#64 - Dale Woerner, Redoing Red Meat Yield

Dr. Dale R. Woerner, Professor and Cargill Endowed Professor, Department of Animal and Food Sciences, Texas Tech University, is an academic professional and meat and food industry consultant. Dr. Woerner earned his B.S. and M.S. degrees in Animal Science, Meat and Food Industry Management from Texas Tech University in 2003 and 2005, respectively, and earned his Ph.D. in Animal Science/Meat Science from Colorado State University in 2009.  He served on the faculty at Colorado State University for 9 years, and recently joined the faculty at Texas Tech University in an endowed chair position. He has conducted more that 12 million dollars in industry funded research and has published more than 300 scholarly works. Dale has served as a member of the National Cattlemen's Beef Association's Beef Innovations Group, has served as the president of the AMSA Intercollegiate Meat Coaches Association, serves as chairman of the National 4-H Meat Judging Advisory Committee,  and is a member of the American Meat Science Association Board of Directors.  In 2013, Dale was selected by Vance Publishing as a “40 Under 40” honoree as a young leader in the agricultural industry contributing to the 2050 challenge, the challenge of feeding 9 billion people by 2050. Also, Dale was named one of the Top Ten Industry Leaders for 2014 by Cattle Business as a “Difference Maker”, was recognized along with his wife Wendy in 2014 by Texas Tech as Horizon Award Recipients, in 2015 he received the American Meat Science Associations Achievement Award, in 2018 was recognized by the American Society of Animal Sciences with the Early Career Achievement Award, and, was recognized in 2018 as a Distinguished Alumni by the TTU College of Agricultural Sciences and Natural Resources. Also, in 2018, Dr. Woerner was selected by the American Meat Science Association membership to be the chairman of the Reciprocal Meat Conference, the premier meat science conference in North America. Most recently, he was awarded the North American Meat Institute's 2018 Harry L. Rudnick Educator's Award.

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Episode Transcript

Microphone (Yeti Stereo (00:02):
Thanks for joining us for episode 64 of
practically ranching.
I'm Matt Perrier and we're here.
Thanks to Dalebanks Angus,Eureka, Kansas.
Our catalogs are available forour 52nd annual bull sale to be
held Saturday, November 23rd,Northwest of Eureka.
If you'd like to receive acatalog, go to Dalebanks.com,

(00:23):
drop us a request, or just usethe sortable online version to
custom sort doubles specificallyfor your herds needs.
Dr.
Dale Woerner is the Cargillendowed professor of sustainable
meat science at Texas techuniversity.
He spent 15 years in the meatscience space.
And he's been involved invarious industry leading

(00:45):
research projects.
In addition to coaching andleading meats, judging teams at
various levels.
And now serving as chairman ofthe national four H meat judging
advisory committee.
And through that part in for mymeat judging kids who will
appreciate that.
One of his most visible projectsthat he is involved in today,
surrounds updating or totallyrebuilding really the system for

(01:10):
estimating red meat yield andbeef carcasses that age old one
through five numbering systemthat most of us know as yield
grade.
We cover some of the history ofyield grading, the advent of
some new tools and technologiesthat have inspired this much
discussed change.
And what it could mean forcarcass valuation.

(01:32):
Evaluation and even geneticselection.
And while most of us don'tlikely spend a ton of time in
the meat cooler anymore.
And we certainly probably can'trecite the old equation that we
learned in meat science or onthe grading rail.
It nevertheless has bearing onhow we value carcasses and
cattle today.
So, whether you're a meat heador you just want to stay current

(01:55):
with how we could be evaluatingand selecting for credibility
and rep red meat yield in thefuture.
I think this discussion will bea good one.
So as always, thanks again forlistening and enjoy this
conversation with Dale Woerner.

matt_2_10-29-2024_141856 (02:12):
So the last time that I got to hear you
speak was actually about a monthago in Kansas city.

I was impressed in two ways: number one, I, I was fascinated. (02:18):
undefined
I'd read some of your stuff andknew some of the projects you
were working on and meet scienceside of things and credibility.
And, and so I kind of, that partwas a given, but then after you
spoke, you vanished and, theybrought everybody back on stage
to have this panel discussionand they said, well, Dr.

(02:39):
Werner can't be here.
He has a son playing footballtonight and he's going home to
see his kid.
And I thought.
That's my kind of people.
I like it.
I like it.
So that would have beenThursday, late September.
I don't even know where the gamewould have been, but hopefully
you won.

squadcaster-2ab1_2_10-29-2 (02:58):
Yes, we did.
And it was just just a juniorhigh game.
he's a seventh grader.
So it's his first year.
And I vowed never to miss any ofthose football games.
And then Kelly Ritalik, youknow, at Angus called and was
like, Hey, I really want you todo this.
And I was like, Well, I can'tbecause Because I can't make the

(03:19):
schedule work and so Anyway, sheultimately Decided that I didn't
have to be on the panel that Icould just talk and leave but I
was going to join virtually andthey had a they had an issue
there with something on theirend Not being able to make that
happen.
So I wasn't able to do that.

matt_2_10-29-2024_141856 (03:41):
I remember, I remember them saying
that they thought they weregoing to be able to get it done,
but, uh, but weren't, but, uh,you were, you were missed, but
you were in the right spot andI, uh, I applaud your
priorities.
I've actually got, uh, my wifeand I have a, uh, Junior high
football player had a game thatnight as well.
Henry is in eighth grade andluckily I was in driving

(04:01):
distance to his game prettyeasily and so I was, I was able
to make it in plenty of time.
But, uh, you had a little, alittle longer haul than what I
did to get back to, to Lubbockor wherever he was playing.
So is he, are they still goingor are they season over?

squadcaster-2ab1_2_10-29-20 (04:16):
no, they are yet this week and next
and then Then we're done.
Um, but I did look into thedriving too.
I was like, well, you know, theflight doesn't work mainly
because of the connection time.
And, uh, well, I mean the flightdid work, but I couldn't get a
later flight was the reality.

(04:37):
And so, but anyway, it was toofar to drive.
It was like 10 hours.
And so it wouldn't work,

matt_2_10-29-2024_141856 (04:42):
Yep.
Well, I'm glad you made it.
And, and I was glad that I gotto hear at least your main
comments, even if you weren'tthere for the panel.
So before we get into that, andthat's, that's the main thing I
would like to talk about todayis the, your thoughts on the
beef yield grade equation and,and, um, how we make that a
little bit more current, uh, butgive us a feel for what all you

(05:05):
do there at tech.
And I guess some of yourbackground brings you to where
you are today.
I know you've a long list onyour, on your CV and resume,
everything from coachingintercollegiate meats judging to
all kinds of different meatscience, but give us a feel for,
for what what brought you toLubbock up to now.

squadcaster-2ab1_2_10-29- (05:24):
yeah, absolutely.
You know, traditionally trainedas a meat scientist.
I've been working, I guess,professionally in that space for
15 years now.
I spent my first nine years as afaculty member at Colorado State
in Fort Collins, working with areally great team of meat

(05:44):
scientists there.
Before going to Colorado State,however, I did get an undergrad
and a masters here at TexasTech.
And so I did spend six yearsworking on my early education
here.
Prior to being a faculty at CSU,I worked on a PhD, with, uh,
Daryl Tatum and Keith Belk, inthat meat science group.

