Practically Ranching
Join Matt Perrier as he visits weekly with interesting, thoughtful, entertaining individuals within the beef community. Conversations will inspire curiosity and creativity while maintaining the independent spirit and practical nature for which ranchers are known.
Practically Ranching
#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.
dale.woerner@ttu.edu
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 for our 52nd annual bull sale to be held Saturday, November 23rd, Northwest of Eureka. If you'd like to receive a catalog, go to Dalebanks.com, drop us a request, or just use the sortable online version to custom sort doubles specifically for your herds needs. Dr. Dale Woerner is the Cargill endowed professor of sustainable meat science at Texas tech university. He spent 15 years in the meat science space. And he's been involved in various industry leading research projects. In addition to coaching and leading meats, judging teams at various levels. And now serving as chairman of the national four H meat judging advisory committee. And through that part in for my meat judging kids who will appreciate that. One of his most visible projects that he is involved in today, surrounds updating or totally rebuilding really the system for estimating red meat yield and beef carcasses that age old one through five numbering system that most of us know as yield grade. We cover some of the history of yield grading, the advent of some new tools and technologies that have inspired this much discussed change. And what it could mean for carcass valuation. Evaluation and even genetic selection. And while most of us don't likely spend a ton of time in the meat cooler anymore. And we certainly probably can't recite the old equation that we learned in meat science or on the grading rail. It nevertheless has bearing on how we value carcasses and cattle today. So, whether you're a meat head or you just want to stay current with how we could be evaluating and selecting for credibility and rep red meat yield in the future. I think this discussion will be a good one. So as always, thanks again for listening and enjoy this conversation with Dale Woerner.
matt_2_10-29-2024_141856:So the last time that I got to hear you speak was actually about a month ago in Kansas city. I was impressed in two ways: number one, I, I was fascinated. I'd read some of your stuff and knew some of the projects you were working on and meet science side of things and credibility. And, and so I kind of, that part was a given, but then after you spoke, you vanished and, they brought everybody back on stage to have this panel discussion and they said, well, Dr. Werner can't be here. He has a son playing football tonight 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 been Thursday, late September. I don't even know where the game would have been, but hopefully you won.
squadcaster-2ab1_2_10-29-2024_141856:Yes, we did. And it was just just a junior high game. he's a seventh grader. So it's his first year. And I vowed never to miss any of those football games. And then Kelly Ritalik, you know, at Angus called and was like, Hey, I really want you to do this. And I was like, Well, I can't because Because I can't make the schedule work and so Anyway, she ultimately Decided that I didn't have to be on the panel that I could just talk and leave but I was going to join virtually and they had a they had an issue there with something on their end Not being able to make that happen. So I wasn't able to do that.
matt_2_10-29-2024_141856:I remember, I remember them saying that they thought they were going 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 and I, uh, I applaud your priorities. I've actually got, uh, my wife and I have a, uh, Junior high football player had a game that night as well. Henry is in eighth grade and luckily I was in driving distance to his game pretty easily and so I was, I was able to make it in plenty of time. But, uh, you had a little, a little longer haul than what I did to get back to, to Lubbock or wherever he was playing. So is he, are they still going or are they season over?
squadcaster-2ab1_2_10-29-2024_141856:no, they are yet this week and next and then Then we're done. Um, but I did look into the driving too. I was like, well, you know, the flight doesn't work mainly because of the connection time. And, uh, well, I mean the flight did work, but I couldn't get a later flight was the reality. And so, but anyway, it was too far to drive. It was like 10 hours. And so it wouldn't work,
matt_2_10-29-2024_141856:Yep. Well, I'm glad you made it. And, and I was glad that I got to hear at least your main comments, even if you weren't there for the panel. So before we get into that, and that's, that's the main thing I would like to talk about today is the, your thoughts on the beef yield grade equation and, and, um, how we make that a little bit more current, uh, but give us a feel for what all you do there at tech. And I guess some of your background brings you to where you are today. I know you've a long list on your, on your CV and resume, everything from coaching intercollegiate meats judging to all kinds of different meat science, but give us a feel for, for what what brought you to Lubbock up to now.
