WEBVTT
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Welcome everybody to another episode of the Prophet Through Science podcast hosted by the American Semitol Association with Dr.
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John DeClerc and myself.
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And John, today's a really exciting day and fortunate to have our first guest joining us all the way from the state of Tennessee, the University of Tennessee, Dr.
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Troy Rowan.
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Troy actually joined us for Fall Focus in Bozeman, Montana back in August.
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And fortunate to have Troy join us on here today.
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And Troy, if you wouldn't mind for folks that are listening in here, would you give us a brief introduction on yourself and your role at the University of Tennessee?
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Yeah, thanks, Fan.
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And thanks for making me the and trusting me with being the first non-Sementol employee to be on the podcast.
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So we'll uh we'll see how that how this goes.
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And um maybe after this you'll decide to keep everything in-house.
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But but excited to be here and talk with you guys today.
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But like I said, Troy Rowan, um, a faculty member here at the University of Tennessee, have a split research and extension appointment and focus broadly on beef cattle, genetics, and genomics, have a real interest in cow efficiency, how we can use phenotypes to move our breeding programs towards making more efficient cows, which I think will certainly feed into our conversation today.
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But have been at Tennessee for about five and a half years now.
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And and yeah, have always admired what happens at the Cementol Association, all the way back through my PhD working with data sets and collaborating with you guys.
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So again, love our partnership that we have and excited to chat with you today.
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Yeah, thank you, Troy.
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I know we first got to meet each other when I was wrapping up my graduate work at Kentucky and uh had some mutual friends from a faculty standpoint between Kentucky and Tennessee.
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And one of the things that I've always admired about you and your your role in extension is taking really complex topics and sharing that information that's really easy for folks to understand.
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And we'd like to kind of visit today with you a little bit about genetic evaluations and EPDs and and really selection indexes and those tools that are available for breeders, for registered breeders, for commercial bull buyers that are out in the marketplace as they make their genetic decisions for for their herds.
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And as we kind of just hop right into it, would really like to focus today on selection indexes.
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You know, the American Semitol Association and International Genetic Solutions would produce several economic indexes, the all-purpose index, the terminal index, for example, and would just like to kind of get your thoughts as you know, give us the basics.
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What really is a selection index and what's it designed to do?
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Yeah, I think that at its core is selection index is really just a combination of traits, a combination of EPDs rather, that are weighted and economically optimized to maximize profit in a given scenario, right?
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So the two ways that you guys develop indexes, right?
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You define an endpoint, you define whether you're replacing your own females in the herd, and then you let the economics do the talking in figuring out how to weight each of those individual EPDs such that you can arrive at a single number.
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So I think for a lot of producers, selection indexes are really valuable because instead of looking at 15 or 20 traits that we know are profit drivers, but focuses our attention just on a single number.
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When we think about sort of the information overload that a lot of commercial producers get when faced with the bull sale catalog, I think that indexes are really useful at helping them focus their attention on generally the suite of traits that would help them be the most profitable in their given scenario.
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And of course, there's a whole sort of subfield of animal breeding that goes into the calculation of these weights and the simulations that live behind.
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But at the end of the day, they're they're tools that allow producers to directly select for the genetics that are going to make them more profitable.
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And so, Dr.
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Rowan, I guess that that comes to the question like who are we really building these selection indexes for?
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Is it for commercial producers to simplify decisions, or is this something that you know ASA should sit around and say, we think semitol cattle need to be as a you know more docile or have more red meat yield or have more fertility, and we're trying to drive the breed in a particular space or direction.
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What is a selection index created for?
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What audience?
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Yeah, to to me, the selection index is is made for the commercial producer, right?
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It's it's made to simplify that decision.
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The assumptions that it makes as it tries to optimize profitability are all commercial in nature, right?
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They they assume some sort of a base level of cows.
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There are simulations that are happening behind the scenes to calculate these things that sort of play out all of these scenarios.
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Prices go up for one thing, corn gets more expensive, labor changes in the feedlot, right?
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All of these things are going to slightly change the emphasis, maybe that in the real world, right?
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In the real commercial world where we're sending these cattle, the feed yards, we're bringing them these heifers back into our herds as replacements.
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There are real economic drivers of those things.
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And again, that's in the commercial herd, I think, is where we design these things to function.
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So, and again, in addition to helping those producers make that decision maybe a little bit easier, right?
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Instead of trying to sort through 15 different EPDs, really it helps them drill into a single value.
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And I think that's just incredibly helpful as we think about trying to simplify the genetic decision-making process for our commercial producers.
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As a seed stock producer, I think these things are certainly they're useful, right?
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Just as with commercial producers, your focus as a seed stock breeder is making the right type of bull to go into that commercial herd that's your customer.
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And so being aware of these things, I think is is incredibly important.
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Building breeding, I guess, breeding objectives around these things solely.
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I would sort of tell a seed stock producer, you've got, you know, the 15 or 20 different dials that you can turn individually, right?
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You can understand how they affect that index at the end of the day.
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But your job is sort of another level up as far as complexity goes.
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There's lots of different ways to get to high index value.
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Making sure that those things are aligned with your breeding program is really, really important.
