:25:33.339
they use. to breed the next generation of bulls.

00:25:33.359 --> 00:25:36.279
And this creates what we might call a two -speed

00:25:36.279 --> 00:25:39.440
market. You've got the nucleus herds racing ahead

00:25:39.440 --> 00:25:42.200
on these cutting -edge lines using the newest,

00:25:42.240 --> 00:25:45.400
most elite genetics for their own internal genetic

00:25:45.400 --> 00:25:48.460
progress and, importantly, to develop the next

00:25:48.460 --> 00:25:50.920
generation of top sires they'll sell. And everyone

00:25:50.920 --> 00:25:53.799
else. Meanwhile, the broader commercial base,

00:25:53.960 --> 00:25:56.460
you know, the everyday producer... often gets

00:25:56.460 --> 00:25:59.319
later waves of these genetics, sometimes after

00:25:59.319 --> 00:26:01.480
those lines have already begun to saturate the

00:26:01.480 --> 00:26:04.200
population somewhat. So it reinforces the dominance

00:26:04.200 --> 00:26:06.460
of the big players. It certainly helps reinforce

00:26:06.460 --> 00:26:09.500
their market position, yeah. And critically,

00:26:09.740 --> 00:26:12.140
it accelerates the rate of inbreeding across

00:26:12.140 --> 00:26:15.039
the entire system because those foundational

00:26:15.039 --> 00:26:18.299
lines become even more widespread. So in essence,

00:26:18.380 --> 00:26:20.240
it sounds like a classic tragedy of the commons

00:26:20.240 --> 00:26:22.019
playing out, but this time it's for the bovine

00:26:22.019 --> 00:26:25.220
genome itself. That's a really... Everyone's

00:26:25.220 --> 00:26:27.039
doing what appears to be best for their own short

00:26:27.039 --> 00:26:29.779
-term gain, but it leads to a collective long

00:26:29.779 --> 00:26:33.259
-term risk for the entire breed. Precisely. For

00:26:33.259 --> 00:26:35.500
an individual producer, choosing the highest

00:26:35.500 --> 00:26:38.460
index sire seems like the most logical, profitable

00:26:38.460 --> 00:26:42.400
decision in that moment. For an AI company, aggressively

00:26:42.400 --> 00:26:44.319
marketing their next chart -topping role makes

00:26:44.319 --> 00:26:46.960
perfect business sense and maximizes shareholder

00:26:46.960 --> 00:26:50.440
value. But when every actor in the system makes

00:26:50.440 --> 00:26:52.460
their own most rational short -term decision,

00:26:52.720 --> 00:26:55.140
the collective long -term outcome is this rapid

00:26:55.140 --> 00:26:58.200
narrowing of the gene pool and a dangerous increase

00:26:58.200 --> 00:27:01.819
in systemic genetic risk. There's just no inherent

00:27:01.819 --> 00:27:04.579
market -based incentive for any single actor

00:27:04.579 --> 00:27:07.059
to prioritize collective genetic diversity over

00:27:07.059 --> 00:27:09.700
their own individual index -driven gain. It's

00:27:09.700 --> 00:27:11.539
a structural problem, like you said, not necessarily

00:27:11.539 --> 00:27:13.900
a villain story. Exactly. It's baked into the

00:27:13.900 --> 00:27:16.259
current system's incentives. This leads directly

00:27:16.259 --> 00:27:18.630
to what you call the illusion. of choice. You

00:27:18.630 --> 00:27:21.289
know, you might look at a Sire catalog with dozens,

00:27:21.450 --> 00:27:24.289
maybe even hundreds of bowls listed from multiple

00:27:24.289 --> 00:27:26.470
companies giving this impression of wide diversity.

00:27:26.710 --> 00:27:29.670
But a deeper look often reveals something quite

00:27:29.670 --> 00:27:31.750
different. Yeah, that's a really important point.

00:27:31.869 --> 00:27:34.230
You as a producer might receive catalogs from

00:27:34.230 --> 00:27:36.289
several different companies. They're all well

00:27:36.289 --> 00:27:38.849
produced, showing lots of bowls, which naturally

00:27:38.849 --> 00:27:41.150
gives the impression of a diverse and competitive

00:27:41.150 --> 00:27:44.309
marketplace. Lots of options. Right. Looks like

00:27:44.309 --> 00:27:47.220
plenty of choice. However. If you do a closer

00:27:47.220 --> 00:27:49.539
pedigree analysis, really dig into the family

00:27:49.539 --> 00:27:52.119
trees, you often find that a large percentage

00:27:52.119 --> 00:27:55.140
of these commercially distinct bulls are actually

00:27:55.140 --> 00:27:58.859
sons or maybe grandsons of the same few globally

00:27:58.859 --> 00:28:02.059
dominant sires of sons. So they all trace back

00:28:02.059 --> 00:28:05.579
to the same few ancestors. Very often, yes. So

00:28:05.579 --> 00:28:07.460
while there appears to be commercial diversity,

00:28:07.680 --> 00:28:10.279
different bull names, different company codes,

00:28:10.460 --> 00:28:13.119
it can mask a severe lack of underlying genetic

00:28:13.119 --> 00:28:15.799
diversity. You're often choosing between slight

00:28:15.799 --> 00:28:18.700
variations within the same core genetic families

00:28:18.700 --> 00:28:21.839
rather than truly diverse unrelated options.

00:28:22.099 --> 00:28:24.240
And the impact of this concentration isn't just

00:28:24.240 --> 00:28:27.039
theoretical is it? It's actually showing up in

00:28:27.039 --> 00:28:30.380
very real regional reality checks across different

00:28:30.380 --> 00:28:32.539
parts of the U .S. What are producers experiencing

00:28:32.539 --> 00:28:35.039
on the ground because of this? Yeah, we're definitely

00:28:35.039 --> 00:28:38.640
seeing clear and concerning patterns emerge in

00:28:38.640 --> 00:28:41.059
different regions. For example, in the upper

00:28:41.059 --> 00:28:43.240
Midwest, think places like Wisconsin, Minnesota,

00:28:43.599 --> 00:28:46.299
you'll find large, well -managed herds, sometimes

00:28:46.299 --> 00:28:50.339
hundreds of miles apart, that still show... eerily

00:28:50.339 --> 00:28:52.720
similar sire stacks when you look closely at

00:28:52.720 --> 00:28:54.920
their pedigrees, even though they might have

00:28:54.920 --> 00:28:56.559
different nutritionists, different management

00:28:56.559 --> 00:28:58.720
styles, maybe even different breeding advisors

00:28:58.720 --> 00:29:01.259
telling them what to use. So it's proof that

00:29:01.259 --> 00:29:03.460
everyone's fishing in the same small pond. It's

00:29:03.460 --> 00:29:06.920
direct on -farm proof of how just a handful of

00:29:06.920 --> 00:29:09.940
popular bull families can dominate selection

00:29:09.940 --> 00:29:12.799
decisions regionally simply because everyone

00:29:12.799 --> 00:29:15.380
is buying off the same highly concentrated list

00:29:15.380 --> 00:29:18.039
of top index sires. Okay, what about other regions?

00:29:18.420 --> 00:29:22.470
Well, over to California's Central Valley. Producers

00:29:22.470 --> 00:29:24.809
there are battling extreme heat, serious water

00:29:24.809 --> 00:29:27.430
scarcity issues, completely different environment.

00:29:27.960 --> 00:29:29.880
And they're finding that many of the top index

00:29:29.880 --> 00:29:32.799
bulls, the ones optimized perhaps for production

00:29:32.799 --> 00:29:34.960
in more moderate climates or confinement systems,

00:29:35.359 --> 00:29:37.839
simply weren't bred for their specific climate

00:29:37.839 --> 00:29:41.000
challenges. They struggle to find good slick

00:29:41.000 --> 00:29:43.839
coat gene options or genuinely heat tolerant

00:29:43.839 --> 00:29:47.339
bulls or even pasture efficient outcross options

00:29:47.339 --> 00:29:50.240
within that elite commercial stream. Because

00:29:50.240 --> 00:29:52.180
those traits aren't weighted as heavily in the

00:29:52.180 --> 00:29:54.980
main index. Exactly. Those specific adaptation

00:29:54.980 --> 00:29:57.460
traits often aren't showing up high enough. on

00:29:57.460 --> 00:29:59.640
the leaderboards to get widespread use, leaving

00:29:59.640 --> 00:30:02.140
those producers with fewer suitable choices from

00:30:02.140 --> 00:30:04.740
the mainstream suppliers. So those cop performing

00:30:04.740 --> 00:30:08.240
bulls on paper aren't always translating to top

00:30:08.240 --> 00:30:10.619
performance in every single environment. And

00:30:10.619 --> 00:30:12.700
you're seeing dairies quietly trying different

00:30:12.700 --> 00:30:15.099
approaches in other regions, too, because of

00:30:15.099 --> 00:30:17.339
this disconnect. Precisely. Down in the southeast,

00:30:17.660 --> 00:30:19.720
you know, states like Georgia, Florida, they're

00:30:19.720 --> 00:30:22.609
dealing with. intense heat, high humidity, significant

00:30:22.609 --> 00:30:24.990
parasite pressure. Very different challenges

00:30:24.990 --> 00:30:28.349
again. And dairies there are increasingly and

00:30:28.349 --> 00:30:31.490
often quite quietly experimenting with crossbreeding

00:30:31.490 --> 00:30:34.950
or seeking out genuine outcross sires from different

00:30:34.950 --> 00:30:38.390
populations. Because the high input, confinement

00:30:38.390 --> 00:30:41.009
optimized, mainstream genetics that dominate

00:30:41.009 --> 00:30:44.009
the leaderboards often just don't fit the reality

00:30:44.009 --> 00:30:46.049
of their specific environmental challenges. They

00:30:46.049 --> 00:30:48.390
need resilience, maybe more than that extra pound

00:30:48.390 --> 00:30:50.710
of milk on paper. and they're finding they have

00:30:50.710 --> 00:30:53.130
to look outside the most popular genetic lines

00:30:53.130 --> 00:30:55.410
to find it. It really sounds like a powerful

00:30:55.410 --> 00:30:57.410
disconnect between what's available at the very

00:30:57.410 --> 00:31:00.250
top of the leaderboard and what's truly needed

00:31:00.250 --> 00:31:02.250
on the ground in some of these diverse, challenging

00:31:02.250 --> 00:31:04.309
environments. It certainly seems that way, yeah.