squadcaster-2ab1_2_10-29-2024_141856:yeah, absolutely. You know, traditionally trained as a meat scientist. I've been working, I guess, professionally in that space for 15 years now. I spent my first nine years as a faculty member at Colorado State in Fort Collins, working with a really great team of meat scientists there. Before going to Colorado State, however, I did get an undergrad and a masters here at Texas Tech. And so I did spend six years working on my early education here. Prior to being a faculty at CSU, I worked on a PhD, with, uh, Daryl Tatum and Keith Belk, in that meat science group. Had some exposure to the great Gary Smith, uh, in the meat science area as well, uh, served on my committee there. So, that's why I went to CSU, was to work with those great, meat scientists on the faculty there at that time. And was lucky enough at the end of that PhD to have an opportunity to stay. So during that time, just kind of cutting my teeth as a academic faculty, uh, stayed pretty wide open to anything that, uh, was fundable to do research on. So wide variety of, you know, livestock production systems, grading, nutritional work, uh, composition of meat measurement, uh, food safety, lots and lots of opportunities there, during my early career and, would generally have characterized myself as a jack of all trades and kind of a master of none at that point. opportunity Came up here at Texas Tech. Cargill had provided a substantial gift to the university here for the purpose of securing an additional meat science position. Cargill's concern then and perhaps still now is making sure that we have enough people trained in the meat industry to provide leadership and workforce for meat processing plants, but also research and development and innovation, things like that. So, My title here is Cargill Endowed Professor of Sustainable Meat Science. With that, I've worked a little bit on some sustainability work as we traditionally would describe it relative to efficiencies of production, looking at things like water saving technology or processes. Worked a lot of the beef on dairy space here in the last six years or so. Um, just trying to make better utilization of the dairy cow and what she has to offer. with that beef on dairy work, uh, we kind of opened the eyes to, more opened the eyes, maybe knew about these issues, in the past, but exaggerated liver abscess issues, exaggerated issues with red meat yield, in that area. population of cattle. We have cattle with beef on dairy that have really big ribeyes on average, but don't necessarily have the rest of the carcass muscling to go with it. So there's some confirmation issues there relative to round muscling, chuck muscling, disproportionate to the ribeye. And so again, beef on dairy, help to exaggerate those differences and kind of drew our attention closer to some of those issues. Um, worked also, uh, at the university, but, but in an external way, uh, with, with a technology company, um, introduced to things like 3D imagery, uh, CT scanning, some of the technologies we're using now, uh, with a group of engineers. working outside of the academic space. And so with all of those things combined, uh, trying to get at the, the issue with yield grade and its current inaccuracy, um, you know, they're just being creative and finding some ideas as to how we could do a better job. And that's where we landed on 3d imagery at first. Um, and then that's evolved, from the NCBA working group into, uh, Uh, MRI and CT scanning, so we've experimented with both of those. And now, continuing to work, uh, with industry through NCBA, to better understand the capacity of these technologies to measure red meat yield. That's where we're at today,
matt_2_10-29-2024_141856:So on the, on the cutability portion of that, uh, you talked about in reference beef on dairy and that, uh, that was something that, um, shed a lot more light on some of those issues on how we estimate cutability, but that discussion was it was taking place way before, the beef on dairy space. I know that's just like you said, it's kind of exacerbated or, or ramped it up just like liver abscesses and, and some of these other things. Correct.
squadcaster-2ab1_2_10-29-2024_141856:Absolutely, I mean the discussion about inadequacy of yield grade has been occurring, uh, since the adoption of yield grade in the 1960s. So, um, anytime you use a small number, a hundred and sixty or so cattle to develop a prediction equation, you're very limited, by that sample size and number. And so, there was some very early work, literally done within a decade of yield grades development and creation, inception into the industry, already questioning its accuracy. So This has been an age old question. Uh, you know, Ty Lawrence at West Texas A& M has written a lot of articles, done a lot of interviews over the last 10 years or so, talking about this as well. Uh, yeah, beef on dairy for us, you know, really allowed for us to collect a lot of data on it specifically, and it drew closer attention for us and our program, but even Ty, you know, his narrative was based largely on the inadequacy of the yield grade 4 Holsteins, you know, particularly, it's really inaccurate on that population or was, and, uh, but yeah, I mean, the issue of inaccuracy of yield grade has existed across all cattle types for decades and it's time to fix it, you know.
matt_2_10-29-2024_141856: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 yield grade equations or the yield grade equation that we're currently using was adopted and then kind of what that change has been. Uh, and we, I think we know it in the cattle industry, but, um, give us some history of, of the yield grade equation we currently use.
squadcaster-2ab1_2_10-29-2024_141856:Yeah, so we started grading cattle and carcasses, uh, in the United States in the 1920s. that system has been voluntary, but is overseen by USDA and their Agricultural Marketing Service, the AMS branch of USDA. It wasn't until, uh, late 1962 that, um, USDA implemented the yield grading system as we know it today, uh, in that, uh, 162 head of cattle. Of course, at that point in time, 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 that original equation., Um, A guy named Charlie Murphy, uh, working at Texas A& M at that time, helped to develop that equation, uh, for the industry to use. And it gets stuck, right? It, it worked well enough at that time, at that point. Uh, I think Dr. Murphy had determined they were 70 to 80 percent accurate in, in that equation at estimating boneless, closely trimmed retail cuts of carcasses. so, Essentially that equation has remained unchanged over time. Most recently, although it's been 20 years now, or more, the industry adapted and began to use camera grading technologies to measure things like fat thickness and rib eye area with greater precision and accuracy. So that was still being inputted, however, into the same old equation you know, developed in the 60s. And so we've been computing yield grade and transacting on yield grade since 1962. And, uh, it's grown more and more inaccurate over time. And, and that's at least in part due to cattle type changing, cattle conformation changing, cattle weights changing, uh, ribeye area gotten larger on average, uh, most every year, but seemingly without any growing relationship between ribeye area and true carcass muscling. So one thing that the camera data really, uh, expedited, if you will, in our industry is the ability to select for ribeye area genetically allowed for us to increase ribeye area rapidly. But because ribeye area was a single measurement, a single trait measurement, it wasn't being related to muscling in the hindquarter or muscling in the forequarter. So we were single trait selecting for a muscling trait that wasn't translating well, uh, with total carcass muscling. So as a result, at least in my mind, 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 of the overall red meat yield. So as a single indicator of muscling or yield, it's, it's very poor. You know, we put it in combination 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 math and figured out that we could adjust the coefficients on that equation 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 better job. at accurately depicting or measuring carcass conformation, carcass fatness, carcass muscling as a whole. Um, you know, now that we have the capacity, not only with the hardware in the way of camera systems or, or x ray machines, but maybe even more importantly is the ability with computing speeds to manage the data. Large volumes of data coming off of these images and x ray type scans, artificial intelligence models are able to handle these volumes of data and actually give us a meaningful result. And so that computing speed, computing capacity is paramount to this discussion today.