(06:08):
Had some exposure to the greatGary Smith, uh, in the meat
science area as well, uh, servedon my committee there.
So, that's why I went to CSU,was to work with those great,
meat scientists on the facultythere at that time.
And was lucky enough at the endof that PhD to have an
opportunity to stay.

(06:30):
So during that time, just kindof cutting my teeth as a
academic faculty, uh, stayedpretty wide open to anything
that, uh, was fundable to doresearch on.
So wide variety of, you know,livestock production systems,
grading, nutritional work, uh,composition of meat measurement,

(06:54):
uh, food safety, lots and lotsof opportunities there, during
my early career and, wouldgenerally have characterized
myself as a jack of all tradesand kind of a master of none at
that point.
opportunity Came up here atTexas Tech.
Cargill had provided asubstantial gift to the

(07:15):
university here for the purposeof securing an additional meat
science position.
Cargill's concern then andperhaps still now is making sure
that we have enough peopletrained in the meat industry to
provide leadership and workforcefor meat processing plants, but

(07:39):
also research and developmentand innovation, things like
that.
So, My title here is CargillEndowed Professor of Sustainable
Meat Science.
With that, I've worked a littlebit on some sustainability work
as we traditionally woulddescribe it relative to

(08:02):
efficiencies of production,looking at things like water
saving technology or processes.
Worked a lot of the beef ondairy space here in the last six
years or so.
Um, just trying to make betterutilization of the dairy cow and
what she has to offer.

(08:22):
with that beef on dairy work,uh, we kind of opened the eyes
to, more opened the eyes, maybeknew about these issues, in the
past, but exaggerated liverabscess issues, exaggerated
issues with red meat yield, inthat area.
population of cattle.

(08:43):
We have cattle with beef ondairy that have really big
ribeyes on average, but don'tnecessarily have the rest of the
carcass muscling to go with it.
So there's some confirmationissues there relative to round
muscling, chuck muscling,disproportionate to the ribeye.

(09:03):
And so again, beef on dairy,help to exaggerate those
differences and kind of drew ourattention closer to some of
those issues.
Um, worked also, uh, at theuniversity, but, but in an
external way, uh, with, with atechnology company, um,
introduced to things like 3Dimagery, uh, CT scanning, some

(09:27):
of the technologies we're usingnow, uh, with a group of
engineers.
working outside of the academicspace.
And so with all of those thingscombined, uh, trying to get at
the, the issue with yield gradeand its current inaccuracy, um,
you know, they're just beingcreative and finding some ideas

(09:49):
as to how we could do a betterjob.
And that's where we landed on 3dimagery at first.
Um, and then that's evolved,from the NCBA working group
into, uh, Uh, MRI and CTscanning, so we've experimented
with both of those.
And now, continuing to work, uh,with industry through NCBA, to

(10:12):
better understand the capacityof these technologies to measure
red meat yield.
That's where we're at today,

matt_2_10-29-2024_141856 (10:21):
So on the, on the cutability portion
of that, uh, you talked about inreference beef on dairy and
that, uh, that was somethingthat, um, shed a lot more light
on some of those issues on howwe estimate cutability, but that
discussion was it was takingplace way before, the beef on
dairy space.
I know that's just like yousaid, it's kind of exacerbated

(10:45):
or, or ramped it up just likeliver abscesses and, and some of
these other things.
Correct.

squadcaster-2ab1_2_ (10:51):
Absolutely, I mean the discussion about
inadequacy of yield grade hasbeen occurring, uh, since the
adoption of yield grade in the1960s.
So, um, anytime you use a smallnumber, a hundred and sixty or
so cattle to develop aprediction equation, you're very

(11:11):
limited, by that sample size andnumber.
And so, there was some veryearly work, literally done
within a decade of yield gradesdevelopment and creation,
inception into the industry,already questioning its
accuracy.
So This has been an age oldquestion.
Uh, you know, Ty Lawrence atWest Texas A& M has written a

(11:36):
lot of articles, done a lot ofinterviews over the last 10
years or so, talking about thisas well.
Uh, yeah, beef on dairy for us,you know, really allowed for us
to collect a lot of data on itspecifically, and it drew closer
attention for us and ourprogram, but even Ty, you know,
his narrative was based largelyon the inadequacy of the yield

(11:59):
grade 4 Holsteins, you know,particularly, it's really
inaccurate on that population orwas, and, uh, but yeah, I mean,
the issue of inaccuracy of yieldgrade has existed across all
cattle types for decades andit's time to fix it, you know.

matt_2_10-29-2024_141856 (12:20):
So I should have done this first, but
for those of us that may have,uh, missed that lecture and meet
science or have forgotten it,give us a quick and dirty
history of when and why yieldgrade equations or the yield
grade equation that we'recurrently using was adopted and
then kind of what that changehas been.

(12:43):
Uh, and we, I think we know itin the cattle industry, but, um,
give us some history of, of theyield grade equation we
currently use.

squadcaster-2ab1_2_10-29- (12:51):
Yeah, so we started grading cattle and
carcasses, uh, in the UnitedStates in the 1920s.
that system has been voluntary,but is overseen by USDA and
their Agricultural MarketingService, the AMS branch of USDA.
It wasn't until, uh, late 1962that, um, USDA implemented the

(13:15):
yield grading system as we knowit today, uh, in that, uh, 162
head of cattle.
Of course, at that point intime, those cattle were almost
exclusively, if not exclusively,British influenced cattle.
Angus Hereford type genetics,uh, being fed in, in feedlots,
uh, were used to develop thatoriginal equation., Um, A guy

(13:38):
named Charlie Murphy, uh,working at Texas A& M at that
time, helped to develop thatequation, uh, for the industry
to use.
And it gets stuck, right?
It, it worked well enough atthat time, at that point.
Uh, I think Dr.
Murphy had determined they were70 to 80 percent accurate in, in

(13:59):
that equation at estimatingboneless, closely trimmed retail
cuts of carcasses.
so, Essentially that equationhas remained unchanged over
time.
Most recently, although it'sbeen 20 years now, or more, the

(14:19):
industry adapted and began touse camera grading technologies
to measure things like fatthickness and rib eye area with
greater precision and accuracy.
So that was still beinginputted, however, into the same
old equation you know, developedin the 60s.
And so we've been computingyield grade and transacting on

(14:40):
yield grade since 1962.
And, uh, it's grown more andmore inaccurate over time.
And, and that's at least in partdue to cattle type changing,
cattle conformation changing,cattle weights changing, uh,

(15:01):
ribeye area gotten larger onaverage, uh, most every year,
but seemingly without anygrowing relationship between
ribeye area and true carcassmuscling.
So one thing that the cameradata really, uh, expedited, if
you will, in our industry is theability to select for ribeye

(15:25):
area genetically allowed for usto increase ribeye area rapidly.
But because ribeye area was asingle measurement, a single
trait measurement, it wasn'tbeing related to muscling in the
hindquarter or muscling in theforequarter.
So we were single traitselecting for a muscling trait

(15:48):
that wasn't translating well,uh, with total carcass muscling.
So as a result, at least in mymind, those two factors were
allowed to drift apart.
And so today we see that, uh,ribeye area as a single factor
explains less than 5 percent ofthe overall red meat yield.