00:31:04.529 --> 00:31:07.849
The system rewards one type of cow, but not all

00:31:07.849 --> 00:31:10.309
environments suit that type. And that brings

00:31:10.309 --> 00:31:12.390
us to a really critical point about how we even

00:31:12.390 --> 00:31:14.809
measure this risk. You've pointed out that the

00:31:14.809 --> 00:31:17.140
traditional ways we measure inbreeding might

00:31:17.140 --> 00:31:19.720
actually be misleading us potentially making

00:31:19.720 --> 00:31:22.039
the problem seem less severe than it truly is

00:31:22.039 --> 00:31:24.839
and then tied to that we have a stark historical

00:31:24.839 --> 00:31:26.640
example that should probably give everyone pause

00:31:27.160 --> 00:31:29.700
a real cautionary tale from our own industry's

00:31:29.700 --> 00:31:32.000
history. Yeah, what's fascinating here is the

00:31:32.000 --> 00:31:34.720
science, how our understanding of measuring inbreeding

00:31:34.720 --> 00:31:38.059
has evolved so much. For decades, we relied heavily

00:31:38.059 --> 00:31:40.920
on pedigree -based methods, just looking at family

00:31:40.920 --> 00:31:43.559
trees. Right. But genomic science has now shown

00:31:43.559 --> 00:31:46.500
us a much clearer and often, frankly, much more

00:31:46.500 --> 00:31:49.980
alarming picture of the actual genetic landscape

00:31:49.980 --> 00:31:52.720
within our herds. And the historical example.

00:31:53.000 --> 00:31:55.059
The historical example you're referring to, Pawnee

00:31:55.059 --> 00:31:58.799
Farm, our Linda Chief. His story is truly the

00:31:58.799 --> 00:32:00.619
ghost in the machine for the dairy industry.

00:32:01.039 --> 00:32:03.500
It powerfully highlights the profound lessons

00:32:03.500 --> 00:32:05.700
we've learned, or maybe should have learned,

00:32:05.839 --> 00:32:08.359
about the hidden, potentially devastating risks

00:32:08.359 --> 00:32:11.079
of extreme genetic concentration. Okay, let's

00:32:11.079 --> 00:32:12.619
start with measuring what matters. You mentioned

00:32:12.619 --> 00:32:15.119
the old standard pedigree -based inbreeding,

00:32:15.220 --> 00:32:19.539
or FPED. What exactly is that, and what were

00:32:19.539 --> 00:32:21.539
its inherent limitations? Why wasn't it giving

00:32:21.539 --> 00:32:24.930
us the full picture? Okay, so FPED, or pedigree

00:32:24.930 --> 00:32:27.109
inbreeding, is essentially an estimate based

00:32:27.109 --> 00:32:29.710
entirely on the family tree, the known ancestors.

00:32:30.349 --> 00:32:32.849
It's defined as the probability that an individual

00:32:32.849 --> 00:32:35.650
possesses two alleles at any given gene, that's

00:32:35.650 --> 00:32:37.690
two copies of a gene that are identical simply

00:32:37.690 --> 00:32:40.309
because they were inherited from a common ancestor

00:32:40.309 --> 00:32:42.849
somewhere back in the lineage. So you trace the

00:32:42.849 --> 00:32:46.450
lines back. Exactly. You calculate it by meticulously

00:32:46.450 --> 00:32:49.049
tracing an animal's lineage back through generations,

00:32:49.289 --> 00:32:51.630
looking for those loops where ancestors appear

00:32:51.630 --> 00:32:54.549
on. both the sires and dam side. While it was

00:32:54.549 --> 00:32:57.400
the best tool we had for a long, long time, Its

00:32:57.400 --> 00:33:00.619
accuracy depends entirely on the depth and, crucially,

00:33:00.819 --> 00:33:03.759
the completeness of those pedigree records. If

00:33:03.759 --> 00:33:06.279
you have missing ancestors or even small errors

00:33:06.279 --> 00:33:08.740
in the database, it can lead to a significant

00:33:08.740 --> 00:33:11.200
underestimation of the true relatedness between

00:33:11.200 --> 00:33:13.160
animals. Garbage in, garbage out, basically.

00:33:13.440 --> 00:33:15.599
Somewhat, yeah. And maybe more fundamentally,

00:33:15.859 --> 00:33:18.599
FPD is just a statistical expectation, a probability.

00:33:18.900 --> 00:33:21.539
It can't account for the actual random process

00:33:21.539 --> 00:33:24.519
of Mendelian sampling, you know, how genes actually

00:33:24.519 --> 00:33:26.460
get shuffled and passed down during reproduction.

00:33:26.509 --> 00:33:29.210
So an animal might get more or less DNA from

00:33:29.210 --> 00:33:32.009
an ancestor than predicted. Exactly. An individual

00:33:32.009 --> 00:33:35.230
might actually inherit more or less of their

00:33:35.230 --> 00:33:38.589
genome from a particular ancestor than the pedigree

00:33:38.589 --> 00:33:43.009
-based probability suggests. So FPED is an educated

00:33:43.009 --> 00:33:45.990
guess, a good one based on what we knew, but

00:33:45.990 --> 00:33:48.109
it can miss a lot of what's truly happening right

00:33:48.109 --> 00:33:50.410
there at the DNA level. So it's like trying to

00:33:50.410 --> 00:33:53.089
figure out how related two people are just by

00:33:53.089 --> 00:33:55.230
looking at their family tree on paper without

00:33:55.230 --> 00:33:58.109
actually doing a DNA test. It's helpful, but

00:33:58.109 --> 00:33:59.789
it's definitely not the full picture. That analogy

00:33:59.789 --> 00:34:02.009
is absolutely spot on. And the new gold standard

00:34:02.009 --> 00:34:04.410
you've said is based on genomics looking at runs

00:34:04.410 --> 00:34:07.369
of homozygosity or FROH. How does that differ?

00:34:07.490 --> 00:34:09.650
And why is it considered so much more robust

00:34:09.650 --> 00:34:13.260
and accurate? Right. FROH, or runs of homozygosity,

00:34:13.300 --> 00:34:15.139
is like doing that direct genetic scan, that

00:34:15.139 --> 00:34:17.239
DNA test. It's a far more direct and therefore

00:34:17.239 --> 00:34:20.400
more accurate measure of actual inbreeding. A

00:34:20.400 --> 00:34:23.380
run of homozygosity, an ROH, is defined as a

00:34:23.380 --> 00:34:25.679
long, continuous segment of an animal's genome

00:34:25.679 --> 00:34:28.239
where the alleles inherited from both the sire

00:34:28.239 --> 00:34:30.820
and the dam are identical. Okay, long, identical

00:34:30.820 --> 00:34:33.949
stretches. Exactly. These long homozygous stretches

00:34:33.949 --> 00:34:37.289
occur when an individual inherits the exact same

00:34:37.289 --> 00:34:39.969
ancestral chromosome segment from both parents

00:34:39.969 --> 00:34:42.590
because of that shared ancestry further back.

00:34:43.369 --> 00:34:45.969
FROH is then calculated simply as the total length

00:34:45.969 --> 00:34:48.349
of all these ROH segments in an animal's genome

00:34:48.349 --> 00:34:50.909
divided by the total length of the genome itself.

00:34:51.090 --> 00:34:53.110
So it's a direct measurement, not a probability.

00:34:53.659 --> 00:34:56.119
Precisely. It's not a probability or an estimate

00:34:56.119 --> 00:34:59.300
based on history. It's a direct empirical measurement

00:34:59.300 --> 00:35:02.539
of the actual proportion of an animal's DNA that

00:35:02.539 --> 00:35:04.960
is autoxygous meaning, identical by descent.

00:35:05.260 --> 00:35:07.880
This makes it far more robust and accurate at

00:35:07.880 --> 00:35:10.400
quantifying the true level of inbreeding because

00:35:10.400 --> 00:35:12.659
it directly reveals those shared segments of

00:35:12.659 --> 00:35:15.199
DNA inherited from common ancestors. And you

00:35:15.199 --> 00:35:16.920
mentioned there's a significant measurement gap

00:35:16.920 --> 00:35:20.800
between these two methods, FPD and FROH. This

00:35:20.800 --> 00:35:23.239
gap has profound economic implications. right?

00:35:23.320 --> 00:35:25.519
We were, as you so aptly put it earlier, potentially

00:35:25.519 --> 00:35:28.559
flying with a faulty altimeter, underestimating

00:35:28.559 --> 00:35:30.840
the real problem. That's a perfect analogy, and

00:35:30.840 --> 00:35:32.619
it's truly unsettling when you see the numbers.

00:35:33.179 --> 00:35:36.079
Studies consistently show that FROH values are

00:35:36.079 --> 00:35:38.480
significantly higher, often much higher, than

00:35:38.480 --> 00:35:41.099
the FPED values calculated for the same animals.

00:35:41.280 --> 00:35:43.480
How much higher? Well, for example, that 2023

00:35:43.480 --> 00:35:46.219
study of Italian Holstein cattle we talked about,

00:35:46.380 --> 00:35:49.840
it found an average FPED of 7 % in their population.

00:35:50.019 --> 00:35:52.500
But when they measured FROH on the same animals

00:35:52.500 --> 00:35:56.300
using genomic data, the average was 16%. 16%.

00:35:56.300 --> 00:35:58.119
That's more than double. More than double. And

00:35:58.119 --> 00:36:00.800
this isn't an isolated finding. It means pedigree

00:36:00.800 --> 00:36:03.440
-based methods often fail to capture a large

00:36:03.440 --> 00:36:06.460
portion of the actual homozygosity, the true

00:36:06.460 --> 00:36:08.659
inbreeding, that's present in our herds today.

00:36:08.820 --> 00:36:11.079
And that gap has huge economic implications.

00:36:11.369 --> 00:36:14.690
Huge. The same Italian study modeled inbreeding's

00:36:14.690 --> 00:36:16.769
impact on milk production using both methods.

