EPISODE NAME: It’s an Industry Issue: Red Meat Yield’s Challenges and Solutions with Stika and Foraker 

Red meat yield has always mattered to the beef industry, but there have only been mediocre tools available to quantify it. This episode covers the surprising history of the subjective measurement before switching to new technology that could completely modernize the system for the better. Every breeder knows the way to drive directional change is to first start with good data, and that’s the goal of a group of ranchers, feeders, packers and academia who are all working together to tackle this issue.  

HOSTS: Miranda Reiman and Mark McCully 

GUESTS: Blake Foraker, Texas Tech University, and John Stika, Certified Angus Beef 

Blake Foraker is an assistant professor of meat science at Texas Tech University. He grew up in Burrton, Kan., and earned his bachelor’s degree in animal science from Kansas State University and his master’s degree in meat science from Colorado State. He holds a meat science doctorate from Texas Tech and worked at Washington State University before returning to his alma mater. Foraker is a member of the American Society of Animal Science, Intercollegiate Meat Coaches Association, and the American Meat Science Association. He has coached and participated on many nationally acclaimed meats and livestock judging teams and was named Texas Tech’s Department of Animal & Food Science Outstanding Graduate Student in 2022. 

John Stika, president, Certified Angus Beef (CAB), leads the American Angus Association’s consumer-facing subsidiary. The brand is owned by registered Angus breeders and operates to create pull-through demand for Angus cattle. CAB has grown to more than 1.234 billion pounds of sales annually under his leadership. Stika joined CAB in 1999 in what was the feeder-packer relations division, before working in business development and assuming the role of president in 2006. He’s a Kansas farm boy, with degrees in animal and meat science from Kansas State University and the University of Kentucky. 

Miranda Reiman (00:00:03):
Welcome to the Angus Conversation. I'm your host Miranda Reiman with my co-host Mark McCully, CEO of the American Angus Association, and today we're going to bring up a topic that you've probably heard us talk about once or twice, but I guess it's because we think it's that important of a topic, Mark. In fact, I think I have heard you say it's one of the most important topics we have right now in the beef industry.

Mark McCully (00:00:26):
I do think this identification and measurement of red meat yield is a topic that is going to continue to be more important, and I think it's a real opportunity for the Angus breed. I think anytime that we have an opportunity to improve the breed in economically important traits, I think we need to do that. We have diversity in our breed. I think that's truly, truly one of our strengths right now, and we'll get into it, of some of the challenges with our existing yield grade equation and maybe the signals that have been sent or not been sent to improve this area of red meat yield. I think there's an opportunity for us to make some progress really in the near future with some of the exciting things we got to hear about.

Miranda Reiman (00:01:11):
I think a reason in particular that I get really excited about this is my entire time at CAB, I think we often got the criticism that, oh, you're just about marbling or you're just single trait selecting for marbling. And I said, no, we're actually about profitability. That's the most important measure out there to our cattlemen. So this is obviously something that factors into the profitability equation pretty heavily.

Mark McCully (00:01:33):
Absolutely. And not, I think, I'm sure we say this multiple times, not to take our foot off the quality pedal, but with the right kind of tools also be able to improve red meat yield or cutability. Lots of terms we could throw at this and do the two simultaneously. We've got the toolbox and the ability with data and technology today to advance both. But you're right. There's no one element of this that we should be single trait selecting for anything. It's about how do we make progress in multiple traits that are economically important. And I think red meat yield's going to be kind of a new piece of the equation.

Miranda Reiman (00:02:10):
When we were at a cattle industry convention and trade show, we first listened to a session or they had a panel I guess up there, and we talk about that some in this podcast. It was standing room only in there. And I think that signals to us that this is a topic that resonates not just in the Angus business but across the beef business. So if you are a cow-calf producer, if you're a breeder, if you're a cattle feeder listening to this, this topic's for you.

(00:02:38):
Here today on the podcast, we've got a couple of guests with us that share a similar start to their journeys, but we'll hear a little bit more about their differing journey. So Blake Foraker from Texas Tech University, Kansas native and started there at K-State.

Blake Foraker (00:02:52):
Yes. Yeah. Excited to be here this morning with y'all and talk about a topic that I'm very passionate about.

Miranda Reiman (00:02:58):
Excellent. So after you got your undergraduate there, you went on to CSU and then Texas Tech.

Blake Foraker (00:03:02):
Yes. Yeah. I've been fortunate to be at three really great institutions, and I think all had a very special place in my heart and in solidifying my career where I'm now at Texas Tech University as an assistant professor in meat science.

Miranda Reiman (00:03:15):
Excellent. And I think I told somebody that we were interviewing you and you were from Texas Tech and they said no, he is from Washington State, so you had to stop up there in between.

Blake Foraker (00:03:22):
Yes. Yeah. So I graduated with my PhD from Texas Tech in 2022 and kind of a freak life thing. Never thought that I would go to the Pacific Northwest as far away from Kansas as you could possibly get, but that was really the best experience that I could have had to jumpstart my career. Spent two years at Washington State University in a similar type role.

Miranda Reiman (00:03:44):
Excellent. So

Mark McCully (00:03:45):
Today you're teaching, some research, some coaching. What are all your responsibilities? Yes,

Blake Foraker (00:03:51):
So teaching and research responsibilities. And then I also get to work with really great students coaching the meet judging team at Texas Tech.

Mark McCully (00:03:59):
Just had some success coming off a big win here last week,

Blake Foraker (00:04:04):
I guess. Yeah, well, actually Monday, so just a couple days ago, Fort Worth Stock Show and Rodeo, they came away as the southwestern champs.

Mark McCully (00:04:12):
Yeah, congratulations.

Miranda Reiman (00:04:14):
And were also a kind of well-known judger in your own right back in the day, the way I understand it,

Blake Foraker (00:04:19):
Yes. Yeah. I had, again, great coaches and great teammates there at Kansas State, obviously another very storied program, and so was very fortunate to be a part of all the judging teams there. Wool judging, meet judging, livestock judging, and meat animal evaluation. And again, I always tell students that those kinds of activities really solidified my interest in this industry and passion for pursuing it as a career.

Miranda Reiman (00:04:42):
Very good. Well,

Mark McCully (00:04:44):
That's where I met John

Miranda Reiman (00:04:45):
At K-State? Oh, at judging. Okay. Very good. Well, and I said that we had similar journeys, but they kind of differed fairly quickly. So John, you're also a Kansas native and also started your journey at K State?