(16:12):
So as a single indicator ofmuscling or yield, it's, it's
very poor.
You know, we put it incombination with fat thickness
and hot carcass weight.
you know, we could get up to 35,40 percent accuracy out of that
original equation.
we've since played with the mathand figured out that we could

(16:34):
adjust the coefficients on thatequation to get maybe a 60 to 70
percent accuracy, um, but still,you know, performing at a C or D
level on an academic scale.
So not, not good enough, uh,particularly when technology
today exists to do a much betterjob.

(16:56):
at accurately depicting ormeasuring carcass conformation,
carcass fatness, carcassmuscling as a whole.
Um, you know, now that we havethe capacity, not only with the
hardware in the way of camerasystems or, or x ray machines,
but maybe even more importantlyis the ability with computing

(17:19):
speeds to manage the data.
Large volumes of data coming offof these images and x ray type
scans, artificial intelligencemodels are able to handle these
volumes of data and actuallygive us a meaningful result.

(17:40):
And so that computing speed,computing capacity is paramount
to this discussion today.

matt_2_10-29-2024_141856 (17:46):
So before I ask you how and what
kinds of technology and tech andcomputer speeds, we're going to
need to get this done.
I do want to point out somethingthat sounds pretty suspicious to
me.
As my tongue is squarely in sideof my cheek, uh, You said that
A& M and the USDA, I assumeworked together on this research

(18:12):
project back in 62 that puttogether yield a great equation.
I just can't believe with therivalry between tech and A& M
that it's taken you this longto, uh, to throw a flag at, at
something that the Aggies cameup with.
I mean, really, I would, I wouldhave thought sooner than, than,

(18:32):
uh, what It's been 60 someyears, you would have gotten
this done, Dale.

squadcaster-2ab1_2_10-29- (18:37):
Yeah, certainly a good point.
Uh, no, I mean, obviously TexasA& M, uh, 60s and 70s and all
the way to today.
Very much leaders in, inagriculture as a whole.
and definitely in this space ofmeat science, they've had some

(18:58):
really, uh, foundationalinvestigators, people, um, and
projects and, and, you know,they were leaders at that time,
continue to be leaders today,but really the technology didn't
exist.
To this point, right?
Until we can actually dosomething, something different

(19:21):
about it in a way that, thatmade more substantial progress,
you know, in this space and, andto these projects are expensive,
um, expensive as much so in theway of time and manpower and
carcasses, as they are, youknow, financially, uh, most of

(19:44):
the product that we use tocreate this type of work can
still be sold.
So we're not, we're notnecessarily losing product, but
the manpower and the kind of thedistraction or inefficiencies it
creates in the packing plant todo this type of work has really
been an obstacle over thattimeframe as well.

(20:07):
But, yeah, I mean, it's, it'slong overdue.
I think everybody agrees withthat.
It's, it's about time we moveforward with something
different, uh, to in somethingmore accurate.
Every more, ever more importantas we are focusing on
sustainability and efficiency inour production systems today

(20:30):
too, to accurately measure.
what we're trying to produce,right?
We can't just incentivizeefficiency or carbon credits and
things like that based on weightwhenever we don't know what the
composition of that weight is.
And so these carbon markets and,uh, you know, efficiency,
especially accounting for carbonand paying for it really needs

(20:52):
to be based on somethingtruthful.
And, uh, I think that's as muchas anything pushing this forward
as well.

matt_2_10-29-2024_141856 (21:00):
a very good point because, you know,
the old adage, you can't managewhat you can't measure or what
you don't measure.
Uh, we've got technology tomeasure this today.
It's just, if we have the willto, as an industry, Make a
better way, estimate thoseclosely trimmed retail cuts.

(21:20):
And that's, that's where I thinkthat probably the biggest hurdle
is not the tech or the math oranything else.
It is whether we, as an industryare willing to look at this and
really upset the fruit basket,because there's a lot of grids,
there's a lot of value basedmarketing arrangements that are
based off of that old onethrough five yield grade

(21:42):
equation, and you'd discount thefours and fives and you may
reward the ones or twos, or itmay be on par, but regardless, I
think that's, and we'll get tohow, you know, some of the
economic ramifications, um, herelater on, I hope, but, uh, so
tell us, I mean, the cameragrading part of it, and I think
everybody that's listening tothis podcast gets.

(22:04):
The difference between that andwhen a USDA grader was making
that call.
But give us a quick history ofwhat that changed, man, around
2000, 2002, when that occurred,uh, for the way we at least get
these pieces of information thatcould be used in a new equation,

squadcaster-2ab1_2_10-29- (22:23):
Yeah, uh, camera grading systems
evolved with digital imagingtechnology, and so if we can all
remember back to the firstdigital cameras that.
to us to buy, uh, we have tothink back to the late nineties
and early two thousands.
Um, and these developers of thecamera grading systems were,

(22:45):
were maybe slightly ahead of theconsumer's ability to go digital
camera.
So the mid to late nineties isreally when that digital
technology came, uh, becameavailable.
And so there was research beingdone again in the middle of the
late 90s looking at the abilityto segment a digital image, uh,

(23:11):
allowing for measurement ofmuscles, uh, fat, fat thickness,
things like that.
So that's really when this beganevolving more rapidly.
In the late 1990s, there wassome published research done,
mainly at Colorado State,demonstrating the ability of a

(23:33):
camera grading system to takedirect measurements in real time
off of carcasses.
So, they were seeking approval,the instrument manufacturer,
with help from the team atColorado State, uh, doing the
research to seek approval forribeye area measurement, uh, fat
thickness measurement, um, andthose were the first things that

(23:55):
kind of came online, uh, in the,in the very early part of the
two thousands.
And so ultimately USDA ended upaccepting that technology, uh,
approving that technologythrough repeatability and
accuracy estimates of ribeyearea and fat thickness.
So early, very early adopterswould have been Cargill, And

(24:20):
Tyson, who later partnered witha, with a different company
actually, uh, to develop acompeting technology to the
original.
And so you had at least twopackers, Tyson and Cargill,
working, collecting a lot ofdata, you know, putting these
systems online in their plants.