00:36:17.329 --> 00:36:22.010
FPED suggested a 263 kilogram milk loss per lactation

00:36:22.010 --> 00:36:24.449
for a certain shift in inbreeding percentile.

00:36:24.489 --> 00:36:27.409
But FROH, the more accurate measure, showed a

00:36:27.409 --> 00:36:30.389
561 kilogram loss for the exact same shift in

00:36:30.389 --> 00:36:32.610
percentile based on genomics. Wow. More than

00:36:32.610 --> 00:36:35.489
double the predicted loss, too. Exactly. It means

00:36:35.489 --> 00:36:37.750
the true economic cost of inbreeding depression.

00:36:38.409 --> 00:36:41.190
that tax we keep talking about, is far greater

00:36:41.190 --> 00:36:43.369
than what was predicted by pedigree data alone.

00:36:43.730 --> 00:36:45.989
We were indeed underreporting the true level

00:36:45.989 --> 00:36:48.690
of risk, which inevitably leads to misinformed

00:36:48.690 --> 00:36:51.909
breeding decisions. So it's not just that inbreeding

00:36:51.909 --> 00:36:54.489
itself is increasing, but the type of inbreeding,

00:36:54.510 --> 00:36:56.449
particularly the kind generated in the genomic

00:36:56.449 --> 00:36:59.420
era, might be becoming more detrimental. That's

00:36:59.420 --> 00:37:01.420
a critical distinction, isn't it? It absolutely

00:37:01.420 --> 00:37:04.300
is, because not all inbreeding is created equal

00:37:04.300 --> 00:37:07.539
in terms of its immediate impact. Long ROH segments,

00:37:07.739 --> 00:37:10.340
those long stretches of identical DNA, are indicators

00:37:10.340 --> 00:37:13.280
of recent inbreeding. Meaning the shared ancestor

00:37:13.280 --> 00:37:15.320
is close up in the pedigree. Generally, yes.

00:37:15.480 --> 00:37:18.300
It means there hasn't been much time, evolutionarily

00:37:18.300 --> 00:37:21.219
speaking, for recombination, the natural shuffling

00:37:21.219 --> 00:37:23.539
of genetic material during meiosis, to break

00:37:23.539 --> 00:37:26.440
up those ancestral chromosome segments. Shorter

00:37:26.440 --> 00:37:28.809
ROH segments, on the other hand, are usually

00:37:28.809 --> 00:37:31.230
remnants of more ancient inbreeding, further

00:37:31.230 --> 00:37:33.389
back in the lineage. And the recent stuff is

00:37:33.389 --> 00:37:35.599
worse. Research consistently shows that these

00:37:35.599 --> 00:37:38.500
long, recent ROH segments have a more potent

00:37:38.500 --> 00:37:41.260
negative effect on production traits, fertility,

00:37:41.599 --> 00:37:44.219
and overall health compared to the same amount

00:37:44.219 --> 00:37:46.820
of homozygosity coming from shorter, older segments.

00:37:47.019 --> 00:37:49.760
And the rapid generation turnover and intense

00:37:49.760 --> 00:37:52.579
selection pressure that are characteristic of

00:37:52.579 --> 00:37:54.960
the genomic era. They are perfectly designed

00:37:54.960 --> 00:37:57.300
to create and then quickly propagate these more

00:37:57.300 --> 00:38:00.380
harmful, long ROH segments throughout the population.

00:38:00.380 --> 00:38:03.260
It makes the problem potentially even more acute.

00:38:03.440 --> 00:38:05.300
than just the overall percentage might suggest.

00:38:05.619 --> 00:38:07.679
This brings us right back to that cautionary

00:38:07.679 --> 00:38:10.340
tale you mentioned, Pawnee Farm Arlinda Chief.

00:38:10.920 --> 00:38:13.599
For listeners who might be unfamiliar, who was

00:38:13.599 --> 00:38:16.679
Chief and what was his, well, almost unparalleled

00:38:16.679 --> 00:38:18.869
influence on the Holstein breed? Chief. Well,

00:38:18.929 --> 00:38:21.710
born back in 1962, he was truly a legend of the

00:38:21.710 --> 00:38:24.110
dairy world, arguably one of the single most

00:38:24.110 --> 00:38:26.650
impactful sires in the history of any livestock

00:38:26.650 --> 00:38:28.989
breed, really. His daughters were just renowned

00:38:28.989 --> 00:38:31.510
for their exceptional milk production. They consistently

00:38:31.510 --> 00:38:33.849
rewrote the record books and fundamentally changed

00:38:33.849 --> 00:38:36.050
the industry's expectations for what a Holstein

00:38:36.050 --> 00:38:38.650
cow could produce. He set a new standard. He

00:38:38.650 --> 00:38:41.610
absolutely did. And his genetic influence, thanks

00:38:41.610 --> 00:38:44.829
primarily to the power of AI, spread like wildfire

00:38:44.829 --> 00:38:48.539
across the globe. He directly produced over 16

00:38:48.539 --> 00:38:51.380
,000 registered daughters. He sired something

00:38:51.380 --> 00:38:54.739
like 500 ,000 granddaughters. And estimates are

00:38:54.739 --> 00:38:57.119
over 2 million great -granddaughters. That's

00:38:57.119 --> 00:39:00.179
staggering reach. It is. And his sons also became

00:39:00.179 --> 00:39:02.639
hugely popular and influential sires in their

00:39:02.639 --> 00:39:05.079
own right. Today, even now, decades later, it's

00:39:05.079 --> 00:39:08.940
estimated that a staggering 14 -15 % of the entire

00:39:08.940 --> 00:39:11.559
modern North American Holstein genome traces

00:39:11.559 --> 00:39:14.000
directly back to him. One bull accounts for 15

00:39:14.000 --> 00:39:16.579
% of the breed's DNA. Roughly, yes. His genetic

00:39:16.579 --> 00:39:19.119
shadow is incredibly long, incredibly wide, and

00:39:19.119 --> 00:39:21.059
almost inescapable in many Holstein pedigrees.

00:39:21.320 --> 00:39:23.480
It's a testament to his perceived value back

00:39:23.480 --> 00:39:25.679
then and the incredible power of AI to propagate

00:39:25.679 --> 00:39:28.400
specific genes very quickly. So he was a production

00:39:28.400 --> 00:39:31.380
superstar revolutionizing milk yields. But baked

00:39:31.380 --> 00:39:33.539
into that unparalleled influence, as you mentioned,

00:39:33.719 --> 00:39:36.840
was a hidden ticking time bomb, a genetic defect.

00:39:37.079 --> 00:39:39.380
What was it and how did it manifest itself over

00:39:39.380 --> 00:39:41.800
time? Yeah, that's the tragic irony of Chief's

00:39:41.800 --> 00:39:44.420
story, isn't it? While he brought immense progress

00:39:44.420 --> 00:39:47.320
in production, he was also, unknowingly at the

00:39:47.320 --> 00:39:50.340
time, a carrier of a lethal recessive genetic

00:39:50.340 --> 00:39:52.800
defect. What kind of defect? This defect was

00:39:52.800 --> 00:39:55.019
later identified and named Holstein haplotype

00:39:55.019 --> 00:39:58.920
1, or HH1. Specifically, it turned out to be

00:39:58.920 --> 00:40:01.820
what's called a nonsense mutation in the APAF1

00:40:01.820 --> 00:40:05.340
gene. Essentially, it's a critical genetic typo

00:40:05.340 --> 00:40:07.920
that prematurely stops the production of a vital

00:40:07.920 --> 00:40:10.469
protein. And what does that protein do? This

00:40:10.469 --> 00:40:13.690
APAF1 protein is absolutely critical for normal

00:40:13.690 --> 00:40:15.889
fetal development, particularly for the central

00:40:15.889 --> 00:40:19.190
nervous system. Without it, the fetus can't develop

00:40:19.190 --> 00:40:22.190
properly. So it's lethal. It's lethal when homozygous,

00:40:22.250 --> 00:40:25.010
yes. Now, as a recessive condition, carriers

00:40:25.010 --> 00:40:27.389
like Chief himself appear perfectly normal. They're

00:40:27.389 --> 00:40:29.630
healthy, high -performing animals. The problem

00:40:29.630 --> 00:40:32.750
only arises if two carriers happen to mate. Right,

00:40:32.809 --> 00:40:35.309
the classic recessive inheritance pattern. Exactly.

00:40:35.800 --> 00:40:38.659
In that scenario, there's a 25 % chance that

00:40:38.659 --> 00:40:40.920
the resulting offspring will inherit two copies

00:40:40.920 --> 00:40:44.440
of the mutated APAF1 gene. And that homozygous

00:40:44.440 --> 00:40:47.360
condition is fatal. It typically leads to early

00:40:47.360 --> 00:40:50.400
embryonic loss or spontaneous abortion, often

00:40:50.400 --> 00:40:52.820
happening so early that the producer might not

00:40:52.820 --> 00:40:54.639
even realize the cow was pregnant in the first

00:40:54.639 --> 00:40:56.920
place. So for decades, this was just a mystery.