matt_2_10-29-2024_141856:So before I ask you how and what kinds of technology and tech and computer speeds, we're going to need to get this done. I do want to point out something that sounds pretty suspicious to me. As my tongue is squarely in side of my cheek, uh, You said that A& M and the USDA, I assume worked together on this research project back in 62 that put together yield a great equation. I just can't believe with the rivalry between tech and A& M that it's taken you this long to, uh, to throw a flag at, at something that the Aggies came up with. I mean, really, I would, I would have thought sooner than, than, uh, what It's been 60 some years, you would have gotten this done, Dale.
squadcaster-2ab1_2_10-29-2024_141856:Yeah, certainly a good point. Uh, no, I mean, obviously Texas A& M, uh, 60s and 70s and all the way to today. Very much leaders in, in agriculture as a whole. and definitely in this space of meat science, they've had some really, uh, foundational investigators, 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 do something, something different about it in a way that, that made more substantial progress, you know, in this space and, and to these projects are expensive, um, expensive as much so in the way of time and manpower and carcasses, as they are, you know, financially, uh, most of the product that we use to create this type of work can still be sold. So we're not, we're not necessarily losing product, but the manpower and the kind of the distraction or inefficiencies it creates in the packing plant to do this type of work has really been an obstacle over that timeframe as well. But, yeah, I mean, it's, it's long overdue. I think everybody agrees with that. It's, it's about time we move forward with something different, uh, to in something more accurate. Every more, ever more important as we are focusing on sustainability and efficiency in our production systems today too, to accurately measure. what we're trying to produce, right? We can't just incentivize efficiency or carbon credits and things like that based on weight whenever we don't know what the composition of that weight is. And so these carbon markets and, uh, you know, efficiency, especially accounting for carbon and paying for it really needs to be based on something truthful. And, uh, I think that's as much as anything pushing this forward as well.
matt_2_10-29-2024_141856:a very good point because, you know, the old adage, you can't manage what you can't measure or what you don't measure. Uh, we've got technology to measure this today. It's just, if we have the will to, as an industry, Make a better way, estimate those closely trimmed retail cuts. And that's, that's where I think that probably the biggest hurdle is not the tech or the math or anything else. It is whether we, as an industry are willing to look at this and really upset the fruit basket, because there's a lot of grids, there's a lot of value based marketing arrangements that are based off of that old one through five yield grade equation, and you'd discount the fours and fives and you may reward the ones or twos, or it may be on par, but regardless, I think that's, and we'll get to how, you know, some of the economic ramifications, um, here later on, I hope, but, uh, so tell us, I mean, the camera grading part of it, and I think everybody that's listening to this podcast gets. The difference between that and when a USDA grader was making that call. But give us a quick history of what that changed, man, around 2000, 2002, when that occurred, uh, for the way we at least get these pieces of information that could be used in a new equation,
squadcaster-2ab1_2_10-29-2024_141856:Yeah, uh, camera grading systems evolved with digital imaging technology, and so if we can all remember back to the first digital cameras that. to us to buy, uh, we have to think back to the late nineties and early two thousands. Um, and these developers of the camera grading systems were, were maybe slightly ahead of the consumer's ability to go digital camera. So the mid to late nineties is really when that digital technology came, uh, became available. And so there was research being done again in the middle of the late 90s looking at the ability to segment a digital image, uh, allowing for measurement of muscles, uh, fat, fat thickness, things like that. So that's really when this began evolving more rapidly. In the late 1990s, there was some published research done, mainly at Colorado State, demonstrating the ability of a camera grading system to take direct measurements in real time off of carcasses. So, they were seeking approval, the instrument manufacturer, with help from the team at Colorado State, uh, doing the research to seek approval for ribeye area measurement, uh, fat thickness measurement, um, and those were the first things that kind of came online, uh, in the, in the very early part of the two thousands. And so ultimately USDA ended up accepting that technology, uh, approving that technology through repeatability and accuracy estimates of ribeye area and fat thickness. So early, very early adopters would have been Cargill, And Tyson, who later partnered with a, with a different company actually, uh, to develop a competing technology to the original. And so you had at least two packers, Tyson and Cargill, working, collecting a lot of data, you know, putting these systems online in their plants. And like you said earlier, you can't change or improve what you don't measure. So This was an ultimate measuring tool, uh, for them to begin to capture a lot of data. Uh, they incorporated those measurements into the yield grade equation, so began to develop that camera as a yield grading instrument or tool. And, uh, USDA eventually adopted that as well, um, as, you know, having the capacity to assign official USDA yield grade. up To that point, the human grader was. assessing, I should say, reality would be guessing, you know, as to how big the ribeye areas actually were. and the reason I say guessing is is because at production speeds, there's no way for them to physically measure with the tools that they had, you know, the ribeye areas or even physically measure fat thickness. Speed that we were operating. So that is in no way a stab at, at USDA. It was just the fact of the matter. They were, they were site grading carcasses, based on their experience. And then they would, of course, be checked through a series of audits and things that USDA had in place at the time. So, but nonetheless, you know, the camera was going to be more consistent, more accurate, in most every scenario than a human grader could be at a high rate of speed. So that was an improvement. But one of the biggest improvements made was the ability to capture that data in real time and, and share that data back with cattle groups, right? With, with breed associations, with individual producers. And so now all of a sudden we go from, a handful of measurements of ribeye area to thousands, tens of thousands or hundreds of thousands of measurements of ribeye area that was just, you know, occurring automatically in these plants and that data was made available back to those producers. So we started to make genetic advancements more so with that ability to measure and share that data. It wasn't until 2006, however, that Researchers and, and camera providers or developers actually got the cameras approved for marbling score. So, you know, marbling score, not necessarily a part of our red meat yield discussion here today, but once that became the case and, and more plants, essentially all of the big plants, you know, had camera grading systems. In place to do not only yield grade, but marbling score. That's when the data really, you know began to pile up and Now we had marbling score, rib eye area, fat thickness, all of these things coming back from these camera grading systems which You know made that data capture so much easier and facilitated genetic selection as a result
matt_2_10-29-2024_141856:Yeah. I remember the discussions back in that time period when some of those Plants were beginning to both research and then experiment and test and then use it for their own in house data. In fact, some of them, I believe, were paying on the camera data on grids before USDA was actually using it for their yield grade and then eventually quality grade equations. And I remember the discussions of people saying, you know, it'll be a Cold day in hell before you don't have a USDA grader standing there making the actual call. No, no computer, no, no camera is going to do what a USDA grader has done for decades. And man, oh man, you talk about something happening fast. I don't know if, if USDA just decided in the unions that represented their graders decided they couldn't fight this or what, but it happened quicker than I thought that it probably would.