(24:40):
And like you said earlier, youcan't change or improve what you
don't measure.
So This was an ultimatemeasuring tool, uh, for them to
begin to capture a lot of data.
Uh, they incorporated thosemeasurements into the yield
grade equation, so began todevelop that camera as a yield
grading instrument or tool.

(25:01):
And, uh, USDA eventually adoptedthat as well, um, as, you know,
having the capacity to assignofficial USDA yield grade.
up To that point, the humangrader was.
assessing, I should say, realitywould be guessing, you know, as
to how big the ribeye areasactually were.

(25:24):
and the reason I say guessing isis because at production speeds,
there's no way for them tophysically measure with the
tools that they had, you know,the ribeye areas or even
physically measure fatthickness.
Speed that we were operating.
So that is in no way a stab at,at USDA.

(25:45):
It was just the fact of thematter.
They were, they were sitegrading carcasses, based on
their experience.
And then they would, of course,be checked through a series of
audits and things that USDA hadin place at the time.
So, but nonetheless, you know,the camera was going to be more
consistent, more accurate, inmost every scenario than a human

(26:06):
grader could be at a high rateof speed.
So that was an improvement.
But one of the biggestimprovements made was the
ability to capture that data inreal time and, and share that
data back with cattle groups,right?
With, with breed associations,with individual producers.

(26:27):
And so now all of a sudden we gofrom, a handful of measurements
of ribeye area to thousands,tens of thousands or hundreds of
thousands of measurements ofribeye area that was just, you
know, occurring automatically inthese plants and that data was
made available back to thoseproducers.

(26:48):
So we started to make geneticadvancements more so with that
ability to measure and sharethat data.
It wasn't until 2006, however,that Researchers and, and camera
providers or developers actuallygot the cameras approved for
marbling score.

(27:09):
So, you know, marbling score,not necessarily a part of our
red meat yield discussion heretoday, but once that became the
case and, and more plants,essentially all of the big
plants, you know, had cameragrading systems.
In place to do not only yieldgrade, but marbling score.

(27:30):
That's when the data really, youknow began to pile up and Now we
had marbling score, rib eyearea, fat thickness, all of
these things coming back fromthese camera grading systems
which You know made that datacapture so much easier and
facilitated genetic selection asa result

matt_2_10-29-2024_141856 (27:50):
Yeah.
I remember the discussions backin that time period when some of
those Plants were beginning toboth research and then
experiment and test and then useit for their own in house data.
In fact, some of them, Ibelieve, were paying on the
camera data on grids before USDAwas actually using it for their

(28:12):
yield grade and then eventuallyquality grade equations.
And I remember the discussionsof people saying, you know,
it'll be a Cold day in hellbefore you don't have a USDA
grader standing there making theactual call.
No, no computer, no, no camerais going to do what a USDA
grader has done for decades.
And man, oh man, you talk aboutsomething happening fast.

(28:35):
I don't know if, if USDA justdecided in the unions that
represented their gradersdecided they couldn't fight this
or what, but it happened quickerthan I thought that it probably
would.

squadcaster-2ab1_2_10-29- (28:47):
Well, yeah, I mean, to be clear,
right?
We haven't replaced the USDA

matt_2_10-29-2024_141856 (28:51):
there.
That's true.
Yeah.

squadcaster-2ab1_2_10-29 (28:53):
plants and they are still there
certifying the grade, applyingthe grade to the carcasses.
Yes, relying on and using thecamera system to, to augment or
to help them do that, but theystill are the, the certifying
and governing body, providingthose official grades.

(29:14):
But you're right.
I mean, I think ultimately whatit came from was a partnership
and an agreement between USDA,and producers and packing
companies that this was theright thing to do.
You know, to change with the,with the technology and allow
for the technology to ultimatelyprovide a more consistent grade,

(29:37):
across the country.
I mean, there were, and to someextent still are, with human
graders, a range, right, a lackof consistency from north to
south.
Specifically in, in qualitygrading cattle.
And what the camera gradingsystems did was, was kind of

(29:57):
neutralize that discrepancy orrange in quality grade
performance.
and we've worked a lot onquality grade as an industry for
the last 25 years as a result.
the question should be asked,well why weren't we working on
Improving, you know, red meatyield, well, because the yield

(30:18):
grade equation was broken.
And even though we had all ofthat data for yield grade, um,
it wasn't telling us anything.
And that's, you know, what we'vediscussed thus far is just the
general inaccuracy of that yieldgrade has made it somewhat
meaningless, right, to, to, thepacker and the producer.

(30:41):
And so that's what we're workingon changing is the relativity
and the meaningfulness of thatdata.

matt_2_10-29-2024_141856 (30:48):
Okay, so now what I've been wanting
and waiting to talk to you abouthow, how do we do this?
What kind of new tools, uh, whatkind of new formula and math and
technology and everything elsegets us to where you feel we
need to be?

squadcaster-2ab1_2_10-29- (31:06):
Yeah, absolutely.
Um, I'm glad you're asking thatin the form of a question.
I kind of respond in telling youthat this is very early on in
development.
We don't know exactly what thetechnology will be.
we have some ideas and.
Experimenting with, withtechnologies that have been very

(31:26):
promising and, and provide somehope, right, for what we're
trying to do.
I think ultimately what we'retrying to achieve as an industry
is to get a technology, that isaccurate, that is repeatable,
um, although we haven'tdetermined exactly how accurate
it needs to be yet.
you know, when you compare tocurrent yield grade that's

(31:48):
performing in the 40 percentrange.
An adapted new yield grade thatcan perform in the 70 percent
range.
Then if we're going to move to adifferent technology, it needs
to perform better than that.
You know, so we're looking atsomething personally, I would
hope we could get into atechnology that at minimum

(32:08):
provides at a 90 percentile.
Um, in terms of accuracy, sowe're getting an A from an
academic standard or viewpoint.
Uh, but I think we could easilyattain that.
So, ultimately, we learn fromthe inadequacy of ribeye area
that we have to have a moreholistic measurement of carcass

(32:32):
muscling.
to go right along with that weneed a holistic measurement of
fat.
Alright, those are the twoprimary factors, of course bone
is there as well, uh, bonesomewhat invisible, to the
external parts of the carcassobviously, so that becomes more
of a challenge.
the most accurate technologieswe have experimented with thus

(32:55):
far are x ray based.
That would be primarily CTscanning.
And CT scanning allows not onlyfor the differentiation and
measurement of soft tissues likemuscle and fat, but also allows
for us to see and measure thebone.