00:40:57.599 --> 00:40:59.760
Producers would maybe experience higher pregnancy

00:40:59.760 --> 00:41:02.219
losses in certain cow families, but it was just

00:41:02.219 --> 00:41:04.500
written off as bad luck or maybe some environmental

00:41:04.500 --> 00:41:06.880
factor? Pretty much, yeah. For decades, that

00:41:06.880 --> 00:41:09.179
higher -than -average pregnancy loss observed

00:41:09.179 --> 00:41:11.519
in some of Chief's descendants was an unsolved

00:41:11.519 --> 00:41:14.239
puzzle. It was often attributed to various non

00:41:14.239 --> 00:41:16.599
-genetic factors or just chalked up to the inherent

00:41:16.599 --> 00:41:19.239
difficulties and vagaries of reproduction. It

00:41:19.239 --> 00:41:21.420
wasn't linked back directly. Until genomics came

00:41:21.420 --> 00:41:24.139
along. Exactly. The genomic era was absolutely

00:41:24.139 --> 00:41:27.099
key to finally unraveling this mystery. In the

00:41:27.099 --> 00:41:30.360
early 2010s, USDA researchers, using genomic

00:41:30.360 --> 00:41:33.119
data from thousands of animals, identified a

00:41:33.119 --> 00:41:35.679
specific problematic haplotype, a segment of

00:41:35.679 --> 00:41:38.679
DNA on chromosome 5 that was clearly linked to

00:41:38.679 --> 00:41:41.219
reduced fertility in Holsteins. They found the

00:41:41.219 --> 00:41:43.980
statistical signal. They found the signal. And

00:41:43.980 --> 00:41:46.639
then crucially, they painstakingly traced that

00:41:46.639 --> 00:41:49.199
specific haplotype directly back through the

00:41:49.199 --> 00:41:51.900
pedigrees to Pawnee Farm Arlinda Chief. He was

00:41:51.900 --> 00:41:55.000
the source. Wow. With that critical lead, Geneticists

00:41:55.000 --> 00:41:57.320
at UC Davis, who happen to have Chief's full

00:41:57.320 --> 00:42:00.480
genome sequenced, were able to pinpoint the exact

00:42:00.480 --> 00:42:04.840
APAF1 mutation within that haplotype in remarkably

00:42:04.840 --> 00:42:08.019
about 24 hours. That's incredible speed. And

00:42:08.019 --> 00:42:09.960
what was the estimated impact of this single

00:42:09.960 --> 00:42:12.079
defect once they realized what was happening?

00:42:12.460 --> 00:42:14.219
both in terms of animal loss and the economic

00:42:14.219 --> 00:42:17.079
cost. The economic fallout was just staggering

00:42:17.079 --> 00:42:19.440
when they calculated it. The estimated impact

00:42:19.440 --> 00:42:22.219
included over half a million spontaneous abortions

00:42:22.219 --> 00:42:25.000
worldwide, half a million lost pregnancies, and

00:42:25.000 --> 00:42:28.099
approximately $420 million in global economic

00:42:28.099 --> 00:42:30.539
losses attributed just to this single defect

00:42:30.539 --> 00:42:35.059
carried by one bull. $420 million. That's a catastrophe

00:42:35.059 --> 00:42:38.599
by any measure. To think, one bull's hidden genetic

00:42:38.599 --> 00:42:41.519
flaw caused such widespread, costly devastation.

00:42:41.539 --> 00:42:43.739
It really was. But here's the truly critical

00:42:43.739 --> 00:42:45.659
lesson, perhaps the most challenging part of

00:42:45.659 --> 00:42:47.739
this story, what you've termed the cultural trap.

00:42:48.219 --> 00:42:51.920
Despite those massive quantifiable losses, the

00:42:51.920 --> 00:42:55.579
$420 million chief's production upside, the value

00:42:55.579 --> 00:42:57.940
he brought through milk, still made him a net

00:42:57.940 --> 00:43:01.360
positive sire overall. This set a really dangerous

00:43:01.360 --> 00:43:04.179
precedent, didn't it? This is arguably the most

00:43:04.179 --> 00:43:06.800
profound and deeply unsettling lesson from the

00:43:06.800 --> 00:43:09.829
entire chief saga. Despite causing an estimated

00:43:09.829 --> 00:43:13.110
$420 million in direct losses due to embryonic

00:43:13.110 --> 00:43:16.289
death, Chief's immense positive genetic contribution,

00:43:16.510 --> 00:43:18.349
primarily through the massive milk production

00:43:18.349 --> 00:43:20.429
gains he passed on to millions of descendants,

00:43:20.690 --> 00:43:23.969
was valued at an estimated $30 billion. $30 billion.

00:43:24.210 --> 00:43:26.570
$30 billion in added value versus $420 million

00:43:26.570 --> 00:43:29.550
in losses. So on balance, looking purely at the

00:43:29.550 --> 00:43:32.230
economics, he was overwhelmingly a net positive

00:43:32.230 --> 00:43:34.570
sire. But that created a dangerous precedent.

00:43:34.940 --> 00:43:37.579
It really did. It created a situation where a

00:43:37.579 --> 00:43:39.980
selection system optimized almost exclusively

00:43:39.980 --> 00:43:43.340
for maximizing net gain can tolerate and perhaps

00:43:43.340 --> 00:43:46.559
even implicitly reward sires carrying significant

00:43:46.559 --> 00:43:49.920
hidden risks. The system heavily weighted by

00:43:49.920 --> 00:43:52.699
production traits is designed to maximize overall

00:43:52.699 --> 00:43:55.820
net economic gain, not necessarily to preemptively

00:43:55.820 --> 00:43:58.360
avoid potential genetic catastrophes. The upside

00:43:58.360 --> 00:44:01.130
was just too tempting. A bull -like chief offering

00:44:01.130 --> 00:44:03.369
truly revolutionary production potential became

00:44:03.369 --> 00:44:06.510
almost irresistible to the industry. His immense

00:44:06.510 --> 00:44:09.929
positive value effectively overshadowed, or excused,

00:44:09.929 --> 00:44:12.730
the massive but largely hidden negative consequences

00:44:12.730 --> 00:44:15.929
of the HH1B effect. It subtly taught the industry

00:44:15.929 --> 00:44:18.010
that if the good outweighs the bad, even if the

00:44:18.010 --> 00:44:20.789
bad is significantly bad, it's acceptable, maybe

00:44:20.789 --> 00:44:23.130
even desirable, in the pursuit of progress. Which

00:44:23.130 --> 00:44:25.429
highlights a deep systemic vulnerability, doesn't

00:44:25.429 --> 00:44:27.829
it? Yeah. Extreme genetic concentration, like

00:44:27.829 --> 00:44:30.010
what we saw happen with Chief, acts as a powerful

00:44:30.010 --> 00:44:33.010
detonator for otherwise rare, maybe even benign,

00:44:33.010 --> 00:44:35.360
recessive alleles to suddenly become one. Widespread,

00:44:35.360 --> 00:44:37.820
costly epidemics. Exactly. That's the perfect

00:44:37.820 --> 00:44:41.519
word. Detonator. Deleterious recessive alleles

00:44:41.519 --> 00:44:44.280
exist at low frequencies in almost any large

00:44:44.280 --> 00:44:47.440
population. Usually they're harmless, just part

00:44:47.440 --> 00:44:49.860
of the background genetic noise because the chance

00:44:49.860 --> 00:44:52.960
of two carriers mating is very low. But when

00:44:52.960 --> 00:44:55.360
a single sire's genes are propagated so intensely

00:44:55.360 --> 00:44:58.300
that they eventually account for, say, 14 % of

00:44:58.300 --> 00:45:01.480
an entire breed's genome, like with Chief, the

00:45:01.480 --> 00:45:04.800
frequency of his specific recessive alleles skyrockets

00:45:04.800 --> 00:45:06.940
across that whole population. Making carrier

00:45:06.940 --> 00:45:09.079
-to -carrier matings much more likely. Dramatically

00:45:09.079 --> 00:45:11.659
more likely. It turns what was once a rare, maybe

00:45:11.659 --> 00:45:14.760
theoretical risk, into a widespread, costly epidemic

00:45:14.760 --> 00:45:17.199
that causes real economic harm on farms everywhere.

00:45:17.539 --> 00:45:19.800
The concentration itself doesn't create the defect,

00:45:20.000 --> 00:45:22.429
but it acts as that powerful detonator. turning

00:45:22.429 --> 00:45:25.369
a small, localized issue into a potentially breed

00:45:25.369 --> 00:45:28.610
-wide problem. And the article warns. Yeah, the

00:45:28.610 --> 00:45:30.710
article strongly warns that given the current

00:45:30.710 --> 00:45:32.710
levels of genetic concentration we see today

00:45:32.710 --> 00:45:36.590
and the narrowing list of truly influential grandsires

00:45:36.590 --> 00:45:39.050
in the Holstein breed right now, it's highly

00:45:39.050 --> 00:45:41.929
likely that multiple such genetic time bombs

00:45:41.929 --> 00:45:44.329
are being primed simultaneously within the population

00:45:44.329 --> 00:45:46.849
as we speak. That is a truly chilling thought.

00:45:47.280 --> 00:45:49.460
And it really underscores the urgency of this

00:45:49.460 --> 00:45:51.480
conversation for every single producer listening

00:45:51.480 --> 00:45:55.260
today. It absolutely does. So, okay. We've laid

00:45:55.260 --> 00:45:57.320
out the problem, the paradox, the risks, the

00:45:57.320 --> 00:46:00.619
history in pretty stark terms. Now let's pivot

00:46:00.619 --> 00:46:02.880
towards solutions. What can producers actually

00:46:02.880 --> 00:46:05.699
do on their own farms? And what valuable lessons

00:46:05.699 --> 00:46:07.420
can we learn from different approaches being

00:46:07.420 --> 00:46:10.179
used around the world? This brings us to Pathways

00:46:10.179 --> 00:46:12.699
to Genetic Independence Strategies for Diversifying

00:46:12.699 --> 00:46:15.019
Your Herd's Future. Right. And that's the crucial

00:46:15.019 --> 00:46:17.019
next step, isn't it? Because what's encouraging

00:46:17.019 --> 00:46:20.260
here is that the trajectory of relentlessly rising

00:46:20.260 --> 00:46:23.679
inbreeding isn't necessarily an inevitable, unchangeable

00:46:23.679 --> 00:46:26.460
outcome. We do have alternative blueprints out

00:46:26.460 --> 00:46:28.539
there. Models like the Cooperative Breeding Systems

00:46:28.539 --> 00:46:31.639
offer really valuable insights into how you can

00:46:31.639 --> 00:46:34.000
successfully balance aggressive genetic gain

00:46:34.000 --> 00:46:36.940
with long -term diversity and herd sustainability.

00:46:37.420 --> 00:46:38.980
And there are things producers can do themselves.