John Stika (00:04:57):
Yeah, yeah. I grew up in central Kansas just north of Wichita, about an hour. Went to Kansas State as an undergrad, majored in animal science, did a master's in meat science there, coached meets team alongside John Unruh, and then went to the University of Kentucky to coach a livestock judging team and get a PhD in meet science there. And that's during the period of time I met Mark primarily. So yeah, judging's definitely been a part of my career and in my past and something that I really value and I continue to leverage the skills that I learned during meats and livestock judging every day,

Miranda Reiman (00:05:32):
Of course. And of course now president of Certified Angus Beef, and have been in that post for quite a while now. John, I think it was my first year at CAB that you took on the helm as president

Mark McCully (00:05:40):
Almost 20 years now

John Stika (00:05:43):
You have such a short memory. It's been actually 19, actually, Mark and I worked pretty much side by side for 19 years when he was at Certified Angus Beef, but it was actually 26 years on February 1st.

Mark McCully (00:05:59):
But you've been there ... in the role as president

John Stika (00:06:00):
In the role, it's probably been 17, 18 years, whatever that is. Yeah.

Miranda Reiman (00:06:07):
Well, I think you just mentioned that you make a lot of connections in the judging world and through your collegiate experiences. And I would say as we get into the topic today, which we're going to talk about red meat yield, one thing that has probably shown in that is that, John, you've taken a really active role in this and part of that, I'm sure some of those connections, those are people you've worked with for 20 and 30 years that you've been able to pull together into a group that has a brain trust on the subject. I guess I would say

John Stika (00:06:35):
It's been a really fun process and really need to give credit to a lot of producers. James Henderson would be one of them as an Angus breeder that's been passionate about this topic of red meat yield for a long, long time. And then also NCBA really kind of understanding the dynamics that were taking place in the business today. Cattle today are a lot different as a lot of people have talked about, and really took the initiative to pull this working group together. So that's really where the credit is. I think my involvement in it specifically has been as a point of interest all along the way. I think it's very, very important from a Certified Angus Beef standpoint. We're always kind of dubbed the quality folks and we should be. That's kind of where we hang our hat. But I think what keeps us relevant as a brand, and this has been something across the breed mark since we've been working here, is we've, we can't get too tunnel focused on the things that seem to solely align with our core priority.

(00:07:33):
Our core priority is being relevant to the industry, is profitability. It's profitability, right, Miranda, and helping producers make more money targeting a brand that resonates with consumers. And so you start looking at the quality aspect of consumer demand, which thank gosh, we've focused on it as much as we have because I think it's why we're enjoying the best demand we've seen in 30 years right now. But it is profitability and we know that producing fat, those are expensive pounds. And researchers like Blake and others that have been looking into this now for quite some time, it's all coming to fruition and coming to a head. And I would say every issue has its time and now's the time for red meat yield, and we've got a bunch of great minds. Blake's one of the bright young meat scientists in the industry and we're glad to have him and other tenured faculty members at universities, USDA and so forth, really putting a lot of thought into how we evolve red meat yield.

Mark McCully (00:08:32):
And I think you said it yesterday when you were making a presentation at Cattleman's conference. I think this is one of those topics, it's important that I think we frame it up. It's not a, okay, we we're done on quality, now we've got to work on red meat yield. It's an and not an or kind of topic. So let's get into the, talk about it today, how we are evaluating red meat yield

Miranda Reiman (00:08:54):
And how did we get there?

Mark McCully (00:08:55):
And how did we get there.

Blake Foraker (00:08:56):
Yeah, I think, Mark, you bring up a great point there that we're really kind of at a point in our industry where much to John's point, the success that we've obtained with quality grade has really kind built in an insurance factor or some level of resiliency. And I think that's a good way to put it. Just to kind of frame up the topic. I mean, I think I always like to start the topic and end the topic with we're not deemphasizing quality grade or marbling, we're just adding two. And so let's just start with the history of assessment of red meat yield in our industry. And I think maybe first thing we should talk about is what is red meat yield? Because that's a question I get asked all the time, and I think for a lot of our listeners, we should frame that up. I mean, if you're a livestock judger, like many of us around this table, you always talked it in your reasons and you're like, well, it just means more pounds of muscle. You use it as jargon, but

Miranda Reiman (00:09:47):
A buzzword we're going to throw in,

Blake Foraker (00:09:49):
It's a buzzword. But I mean, in all seriousness, I mean it can be defined many different ways. And I guess maybe from a meat science perspective, the way that I would most commonly define it is from a packer perspective, and that is the pounds of closely trimmed sub primals that are going out in a box. And in addition to those sub primals, we also have to think about the trimmings component because we know that those trimmings are a considerable portion of the carcasses and how they get fabricated. So that's probably the most common industry definition of red meat yield is box yield or saleable yield, at least at the point of the packing plant level. Now, others might define it differently. And so we have to think about the retailer level and the trimming of those sub primals into steaks and roasts and the additional trimmings and fat that's removed in that process.

(00:10:42):
So a retailer may define it very differently than a packer. And then another definition of red meat yield might be from an academic standpoint in the sense that we might characterize the muscle fat and bone tissue separately, because I think what it's important for people to realize is that those subprimal that go into a box aren't just muscle. And so there's a lot of seam fat, there's a lot of external fat that's remaining on those cuts, and in some cases, depending on the cutout to a bone end product. And so that tissue remains on some of those cuts. So those are just some variations that I suppose make the topic somewhat confusing at times is how are we defining as red meat yield? Ultimately, I think it comes back to what are producers being paid for and how are packers getting paid? And the predominant consist of that is in box yield.

John Stika (00:11:33):
And I think Blake, you bring up a great point because that's a little bit of, well, it's a main focus of the working group right now is making sure that we align on what it is we're actually trying to measure. And maybe as Blake then talks further about how we measure it, we're still trying to define is it percent saleable yield because the point that he made is extremely important. We do sell fat

Mark McCully (00:11:57):
And bone

John Stika (00:11:58):
And bone. And honestly, the trade expects that to be a part of the cuts that they purchase. We're not talking totally denuded seam separated products. There is an expectation about a certain amount of fat being included, whether that's oftentimes is seam fed, if you think about a chuck roll or something along those lines, and that has value, and I say what it's not is it's not dressing percent. And I think especially as we talk to cattle feeding audiences and we hear yield, they think, well, it's how did the cattle yield? Well, what was the address? And just to be very clear, what we're not talking about here is dressing percent. In all honesty, the work around red meat yield hopefully allows us to clean that up because as we target sometimes yield high yielding cattle, we give no consideration to the composition of that yield. And that's really what this work is really focused on.

Mark McCully (00:12:50):
Yeah, I've always been curious how our industry got there of calling dressing percent yield when we have a USDA yield grade equation that's been around for quite some time. So good clarification, because I do think those are very different terms.