squadcaster-2ab1_2_10-29-2024_141856:Well, yeah, I mean, to be clear, right? We haven't replaced the USDA
matt_2_10-29-2024_141856:there. That's true. Yeah.
squadcaster-2ab1_2_10-29-2024_141856:plants and they are still there certifying the grade, applying the grade to the carcasses. Yes, relying on and using the camera system to, to augment or to help them do that, but they still are the, the certifying and governing body, providing those official grades. But you're right. I mean, I think ultimately what it came from was a partnership and an agreement between USDA, and producers and packing companies that this was the right thing to do. You know, to change with the, with the technology and allow for the technology to ultimately provide a more consistent grade, across the country. I mean, there were, and to some extent still are, with human graders, a range, right, a lack of consistency from north to south. Specifically in, in quality grading cattle. And what the camera grading systems did was, was kind of neutralize that discrepancy or range in quality grade performance. and we've worked a lot on quality 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 meat yield, well, because the yield grade equation was broken. And even though we had all of that data for yield grade, um, it wasn't telling us anything. And that's, you know, what we've discussed thus far is just the general inaccuracy of that yield grade has made it somewhat meaningless, right, to, to, the packer and the producer. And so that's what we're working on changing is the relativity and the meaningfulness of that data.
matt_2_10-29-2024_141856:Okay, so now what I've been wanting and waiting to talk to you about how, how do we do this? What kind of new tools, uh, what kind of new formula and math and technology and everything else gets us to where you feel we need to be?
squadcaster-2ab1_2_10-29-2024_141856:Yeah, absolutely. Um, I'm glad you're asking that in the form of a question. I kind of respond in telling you that this is very early on in development. We don't know exactly what the technology will be. we have some ideas and. Experimenting with, with technologies that have been very promising and, and provide some hope, right, for what we're trying to do. I think ultimately what we're trying to achieve as an industry is to get a technology, that is accurate, that is repeatable, um, although we haven't determined exactly how accurate it needs to be yet. you know, when you compare to current yield grade that's performing in the 40 percent range. An adapted new yield grade that can perform in the 70 percent range. Then if we're going to move to a different technology, it needs to perform better than that. You know, so we're looking at something personally, I would hope we could get into a technology that at minimum provides at a 90 percentile. Um, in terms of accuracy, so we're getting an A from an academic standard or viewpoint. Uh, but I think we could easily attain that. So, ultimately, we learn from the inadequacy of ribeye area that we have to have a more holistic measurement of carcass muscling. to go right along with that we need a holistic measurement of fat. Alright, those are the two primary factors, of course bone is there as well, uh, bone somewhat invisible, to the external parts of the carcass obviously, so that becomes more of a challenge. the most accurate technologies we have experimented with thus far are x ray based. That would be primarily CT scanning. And CT scanning allows not only for the differentiation and measurement of soft tissues like muscle and fat, but also allows for us to see and measure the bone. The current issue with CT is that the readily available technology is built for humans or smaller animals, uh, something smaller than a carcass. So the physical size of a beef carcass is a limitation for CT scanning. Now that's not to say that someone could manufacture a much larger CT scanner, They can. But then you get into the realm and discussion of expense, uh, to do that. And, of course we're adverse to high prices in all of our industry, So there's a limitation there in terms of hardware and physical size. Perhaps more concerning, uh, relative to X ray or CT specifically. is the production speed at which we operate. So as most know, commercial processing speeds today are between 300 and 400 carcasses per hour. at This point and with the technology that, that we're aware of in CT scanning, that is impossible, right? There, we're not going to be able to scan 300 carcasses an hour. through a CT scanner. It's, it's physically impossible at this point to do that with the technologies that are most available. notice I'm communicating in caution here because technology is also such that, you know, a technology that could do that at 300 per hour probably does exist, but, or could be developed, before that. But again, the expense discussion comes in there. So the reality is, is through the research that we've done very recently, we not only understand that CT scanning of carcasses is accurate, but we think that it's more accurate than any other way to measure composition on it, on an animal. So we can't even match the accuracy of a CT scanner with a scalpel and a scale And some extreme level of, of dissection. So what we began to do is discuss CT scanner as, as quote unquote a gold standard for measuring composition. So we have faith that, that the CT scanner can and does measure composition to really full accuracy, 100 percent accuracy if you will. But those limitations that we already discussed are More than likely going to keep CT scanners You know out of this discussion for a full operating speed solution So our next best technology That we've been working on is 3d imagery So what the 3d images do is give us a exterior image Not x ray, so we're not looking inward into the carcass beyond the the surface level, but we're getting a full three dimensional measurement of the confirmation, the volume, the dimensions of the carcass. And then that's where