00:46:39.219 --> 00:46:42.559
Absolutely. And critically for you, the producer

00:46:42.559 --> 00:46:45.400
listening, there are very proactive, practical

00:46:45.400 --> 00:46:48.059
on -farm strategies you can implement right now

00:46:48.059 --> 00:46:50.900
to take more control of your herd's genetic future

00:46:50.900 --> 00:46:53.480
and mitigate some of these risks we've been talking

00:46:53.480 --> 00:46:55.739
about. OK, let's start with those alternative

00:46:55.739 --> 00:46:59.139
blueprints then. The cooperative model, like

00:46:59.139 --> 00:47:02.000
Viking Genetics in Scandinavia, seems to offer

00:47:02.000 --> 00:47:04.099
a fundamentally different set of incentives compared

00:47:04.099 --> 00:47:06.199
to the North American proprietary model we've

00:47:06.199 --> 00:47:08.449
largely been discussing. How does that ownership

00:47:08.449 --> 00:47:10.550
structure actually change their whole approach?

00:47:10.809 --> 00:47:13.409
It's a really critical distinction both in philosophy

00:47:13.409 --> 00:47:16.230
and in practice. Viking Genetics, for instance,

00:47:16.510 --> 00:47:19.929
is owned directly by approximately 16 ,000 dairy

00:47:19.929 --> 00:47:22.769
farmers across Denmark, Sweden, and Finland.

00:47:23.070 --> 00:47:25.929
Owned by the farmers themselves. Exactly. And

00:47:25.929 --> 00:47:27.989
that ownership structure fundamentally shifts

00:47:27.989 --> 00:47:31.010
the core incentives. Whereas a publicly traded

00:47:31.010 --> 00:47:33.989
proprietary company is primarily obligated to

00:47:33.989 --> 00:47:36.789
maximize shareholder return, which often translates

00:47:36.789 --> 00:47:39.949
into aggressively marketing the few sires topping

00:47:39.949 --> 00:47:41.989
those global indices. Right. So the winners.

00:47:42.429 --> 00:47:44.369
A farmer -owned cooperative, on the other hand,

00:47:44.429 --> 00:47:47.710
is primarily incentivized to maximize the long

00:47:47.710 --> 00:47:50.030
-term, holistic profitability and sustainability

00:47:50.030 --> 00:47:52.789
of its members' herds. The people who own the

00:47:52.789 --> 00:47:54.610
company are the same people using the product

00:47:54.610 --> 00:47:56.550
and living with the results. So their goals are

00:47:56.550 --> 00:47:58.690
aligned differently. Completely. The cooperative

00:47:58.690 --> 00:48:01.389
model naturally favors a more balanced, perhaps

00:48:01.389 --> 00:48:03.489
more risk -averse approach because its owners

00:48:03.489 --> 00:48:05.610
are the ones who directly bear the costs of things

00:48:05.610 --> 00:48:08.289
like inbreeding depression, poor health outcomes,

00:48:08.510 --> 00:48:10.909
and reduced longevity right there on their own

00:48:10.909 --> 00:48:13.500
farms. Their success is directly tied to the

00:48:13.500 --> 00:48:15.960
real -world, long -term success of their members'

00:48:16.079 --> 00:48:19.059
POWs. So that fundamental difference in incentives

00:48:19.059 --> 00:48:21.559
translates directly into their breeding goals,

00:48:21.699 --> 00:48:24.320
which they call the Nordic Total Merit, or NTM.

00:48:25.000 --> 00:48:27.980
How does that compare to the heavily production

00:48:27.980 --> 00:48:31.019
-weighted North American indices like NMU that

00:48:31.019 --> 00:48:33.679
we're so used to seeing? Yeah, the NTM is a radically

00:48:33.679 --> 00:48:36.480
different beast, really. It explicitly emphasizes

00:48:36.480 --> 00:48:39.380
a much broader, more balanced range of traits,

00:48:39.500 --> 00:48:41.760
not just focusing heavily on production like

00:48:41.760 --> 00:48:43.920
some other indices tend to do. What kind of traits?

00:48:44.159 --> 00:48:46.119
Well, their breeding objectives are carefully

00:48:46.119 --> 00:48:49.300
balanced across production, yes, but also health,

00:48:49.480 --> 00:48:52.239
reproduction, conformation, and various functional

00:48:52.239 --> 00:48:54.500
traits that contribute to longevity and ease

00:48:54.500 --> 00:48:57.429
of management. They have a long and really robust

00:48:57.429 --> 00:48:59.650
history of collecting extensive data on things

00:48:59.650 --> 00:49:02.309
like hoof health, utter health, resistance to

00:49:02.309 --> 00:49:04.889
mastitis. Things that cost farmers money. Exactly.

00:49:04.889 --> 00:49:07.090
The things that directly impact profitability

00:49:07.090 --> 00:49:10.409
beyond just the milk check. And critically, they

00:49:10.409 --> 00:49:12.610
integrate these treats directly into their selection

00:49:12.610 --> 00:49:15.090
criteria with significant weighting. And they're

00:49:15.090 --> 00:49:17.349
looking at sustainability, too. Yeah, what's...

00:49:17.900 --> 00:49:20.139
Truly pioneering, I think, is their inclusion

00:49:20.139 --> 00:49:23.039
of environmental sustainability traits. They're

00:49:23.039 --> 00:49:25.920
actively selecting for improved feed efficiency

00:49:25.920 --> 00:49:29.119
and by extension, reduced methane emissions.

00:49:29.460 --> 00:49:32.440
They actually treat these as heritable components

00:49:32.440 --> 00:49:35.400
in their index. So they're breeding for greener

00:49:35.400 --> 00:49:38.280
cows. Essentially, yes. They're building sustainability

00:49:38.280 --> 00:49:41.619
directly into the animal's DNA, offering a powerful

00:49:41.619 --> 00:49:43.780
pathway to making permanent reductions in the

00:49:43.780 --> 00:49:46.179
herd's environmental footprint over time. It's

00:49:46.179 --> 00:49:48.440
a much more holistic view, looking way beyond

00:49:48.440 --> 00:49:50.639
just the milk pail. And it's not just about what

00:49:50.639 --> 00:49:52.719
traits they select for. It sounds like they've

00:49:52.719 --> 00:49:54.780
actually built structural safeguards into their

00:49:54.780 --> 00:49:57.619
program to proactively prevent inbreeding from

00:49:57.619 --> 00:49:59.960
getting out of control, rather than just reacting

00:49:59.960 --> 00:50:02.519
to it after the fact. What do those safeguards

00:50:02.519 --> 00:50:05.070
look like? Precisely. They are very intentional

00:50:05.070 --> 00:50:07.110
about preventing those genetic bottlenecks we

00:50:07.110 --> 00:50:08.969
discussed earlier. For instance, they have an

00:50:08.969 --> 00:50:11.769
explicit policy to use a large and diverse group

00:50:11.769 --> 00:50:14.949
of sires of sons each and every year. More variety

00:50:14.949 --> 00:50:17.570
at the top. Exactly. Their Viking Jersey program,

00:50:17.769 --> 00:50:20.190
for example, aims to use around 20 different

00:50:20.190 --> 00:50:22.769
family lines annually just to ensure they maintain

00:50:22.769 --> 00:50:25.750
a broad genetic base. They also enforce strict

00:50:25.750 --> 00:50:29.489
usage limits on individual bulls. No single sire

00:50:29.489 --> 00:50:31.510
is allowed to produce more than three sons that

00:50:31.510 --> 00:50:34.050
go back into their own breeding program. And

00:50:34.050 --> 00:50:36.489
a popular bull's active marketing life is typically

00:50:36.489 --> 00:50:39.250
limited to maybe six to nine months. To keep

00:50:39.250 --> 00:50:41.829
things turning over. Exactly. To ensure constant

00:50:41.829 --> 00:50:44.610
genetic rotation and prevent any single bull

00:50:44.610 --> 00:50:46.179
from becoming a bull. becoming too dominant too

00:50:46.179 --> 00:50:49.889
quickly. Plus, they proactively monitor and manage

00:50:49.889 --> 00:50:52.630
inbreeding rates across their populations, aiming

00:50:52.630 --> 00:50:54.969
to keep the increase below the FAO's recommended

00:50:54.969 --> 00:50:58.690
maximum of 1 % per generation. They even offer

00:50:58.690 --> 00:51:01.550
proprietary mating tools, like VicMate, directly

00:51:01.550 --> 00:51:04.429
to their farmer members to help manage inbreeding

00:51:04.429 --> 00:51:06.750
decisions right there on the farm. It's a really

00:51:06.750 --> 00:51:09.530
comprehensive, proactive strategy from top to

00:51:09.530 --> 00:51:11.489
bottom. The results seem to speak for themselves,

00:51:11.849 --> 00:51:13.590
demonstrating the effectiveness of this more

00:51:13.590 --> 00:51:15.949
balanced approach. What does the actual data

00:51:15.949 --> 00:51:18.250
show in terms of of their herd health, diversity,

00:51:18.369 --> 00:51:20.730
and maybe longevity. Absolutely. The proof is

00:51:20.730 --> 00:51:23.710
in the pudding, as they say. A 2019 analysis,

00:51:23.869 --> 00:51:26.289
for instance, showed that Viking Jersey heifers

00:51:26.289 --> 00:51:28.730
had a projected future inbreeding percentage

00:51:28.730 --> 00:51:32.269
of only 4 .7%. That sounds pretty low. It is

00:51:32.269 --> 00:51:34.349
remarkably low, especially when you compare it

00:51:34.349 --> 00:51:37.230
to the projected 8 .1 % for U .S. Jersey sires

00:51:37.230 --> 00:51:39.900
at that time. That significant difference is

00:51:39.900 --> 00:51:42.460
directly attributed to the more diverse lineages

00:51:42.460 --> 00:51:44.739
and those stringent management practices they

00:51:44.739 --> 00:51:48.719
maintain in the Nordic populations. Beyond just

00:51:48.719 --> 00:51:51.059
inbreeding levels, Nordic cattle consistently

00:51:51.059 --> 00:51:54.179
demonstrate superior fertility metrics, better

00:51:54.179 --> 00:51:56.539
overall health indicators, and significantly

00:51:56.539 --> 00:51:59.239
improved longevity compared to many of the more

00:51:59.239 --> 00:52:01.460
intensively selected North American populations.