(33:16):
The current issue with CT isthat the readily available
technology is built for humansor smaller animals, uh,
something smaller than acarcass.
So the physical size of a beefcarcass is a limitation for CT
scanning.
Now that's not to say thatsomeone could manufacture a much

(33:38):
larger CT scanner, They can.
But then you get into the realmand discussion of expense, uh,
to do that.
And, of course we're adverse tohigh prices in all of our
industry, So there's alimitation there in terms of
hardware and physical size.

(34:00):
Perhaps more concerning, uh,relative to X ray or CT
specifically.
is the production speed at whichwe operate.
So as most know, commercialprocessing speeds today are
between 300 and 400 carcassesper hour.

(34:22):
at This point and with thetechnology that, that we're
aware of in CT scanning, that isimpossible, right?
There, we're not going to beable to scan 300 carcasses an
hour.
through a CT scanner.
It's, it's physically impossibleat this point to do that with
the technologies that are mostavailable.

(34:44):
notice I'm communicating incaution here because technology
is also such that, you know, atechnology that could do that at
300 per hour probably doesexist, but, or could be
developed, before that.
But again, the expensediscussion comes in there.

(35:05):
So the reality is, is throughthe research that we've done
very recently, we not onlyunderstand that CT scanning of
carcasses is accurate, but wethink that it's more accurate
than any other way to measurecomposition on it, on an animal.
So we can't even match theaccuracy of a CT scanner with a

(35:28):
scalpel and a scale And someextreme level of, of dissection.
So what we began to do isdiscuss CT scanner as, as quote
unquote a gold standard formeasuring composition.
So we have faith that, that theCT scanner can and does measure

(35:48):
composition to really fullaccuracy, 100 percent accuracy
if you will.
But those limitations that wealready discussed are More than
likely going to keep CT scannersYou know out of this discussion
for a full operating speedsolution So our next best

(36:12):
technology That we've beenworking on is 3d imagery So what
the 3d images do is give us aexterior image Not x ray, so
we're not looking inward intothe carcass beyond the the
surface level, but we're gettinga full three dimensional
measurement of the confirmation,the volume, the dimensions of

(36:38):
the carcass.
And then that's where really theprediction, you know, pieces
come in, the algorithms, themathematics that go on top of
that.
So how predictive is what we canmeasure with a 3D image and how
well related is that to theactual composition.

(36:58):
In some small trials, you know,looking at, uh, you know, less
than 50 carcasses, 40 to 50carcasses, we've demonstrated
accuracies with 3D imagery inthe mid to high 90s.
So being able to match thecomposition that we could
measure with CT scanning withabout 95 or more percent

(37:18):
accuracy.
We, we feel really good aboutthat.
Enough so that we think that 3Dcould be tested on, you know,
hundreds or thousands ofcarcasses to validate that
accuracy over a much largerpopulation or sample size.
There's other technology thathas been demonstrated on the

(37:41):
live animal side to perform asimilar concept using radar to
again create a 3D 3D outline or3D rendering, if you will, of a
live animal, which could beapplied to a carcass as well.
That gives us the same abilityreally to measure dimensions of

(38:04):
carcass and, develop againprediction equations for those
three dimensional measurementsto red meat yields.
Where we've landed at this pointin time, working with NCBA and
an industry working groupthey've put together, is that we

(38:25):
think we can use the CT scannerin a commercial environment to
scan a limited number ofcarcasses.
And when I say limited, I stillthink we're going to get to a
thousand or more, maybe even acouple thousand carcasses that
we scan at a, at a practicalpace to determine the

(38:49):
composition of those carcassesas a gold standard, and then
allow for other technologieslike 3D imagery, like radar
scanning to be developed fromthat data.
And, and in hopes that thosetechnologies are, are fast
enough and predictive enough towork at commercial production

(39:12):
speeds.
So basically we're going tosettle back into a prediction
based on a three dimensionalrendering, hopefully operating
in the 90 percent accuracy rangeat 300 to 400 head per hour.
And so that way we still getindividual carcass measurements
that are accurate for red meatyield.

(39:34):
In addition to just a holisticnumber like, You know, a 60
percent red meat yield, forexample, on a carcass.
We will also have thousands ofindividual measurements, maybe
even tens of thousands ofindividual measurements coming
from that carcass that could beutilized for again, genetic

(39:56):
selection, genetic tools, animaltrait, carcass trait
improvement, and that data, youknow, again and available to be
shared back with, uh, producersand genetics companies to, to
make selection criteria based onthose measurements.

matt_2_10-29-2024_141856 (40:16):
Well, it's To say the least game
changing, I would say, if westart looking at that level of,
of detail in what we get backfrom the pack and plant, um, I
think the first thing that a lotof folks who have fed cattle and
been paid on a value based gridand seeing the discounts for

(40:40):
fours and fives or the rewardsfor ones and twos or whatever
the case may be, are going toask is, and maybe the This gets
you out of your meat scienceoffice and into that jack of all
trades, uh, that you said thatyou've kind of always been,
which I think is valuable in aresearcher or a college
professor of any kind, but howdoes it affect us as beef

(41:03):
producers from an economicstandpoint?
Let's say that the technologyand the math and the, uh, And
you're able to write the algosand, and get to that level of
confidence compared to your goldstandard of the CT scan, let's
say that, that all the scienceworks, how do we buy and sell

(41:25):
cattle from a credibilitystandpoint?
Now, going from the feed yard tothe packing plant, do you, have
you looked through that or arewe too far away to even consider
those economic ramifications?

squadcaster-2ab1_2_10-29- (41:35):
Well, I think we can speculate, um,
you know, at this point as towhat that might look like.
I think, in general, I don'tthink it looks a whole lot
different than what we'realready doing with grid, grid
based systems.
I mean, most grids today still,paying on quality and yield
grade, and the issue is, ofcourse, the inaccuracy of the

(41:58):
yield grade.
But I think how we incentivizeboth quality and yield together
is still in a two part pricingsystem.
So instead of a numerical yieldgrading system, we may go into a
percent or point basis, youknow, on, on red meat yield as a

(42:19):
whole.
And so that's more than likelywhat's going to happen.
The industry needs to maintainthe ability to emphasize quality
grade.
Quality grade is what puts beefon the table every single day.
Quality grade and eatingexperience will remain
paramount, you know, in ourlifetime.
I think people will continue topay for beef because of the way

(42:42):
beef tastes.
and I think one of our greatestchallenges is to not get too
carried away with red meat yieldto the point where, uh, you
know, we're over emphasizing redmeat yield traits and
subtracting eating quality fromcattle, but, you know,
incentivizing the red meatyield, being able to measure

(43:05):
that more accurately willultimately place more value on
red meat yield and the greatestway for us to improve efficiency
in raising and feeding cattle isto increase very specifically
the lean portion of the carcassand with the ability to measure