really the prediction, you know, pieces come in, the algorithms, the mathematics that go on top of that. So how predictive is what we can measure with a 3D image and how well related is that to the actual composition. In some small trials, you know, looking at, uh, you know, less than 50 carcasses, 40 to 50 carcasses, we've demonstrated accuracies with 3D imagery in the mid to high 90s. So being able to match the composition that we could measure with CT scanning with about 95 or more percent accuracy. We, we feel really good about that. Enough so that we think that 3D could be tested on, you know, hundreds or thousands of carcasses to validate that accuracy over a much larger population or sample size. There's other technology that has been demonstrated on the live animal side to perform a similar concept using radar to again create a 3D 3D outline or 3D rendering, if you will, of a live animal, which could be applied to a carcass as well. That gives us the same ability really to measure dimensions of carcass and, develop again prediction equations for those three dimensional measurements to red meat yields. Where we've landed at this point in time, working with NCBA and an industry working group they've put together, is that we think we can use the CT scanner in a commercial environment to scan a limited number of carcasses. And when I say limited, I still think we're going to get to a thousand or more, maybe even a couple thousand carcasses that we scan at a, at a practical pace to determine the composition of those carcasses as a gold standard, and then allow for other technologies like 3D imagery, like radar scanning to be developed from that data. And, and in hopes that those technologies are, are fast enough and predictive enough to work at commercial production speeds. So basically we're going to settle back into a prediction based on a three dimensional rendering, hopefully operating in the 90 percent accuracy range at 300 to 400 head per hour. And so that way we still get individual carcass measurements that are accurate for red meat yield. In addition to just a holistic number like, You know, a 60 percent red meat yield, for example, on a carcass. We will also have thousands of individual measurements, maybe even tens of thousands of individual measurements coming from that carcass that could be utilized for again, genetic selection, genetic tools, animal trait, carcass trait improvement, and that data, you know, again and available to be shared back with, uh, producers and genetics companies to, to make selection criteria based on those measurements.
matt_2_10-29-2024_141856:Well, it's To say the least game changing, I would say, if we start looking at that level of, of detail in what we get back from the pack and plant, um, I think the first thing that a lot of folks who have fed cattle and been paid on a value based grid and seeing the discounts for fours and fives or the rewards for ones and twos or whatever the case may be, are going to ask is, and maybe the This gets you out of your meat science office and into that jack of all trades, uh, that you said that you've kind of always been, which I think is valuable in a researcher or a college professor of any kind, but how does it affect us as beef producers from an economic standpoint? Let's say that the technology and the math and the, uh, And you're able to write the algos and, and get to that level of confidence compared to your gold standard of the CT scan, let's say that, that all the science works, how do we buy and sell cattle from a credibility standpoint? Now, going from the feed yard to the packing plant, do you, have you looked through that or are we too far away to even consider those economic ramifications?
squadcaster-2ab1_2_10-29-2024_141856:Well, I think we can speculate, um, you know, at this point as to what that might look like. I think, in general, I don't think it looks a whole lot different than what we're already doing with grid, grid based systems. I mean, most grids today still, paying on quality and yield grade, and the issue is, of course, the inaccuracy of the yield grade. But I think how we incentivize both quality and yield together is still in a two part pricing system. So instead of a numerical yield grading system, we may go into a percent or point basis, you know, on, on red meat yield as a whole. And so that's more than likely what's going to happen. The industry needs to maintain the ability to emphasize quality grade. Quality grade is what puts beef on the table every single day. Quality grade and eating experience will remain paramount, you know, in our lifetime. I think people will continue to pay for beef because of the way beef tastes. and I think one of our greatest challenges is to not get too carried away with red meat yield to the point where, uh, you know, we're over emphasizing red meat yield traits and subtracting eating quality from cattle, but, you know, incentivizing the red meat yield, being able to measure that more accurately will ultimately place more value on red meat yield and the greatest way for us to improve efficiency in raising and feeding cattle is to increase very specifically the lean portion of the carcass and with the ability to measure that. we can make genetic progress as well as management decisions like over versus under feeding or accurately feeding to a terminal endpoint that optimizes red meat yield is, is where we need to go. And even though yield grade one and two premium, yield grade four and five discount have long been in place, they've really just kind of gone by the wayside because of their inadequacy. You know, to, to tell us a true difference. Once a packer specifically has the ability to quantify what a percentage in red meat yield does to their bottom line, to their efficiency, then they can begin to place more emphasis on what that matters to them. selfishly, and I think logically, We have to figure out a solution for not over fattening cattle the way that we do today.
matt_2_10-29-2024_141856:Okay.