00:52:02.079 --> 00:52:04.440
They're basically building cows that last longer

00:52:04.440 --> 00:52:06.579
and likely cost less in terms of vet bills and

00:52:06.579 --> 00:52:08.539
replacement rates over their lifetime. OK, so

00:52:08.539 --> 00:52:10.820
that's the cooperative model. But beyond these

00:52:10.820 --> 00:52:12.659
types of programs, there's another alternative

00:52:12.659 --> 00:52:15.320
pathway that you mentioned is quietly gaining

00:52:15.320 --> 00:52:18.949
ground, and that's crossbreeding. For producers

00:52:18.949 --> 00:52:21.469
grappling with these inbreeding concerns in their

00:52:21.469 --> 00:52:24.590
purebred herds, what does the research say about

00:52:24.590 --> 00:52:27.130
the potential economic advantages of strategic

00:52:27.130 --> 00:52:29.750
crossbreeding? Yeah, crossbreeding is increasingly

00:52:29.750 --> 00:52:33.190
emerging as a really compelling and very practical

00:52:33.190 --> 00:52:35.889
economic alternative to sticking strictly with

00:52:35.889 --> 00:52:38.670
purebred holstein breeding, especially for producers

00:52:38.670 --> 00:52:40.809
who are looking to specifically improve resilience

00:52:40.809 --> 00:52:43.849
and maybe reduce some of those input costs. What

00:52:43.849 --> 00:52:45.920
does the research show? Research pretty consistently

00:52:45.920 --> 00:52:48.860
shows that crossbred dairy cattle often outperform

00:52:48.860 --> 00:52:51.079
their purebred contemporaries, particularly for

00:52:51.079 --> 00:52:53.480
those crucial functional traits like fertility,

00:52:53.719 --> 00:52:56.079
health, and longevity. You get that benefit of

00:52:56.079 --> 00:52:59.559
hybrid vigor or heterosis. A comprehensive Swedish

00:52:59.559 --> 00:53:02.079
study, for example, looked at systematic crossbreeding,

00:53:02.119 --> 00:53:05.039
specifically using Holstein and Swedish red in

00:53:05.039 --> 00:53:07.280
rotation, and found that it increased annual

00:53:07.280 --> 00:53:10.460
profit margins by a significant 20 to 59 euros

00:53:10.460 --> 00:53:13.789
per cow. Just from crossbreeding. from the crossbreeding

00:53:13.789 --> 00:53:16.510
system. And this improvement was primarily driven

00:53:16.510 --> 00:53:18.670
by things like enhanced fertility, getting cows

00:53:18.670 --> 00:53:21.650
pregnant faster and easier, and notably reduced

00:53:21.650 --> 00:53:24.610
replacement rates. The culling rate dropped from

00:53:24.610 --> 00:53:28.210
39 % in the purebred herds down to a much more

00:53:28.210 --> 00:53:31.289
sustainable 30 % in the rotational crossbreeding

00:53:31.289 --> 00:53:33.349
systems. That's a huge difference in replacement

00:53:33.349 --> 00:53:35.590
costs right there. It really is. That alone can

00:53:35.590 --> 00:53:38.170
have a massive impact on your bottom line. And

00:53:38.170 --> 00:53:39.750
it's not just dairy -on -dairy crossbreeding

00:53:39.750 --> 00:53:41.570
that's gaining traction, right? Yeah. Beef -on

00:53:41.570 --> 00:53:43.380
-dairy. seems to be exploding and becoming a

00:53:43.380 --> 00:53:46.420
major income stream for many operations. Indeed.

00:53:46.840 --> 00:53:49.099
Beef on dairy crossbreeding programs are definitely

00:53:49.099 --> 00:53:51.519
seeing strong interest and often command significant

00:53:51.519 --> 00:53:54.480
premiums. They're becoming a major profit center

00:53:54.480 --> 00:53:58.239
for many dairies. A 2024 Purina survey actually

00:53:58.239 --> 00:54:01.199
found that 80 % of dairy farmers reported receiving

00:54:01.199 --> 00:54:04.980
$350 to $700 per head more for their beef dairy

00:54:04.980 --> 00:54:07.159
cross calves compared to what they'd get for

00:54:07.159 --> 00:54:09.280
purebred Holstein bull calves. Wow, that's serious

00:54:09.280 --> 00:54:12.269
money. It is. And these programs also offer direct

00:54:12.269 --> 00:54:14.570
benefits back on the dairy side, too. Things

00:54:14.570 --> 00:54:16.769
like potentially improved conception rates when

00:54:16.769 --> 00:54:19.670
using beef semen on certain cows, reduced calving

00:54:19.670 --> 00:54:22.170
difficulties, lower stillbirth rates compared

00:54:22.170 --> 00:54:25.289
to using some large frame dairy sires. And for

00:54:25.289 --> 00:54:28.480
specific challenges like heat stress. Yeah. For

00:54:28.480 --> 00:54:30.739
challenges like heat tolerance, crossbreeding

00:54:30.739 --> 00:54:33.079
with genetics from naturally heat adapted breeds,

00:54:33.380 --> 00:54:36.159
maybe some boss indicus influence or breeds selected

00:54:36.159 --> 00:54:38.659
in hot climates can offer much more immediate

00:54:38.659 --> 00:54:41.159
and robust solutions than solely relying on the

00:54:41.159 --> 00:54:44.340
slower incremental process of genomic selection

00:54:44.340 --> 00:54:46.380
for heat tolerance within a purebred population

00:54:46.380 --> 00:54:49.440
like Holstein's. It's often a faster path to

00:54:49.440 --> 00:54:51.320
getting resilience for that specific trait. You

00:54:51.320 --> 00:54:54.260
get a dual benefit of the calf value and the

00:54:54.260 --> 00:54:56.340
adaptation. All these insights are incredibly

00:54:56.340 --> 00:54:58.500
valuable. looking at co -ops, crossbreeding.

00:54:58.679 --> 00:55:00.960
But for the busy producer, maybe sticking with

00:55:00.960 --> 00:55:03.539
purebreds for now, what about technology solutions

00:55:03.539 --> 00:55:06.619
that might be on the horizon? We're awash in

00:55:06.619 --> 00:55:08.719
data these days, but how can that data infrastructure

00:55:08.719 --> 00:55:11.679
truly help manage this complex genetic challenge

00:55:11.679 --> 00:55:14.719
of balancing gain and inbreeding? This is where

00:55:14.719 --> 00:55:17.300
the future truly lies, I believe. The Council

00:55:17.300 --> 00:55:20.500
on Dairy Cattle Breeding, or CDCB, maintains

00:55:20.500 --> 00:55:22.940
the National Cooperator Database here in the

00:55:22.940 --> 00:55:25.320
U .S. And that is absolutely the foundational

00:55:25.320 --> 00:55:28.159
asset for everything we do genetically. How big

00:55:28.159 --> 00:55:30.599
is it? It contains performance records on over

00:55:30.599 --> 00:55:33.400
87 million animals, and it integrates something

00:55:33.400 --> 00:55:36.769
like 10 million genotypes now. It is, without

00:55:36.769 --> 00:55:39.130
question, the world's largest and most comprehensive

00:55:39.130 --> 00:55:41.650
database of its kind for any livestock species.

00:55:42.150 --> 00:55:45.150
This massive repository powers all the genetic

00:55:45.150 --> 00:55:47.829
tools we currently use, and it's where future

00:55:47.829 --> 00:55:49.989
solutions will come from. So what tools exist

00:55:49.989 --> 00:55:52.230
now, maybe under the hood? Well, we actually

00:55:52.230 --> 00:55:54.860
already have... tools within that system, such

00:55:54.860 --> 00:55:57.239
as something called expected future inbreeding,

00:55:57.300 --> 00:56:01.260
or EFI. The CDCB uses EFI internally right now.

00:56:01.400 --> 00:56:04.000
It essentially measures an animal's average genetic

00:56:04.000 --> 00:56:06.380
relationship to the current population. Okay.

00:56:06.559 --> 00:56:09.260
And they use it to subtly adjust the published

00:56:09.260 --> 00:56:12.119
predicted transmitting abilities, the PTAs, to

00:56:12.119 --> 00:56:14.420
account for expected inbreeding depression in

00:56:14.420 --> 00:56:17.119
future offspring. It effectively penalizes sires

00:56:17.119 --> 00:56:19.260
that are highly related to the current population.

00:56:19.559 --> 00:56:21.760
But you're saying that's hidden. Exactly. Here's

00:56:21.760 --> 00:56:24.739
the rub. This crucial piece of information, this

00:56:24.739 --> 00:56:26.860
EFI adjustment, is currently done behind the

00:56:26.860 --> 00:56:28.960
scenes. It's hidden from you, the end user, the

00:56:28.960 --> 00:56:31.739
producer, making the mating decision. You don't

00:56:31.739 --> 00:56:33.219
see that penalty being applied when you look

00:56:33.219 --> 00:56:35.659
at the final index number. So the next frontier

00:56:35.659 --> 00:56:38.260
isn't just generating more data. It's about making

00:56:38.260 --> 00:56:41.420
that existing data and maybe new calculations,

00:56:41.599 --> 00:56:45.019
actionable, visible, and maybe even prescriptive

00:56:45.019 --> 00:56:47.440
to the producer. What should we realistically

00:56:47.440 --> 00:56:50.599
expect to see in the next, say, 5 to 10 years

00:56:50.599 --> 00:56:53.139
that will really empower farmers in this area?

00:56:53.300 --> 00:56:55.820
Precisely. Visibility and usability are key.

00:56:56.079 --> 00:56:59.800
The goal for the near future, say 2025 to 2030,

00:57:00.159 --> 00:57:02.219
is a fundamental shift from these hidden background

00:57:02.219 --> 00:57:05.099
adjustments to truly actionable, breeder -facing

00:57:05.099 --> 00:57:07.500
dashboards. What would that look like? Well,

00:57:07.519 --> 00:57:09.940
we envision relatedness risk dashboards integrated

00:57:09.940 --> 00:57:12.599
directly into your everyday herd management software.

00:57:13.199 --> 00:57:15.059
These dashboards would clearly show a bull's

00:57:15.059 --> 00:57:17.840
EFI number, sure, but also, crucially, for any

00:57:17.840 --> 00:57:19.760
mating you propose on screen, it would calculate

00:57:19.760 --> 00:57:22.219
and display a precise estimate of the expected

00:57:22.219 --> 00:57:25.159
FROH, that accurate genomic inbreeding measure

00:57:25.159 --> 00:57:27.719
for the resulting calf. So you'd see the inbreeding

00:57:27.719 --> 00:57:29.920
consequence before you make the mating. Exactly.