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So I think as Cimitol sits down and thinks about your future, certainly those conversations should come into, you know, how might this thing affect profitability for our commercial producer?
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But at the end of the day, I think you make those tools, your breeders move those forward, but that decision making, that decision support that the index provides is really tailor-made for that commercial producer.
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I think that's great.
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I think about my dad, you know, he's got a hundred commercial cows, and he maybe looks at EPDs twice a year, you know.
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And if we're going to simplify the decision, I think we need to simplify the decision for a guy like him that's buying commercial bowls instead of our seed stock producers that have all those different tools at their hand.
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And I I really enjoyed a research study that you were a part of with one of your former colleagues, Charlie Martinez there at the University of Tennessee, where you guys did a simulated bull test and eyes don't lie.
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And you talk about those 15 to 20 dials to commercial producer, they study every one of those, or can you tell us a little bit about the results of that and how that ties into this discussion?
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Yeah, John, that's one of my favorite things that we've done using Dr.
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Martinez's eye tracking technology.
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Our question is really just we put all this stuff out there in a bull sale catalog.
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We expect that people are utilizing it to some degree.
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Let's just let their actual behavior, their actual eyes, tell us what they're doing.
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And what we find is there's an enormous amount of variation, right?
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It's just like when you think about the genetics inside your breed, right?
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There's some people that are on each side of that normal distribution, all the way from people that when you put a sort of online auction in front of them, they spend about 10 seconds looking at the animal's phenotype and then make their decision.
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Others that study absolutely every last piece of data three times, they take the time to digest it and then make a decision and everything else in between.
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And one of the big takeaways related to collection indexes, we were curious, are are people looking at these things?
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And I think there's probably our our sample would have been biased to the southeast here, but people are gonna look more at things that they understand, right?
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And a lot of times that's phenotypically evaluating the animal, that's a a birth weight EPD, a weaning weight EPD, and that's it.
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And so we find that was probably the most common was that that phenotypic evaluation, birth weight and weaning weight.
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And and for good reason.
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I think that when we talk about profitability, that's a function of revenue and costs.
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I think our producers have a really good handle on their costs, the things or on their on their revenue rather, and less of a good idea about their costs.
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And so those things, the live calf and the weaning weight that make that check bigger as they deliver cattle to the stockyard at the end of the year, that's easy for them to understand.
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All the other stuff, I think um, sometimes it just piles on, and it's easier just to skip over it all together than to try and sort through everything.
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And so I think probably not a ton of time being spent looking at those indexes, but we've we've been trying recently, sort of given all this.
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One of the things we tested is if you just invert the EPD profile, put your indexes at the front, weaning weight and birth weight at the back, do people spend more time actually looking at indexes?
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And we find that they do, and we find that not only do they spend more time looking at them, but they end up making better buying decisions at the end of the exercise.
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So encouraging us that there's some little things that we can do, maybe in addition to all the education that we do from an extension standpoint, you all do through your breeders to educate their commercial customers.
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But again, just pointing to the fact that that these indexes really are invaluable tools for that commercial producer, making sure they have an understanding of what they are, how to use them, and then putting them front and center so that they can use it to help make that decision more effectively.
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Yeah, you made a really good point that I think is is hard for some folks to recognize, right?
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We have all sorts of, you know, choice prime premium is worth X amount of dollars, or feeder calves are worth X, but it's a lot harder to get at, well, how much grass is my cow eating every day, right?
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Or when we put a set of cattle in the feed yard and and we're looking at what their true fee to gain efficiency is when we maybe don't have some of that data, you brought it up earlier.
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We're trying to measure profit, right?
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So we need both the revenue and the expense side.
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I hear some of our producers, because our some of our expense side EPDs are newer traits like mature cow weight and dry matter intake, and they get a little nervous about putting those into the index.
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And you know, I I think you know, one thing to me is if you had an accountant that said, Hey, I'm I'm not gonna worry about how much money you spend on your cattle, I'm just gonna worry about how much you sell them for.
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Yep.
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Yeah, he might be popular accountant, but you still could go broke, right?
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So tell us, is it important that even though maybe some of those indexes we or some of those traits we don't have as many phenotypes on, do we still need to have those into the index to do a good job of estimating profit?
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Yeah, no, it's that's absolutely right, John.
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If you don't have a handle on both sides of the profit equation, you're not actually calculating profit.
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And yeah, you end up looking like some of these, uh, some of these startup companies, right?
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They said we've got however many million dollars of revenue, um, but they don't say uh what their their expenses have been as they've blown through all their venture capital cash.
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So it's the same thing on an index.
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You've got to have both sides of that profitability equation represented.
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And and again, I think to your point, there's good reason, right, to say, hey, we've got an order of magnitude or two orders of magnitude, more of these records on the output traits.
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But I think still, from everything I've seen, your even early stages evaluations on things like mature weight, which is going to be one of the big drivers of cow costs.
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Those are well-informed, accurate EPDs.
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Um, I think that skipping out on those, just because they're maybe not to the um the same degree of phenotypes accumulated as something like weaning weight, um, skipping over those, I think you lose way, way more than you gain from maybe a little bit different accuracies in those EPDs.