squadcaster-2ab1_2_10-29-2024_141856:we are producing cattle today on the basis of weight, uh, and dressing percentage. Because those are the two measurements that, are being translated back in the market signal. Basically, heavier cattle, higher dressing percentage equals more dollars. The issue with that is higher weight and higher dressing percentage directly related to more fat. And looking at really high volumes of data, millions and millions of data points off of camera grading systems, we realize that we don't need all of that fat to achieve marbling score on most cattle. So we're overfeeding all cattle in hopes of bringing up the below average cattle to a level of marbling that's desirable. But with this two part system with greater accuracy, hopefully we can identify high marbled cattle, cattle that have the propensity to marble at an earlier time point. So that we can not have to feed them so long. And we would not have to over finish or over fatten those cattle. Which gets into more of a sustainability discussion. In that, we really have to have a tool to help us manage cattle better. To optimize marbling first. But keep that in balance with red meat yield. And, I just hope. We can provide economic signal that, that shapes that because I think we all know that if you're not going to pay us for it, we're not going to do it.
matt_2_10-29-2024_141856:Right.
squadcaster-2ab1_2_10-29-2024_141856:that's very clear. Um, if you're not going to provide an economic incentive, then you're just not going to get results. So we do have to emphasize red meat yield enough economically. to force the change or to pull the change through the system. And so I'm hoping that with more confidence in red meat yield measurement, we can place greater emphasis on red meat yield. That's going to require the packers having the ability to monetize that. They're going to have to be able to monetize the red meat yield before they can pay it back. And so it'll take some time. You know, with these newly developed systems in place for them to fully understand what they can afford to do, right, in their facility, or what becomes profitable for them. And then we realign with what the target should be. The target will not be the highest red meat yield animal that we can make. The target will be the balance. Between the red meat yield, the quality of the product, the marbling score coming in the door, and the ability to sell those two things together. So, it's kind of like the pork industry, right? I mean, the pork industry, in the 90s, decided that we needed leaner pork to compete with poultry. And, uh, they went by the way of red meat yield almost exclusively and subtracted Not just marbling from pork, but the processing value of pork. I mean, bacon didn't have any fat in it. We subtracted enough fat from, from trimmings to not make high quality sausages and, and further processed items. So, you know, we, we can learn from that, mistake that the pork industry made at overemphasizing without attention to the quality. I don't think we're going to make that mistake in the beef industry. It will take time to, to find those cattle, and management styles that, that optimize those things together. But I have to say this discussion goes well beyond red meat yield measurements. It goes into the world of genomics. Um, and those tools that are now so much more available, so much more accurate, from a seed stock side of things, um, the ability to make those breeding decisions to really find these animals that can do both, in the way of marbling and red meat yield, I predict that once this system is in place, uh, that will manage cattle differently as a result. And that's my hope. My hope is that we can effectively feed cattle less, to improve our carbon footprint, to improve our water situation,
matt_2_10-29-2024_141856:hmm.
squadcaster-2ab1_2_10-29-2024_141856:in growing grain and crops and forages to feed cattle. You know, that's my hope. That's a dangerous territory to talk about too. I mean, the last thing a feedlot operator owner wants to hear is that we're going to put less cattle in the yard for, you know, for fewer days or put cattle in the yard for days. So I understand that the industry will have to adjust, but in that same discussion, it may allow for us to, to, grow our inventory. You know, if we have the capacity to do that, if mother nature allows for that. More rain and, and, you know, increasing our female number and, and increasing feedlot capacity as a result of fewer days on feed. It's pretty clear that our, our industry wants to grow in terms of, of harvest numbers. We've got kind of two cooperative slash private groups, you know, trying to build packing plants. In the Midwest and in the panhandle of Texas. Candidly, you know, we need to increase cattle numbers for those two things to make it.
matt_2_10-29-2024_141856:For sure.
squadcaster-2ab1_2_10-29-2024_141856:and Mother Nature has not allowed that at this point in time. But, if we subtract, for example, 80 days on feed off of cattle and can effectively do that with a fair percentage of the population, We then open up that capacity in the feedlot for more cattle in the future. And that may be something, that we don't really think about, but could become a reality if all these things work out in a perfect world.
matt_2_10-29-2024_141856:Well, you used a couple of words there that, that I love and get brought up on this podcast a lot. And those were balance, and keeping things in balance, and a holistic approach. And so often in the beef industry, and like you said, the pork industry did it, the poultry industry did it, the corn farmers, everybody else, we, we get fixated on one trait. Or one outcome. And we throw an immense amount of energy at that one piece of the puzzle when quite often there's a whole lot of other pieces that, um, are close to, if not equally important. And I, that's, that's one reason I wanted to have you on here and kind of get your feel for that because
squadcaster-2ab1_2_10-29-2024_141856:to have a
matt_2_10-29-2024_141856:I grew, I was born in 73, grew up through the eighties, saw this. War on fat that the entire world and even the beef industry waged for a little while. And as you said, marbling is what brings beef to the center of the plate. That taste is what we cannot lose. And so yes, red meat and cutability are important. Yes. We need to make sure that we're reducing our carbon footprint and still putting out a product that, uh, that we can represent and sell and sell well. But. Yeah, if we do it at the expense of the quality component and the sizzle and the taste of that, um, we're not going to compete with pork and poultry. I mean, it's, they're monogastric 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 of feeding these cattle and knowing when to harvest them with better data, but then let's make sure that we keep the quality side up too.