Miranda Reiman (00:13:05):
I also think it would be good to unpack a little bit of the history behind that yield grade equation that we use so frequently, because I think as this discussion has evolved, that's been something that's amazing to me to think it was based off

Mark McCully (00:13:18):
Really, really?

Miranda Reiman (00:13:19):
Yeah, that's exactly, so who wants to tell the story?

Blake Foraker (00:13:22):
So let's start back in. Really we're talking 1950s and a group of, obviously we developed the quality grade system at that time, which a lot of producers may not realize, but that quality grading system didn't have as much to do with marbling. It was really just assessment of external fat coverage of the carcass and conformation to a degree of that carcass.

Mark McCully (00:13:44):
So because carcasses weren't even ribbed at that time.

Blake Foraker (00:13:46):
That's right. They weren't ribbed for the assessment of marbling on the quality grade side. And so in addition to, again, trying to predict eating quality or eating satisfaction with the quality grade researchers were attempting to predict the abundance of product that we were getting from carcasses. And so that was kind of how the yield grade discussion started. And so you had a group of researchers at Texas A&M really led by Charlie Murphy at the time that were researching a whole host of traits that would determine the abundance of product that they could get from a carcass.

(00:14:19):
And really how they were defining red meat yield at that time was the percentage of boneless closely trimmed retail cuts, but not from the entire carcass, only from the chuck, the rib, the loin, and the round. And so that kind of leaves out the thin meats as we might call 'em, which frankly are some very high value. We're talking short ribs and flank and skirts. They left that out of the prediction model. So they're just predicting percent boneless, closely trimmed round loin rib chuck and measuring a whole host of traits, not only the traits that producers may be familiar with, fat thickness and rib eye area, but things like carcass length and round circumference and chuck dimensions and a whole host of traits trying to come up with what is now the modern day yield grade. And so ultimately they landed on four traits, those four traits being the fat thickness measured between the 12th and 13th ribs, the ribeye area or the surface area of the longissimus muscle and the ribeye steak or the striploin steak, as many might know it in addition to hot carcass weight and the percent kidney, pelvic and heart fat.

(00:15:27):
So those four factors, they assign coefficients to those factors to derive a yield grading system that ranges from a yield grade 1 to a yield grade 5, where a yield grade 1 would indicate that those carcasses are going to produce the highest abundance of closely trimmed retail cuts, be the trimmed and the heaviest muscle to yield grade 5, which would be the fattest lightest muscle, lowest yielding.

John Stika (00:15:50):
One thing, we talk about yield grade today and we hear the realities of it and the cattle that went into that equation, 162 head from the sixties with an average carcass weight of 675. And I am reminded that we tend to be a little critical of Dr. Murphy yet keeping in mind that that was progress back in the day that was helping to move an industry forward. It's just always a good reminder that sometimes our work in science does have a lifespan, and we've just got to be aware of how the world and the market and the industry evolves around us and always be willing to circle back and reconfirm whether or not the results, the sound results we found at that time are still relevant. And that's where we've come to, is that still relevant? And I know the work of Blake and his colleagues at Texas Tech and others would suggest that that's not relevant anymore, and that's really the impetus for looking at a better system.

Miranda Reiman (00:16:48):
I was thinking about that yesterday. I'm sure if you had Dr. Murphy around today and he was like, you're still using that guys?

John Stika (00:16:56):
Even I wouldn't have used it.

Miranda Reiman (00:16:57):
Yeah, right.

John Stika (00:16:59):
That's right. That's right.

Mark McCully (00:17:00):
And maybe get into a little of the details there of the job it's doing today, and we talk about it's not doing as good of a job as a predictor as we'd like it to be. Obviously some of that is some of the way the equation is built of an assumption of linear, of a linear relationship between carcass weight and ribeye area. But then there's also other things in there like kidney pelvic and heart fat where we make some pretty base assumptions normally.

Miranda Reiman (00:17:28):
And packers are dealing with it differently

Mark McCully (00:17:30):
But we really don't measure that, that right, it is an estimate. So maybe just talk about the job it's doing and maybe the difference between beef type cattle and beef on dairy or dairy, full dairy influence

Blake Foraker (00:17:40):
So I mean, I think to John's point, we should give, I guess some credit to the yield grade today. And so I mean the fact that we're 60 plus years later, and on an average,

Miranda Reiman (00:17:53):
Is it like that because it's A&M? Is that

Blake Foraker (00:17:56):
No, no, no, no. But I am saying that directionally it still works, so we can't exclude that fact. And so our yield grade fives today still fatter.. muscle and lower yielding, then you'll grade ones on an average basis. Yes. And so that's not really what we're talking about though in this era of precision technology, what we're talking about is today so many of our genetic decisions, and you guys as a breed association know this better than anybody is that producers are making decisions on individual animals which bull to breed to which cow to maximize or optimize which trait. And so the fact of the matter is that that's where yield grade comes into question is it's not on an aggregate basis. I think our industry has proven time and time again pending what trait, whatever trait it is, we've moved more towards granular data, individual animal type data, and that's kind of where we're at with yield grade today.

(00:18:51):
Now certainly cattle have changed over time, and obviously cattle today are heavier than what they've ever been. You're exactly right, Mark that there's a disconcert and the linearity, if you will, between hot carcass weight and ribeye area. In other words, as we increase in carcass weight, ribeye area, the biological relationship of that trait and Dr. Ty Lawrence has demonstrated that perfectly well, that biological relationship is not linear. And so it's actually curvilinear such that as we get to heavier carcass weights, those cattle have a much harder time reaching what the yield grade assumption would assume is an average ribeye area for that weight in addition to ribeye area. One of the major challenges with yield grade, and frankly one of the reasons why, and many may not know this, only 25% of cattle in our industry are assigned an official USDA yield grade.

(00:19:47):
Now, you may get a yield grade back from the packer, but it's not an official USDA yield grade. And one of the reasons for that is because many packers are removing kidney, pelvic and heart fat after hot carcass weight is obtained. It should clarify that because we know that carcass weight is defined as KPH fat in, but they're calculating yield grade and paying producers on an internal plant derived yield grade. So only 25% of a cattle in our industry are assigned an official USDA yield grade. And part of that official USDA designation requires either actual USDA assessment of the percent kidney, pelvic and heart fat or a direct measurement of its weight. And so there was a clause that was passed a decade or more ago where they allowed that. But again, it's important to remember that many packers are actually removing KPH and other fat like over the inside round.