00:57:30.019 --> 00:57:33.119
It makes the tradeoff between the potential genetic

00:57:33.119 --> 00:57:35.980
gain from that bull, and the resulting inbreeding

00:57:35.980 --> 00:57:38.679
level completely visible right there at the exact

00:57:38.679 --> 00:57:40.599
moment you're making the breeding decision. No

00:57:40.599 --> 00:57:42.940
more flying blind. And the ultimate goal? The

00:57:42.940 --> 00:57:45.599
ultimate goal, maybe a bit further out, is AI

00:57:45.599 --> 00:57:49.840
-powered portfolio optimization. Think prescriptive,

00:57:49.840 --> 00:57:53.219
multi -objective mating programs. You input your

00:57:53.219 --> 00:57:56.119
herd's genomic data, your specific breeding goals,

00:57:56.300 --> 00:57:59.719
milk, health, fertility, et cetera, and importantly,

00:57:59.940 --> 00:58:02.059
your defined risk tolerance for inbreeding, say,

00:58:02.239 --> 00:58:04.739
you tell the system, I don't want any calves

00:58:04.739 --> 00:58:08.840
over 8 % ROH. The program would then analyze

00:58:08.840 --> 00:58:11.519
all available sires and recommend an optimized

00:58:11.519 --> 00:58:14.099
portfolio of bulls to use across your herd to

00:58:14.099 --> 00:58:16.199
best meet your goals while staying within your

00:58:16.199 --> 00:58:18.409
risk limits. It's a fundamental shift away from

00:58:18.409 --> 00:58:21.010
just picking the single best bull toward strategically

00:58:21.010 --> 00:58:23.530
engineering the optimal genetic future for your

00:58:23.530 --> 00:58:25.769
entire herd, consciously balancing immediate

00:58:25.769 --> 00:58:27.929
gains with that crucial long -term resilience.

00:58:28.349 --> 00:58:30.869
That sounds like a really powerful future, putting

00:58:30.869 --> 00:58:32.769
more control back in the hands of the producer.

00:58:33.530 --> 00:58:36.409
Okay, so after this incredibly detailed deep

00:58:36.409 --> 00:58:39.449
dive, for a farmer listening today, maybe feeling

00:58:39.449 --> 00:58:41.230
a bit overwhelmed but wanting to take action,

00:58:41.409 --> 00:58:45.869
what are the truly key takeaways? practical actionable

00:58:45.869 --> 00:58:49.030
steps can they implement starting like tomorrow

00:58:49.030 --> 00:58:52.250
to navigate this genomic paradox? Yeah, it absolutely

00:58:52.250 --> 00:58:54.530
can feel like a lot, but it really boils down

00:58:54.530 --> 00:58:57.590
to a few critical practical steps you can take

00:58:57.590 --> 00:58:59.909
right now to start reclaiming some genetic independence

00:58:59.909 --> 00:59:02.510
and protect your herd's long -term profitability

00:59:02.510 --> 00:59:05.090
and resilience. You don't have to wait for future

00:59:05.090 --> 00:59:07.159
tech. Okay, first up, it sounds like it's all

00:59:07.159 --> 00:59:09.019
about knowing your numbers, right? You have to

00:59:09.019 --> 00:59:11.179
calculate your herd's actual genomic inbreeding

00:59:11.179 --> 00:59:13.739
level and then critically set a hard target for

00:59:13.739 --> 00:59:16.460
it. Don't just let it drift. Exactly. Step one

00:59:16.460 --> 00:59:18.760
is know where you stand. You need to leverage

00:59:18.760 --> 00:59:21.039
the tools that are already available, likely

00:59:21.039 --> 00:59:23.219
through your herd management software or maybe

00:59:23.219 --> 00:59:25.940
your breed association or AI company nominator

00:59:25.940 --> 00:59:29.099
portals, which link to the CDCB data. Use them

00:59:29.099 --> 00:59:32.019
to actually find your herd's average genomic

00:59:32.019 --> 00:59:34.659
inbreeding level specifically. Look for that

00:59:34.659 --> 00:59:36.860
FROH number if it's available, as that's the

00:59:36.860 --> 00:59:39.159
most accurate. And then quantify the cost. Yes.

00:59:39.260 --> 00:59:42.280
Then quantify that inbreeding tax for your specific

00:59:42.280 --> 00:59:45.800
operation. Do the math. Multiply each percentage

00:59:45.800 --> 00:59:48.679
point your herd average is above, say, a baseline

00:59:48.679 --> 00:59:53.079
of 5 % FROH by that. $23 figure per cow. That

00:59:53.079 --> 00:59:55.099
gives you a real dollar estimate of your current

00:59:55.099 --> 00:59:57.639
annual losses due to inbreeding. Seeing that

00:59:57.639 --> 00:59:59.760
number can be very motivating. I bet. And then

00:59:59.760 --> 01:00:02.039
set a target. And once you have that data, establish

01:00:02.039 --> 01:00:04.880
a firm, explicit target for your herd. Maybe

01:00:04.880 --> 01:00:07.519
that's keeping the average below 8 % FROH, or

01:00:07.519 --> 01:00:09.500
maybe ensuring no individual mating exceeds 10

01:00:09.500 --> 01:00:12.119
% FROH. Whatever it is, write it down and force

01:00:12.119 --> 01:00:14.039
it at mating time. Don't let breeding decisions

01:00:14.039 --> 01:00:16.760
be purely index -driven guesswork when it comes

01:00:16.760 --> 01:00:19.260
to inbreeding risk. Make it data -driven. That

01:00:19.260 --> 01:00:22.880
makes perfect sense. Okay, step two. It's about

01:00:22.880 --> 01:00:25.519
consciously breaking free from that narrow genetic

01:00:25.519 --> 01:00:28.000
funnel we discussed earlier. You recommend something

01:00:28.000 --> 01:00:30.539
called portfolio breeding, a more strategic,

01:00:30.719 --> 01:00:33.619
diversified way to select sires. Yeah, this is

01:00:33.619 --> 01:00:35.619
about not putting all your genetic eggs in one

01:00:35.619 --> 01:00:38.300
basket. Implement a disciplined, maybe something

01:00:38.300 --> 01:00:42.000
like a 40 -20 breeding split as a starting point.

01:00:42.139 --> 01:00:45.159
How does that work? 40 -40 -20? Okay, so... Allocate

01:00:45.159 --> 01:00:47.260
maybe 40 % of your matings for what you might

01:00:47.260 --> 01:00:50.659
call income insurance. Use those proven high

01:00:50.659 --> 01:00:53.420
index bulls, the chart toppers, but use them

01:00:53.420 --> 01:00:55.840
strategically on your best, most productive cows.

01:00:56.059 --> 01:00:57.960
Maybe those in their optimal breeding windows

01:00:57.960 --> 01:00:59.599
where you want the highest chance of a great

01:00:59.599 --> 01:01:02.059
replacement. Okay, 40 % top index. Then dedicate

01:01:02.059 --> 01:01:05.780
another 40 % to balanced performers. Select solid,

01:01:05.900 --> 01:01:08.179
reliable bulls, but specifically look for ones

01:01:08.179 --> 01:01:10.500
from less related sire families. Maybe slightly

01:01:10.500 --> 01:01:13.059
lower index, but still good all around bulls.

01:01:13.340 --> 01:01:15.460
This helps maintain steady progress while actively

01:01:15.460 --> 01:01:17.659
managing and diluting the overall inbreeding

01:01:17.659 --> 01:01:20.159
level in the herd. Right. Good solid bulls, less

01:01:20.159 --> 01:01:23.960
related. And the last 20%. And finally, commit

01:01:23.960 --> 01:01:27.280
to that last 20 % explicitly to diversity builders.

01:01:27.599 --> 01:01:31.039
This means actively, intentionally choosing true

01:01:31.039 --> 01:01:33.320
outcrossed genetics, maybe from different breeds

01:01:33.320 --> 01:01:36.420
in a crossbreeding system, or maybe sires from

01:01:36.420 --> 01:01:38.739
completely unrelated lines within your breed,

01:01:38.880 --> 01:01:42.139
even if their index isn't stellar. or perhaps

01:01:42.139 --> 01:01:45.539
engaging in strategic beef on dairy here, this

01:01:45.539 --> 01:01:48.440
portion is your hedge against those systemic

01:01:48.440 --> 01:01:51.579
risks, like a new genetic defect emerging or

01:01:51.579 --> 01:01:53.719
unexpected environmental shifts down the road.

01:01:53.860 --> 01:01:55.820
And you mentioned thinking about timing, too.

01:01:56.000 --> 01:01:57.800
Yeah, you could even think about seasonal timing.

01:01:57.940 --> 01:02:00.239
Maybe use more diversity builders in the spring,

01:02:00.400 --> 01:02:02.420
test some heat -tolerant outcrosses in the summer

01:02:02.420 --> 01:02:04.780
matings, and then blend your index leaders with

01:02:04.780 --> 01:02:07.340
balanced performers in the fall. The point is

01:02:07.340 --> 01:02:09.719
to build a balanced genetic portfolio over time,

01:02:09.840 --> 01:02:11.579
just like you would with your financial investments.

01:02:11.760 --> 01:02:14.340
Don't chase just one number. So those outcrossed

01:02:14.340 --> 01:02:16.440
doses, the ones in that 20 % maybe, which might

01:02:16.440 --> 01:02:19.260
not top the charts on conventional indices, they're

01:02:19.260 --> 01:02:21.460
actually incredibly valuable in the long run.

01:02:21.539 --> 01:02:23.500
It's about treating them like an insurance premium

01:02:23.500 --> 01:02:25.619
for your herd's future resilience, isn't it?

01:02:25.920 --> 01:02:28.039
Absolutely. You have to shift your mindset here.