squadcaster-2ab1_2_10-29-2024_141856:Yeah, 100%, you know, I think, again, incorporating more of that holistic mentality in using genomics and genetic testing to find those cattle that can afford to be fed less and still perform at a very high level will be critical to the sustainability of our industry moving forward. And, You know, I got to be honest, when we first started having these discussions, you know, the last year or two, the first questions were exactly what you're talking about. How are you going to do that? You know, what kind of cattle are you going to remove from the system when you start talking about red meat yield? And I think, you know, leaders in the marbling space and that that would primarily be Angus as a breed. I think when you start talking about red meat yield, they start backpedaling pretty hard, thinking, oh my, you know, we're going to go back 30 years here in time. But I actually think quite the opposite is true. I think we don't improve red meat yield initially by pursuing purely continental breeds of cattle or, you know, going to an extreme of muscling homes. I think we actually achieve that most rapidly and effectively by shortening days on feed. And the only way we could do that is to have cattle that have a high capacity for marbling at a younger age, at an age and composition that doesn't require so much weight and fat. So, You know, getting further on into that carbon discussion right now, we're accounting for carbon, you know, there's a handful of third party programs that are trying to market carbon, uh, out to the customers of the world, the basis is, you know, pounds of beef produced in the form of carcass weight, or maybe even live animal weight in some scenarios. Well, if we're incentivizing carcass weight, we're incentivizing fat, which is not anywhere close to carbon responsible or carbon neutral. So that signal in and itself is just wrong. And if that's going to be corrected, it needs to be corrected with what we're talking about. It needs to be an assessment and measurement of what the red meat yield is, the edible portion That's being produced by that animal over its lifetime. So I really think that carbon accounting will evolve with this technology as well. In order to accurately depict what carbon responsibility is, we need a better measurement of efficient production. You know, production of edible product. And, and so I think all of these things kind of come together hand in hand when this thing's all said and done.
matt_2_10-29-2024_141856:That's, that's the fascinating part to me is, is not just the sustainability piece, not just the value piece from the retail consumer back, but even as you said, the genetics piece, because anybody who's had to go into a plant and do tag transfers and Trace ribeye areas and make marbling scores based off 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're talking about, and of course with camera grading, we're already there from a marbling standpoint, we're already there probably from a ribeye area standpoint. But as you said, we can argue whether that's whole carcass muscle and estimate of cutability. But if we can get through 3d imagery tied to that CT scanning all those thousands of pieces of data per carcass put into some kind of a useful information that goes back to sire identified pens of cattle and now we know beyond a shadow of the doubt what those phenotypes are from That bull or that dam or whatever the case may be. You, you want to talk about some powerful information, not just in how we price and sell those cattle, but how we make the next generation even better. Um, then we can really get somewhere.
squadcaster-2ab1_2_10-29-2024_141856:Yeah, I think that's the greatest opportunity of all is, is the ability to select and create generation after generation of improvement in cattle. cattle that are more efficient, cattle that, uh, produce more saleable product and, uh, you know, truly allow for us to be sustainable over time. I think one of the things people don't realize necessarily as well as that in order to make a more efficient animal, we have to make an animal that has more muscling versus fat. Heavier muscled animals are more feed efficient than fat animals. And that seems counterproductive because I think everybody looks at a, a soggy belly, deep bodied British made steer and says, wow, look at the conversion on that steer. Look how much he eats and look how many pounds he's putting on. Well, the reality is, is he's putting on fat at double the calorie cost of protein. Protein cost us four and a half kilocalories per gram and, and fat cost us nine. So just simple math and logic is, is it took twice as many calories to produce that pound of fat as it did to produce that same pound of muscle. So just by increasing confirmation relative to muscle, we're improving The feed efficiency of those cattle. We're getting more conversion per dollar and per pound of feed we're putting in that animal. And so transitioning more towards a muscle, an animal that has the ability to put in muscle and red meat yield actually simultaneously improves efficiency. And the most popular question we get, everybody likes metrics, you know. Producers like metrics and we like EPDs and, single trait selection type concept. So if we get rid of rib eye area, you know, then what are we going to be selecting for next? How do I holistically select for muscularity if I don't have rib eye area to lean on anymore? And so one of the beauties of this technology as well is now we can get a round muscling score. And we can get a chuck muscling score. and I think, develop those metrics that are more translatable, you know, visible, even for the producer that's just solely basing their assessment on phenotype. At least they'll know, hey, in order for me to improve my red meat yield, I have to improve round muscling. And I can see that. I can see hindquarter muscling. better than I can see ribeye area, right on the hoof. And so some of those things may even become more clear, for the producers as to what they're going 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 the most predictive of red meat yield are like forearm circumference and, um, hind shank circumference. Some of the work that, uh, my counterpart here, colleague, I should say, has done, Brad Johnson and his graduate student, Luke Furness, who's working out of the industry today. I mean, things like measuring the forearm circumference of calves, even at the time of birth or time of weaning, strongly correlated to, you know, muscle attributes later on. And so these might even be metrics or tools that we can use to measure at calving or weaning or both, getting at a measurement that's going to ultimately be taken on the carcass that's highly predictive and related to red memeat yield more so than ribeye area, you know, ever was. And so, there's a lot of neat stuff 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. They want to know what, what do we need to be selecting on. You've, you know, you've ribeye area doesn't work. That's what we've been selecting on. So what are we, where do we go now? so everybody's kind of scrambling at this point, I think, for a live animal measurement of muscularity, individual measurement, you know, metrics like what we just mentioned. Those things are becoming of greater interest now, for sure. And these radar technologies, that have the ability to measure animals as they move through the chute. I think will prove to be very effective tools, for management, you know, days on feet, closeout dates, things of that nature. I think they'll be used as management tools along the way. Live cattle to improve red meat yield.