(00:20:41):
with all that hot fat trimming on the kill floor. And so that kind of excludes their ability to assign an official USDA yield grade. The other point of it is is that KPH fat is highly variable from a student perspective and meat judging or whatever, we always talk about 2%, two and a half percent. We say, well, that's the percent of the carcass that we might expect is comprised of KPH fat, when in reality the average in our industry is 3.5%. That's of all cattle, all cattle types. Obviously dairy influence cattle, whether those beef on dairy or straight breaded dairy would be closer to four point a half or 5%, but it's frankly not unheard of to have cattle with six, seven, 8% of the carcass weight comprised of kidney, pelvic and heart fat. And I think there's lots of theories as to why that may be. As we talk about breeding for deeper bodied cattle or cattle that convert more efficiently, perhaps they require greater energy stores around their mesentery or wherever else. And so the point is is that KPH fat is much higher in today's cattle population than what it was in the 1960s certainly. And so that's something that we absolutely have to account for. Recently published a paper that pretty much shows zero relationship between percent kidney, pelvic and heart fat, and the percent subprimal yield or boxed yield that we're getting from carcasses.

John Stika (00:22:04):
I think along with that, at the end of the day, Mark, when you were still at Certified Angus Beef, we commissioned a paper, a white paper with Dr. Ty Lawrence at West Texas A&M. And that paper, that white paper has since been updated, NCBA when beef on dairy really expanded. That wasn't a part of the original study. And Dr. Lawrence in his efforts would've more carcass data coming out of plants than probably any academic institution. And you'll recall that in both the original and the revised paper, yield grade only accounts for 40% of the variation in red meat yield in native cattle,

Miranda Reiman (00:22:40):
in beef cattle

John Stika (00:22:40):
in native. That's right, Miranda. It accounts for zero of the variation in Holsteins and probably perhaps may be more relevant to our business today, only about 20 or an intermediate amount of the variation in red meat yield for beef on dairy.

(00:22:54):
And so I think one of the big topics that was discussed early on within the working group was can we just rework the yield grade that Blake just walked us through? And I think the reality was, and Blake, you might have to correct me here, but I want to say that Dr. Woerner, in presentations, I've heard him share that we can go from 40% variation accounting for 40% of the variation in native cattle. If we adjust the equation, adjust the way that we work this, maybe do an equation for steers, an equation for heifers, and we can maybe get that up to 60, 65%. And so as I've heard him say, we can get a D, but can we do better than a D? I know a D wasn't acceptable in my household growing up, and I think that's been the impetus for let's look something different. Let's move beyond this. We understand that there's just a lot of things we just can't account for in the current system, and we're at a stage to move forward, I think,

Mark McCully (00:23:54):
And we have some new technologies and some new capabilities we've simply haven't had in the past. And I think from a breeder standpoint, a genetic standpoint, I think there's also obviously a really important element to this as well. I mean, the tool that we've largely put in the toolbox of breeders around improving red meat yield is ribeye area. That is basically the tool that breeders have. And again, it's not directionally taking us the wrong way. I've had someone said, well, do we just need to throw it away? No, I don't think we need to throw it away. I just think

Miranda Reiman (00:24:24):
Don't take away tools.

Mark McCully (00:24:25):
We have the opportunity to, we don't want to lose the baby in the bathwater kind of thing, but we have the opportunity potentially to collect some new phenotypes that could put into a genetic evaluation that put tools in breeders' hands to truly make some more advanced improvement on this whole topic. Then maybe some of the limitations we have with simply having with ribeye area.

Miranda Reiman (00:24:50):
That was kind of though I have a question that says why is it so important? Some people might say, well, it's working okay. I mean, it's directionally you say, but why is it so important that we measure it more accurately than we're today?

John Stika (00:25:02):
Yeah. Well, I think it comes back to are we going to give folks tools to make improvement or tools just to use with no, that really don't align with any meaningful outcome. And I think that's the kicker. We've seen it within the Angus breed that give producers the right tool and significant change and progress can be made in a relatively short period of time, and I think we're kind of where we are because we've only had really one measure that's ribeye area, but if we can give them a better tool, then the reality is we can make cattle that are more profitable to the entire segment. We've come so far in terms of quality. If you look at the National Beef Quality Audit from 2022, we've made such progress from where we were in 91 and even where we were just 10 years ago, and we continue to grab that lost opportunity that has been in the industry by not meeting the right mix of quality and so forth. And quality still is the biggest driver. I will always circle back to that quality still is the biggest driver, and it's still where the most dollars are for producers to continue to win. And I think that's important to point out,

Miranda Reiman (00:26:15):
And it's what the consumers care about

John Stika (00:26:17):
And it's what the consumers care about. Glen Dolezal made the comment though, that quality pays a lot of bills, but red meat yield is there every day. You get paid and there's value in red meat yield every day. The challenge has been that the quality message with consumers has been so loud, it's overshadowed the need to probably give quite the intentional focus on red meat yield that we need today. He made the comment yesterday that when he started with Cargill, they were discounting yield grade fours, 15 to 20 bucks a hundred, and here now they're discounting 'em about four, $4

Miranda Reiman (00:26:54):
With allowances.

John Stika (00:26:55):
Yeah, with allowances

Mark McCully (00:26:56):
Thresholds.

John Stika (00:26:57):
Exactly. And that's because the quality message is so important, but we're at that point where we're trying to find ways to eke every dollar out of the process of producing great beef. And when you look at where we're at, a lot of the work that has been done at Texas Tech and other universities says, we're spending a lot of resources to just cut it off and throw it away or go to biodiesel. And I got to believe that some work with Dr. Kristin Hales at Texas Tech is there are better ways to produce biodiesel than run corn through cattle to produce fat to fuel the biodiesel industry. So

Miranda Reiman (00:27:35):
James Henderson said that it takes a lot of energy going in and it takes a lot of energy coming.

Mark McCully (00:27:39):
Yeah, that's right. Think about too, I mean just the energy to put the fat on, the energy to cool the fat down, the energy to take the fat off of the carcass, the energy to render it back to where we need it. I mean, that's probably not, if we were going to draw it out on a whiteboard, the ideal way to do this is probably isn't out where we'd end up. Yeah, indeed. And I think you bring it up the fact that this is not really a consumer issue, and I think

Miranda Reiman (00:28:10):
Unless they hear that statement you just made about all the energy. Well,

Mark McCully (00:28:13):
That's right. Good point. They could potentially be a little critical of us there. But if you think about too, I mean, and I don't know what the percentage is, but if you think about the amount of closely trimmed product today, fully case ready, trim, block, ready trim, this product that's ending up even at a grocery store that's maybe still cutting meat, that's not buying case ready, they don't even have a lot of trim. A lot of that's being absorbed back further in the processing. So it's a huge economic element here, but it's truly the consumer. I'm not sure they see a whole lot of difference if it's a yield grade 5 or a yield grade 2 at the case by the time it hits their eyes.