(43:26):
that.
we can make genetic progress aswell as management decisions
like over versus under feedingor accurately feeding to a
terminal endpoint that optimizesred meat yield is, is where we
need to go.
And even though yield grade oneand two premium, yield grade

(43:48):
four and five discount have longbeen in place, they've really
just kind of gone by the waysidebecause of their inadequacy.
You know, to, to tell us a truedifference.
Once a packer specifically hasthe ability to quantify what a
percentage in red meat yielddoes to their bottom line, to

(44:12):
their efficiency, then they canbegin to place more emphasis on
what that matters to them.
selfishly, and I thinklogically, We have to figure out
a solution for not overfattening cattle the way that we
do today.

matt_2_10-29-2024_141856 (44:31):
Okay.

squadcaster-2ab1_2_10-29-202 (44:32):
we are producing cattle today on
the basis of weight, uh, anddressing percentage.
Because those are the twomeasurements that, are being
translated back in the marketsignal.
Basically, heavier cattle,higher dressing percentage
equals more dollars.
The issue with that is higherweight and higher dressing

(44:54):
percentage directly related tomore fat.
And looking at really highvolumes of data, millions and
millions of data points off ofcamera grading systems, we
realize that we don't need allof that fat to achieve marbling
score on most cattle.

(45:15):
So we're overfeeding all cattlein hopes of bringing up the
below average cattle to a levelof marbling that's desirable.
But with this two part systemwith greater accuracy, hopefully
we can identify high marbledcattle, cattle that have the

(45:35):
propensity to marble at anearlier time point.
So that we can not have to feedthem so long.
And we would not have to overfinish or over fatten those
cattle.
Which gets into more of asustainability discussion.
In that, we really have to havea tool to help us manage cattle

(45:56):
better.
To optimize marbling first.
But keep that in balance withred meat yield.
And, I just hope.
We can provide economic signalthat, that shapes that because I
think we all know that if you'renot going to pay us for it,

(46:17):
we're not going to do it.

matt_2_10-29-2024_141856 (46:18):
Right.

squadcaster-2ab1_2_10-29 (46:19):
that's very clear.
Um, if you're not going toprovide an economic incentive,
then you're just not going toget results.
So we do have to emphasize redmeat yield enough economically.
to force the change or to pullthe change through the system.

(46:42):
And so I'm hoping that with moreconfidence in red meat yield
measurement, we can placegreater emphasis on red meat
yield.
That's going to require thepackers having the ability to
monetize that.
They're going to have to be ableto monetize the red meat yield
before they can pay it back.
And so it'll take some time.

(47:05):
You know, with these newlydeveloped systems in place for
them to fully understand whatthey can afford to do, right, in
their facility, or what becomesprofitable for them.
And then we realign with whatthe target should be.
The target will not be thehighest red meat yield animal
that we can make.

(47:26):
The target will be the balance.
Between the red meat yield, thequality of the product, the
marbling score coming in thedoor, and the ability to sell
those two things together.
So, it's kind of like the porkindustry, right?
I mean, the pork industry, inthe 90s, decided that we needed

(47:47):
leaner pork to compete withpoultry.
And, uh, they went by the way ofred meat yield almost
exclusively and subtracted Notjust marbling from pork, but the
processing value of pork.
I mean, bacon didn't have anyfat in it.
We subtracted enough fat from,from trimmings to not make high

(48:10):
quality sausages and, andfurther processed items.
So, you know, we, we can learnfrom that, mistake that the pork
industry made at overemphasizingwithout attention to the
quality.
I don't think we're going tomake that mistake in the beef
industry.
It will take time to, to findthose cattle, and management

(48:34):
styles that, that optimize thosethings together.
But I have to say thisdiscussion goes well beyond red
meat yield measurements.
It goes into the world ofgenomics.
Um, and those tools that are nowso much more available, so much
more accurate, from a seed stockside of things, um, the ability

(48:57):
to make those breeding decisionsto really find these animals
that can do both, in the way ofmarbling and red meat yield, I
predict that once this system isin place, uh, that will manage
cattle differently as a result.
And that's my hope.

(49:18):
My hope is that we caneffectively feed cattle less, to
improve our carbon footprint, toimprove our water situation,

matt_2_10-29-2024_141856 (49:29):
hmm.

squadcaster-2ab1_2_10-29-202 (49:30):
in growing grain and crops and
forages to feed cattle.
You know, that's my hope.
That's a dangerous territory totalk about too.
I mean, the last thing a feedlotoperator owner wants to hear is
that we're going to put lesscattle in the yard for, you
know, for fewer days or putcattle in the yard for days.

(49:50):
So I understand that theindustry will have to adjust,
but in that same discussion, itmay allow for us to, to, grow
our inventory.
You know, if we have thecapacity to do that, if mother
nature allows for that.
More rain and, and, you know,increasing our female number
and, and increasing feedlotcapacity as a result of fewer

(50:12):
days on feed.
It's pretty clear that our, ourindustry wants to grow in terms
of, of harvest numbers.
We've got kind of twocooperative slash private
groups, you know, trying tobuild packing plants.
In the Midwest and in thepanhandle of Texas.

(50:36):
Candidly, you know, we need toincrease cattle numbers for
those two things to make it.

matt_2_10-29-2024_141856 (50:40):
For sure.

squadcaster-2ab1_2_10-29-20 (50:41):
and Mother Nature has not allowed
that at this point in time.
But, if we subtract, forexample, 80 days on feed off of
cattle and can effectively dothat with a fair percentage of
the population, We then open upthat capacity in the feedlot for

(51:02):
more cattle in the future.
And that may be something, thatwe don't really think about, but
could become a reality if allthese things work out in a
perfect world.

matt_2_10-29-2024_141856 (51:14):
Well, you used a couple of words there
that, that I love and getbrought up on this podcast a
lot.
And those were balance, andkeeping things in balance, and a
holistic approach.
And so often in the beefindustry, and like you said, the
pork industry did it, thepoultry industry did it, the

(51:34):
corn farmers, everybody else,we, we get fixated on one trait.
Or one outcome.
And we throw an immense amountof energy at that one piece of
the puzzle when quite oftenthere's a whole lot of other
pieces that, um, are close to,if not equally important.

(51:56):
And I, that's, that's one reasonI wanted to have you on here and
kind of get your feel for thatbecause

squadcaster-2ab1_2_10-29-202 (52:01):
to have a

matt_2_10-29-2024_141856 (52:02):
I grew, I was born in 73, grew up
through the eighties, saw this.
War on fat that the entire worldand even the beef industry waged
for a little while.
And as you said, marbling iswhat brings beef to the center
of the plate.
That taste is what we cannotlose.