matt_2_10-29-2024_141856:So do you have a timeline where you think in your mind, Hey, we could be here, we could be using the CT scan to train the 3d imagery by five years? 15 years? What's your estimate?
squadcaster-2ab1_2_10-29-2024_141856:Yeah, so right now we're working on a two year timeline to establish CT scans as a gold standard. So, I fully expect that that will be successful. So, within two years, we'll be utilizing CT to provide the opportunity for 3D technologies or other rapid technologies to be developed to be used for measurement purposes. so, the time frame I've been telling people is three to five years. I think in three years it's it's possible, maybe even likely that we'll see systems in plants assessing carcass confirmation or red meat yield at chain speed. I think three years is a reasonable timeline for that. How long will it take the industry to adapt and accept, prove instrumentation for official grading purposes? You know, that's where I think the five year timeline comes in. But the industry has already proven that we don't need official designation of grade to use it. And so I think if, if the industry becomes comfortable enough, Packers specifically become comfortable enough with that technology in their plant, then I think we can see something as soon as three or three to four years where we're actually paying on the basis of red meat yield with a new system. Now, I mean, I am completely speculating that, but it's, it's based on some of the timelines that we're working with on research and partnership with packers, partnership with cattle feeders through NCBA. So I think, I think there's some reality to that. So I'm going to say three to five years. Kind of the wake up call, however, is, you know, a heifer or steer entering a packing plant in three years is being bred today, right? I mean, the cows are being bred today to make that calf that's fully developed, weaned, and fed, going into that packing plant in three years, so We're within, you know, months Months to a year at needing to make a decision With a straw of semen, 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 three years. So, this is now, I mean the timing is now, for this, in making decisions for breeding and genetic selection. Which is why breed associations and bull studs are, are really of peak interest here. Uh, at this stage of development, and they do want to know. So, uh. Without naming names, uh, you know, I had a, an email exchange today, uh, with one of the world's largest genetics, most, uh, companies, you know, wanting to have the same conversation, trying to figure out what the metrics should be. and we've had conversation with most every large genetics company in the United States talking about this concept. So things are moving, breed associations and bull studs very much trying to be prepared in marketing red meat yield, um, through genotypes and phenotypes, primarily on the sire side of the equation. But, uh, of course we know that the dam side is equally, if not more important than the sire side of the discussion. So This will become, you know, somewhat universal.
matt_2_10-29-2024_141856:Well, and I think that, uh, is a good spot to kind of close our conversation because I'll bring it up again. You said the word balance and really the holistic piece of it. I mean, regardless of what we're using to determine red meat yield in a sire, He also has to do all the other things. He has to provide the marbling that makes beef better than pork and poultry. He has to make daughters, at least in some herds that'll go out there and breed under range type environment. He has to make calves that are born unassisted and grow as rapidly as they can as efficiently as they can. So, yeah, I mean, I think if I were in your shoes and have those people calling me and asking, you know, what do we need to look at? What measurement do we need to look at? Probably at least right now, until we see what this imaging and the math does and indicates it's Word of balance, make sure everything is in a pretty good spot somewhere in that optimum range and don't just put all your eggs in the ribeye basket or something like that.
squadcaster-2ab1_2_10-29-2024_141856:Yeah.
matt_2_10-29-2024_141856:Well, this has been a great conversation and I appreciate it. If it's okay with you, I will put in our show notes, um, your contact information, or at least a website. And, uh, folks do want to drill down any further or have further questions. Um, yeah. Any of those AI studs that are listening to Practically Ranching, uh, that didn't get all their hands, their questions answered. Um, uh, I assume that you would be welcome to respond to any emails like that.
squadcaster-2ab1_2_10-29-2024_141856:Absolutely. My email is a great way to make contact, so I'll be happy to
matt_2_10-29-2024_141856:great. We'll put that in there. And, and again, thanks so much for being here with us today. Uh, great information, exciting stuff, sometimes a little scary stuff, but, uh, but stuff that I think anytime we can better characterize and quantify, uh, Cattle and use that information to make more of the good ones and manage in a way that we can produce more of the good beef sustainably. It's a win. Uh, is it change? Yeah. Is it maybe going to give us a little heartburn as we make that transition? Sure. But it's a win for the beef industry. And so I applaud your efforts on that and we look forward to kind of staying tuned.
squadcaster-2ab1_2_10-29-2024_141856:Sounds great, look forward to it.
Microphone (Yeti Stereo Microphone):Thanks. Again, for listening to practically ranching brought to you by Dale banks, Angus, as we've said before, if you like what we're doing here, give us a five star rating, drop us a comment and be sure to follow us, to hear future episodes when they're out. And be sure to make plans to join us for our annual bull sale, november 23rd. At the ranch Northwest of Eureka. As I mentioned, catalogs. Blogs are available. Now, if you'd like to receive one, drop me a note@mattperrieratdalebanks.com. Or just fill out the form. form. Dalebanks.com. God bless each of you. We'll be back with our bull sale preview in two weeks.