John Stika (00:28:58):
Mark, your point is exactly why back in 2006, 2007, we moved away from a yield grade spec. There was a time where Certified Angus Beef's specification was a yield grade 3.9 or leaner, and the reality became that was an irrelevant spec to where what to the customer was expected. And that's when we migrated to at the time, the component traits of yield grade, which were a carcass weight limit, a ribeye area range, and a fat limit.

Miranda Reiman (00:29:29):
And thank goodness we did because what would your supply look like today with the trends?

John Stika (00:29:33):
If we didn't have the conversation in 2006, we would've had it before that. That's all I would say. But that's how we've gotten to where we are, and it really is an industry opportunity to recoup lost opportunity. And one of the things I'll share about the working group that has been pulled together, that working group is made up of over 30 individuals now with a lot of expertise in different areas that range from cattle producers, cattle feeders, packers obviously are at the table. We've got researchers, both academic institutions and USDA, we've got branded beef and beef merchandisers, and we've got tech individuals that have strong meat science background that can help us better understand where some of these technologies are that can help us move this forward. And that's a very important thing because as we talk about this being an industry opportunity, it's got to be good for everybody,

(00:30:36):
Meaning it can't just benefit the packer. It has to ultimately produce dollars that come back to the feeder and the cow calf producer and ultimately increase the value of genetics that are able to hit these targets. Just like quality genetics have increased in value because of the messages that have been sent. So we're definitely kind of trying to make sure we don't lose sight that everybody has to win as we transition to a better, more effective system. It can't just benefit one segment of the industry because the consumer's not the driver on this, the industry's the driver.

Miranda Reiman (00:31:12):
So I think we've IDed the problem pretty well. We've also IDed, I guess, the importance of it, but I get real excited when you guys start talking about some of those possible solutions and things that are on the horizon and new technologies.

John Stika (00:31:28):
Let the old guy talk about the new technologies.

Miranda Reiman (00:31:31):
I was looking right at Blake when I said that, but we've gotten to hear a couple of different presentations that I think our listeners would like to hear a little bit more about some of that, everything from CT scanning to radio waves to just talk a little bit about some of the technology that's on their horizon or that's here, but just isn't practical yet

Blake Foraker (00:31:50):
Yeah, it's pretty exciting stuff and stuff that is novel for our industry. I think as we look at other industries, and frankly as we look at other countries because they're not the size and the scope and as fast as what we are, I think that to a degree sometimes hinders our ability to adopt technology so rapidly. But I think we're ready for that now. I'll just kind of go back to the first question that we talked about here of what is red meat yield? And so some of the research that I've been working on kind of tried to start defining what is red meat yield and how can we measure carcass composition, how's it defined, and then can we tie some applied application to that? And so trying to rethink Charlie Murphy, what was going through his mind in the 1950s when they were conducting that work.

(00:32:40):
And so one of the things that's important to realize as we talk about red meat yield is if I take one carcass and I throw it into a packing plant and we cut it with a standard industry cut out, the likelihood of me being able to take that exact same carcass and end up with the exact same result is very, very low. In fact, probably nearly impossible. What I think a lot of people don't understand is that there's a lot of error when you have a person with a knife fabricating a carcass. Even the most skilled butcher, even if you just had one person doing it, is not going to be able to replicate that the same way twice.

Miranda Reiman (00:33:17):
Call that statistically insignificant.

Blake Foraker (00:33:19):
Yeah, that's exactly right. Yeah, even on the same carcass with the same fatness and the same muscle. And so there's a lot of challenge in that when we're trying to predict that number. And so removing error in this topic or around the measure of red meat yield is really, really important. And so that's where Miranda, we started looking at computed tomography or CT scanning as a means to provide a gold standard measure of carcass composition. And I'm going to use carcass composition because I think it's different than red meat yield, albeit I think those two things are related. In fact, I think we have pretty strong evidence to believe that. Now, how highly related, we may not know that answer, and it probably depends on the cutout, but what CT scanning allows us to do is through the use of x-ray technology, we're able to measure the muscle fat and bone have very different densities.

(00:34:11):
And so those x-rays have different attenuation rates to the different densities of those three tissues. And so from that, then we're able to quantify the abundance of those tissues from a CT scan with a very, very high degree of accuracy. In fact, one of the first studies that I conducted while I was still at Washington State was to relate the CT composition to chemical composition. In other words, if I was to grind an entire carcass, kind of the old school method, Berg and Butterfield type, those scientific works, or they grind an entire carcass and get a chemical measure of lean, a chemical measure of fat and bone, the relationship between CT composition and chemical composition is well over 90%. R squared value is just 0.95. I mean, it's perfect. Frankly, there's probably more error in the chemical measure and my pipetting skills than what there is in the ct.

(00:35:09):
And so that gives us a lot of confidence, at least in the fact that, look, there's a measure by which we can chase that has essentially no error. It's a gold standard measurement, and that is CT scanning. Now, you might ask, well, are we going to be putting a CT scanner into a plant tomorrow and absolutely not. Now, might it happen in our lifetime potentially, right? I mean, it's

Miranda Reiman (00:35:29):
Maybe in your lifetime

Blake Foraker (00:35:31):
I don't know. But obviously there's a lot of,

Mark McCully (00:35:36):
I'm depressed now, Miranda.

Blake Foraker (00:35:38):
There's a lot of logistical challenges and a lot of safety issues with a CT scanner and the speed by which we run these packing plants 400 head an hour. I mean, we wouldn't be able to do that tomorrow. But again, from a scientific standpoint, and this is what the working group that John and I are a part of, we're talking about conducting a study to relate that CT or true composition to a standard industry cutout yield.

(00:36:06):
And so how does a 60% CT carcass composition relate to saleable yield going into a box? And so the idea there is that because we've got this true measure with essentially zero error, might this mean that future cutout studies not only this one where we're trying to develop some additional technologies, which I'll talk about here in a minute, but in future studies, instead of having to have a 300 head cutout, might we only have to cut out 50 and substantially reduce the cost of this work? And I think that's another thing for listeners to be aware of is that when we talk about yield studies and cutout studies, these are not just light undertakings. It's one in every five year, 10 year major major investments. We're talking half a million or more, million-dollar studies with 30 grad students on a production floor in addition to 30 folks at a plant. And because it's important to us that we provide realtime industry data back to producers, and so that those cutout yields are reflective of what's going into a box. We can cut cattle at a meat lab all day, but again, it may not be reflective of the error of a line worker at a packing plant.