(52:25):
And so yes, red meat andcutability are important.
Yes.
We need to make sure that we'rereducing our carbon footprint
and still putting out a productthat, uh, that we can represent
and sell and sell well.
But.
Yeah, if we do it at the expenseof the quality component and the
sizzle and the taste of that,um, we're not going to compete

(52:46):
with pork and poultry.
I mean, it's, they'remonogastric or ruminant.
We lose that war just like that.
So yeah, that, that balance,that nuanced approach to saying,
yes, we can do a better job offeeding these cattle and knowing
when to harvest them with betterdata, but then let's make sure
that we keep the quality side uptoo.

squadcaster-2ab1_2_10-29- (53:09):
Yeah, 100%, you know, I think, again,
incorporating more of thatholistic mentality in using
genomics and genetic testing tofind those cattle that can
afford to be fed less and stillperform at a very high level
will be critical to thesustainability of our industry
moving forward.

(53:31):
And, You know, I got to behonest, when we first started
having these discussions, youknow, the last year or two, the
first questions were exactlywhat you're talking about.
How are you going to do that?
You know, what kind of cattleare you going to remove from the
system when you start talkingabout red meat yield?

(53:53):
And I think, you know, leadersin the marbling space and that
that would primarily be Angus asa breed.
I think when you start talkingabout red meat yield, they start
backpedaling pretty hard,thinking, oh my, you know, we're
going to go back 30 years herein time.
But I actually think quite theopposite is true.

(54:13):
I think we don't improve redmeat yield initially by pursuing
purely continental breeds ofcattle or, you know, going to an
extreme of muscling homes.
I think we actually achieve thatmost rapidly and effectively by
shortening days on feed.

(54:35):
And the only way we could dothat is to have cattle that have
a high capacity for marbling ata younger age, at an age and
composition that doesn't requireso much weight and fat.
So, You know, getting further oninto that carbon discussion

(54:56):
right now, we're accounting forcarbon, you know, there's a
handful of third party programsthat are trying to market
carbon, uh, out to the customersof the world, the basis is, you
know, pounds of beef produced inthe form of carcass weight, or

(55:17):
maybe even live animal weight insome scenarios.
Well, if we're incentivizingcarcass weight, we're
incentivizing fat, which is notanywhere close to carbon
responsible or carbon neutral.
So that signal in and itself isjust wrong.
And if that's going to becorrected, it needs to be

(55:37):
corrected with what we'retalking about.
It needs to be an assessment andmeasurement of what the red meat
yield is, the edible portionThat's being produced by that
animal over its lifetime.
So I really think that carbonaccounting will evolve with this
technology as well.

(55:59):
In order to accurately depictwhat carbon responsibility is,
we need a better measurement ofefficient production.
You know, production of edibleproduct.
And, and so I think all of thesethings kind of come together
hand in hand when this thing'sall said and done.

matt_2_10-29-2024_14185 (56:18):
That's, that's the fascinating part to
me is, is not just thesustainability piece, not just
the value piece from the retailconsumer back, but even as you
said, the genetics piece,because anybody who's had to go
into a plant and do tagtransfers and Trace ribeye areas

(56:41):
and make marbling scores basedoff of a one dimensional card
telling you what small 50 is,can attest that that is an
imperfect science.
If we get to where you'retalking about, and of course
with camera grading, we'realready there from a marbling
standpoint, we're already thereprobably from a ribeye area
standpoint.
But as you said, we can arguewhether that's whole carcass

(57:02):
muscle and estimate ofcutability.
But if we can get through 3dimagery tied to that CT scanning
all those thousands of pieces ofdata per carcass put into some
kind of a useful informationthat goes back to sire
identified pens of cattle andnow we know beyond a shadow of

(57:24):
the doubt what those phenotypesare from That bull or that dam
or whatever the case may be.
You, you want to talk about somepowerful information, not just
in how we price and sell thosecattle, but how we make the next
generation even better.
Um, then we can really getsomewhere.

squadcaster-2ab1_2_10-29- (57:44):
Yeah, I think that's the greatest
opportunity of all is, is theability to select and create
generation after generation ofimprovement in cattle.
cattle that are more efficient,cattle that, uh, produce more
saleable product and, uh, youknow, truly allow for us to be

(58:06):
sustainable over time.
I think one of the things peopledon't realize necessarily as
well as that in order to make amore efficient animal, we have
to make an animal that has moremuscling versus fat.
Heavier muscled animals are morefeed efficient than fat animals.
And that seems counterproductivebecause I think everybody looks

(58:29):
at a, a soggy belly, deep bodiedBritish made steer and says,
wow, look at the conversion onthat steer.
Look how much he eats and lookhow many pounds he's putting on.
Well, the reality is, is he'sputting on fat at double the
calorie cost of protein.

(58:49):
Protein cost us four and a halfkilocalories per gram and, and
fat cost us nine.
So just simple math and logicis, is it took twice as many
calories to produce that poundof fat as it did to produce that
same pound of muscle.
So just by increasingconfirmation relative to muscle,

(59:13):
we're improving The feedefficiency of those cattle.
We're getting more conversionper dollar and per pound of feed
we're putting in that animal.
And so transitioning moretowards a muscle, an animal that
has the ability to put in muscleand red meat yield actually
simultaneously improvesefficiency.

(59:35):
And the most popular question weget, everybody likes metrics,
you know.
Producers like metrics and welike EPDs and, single trait
selection type concept.
So if we get rid of rib eyearea, you know, then what are we
going to be selecting for next?
How do I holistically select formuscularity if I don't have rib

(01:00:02):
eye area to lean on anymore?
And so one of the beauties ofthis technology as well is now
we can get a round musclingscore.
And we can get a chuck musclingscore.
and I think, develop thosemetrics that are more
translatable, you know, visible,even for the producer that's

(01:00:26):
just solely basing theirassessment on phenotype.
At least they'll know, hey, inorder for me to improve my red
meat yield, I have to improveround muscling.
And I can see that.
I can see hindquarter muscling.
better than I can see ribeyearea, right on the hoof.
And so some of those things mayeven become more clear, for the

(01:00:49):
producers as to what they'regoing to be looking for or what
they're trying to measure.
Um, interestingly enough too,some of the measurements off of
these 3D images that are themost predictive of red meat
yield are like forearmcircumference and, um, hind

(01:01:10):
shank circumference.
Some of the work that, uh, mycounterpart here, colleague, I
should say, has done, BradJohnson and his graduate
student, Luke Furness, who'sworking out of the industry
today.
I mean, things like measuringthe forearm circumference of
calves, even at the time ofbirth or time of weaning,
strongly correlated to, youknow, muscle attributes later

(01:01:36):
on.
And so these might even bemetrics or tools that we can use
to measure at calving or weaningor both, getting at a
measurement that's going toultimately be taken on the
carcass that's highly predictiveand related to red memeat yield

(01:01:57):
more so than ribeye area, youknow, ever was.
And so, there's a lot of neatstuff that's still to be
discovered and communicated.
Every single person we talk to,whether it be a bull stud or a
breed association, you know,they want to get ahead of this.