01:02:28.280 --> 01:02:31.360
Understand that genuine outcross sires will likely

01:02:31.360 --> 01:02:34.820
appear 100, maybe 200 points lower on conventional

01:02:34.820 --> 01:02:38.260
indices like NMR. And that's precisely a feature,

01:02:38.300 --> 01:02:40.860
not a bug. of their genetic difference. That's

01:02:40.860 --> 01:02:43.179
why they're outcrosses. Right. Recognize their

01:02:43.179 --> 01:02:46.599
true long -term value. They pay dividends not

01:02:46.599 --> 01:02:49.139
necessarily on this month's milk check, but when

01:02:49.139 --> 01:02:51.119
environmental stress hits hard, when disease

01:02:51.119 --> 01:02:54.000
pressure amounts unexpectedly, or when, heaven

01:02:54.000 --> 01:02:56.280
forbid, a hidden recessive defect like CHEFS

01:02:56.280 --> 01:02:59.380
HH1 suddenly surfaces in the mainstream lines.

01:02:59.480 --> 01:03:02.059
To protect you from the unknowns. Exactly. Prioritize

01:03:02.059 --> 01:03:04.739
those long -term savings. Remember, avoiding

01:03:04.739 --> 01:03:07.239
just an extra 5 % inbreeding in your herd saves

01:03:07.239 --> 01:03:10.579
you roughly $115. per cow over her lifetime.

01:03:10.860 --> 01:03:14.159
That $150 saving often beats chasing 100 index

01:03:14.159 --> 01:03:16.099
points that never actually make it to your milk

01:03:16.099 --> 01:03:18.039
check anyway because of the hidden drag from

01:03:18.039 --> 01:03:20.239
that inbreeding tax. That's a powerful calculation.

01:03:20.780 --> 01:03:24.099
Okay, fourth point. Producers need to be more

01:03:24.099 --> 01:03:26.559
proactive, maybe more vocal in their interactions

01:03:26.559 --> 01:03:28.480
with their AI providers and in their overall

01:03:28.480 --> 01:03:30.920
sourcing decisions. It's about demanding transparency

01:03:30.920 --> 01:03:33.480
and consciously diversifying your genetic suppliers.

01:03:34.119 --> 01:03:36.699
Yes, absolutely. Don't be a passive consumer.

01:03:37.000 --> 01:03:40.800
Demand transparency from your AI providers. Actively

01:03:40.800 --> 01:03:43.860
ask for genomic relationship data or expected

01:03:43.860 --> 01:03:46.780
future inbreeding EFI information for the bulls

01:03:46.780 --> 01:03:49.440
you're considering using. Ask about the ROH levels

01:03:49.440 --> 01:03:51.539
if they track them. Challenge the status quo.

01:03:51.820 --> 01:03:53.880
Yeah, challenge restrictive contracts if they

01:03:53.880 --> 01:03:55.920
limit your genetic access or lock you into using

01:03:55.920 --> 01:03:59.239
only certain sire lines. And importantly, look

01:03:59.239 --> 01:04:02.320
beyond just the conventional North American catalogs

01:04:02.320 --> 01:04:04.860
from the dominant players. Explore options from

01:04:04.860 --> 01:04:07.139
cooperative programs, maybe European breeding

01:04:07.139 --> 01:04:09.039
programs known for different trade emphasis,

01:04:09.199 --> 01:04:12.280
or look for smaller, specialized AI studs that

01:04:12.280 --> 01:04:14.480
might be focused specifically on providing genuine

01:04:14.480 --> 01:04:17.000
outcross options. Don't be afraid to shop around.

01:04:17.119 --> 01:04:20.000
Exactly. Don't be afraid to negotiate for diversity

01:04:20.000 --> 01:04:22.480
bundles or to work with multiple companies to

01:04:22.480 --> 01:04:24.619
source the different components of that 40 -40

01:04:24.619 --> 01:04:27.300
-20 portfolio we talked about. Remember, you

01:04:27.300 --> 01:04:29.539
are the customer, and collective demand from

01:04:29.539 --> 01:04:31.719
producers has the power to shape the market and

01:04:31.719 --> 01:04:33.869
encourage suppliers. to offer more reverse genetics.

01:04:34.210 --> 01:04:37.530
Good point. Finally, number five. It's about

01:04:37.530 --> 01:04:40.349
maybe changing what you track and value on your

01:04:40.349 --> 01:04:43.469
own farm. Go beyond just looking at milk weights

01:04:43.469 --> 01:04:46.349
to truly understand your herd's overall performance

01:04:46.349 --> 01:04:49.409
and crucially, its long -term profitability.

01:04:50.230 --> 01:04:53.329
Exactly. You need to expand your KPI dashboard

01:04:53.329 --> 01:04:56.389
beyond just the bulk tank readings. Monitor those

01:04:56.389 --> 01:04:58.710
key performance indicators that reflect resilience

01:04:58.710 --> 01:05:01.110
and longevity. Things like daughter pregnancy

01:05:01.110 --> 01:05:04.849
rate, DPR, conception rates, days open, average

01:05:04.849 --> 01:05:07.409
productive life in the herd, and the actual incidence

01:05:07.409 --> 01:05:10.030
rates of costly health events like mastitis,

01:05:10.150 --> 01:05:12.409
metritis, lameness. The things that really determine

01:05:12.409 --> 01:05:15.789
if a cow is profitable long term. Precisely.

01:05:15.789 --> 01:05:18.329
These metrics are absolutely crucial for measuring

01:05:18.329 --> 01:05:20.909
the true long -term profitability and the resilience

01:05:20.909 --> 01:05:23.150
that's being generated per stall in your barn.

01:05:23.329 --> 01:05:25.750
They tell you if your cows are not just producing

01:05:25.750 --> 01:05:27.929
high volumes for a short time but are also staying

01:05:27.929 --> 01:05:30.329
healthy, breeding back consistently and critically,

01:05:30.570 --> 01:05:33.150
staying in the herd for more lactations. Because

01:05:33.150 --> 01:05:34.929
that's ultimately where the real sustainable

01:05:34.929 --> 01:05:38.000
profit lies in dairying. That's incredibly insightful

01:05:38.000 --> 01:05:41.019
and really powerful practical guidance for producers

01:05:41.019 --> 01:05:43.639
listening today. OK, if we leave our listeners

01:05:43.639 --> 01:05:46.820
with just one final provocative thought today,

01:05:46.900 --> 01:05:49.500
something to really mull over after they turn

01:05:49.500 --> 01:05:51.739
this off, what would it be? It would probably

01:05:51.739 --> 01:05:55.000
be this. The industry's current trajectory towards

01:05:55.000 --> 01:05:58.079
ever increasing inbreeding isn't some irreversible

01:05:58.079 --> 01:06:00.519
law of nature. It's the result of collective

01:06:00.519 --> 01:06:04.360
choices. And those choices can be changed. Accepting

01:06:04.360 --> 01:06:07.460
a little less index gain on paper today by consciously

01:06:07.460 --> 01:06:10.559
baking in genetic diversity will very often pay

01:06:10.559 --> 01:06:13.340
far greater real -world dividends in three years'

01:06:13.460 --> 01:06:16.159
time than simply taking another lap around the

01:06:16.159 --> 01:06:18.639
same increasingly narrow pedigrees ever will.

01:06:18.800 --> 01:06:20.989
So the question is... So the fundamental question

01:06:20.989 --> 01:06:23.150
for every producer listening is, is your herd's

01:06:23.150 --> 01:06:25.510
genetic portfolio truly built just for today's

01:06:25.510 --> 01:06:27.670
leaderboard snapshot? Or is it strategically

01:06:27.670 --> 01:06:29.909
constructed for tomorrow's inevitable challenges

01:06:29.909 --> 01:06:32.630
and ultimately for long -term resilience and

01:06:32.630 --> 01:06:34.750
profitability? A very important question indeed.

01:06:35.260 --> 01:06:37.219
Those are truly vital points for anyone in the

01:06:37.219 --> 01:06:39.219
dairy business to consider. And this has been

01:06:39.219 --> 01:06:42.340
a really enlightening deep dive into a critically

01:06:42.340 --> 01:06:44.900
important topic. For more articles and insights

01:06:44.900 --> 01:06:46.940
that matter to dairy producers, be sure to visit

01:06:46.940 --> 01:06:51.079
the website at www .thebullvine .com. And don't

01:06:51.079 --> 01:06:52.719
forget to follow The Bullvine on your favorite

01:06:52.719 --> 01:06:54.780
social media platforms as well. Thanks so much

01:06:54.780 --> 01:06:56.940
for listening. That's a wrap on today's episode

01:06:56.940 --> 01:06:59.699
of The Bullvine Podcast. We've just explored

01:06:59.699 --> 01:07:02.860
how the dairy industry's pursuit of genetic perfection

01:07:03.550 --> 01:07:06.570
has created a billion -dollar hidden tax that's

01:07:06.570 --> 01:07:09.929
bleeding operations nationwide. But more importantly,

01:07:10.150 --> 01:07:12.610
we've shown you practical steps to fight back.

01:07:13.150 --> 01:07:16.309
Remember, the producers building genetic independence

01:07:16.309 --> 01:07:19.170
now, while outcross options are still available,

01:07:19.449 --> 01:07:22.349
will have the most resilient and profitable herds

01:07:22.349 --> 01:07:25.409
by 2030. Don't let your breeding decisions be

01:07:25.409 --> 01:07:27.730
dictated by a system designed to concentrate

01:07:27.730 --> 01:07:30.769
power rather than maximize your long -term success.

01:07:31.760 --> 01:07:34.039
Make sure to subscribe to The Bullvine Podcast

01:07:34.039 --> 01:07:37.000
wherever you listen and share this episode with

01:07:37.000 --> 01:07:39.280
fellow dairy producers who need to hear this

01:07:39.280 --> 01:07:42.019
message. For more cutting -edge dairy intelligence

01:07:42.019 --> 01:07:45.440
and investigative reporting, visit us at www

01:07:45.440 --> 01:07:50.099
.thebullvine .com. Until next time, keep questioning

01:07:50.099 --> 01:07:52.539
the conventional wisdom and keep building the

01:07:52.539 --> 01:07:56.139
future of dairy on your terms. This is The Bullvine,

01:07:56.260 --> 01:07:58.880
where we never stop digging for the truth that

01:07:58.880 --> 01:08:00.369
matters. to your outreach.