John Stika (00:37:19):
And one of the things that we bring up why the work is so important to get done now is timing is everything. When you look at maybe one of the benefits of having such a small cow herd right now from a scientific standpoint is plants aren't running on Saturdays anymore. And because what Blake's talking about is a multi-plan thousand plus head cutout, that would be probably the biggest, that'd be the biggest in the industry in my career. There's an opportunity to go into these plants on a Saturday, whatever it might be, not disrupt production and begin to do some scientific research and data collection that otherwise when plants are running at full bore moving cattle through the system, the cost to pull back to have a product, a project like this going into places is really, really difficult. It's asking a lot. And so timing's really, really good to be able to do this kind of work

Mark McCully (00:38:12):
Well, and I think probably a lot of folks assume this data is just out there. I mean, you would think about we harvest millions and millions of cattle,

Miranda Reiman (00:38:19):
Got all kinds of carcass data.

Mark McCully (00:38:21):
How hard is this? Doesn't every packer know the exact perfect yield calculation of every particular carcass they run through? In fact, they don't. Right? And then you've got all the variability of plant to plant and trims or specs of, is it bone in or not bone in closely all of the variation that happens in these plants every day. And it is a huge undertaking, and I don't think that can be overstated. And yet at the same time it feels like it's like, man, this should be easier, but maybe there's going to be some technologies that you point out and some AI and some things down the road that will allow us to get at this data on a more real time basis and make more informed decisions long term.

John Stika (00:39:04):
Yeah. Well, I think I want to hear Blake talk about it as well, because your whole time in academia, you've been able to work with some of the coolest tools to help move this topic forward that ultimately we hope to be able to relate back to the gold standard of a CT scan.

Blake Foraker (00:39:23):
So I think that brings up the question, what is the applied technology or what are some applications that are at our fingertips today that if we can't put a CT scanner in a plant, what can we do to predict that gold standard or at least get so much better than what the yield grade is doing? And so I think it comes from my line of thinking. It came back to we understand from a meat judging perspective that there are differences in carcass shapes, and those differences in carcass shapes influence what we theorize as muscularity or yield. And also from a livestock judging perspective, we know that the shape of the animal dictates how fat it is, or we learn all that in animal science 101. And so the fact of the matter is that all of that assessment has been somewhat subjective to this point, visual appraisal.

(00:40:14):
And I'm not saying that there's error in all of that, but there is some, I mean, I think all of us can sit around here and look at the same animal and see something just a little bit different. I mean, that's just the beauty of livestock showing and judging and everything. But the point is, is that today we have some tools to objectify some of those measures and some of those measures that may have real meaning when it comes to things like red meat yield. And as you all know, and John, you made the point well is that when we give producers objective measurements, they can really make rapid progress for those specific traits. And so some of the things that I've been working on is measuring live animal morphology or shape. And so at Washington State, we conducted a hundred head study that was funded by many great industry partners up there, the likes of the Washington Cattle Feeders Association, Agri Beef, Simplot, so Beef Northwest, those guys did a great job of supplying us with cattle, all varieties and types.

(00:41:12):
We had everything from a half Piedmontese down to really 1700 pound SimAngus steer that almost got stuck in the knock box. And then we included a few cows in the study as well, just a huge variety, again, trying to mimic what Charlie Murphy had done at the time. But immediately before slaughter, we ran those cattle, a system called SizAR. And so that's a radar technology. It's got about 70 sensors around the circumference of a chute that those sensors are firing about 60 times a second. So if you think it takes about one second for an animal to run through there, we've got 60 sensors running 60 times a second, we're collecting nearly 4,000 data points on that animal. All

Miranda Reiman (00:41:57):
At chute speed.

Blake Foraker (00:41:58):
Yeah, at chute speed. That's what's important is we weren't slowing down the speed of production. This is something that you could implement anywhere and collecting tons of data, all in effort to map the 3D image of that live animal and then correlate that 3D image of the live animal to CT composition. So on the backend, we measured composition of all of those carcasses, and the preliminary results from that data suggests that there is absolutely a relationship between live animal shape as measured by this SizAR technology and CT composition. And so it's absolutely something worth pursuing further. In addition to what we've done on the live side, Dr. Woerner specifically has done a lot of work at Texas Tech. He also sent a graduate student up to Washington State while I was still there to measure those carcasses to take a 3D image of the carcass using a technology that's frankly just very available on our phones. And so they just had it on an iPad. And so we did a 3D image of the carcass. And so they've demonstrated very, very high accuracies of obtaining 3D carcass images and its relationship to CT composition and saleable subprimal yield.

John Stika (00:43:10):
Can I ask a question, Miranda?

Miranda Reiman (00:43:12):
I suppose we'll allow it just this one only if it's a good one.

John Stika (00:43:15):
Well, I'll tell you here during a couple of discussions, I get out in front of my skis a little bit and start trying to talk about some of this technology, but ultimately, if I'm talking to producers, Blake, one of the biggest questions I have with folks anxious to make progress is about the SizAR technology. And you've probably been exposed to it more than anybody. Is it commercially available today? That's a common question I get because folks are wanting to get out in front of this and I think

Miranda Reiman (00:43:44):
Be some of the first.

John Stika (00:43:44):
Yeah, exactly. And so I think I don't really know what the status of SizAR as a commercial technology

Blake Foraker (00:43:51):
It is not. And so it is not commercially available. And again, it's still very much in a pilot phase. In fact, the SizAR technology was never intended to measure red meat yield whatsoever. I mean, I had actually worked with it through a performance cattle company, Max Garrison during my PhD work on beef on dairy when I was still at Texas Tech finishing up my graduate degree. But I was working with it in the sortation of cattle on arrival to a feedyard and projecting when those cattle were ready to ship. And so that was, and still is the primary focus of that technology now, because I was familiar with it and knew that it existed and knew what it was doing. I was curious about, look, can we relate this to live animal shape and relate shape to composition? And so I think that they're kind of swamped in the overwhelming capabilities of that technology. But again, I think it's important to recognize, and again, I'm not a salesman for any one technology provider or the other. The point that I'm trying to make is that look, if we can come up with some technology to measure live animal shape or carcass shape, that we can use those measurements to predict yield.

Mark McCully (00:45:03):
Are there some things going on in the other species? I haven't been in a pork plant or a poultry plant or anything. Is there anything, again, to your point, the technology, what we're talking about is not unique to carcass grading, right? There's I'm sure, all sorts of industries that are using different similar kind of video image kind of analysis and image analysis kind of things. Is there anything going on in the other species or in other countries?