(01:02:18):
They want to know what, what dowe need to be selecting on.
You've, you know, you've ribeyearea doesn't work.
That's what we've been selectingon.
So what are we, where do we gonow?
so everybody's kind ofscrambling at this point, I
think, for a live animalmeasurement of muscularity,
individual measurement, youknow, metrics like what we just

(01:02:41):
mentioned.
Those things are becoming ofgreater interest now, for sure.
And these radar technologies,that have the ability to measure
animals as they move through thechute.
I think will prove to be veryeffective tools, for management,
you know, days on feet, closeoutdates, things of that nature.

(01:03:05):
I think they'll be used asmanagement tools along the way.
Live cattle to improve red meatyield.

matt_2_10-29-2024_141856 (01:03:13):
So do you have a timeline where you
think in your mind, Hey, wecould be here, we could be using
the CT scan to train the 3dimagery by five years?
15 years?
What's your estimate?

squadcaster-2ab1_2_10-29- (01:03:34):
Yeah, so right now we're working on a
two year timeline to establishCT scans as a gold standard.
So, I fully expect that thatwill be successful.
So, within two years, we'll beutilizing CT to provide the

(01:03:56):
opportunity for 3D technologiesor other rapid technologies to
be developed to be used formeasurement purposes.
so, the time frame I've beentelling people is three to five
years.
I think in three years it's it'spossible, maybe even likely that

(01:04:18):
we'll see systems in plantsassessing carcass confirmation
or red meat yield at chainspeed.
I think three years is areasonable timeline for that.
How long will it take theindustry to adapt and accept,
prove instrumentation forofficial grading purposes?

(01:04:40):
You know, that's where I thinkthe five year timeline comes in.
But the industry has alreadyproven that we don't need
official designation of grade touse it.
And so I think if, if theindustry becomes comfortable
enough, Packers specificallybecome comfortable enough with
that technology in their plant,then I think we can see

(01:05:03):
something as soon as three orthree to four years where we're
actually paying on the basis ofred meat yield with a new
system.
Now, I mean, I am completelyspeculating that, but it's, it's
based on some of the timelinesthat we're working with on
research and partnership withpackers, partnership with cattle

(01:05:26):
feeders through NCBA.
So I think, I think there's somereality to that.
So I'm going to say three tofive years.
Kind of the wake up call,however, is, you know, a heifer
or steer entering a packingplant in three years is being

(01:05:48):
bred today, right?
I mean, the cows are being bredtoday to make that calf that's
fully developed, weaned, andfed, going into that packing
plant in three years, so We'rewithin, you know, months Months
to a year at needing to make adecision With a straw of semen,

(01:06:09):
or a bull purchase, or you know,a mating decision that
ultimately could be You know,measured and influenced by a red
meat yield system in threeyears.
So, this is now, I mean thetiming is now, for this, in
making decisions for breedingand genetic selection.

(01:06:29):
Which is why breed associationsand bull studs are, are really
of peak interest here.
Uh, at this stage ofdevelopment, and they do want to
know.
So, uh.
Without naming names, uh, youknow, I had a, an email exchange
today, uh, with one of theworld's largest genetics, most,

(01:06:53):
uh, companies, you know, wantingto have the same conversation,
trying to figure out what themetrics should be.
and we've had conversation withmost every large genetics
company in the United Statestalking about this concept.
So things are moving, breedassociations and bull studs very

(01:07:15):
much trying to be prepared inmarketing red meat yield, um,
through genotypes andphenotypes, primarily on the
sire side of the equation.
But, uh, of course we know thatthe dam side is equally, if not

(01:07:35):
more important than the sireside of the discussion.
So This will become, you know,somewhat universal.

matt_2_10-29-2024_141856 (01:07:45):
Well, and I think that, uh, is a good
spot to kind of close ourconversation because I'll bring
it up again.
You said the word balance andreally the holistic piece of it.
I mean, regardless of what we'reusing to determine red meat
yield in a sire, He also has todo all the other things.
He has to provide the marblingthat makes beef better than pork

(01:08:08):
and poultry.
He has to make daughters, atleast in some herds that'll go
out there and breed under rangetype environment.
He has to make calves that areborn unassisted and grow as
rapidly as they can asefficiently as they can.
So, yeah, I mean, I think if Iwere in your shoes and have

(01:08:28):
those people calling me andasking, you know, what do we
need to look at?
What measurement do we need tolook at?
Probably at least right now,until we see what this imaging
and the math does and indicatesit's Word of balance, make sure
everything is in a pretty goodspot somewhere in that optimum

(01:08:51):
range and don't just put allyour eggs in the ribeye basket
or something like that.

squadcaster-2ab1_2_10-29- (01:08:57):
Yeah.

matt_2_10-29-2024_141856 (01:08:59):
Well, this has been a great
conversation and I appreciateit.
If it's okay with you, I willput in our show notes, um, your
contact information, or at leasta website.
And, uh, folks do want to drilldown any further or have further
questions.
Um, yeah.
Any of those AI studs that arelistening to Practically
Ranching, uh, that didn't getall their hands, their questions

(01:09:20):
answered.
Um, uh, I assume that you wouldbe welcome to respond to any
emails like that.

squadcaster-2ab1_2_ (01:09:28):
Absolutely.
My email is a great way to makecontact, so I'll be happy to

matt_2_10-29-2024_141856 (01:09:31):
great.
We'll put that in there.
And, and again, thanks so muchfor being here with us today.
Uh, great information, excitingstuff, sometimes a little scary
stuff, but, uh, but stuff that Ithink anytime we can better
characterize and quantify, uh,Cattle and use that information

(01:09:51):
to make more of the good onesand manage in a way that we can
produce more of the good beefsustainably.
It's a win.
Uh, is it change?
Yeah.
Is it maybe going to give us alittle heartburn as we make that
transition?
Sure.
But it's a win for the beefindustry.
And so I applaud your efforts onthat and we look forward to kind

(01:10:12):
of staying tuned.

squadcaster-2ab1_2_10-29 (01:10:13):
Sounds great, look forward to it.

Microphone (Yeti Stereo (01:10:16):
Thanks.
Again, for listening topractically ranching brought to
you by Dale banks, Angus, aswe've said before, if you like
what we're doing here, give us afive star rating, drop us a
comment and be sure to followus, to hear future episodes when
they're out.
And be sure to make plans tojoin us for our annual bull
sale, november 23rd.
At the ranch Northwest ofEureka.

(01:10:38):
As I mentioned, catalogs.
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Now, if you'd like to receiveone, drop me a
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God bless each of you.
We'll be back with our bull salepreview in two weeks.