Blake Foraker (00:45:29):
Absolutely. Yeah. Yeah. So well, we'll just start here. In North America, we've got, obviously the swine industry is very progressive on many fronts, and it helps that they're very vertically integrated as well for them to be able to adopt some of these technologies. But they're using something very similar, a video image analysis of, and actually I saw this presented at the Reciprocal Meat Conference last year, found it fascinating, where their foot scoring and basically skeleton scoring gilts for replacements with this video image technology. So where it's calculating angles of the feet and the bone structure to sort 'em one way as keep or one way as cull. Other things that they're doing is CT scanning potential boars. So basically they'll anesthetize them and then CT scan them to

Miranda Reiman (00:46:16):
Keep em still,

Blake Foraker (00:46:18):
Right? But obviously you can fit a boar, a young boar through a CT scanner. You can't a bull, but basically to measure their composition to what might we expect from a percent fat-free lean out of their progeny. And then obviously other countries have adopted these technologies. And frankly though, I think it's important to remember why other countries may have adopted some of these technologies. Not only are they slower pace, but in many of these countries specifically those, like in Europe, marbling isn't nearly as much of a focus, and so they get paid with greater premiums for carcass yield. But if we talk about the lamb industry in Australia, they've adopted a technology known as DEXA or measuring bone densities. And so we've tried that here in the US specifically Dr. Woerner at the Texas Tech Lab has worked with DEXA. The conclusion of that is it's not nearly as highly accurate as CT scanning, but they're looking at some other things in Australia as well related to carcass shape in some of the European countries. Denmark, obviously they're at the forefront specifically in the swine industry and measuring carcass shape and relating that to yield, but they're using some 3D imaging of beef carcasses. Obviously they have the EU ROP or Europe conformation classification system, but they've come up with some 3D imaging technologies to better characterize that. So it's not a subjective assessment.

Mark McCully (00:47:43):
Is it fair? Is this accurate to say that... I mean, there was discussions around near infrared video image analysis 15 plus years ago, but things held though they never got really implemented that I'm aware of. Really on the beef side, some of this is leading up to all of the discussion we've been having of the why now. But is there also, I assume there's also an element here around just technology capability, our ability to process data, store data, transfer data. I assume there's capabilities there in this whole realm that simply weren't around.

Blake Foraker (00:48:17):
That's exactly right. I mean, we were the camera grading system era of the early two thousands taking entire images of carcasses to try to use that as some prediction tool of red meat yield. And it worked to a degree not to the success that we're finding in our current data sets, mainly because it was really just a 2D image at the time. But you're exactly right that our ability to process data and specifically our ability to analyze data to come up with some of these prediction models is far greater today with the advancements in artificial intelligence and machine learning. And so that's something that's really exciting to me, and I think is kind of perhaps the most important piece to all of this puzzle right here is frankly, if we revert back to the old multiple linear regression that was the yield grade equation of the 1960s, I don't think that we find the answer with the kind of accuracy that we really need in our industry.

(00:49:15):
And so I think it's a little bit of foreign territory, certainly for me, and I think many of our group is that the capabilities with artificial intelligence and machine learning is so far out there, but we're working with a group at Texas Tech and our computer science department that that's what they do. And so we describe to them what the problem is, what the outcome needs to be. And they say, oh, yeah, this is easy. We can do this. And they have, I've provided them with data and I have a result within a week. And it's like, okay, now try this.

John Stika (00:49:49):
The hardest part right now is we've got to be able to collect the data. And I think once we're able to collect the right data, there's probably a kid sitting in a garage somewhere that can apply artificial

Miranda Reiman (00:50:00):
Stereotyping.

John Stika (00:50:02):
We always try to remember that the issues we're trying to solve are not that hard. And I think

Miranda Reiman (00:50:08):
We spent an hour talking about how hard they are, John.

John Stika (00:50:10):
But what I mean by that, what I mean by that compared to issues that in other industries are working to solve, there's a lot more crossover between leveraging knowledge from other industries that we can now apply to this topic once we have the technologies available to accurately capture the volume of data that we need that Blake has talked about through whether it's 3D imaging or SizAR or other technologies that are there, you still got to capture the data and then the power then to analyze and apply sophisticated algorithms and regression equations to it then becomes really a data function that does

Blake Foraker (00:50:48):
Well, I want to add to that, John, because you say, well, we still need to capture the data. And I completely agree with that, but I want to mention something that is rather exciting and that we're exploring here is maybe we don't always have to capture the data. And I know that that's maybe a little bit dangerous as a scientist and a researcher for me to say, right? I think we always have to be careful about how we interpret data.

(00:51:09):
But some of these machine learning techniques allow us to employ something that's called data augmentation. And so one of the things that we're working on at Texas Tech with and actually funded through the Beef Checkoff here is we're working with the computer science department. I'm taking that a hundred head study from Pullman, and I told those guys, I want you to take the variation in those a hundred head and composition and body measurements and turn that group of a hundred into 10,000. And they said, okay, yeah, we can do that. So because basically they just mix and match all of the parts of one animal with all of the other animals, the possibilities to determine the biological shape of a beef animal. And we've got the outcome or the phenotypic data to go with it in terms of carcass composition. And so the fact of the matter is maybe we don't need to keep harvesting these $3,000 beasts in order to collect all this information. The challenge with composition is literally we're dealing with $3,000 animals to get one number on the back end of it, and it's very expensive.

Mark McCully (00:52:10):
And I think where, and I've referenced this before, it was a book about disruptive technologies, but there's this idea of convergence of technologies and all of these things feel like they're coming together in a similar time around the capabilities from a technology, the technologies from processor speed, technologies from the ability to move and analyze data. It's not any one of these things by themselves, but when they all come together, that's when some significant disruption tends to occur. Good disruption,

Miranda Reiman (00:52:42):
Good disruption. So I think we've learned that, I mean, our listeners will know that there's a challenge out there. There's people working to solve it. John, maybe to kind of wrap up, you had talked at the end of your presentation yesterday. What are the next steps? What's happening next?

John Stika (00:52:57):
Yeah. After I get a question about whether SizAR is commercially available, the next question is when can we expect a tool?

(00:53:04):
And so if you look at the roadmap for red meat yield right now, we're in that scientific and producer awareness discussion, capturing the data to answer the questions that need answers that can guide us moving forward. And here very early on, as I've learned, socializing the topic with the industry so that we can gather feedback, receive questions that further and better inform the steps that we take moving forward. That's really where we're at. And we've been there now for the last almost two years, and I'm really proud of the progress that collectively the group has made in that space. The next stage then is alignment on the industry. If we actually do need to fully align an industry on, let's say, KPH removal, because it just makes the prediction so much better, that takes alignment to get that done. It sounds easy, just cut it out, well, but there's processes and logistic issues in plants be able to handle

Mark McCully (00:54:06):
Impacts payment on cattle, right?

John Stika (00:54:07):
It affects payment on cattle. And that then moves us to the next stage, which would be a regulatory stage. If we remove that and we want to determine hot carcass weight differently or define it differently, well then that takes regulation. That takes movement, and we've got to make sure that a producer doesn't pay the cost for that lost weight. So then you need adjustments in the market, adjustments in how producers are paid for those cattle. And a meat scientist said, that's why we have ag economists. And so it then becomes a broader discussion. And then ultimately you get the implementation. On the optimistic side, and Blake and his colleagues on the research aspect of this are hopeful that this large project that has been talked about, maybe we can be in a position to capture some data this summer as early as this summer or this fall, and then that'll take some time. It's hard to say when a tool will be available, but we're two to three years probably available to where we're going to feel that we have a tool that we can begin to put out there. I don't know if Blake has a different perspective on it from a research standpoint, but I would say that's the comfort level of the committee to say, we're a ways out, but let's be thinking about this.

Blake Foraker (00:55:17):
I think the academic answer is three to five years.

John Stika (00:55:21):
Industry has a higher sense of urgency.

Miranda Reiman (00:55:23):
Just talk to your computer science department, they'll get it taken care of.

(00:55:27):
Very good. Well, this has been a very informative topic and also just I think there's a lot, we could keep talking for hours, right? There's a lot of different directions you can go, but is there anything else you guys would like to add? Before I get to the random question of the week?

Blake Foraker (00:55:42):
I would just add this because was thinking about it when I was talking about some of the cool stuff. The other thing that I think listeners should be aware of is that from the CT scans, we are attempting to virtually cut a carcass. So essentially we would never have taken a knife to a carcass. We would run it through a CT scanner. And obviously for listeners understanding, we're not able to run an entire carcass intact through a scanner. So it does require some breaking down into some large wholesale cuts, a whole loin or a whole chuck to run through the scanner. But another thing that we're working on at Texas Tech with these computer science guys is can we separate muscles and leave different fat specs on different cuts to optimize yield of that carcass? And I think that that's something that really excites me moving into the future as we talk about this thousand head study, is we may learn from that, that we need to be cutting cattle differently and that we may need to, maybe we need to think totally different about our breaks and all of those things. And so that's just another really cool and exciting thing that's coming out of this work.

Miranda Reiman (00:56:45):
Absolutely.

Mark McCully (00:56:46):
One thing, and I know we've mentioned it a couple of different times, it won't be the Miranda's looking at me like

Miranda Reiman (00:56:52):
I already landed this plane once, Mark

Mark McCully (00:56:54):
You said, is there anything else, and this is anything else is around this data collection. Also, we're looking at opportunities to collect 'em on live cattle too. And I think for so many of our listeners, it's like, well, that's all great. It's going to happen at the plant. But I think what I get excited about is the opportunity with the SizAR, others of being able, while your ultrasound and your yearling bulls, you might be running them through a technology that's doing a full scan and that is an additional really, really informative data that could be plugged into a genetic evaluation coming out of a contemporary group. All those things that we know are so important. So that's my one other thing, and we said it.

Miranda Reiman (00:57:32):
That was a good point worth adding, Mark. I'll give you that. It gives it kind of what's in it for them, for breeders as well.

Mark McCully (00:57:38):
Exactly.

Miranda Reiman (00:57:39):
Yeah, absolutely. OK. Random question of the week. I want to know because as somebody who grew up on a farm and didn't understand the whole meat science part of it until my first meat science class in college, I want to know when you had your moment where you thought, yeah, I could get really into this meat science. Was it a judging competition? Was it 4-H?

Mark McCully (00:58:00):
When did you fall in love with meat?

Miranda Reiman (00:58:01):
Yeah, that's right.

John Stika (00:58:03):
Well, I'll go first. So growing up in central Kansas, every February when it was the coldest, we processed all of our own meat for the entire family, multiple families, and that was hogs and pork and cattle and beef. And we had our own little meat processing room saws, grinders, band saws, sausage stuffers. And I just remember that it was always a special time for me. I just loved it.

Miranda Reiman (00:58:32):
You were all working together

John Stika (00:58:34):
Just loved it. And then after that, it would've been the first meet judging experience that I had as a senior in high school because I needed one more contest to apply for my state FFA degree. And I went to a meet judging contest, and that was the hook that ultimately said, when I get to Kansas State, this is what I'm going to do.

Miranda Reiman (00:58:53):
Sign up for that, love it.

Blake Foraker (00:58:54):
So I grew up in the county where Cargill is headquartered, Wichita, Kansas. And so obviously we showed at the county fair there and we'd go and obviously Cargill had a great representation at the county fair and are great supporters of so much of what we do in agriculture. But there were a couple ladies that came up to me after the shows, Hey, we really want you to join our meat judging team that we're trying to start here in Sedgwick County. I said, I'm not doing meat judging. I'm stuck on livestock judging because only the cool kids do livestock judging. None of them do meat judging. And so it literally took them about two or three years for them to convince me to start doing the meat judging path. I was a junior in high school when I started down that. So they said, look, if you'll do meat judging, we'll make you a better livestock evaluator. And they were exactly right. But the moment for me when I said, I'm going to be a meat scientist, I think I was a senior in high school. We were preparing for the National 4-H contest, or maybe it was Denver, and we went to the Cargill Dodge City packing facility in preparation, and we got a tour of the harvest floor and it was the blood pit in Dodge City, Kansas, where I said, I'm going to be a meat scientist

Miranda Reiman (01:00:03):
Wow. Not everyone can say that. Well, I would say our industry is certainly better because you two gentlemen have made that decision to make that a kind of life's work. So we appreciate the contribution you've made to the industry and definitely your work on this topic. For sure.

Mark McCully (01:00:17):
Yeah, it's an incredibly exciting topic and you guys bring a lot of energy and vision and leadership to the topic, and so just excited to see where it's going to go, whether it's two to three or three to five years of where we're going to be.

Miranda Reiman (01:00:31):
We'll check back in.

Mark McCully (01:00:32):
We'll record this podcast then, and we we'll recap it all. But no, thank you guys for your leadership and for contributing all of what you are to this subject. It's definitely for the betterment of the industry and for the betterment of the breed. So thank you. Thank you, thank you.

Miranda Reiman (01:00:47):
Thanks for listening today. For continuing coverage on the red meat yield topic, watch for upcoming editions of the Angus Journal or our sister publication, the Angus Beef Bulletin. Not a subscriber? Visit angusjournal.net to learn more. This has been The Angus Conversation, an Angus Journal podcast.