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So, yeah, it's absolutely essential that you try and represent the full profitability equation, all those sources of revenue, as many of those expenses that you can.
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And then I think that's sort of the next frontier for us in genetics is figuring out how do you fill in the missing pieces, right?
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Where are we using some sort of an approximation for a trait that we'd be better off getting a little bit closer to it?
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Or is there some expense that's just not at all getting accounted for here?
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And then you continue to develop and innovate in those spaces and make tools that allow you to more comprehensively capture the profitability of an individual animal or a system.
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Oh, yeah, there's no doubt.
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And luckily, I mean, there's some more technology coming on board that we can hopefully do a little better job of capturing some of those phenotypes.
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Yeah, one thing I do think is interesting is some of our breeders will look at a relative emphasis chart and they'll look at a trait like DMI and they'll say, boy, there's a lot of relative emphasis on there.
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But maybe since we don't have as many phenotypes on there, there's not as much spread.
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So, practically speaking, Dr.
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Rowan, is that going to have maybe as as much of a difference on the index if there's not as much spread in individual trait?
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Yeah, so I think that's a really important thing to sort of to bring to mind, John, is the the difference between that relative emphasis.
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So the the pie chart that we're all familiar with looking at, I really like to look at sort of the selection response graph.
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So it basically puts everything on sort of a standardized, a standardized scale and says, if you were to select for this index, how would traits in the population change over X number of years?
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And to me, that gives you a lot better indication of what the index is actually doing.
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To your point about dry matter intake, maybe not a ton of spread, even with a fair bit of relative emphasis, you might not see a huge amount of change in the population.
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Whereas for traits where you've got lots of spread, depending on the heritability of the trait and all those things, right?
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Lots of moving pieces.
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Um, but you'll see that some respond much more strongly, even to relatively small relative emphases in an index.
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But but again, lots of moving pieces that can drive that.
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Well, I think that's a great way to put it.
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I'll I'll try to use an analogy here based on my guest.
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You know, I got two guys in the fescue veld who have good football teams.
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All right.
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So if Dr.
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Rowan, if if Josh Heipel, your football coach, was saying that we're gonna bring in a brand new quarterback, and the number one trait that I'm looking at is completion percentage.
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And there's three guys he's looking at, and that's the most important thing, and the spread is 65 to 68 percent.
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And you know, maybe his third most important trait is rushing touchdowns, and there's one guy that's got 10, and the other two have got two and three, even though that's the third most important trait for him, that might be what makes the decision because there's not as much spread there.
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So, is that two questions?
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Is that similar on something like on DMI?
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And then also, if there's not as much spread in DMI, does that mean that there's not as much genetic potential or difference there, or are we just not capturing it yet?
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Yeah, it's uh I think that's a that's a a good analogy, John.
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And and yeah, so the the spread there is is also going to be a a bit of a function of of the heritability of the trait, the number of records, right?
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As you as you record more dry matter intake records, you'll that normal distribution will start to make itself a little bit more apparent than it does in the early days of data collection.
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And and yeah, that certainly has an effect on the response to selection that an index is is going to generate.
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So again, the relative emphasis can maybe push some of that along a little bit more, trying to identify those animals that are just a little bit in the right direction compared to average.
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But again, as you you get more records, you observe more of that spread, those EPDs will consequently get more spread.
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And then as you readjust these indexes in the future, that that relative emphasis might look different and and as will the corresponding response to selecting with that index.
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Well, I'll tell you another question we get quite a bit.
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Now we we obviously put together, worked with CabalFax to build like a uh historical prices.
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We look, we got over the last 20 years, we use the last 10 years to build uh to to convert to Z-scores to use kind of a future model here.
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But in in your estimation, what seems to have more of an impact on an index?
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Is it the actual genetics and the way the cattle are performing, or is it more some of those economic assumptions?
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Because we'll have people quibble with us that hey, carcass dollars weight should be worth five dollars more than what you're you're modeling, or um, you know, your cow costs are a little higher or lower.
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But practically speaking, for producers, what seems to be driving more of the differences in these indexes?
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Yeah, it's a it's a good question, and and I think it's pretty trait dependent in the the playing around I've done with with making indexes, the the the things that consistently surprise me are how cow costs can really, depending on how big your cows are and how much it costs to keep an animal unit around, that can can take you know a fairly small amount of the index if you're you've got those really small, moderate cows.
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Um they're big and and efficiency is maybe a little bit out of whack.
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Um, that downward pressure on cow size can can really, really take up a big pizza of the index.
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But generally, when it comes to pricing, right, as long as there's not a huge flip in the prioritization of things, right?
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So there's not, you know, let's say carcass weight, right?
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Carcass weight is is valuable.
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The scales at which you get premiums for big carcass weights or or small carcass weights, right?
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All that, all those factors, I guess, they sort of come out in the wash, right?
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Equivalent over$5 a carcass weight is very different than equibling about$100 in cow cost, right?
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So the better you can represent economic reality is always good, but there's also a fair bit of statistical noise around those values, right?
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And that's why you use that 10-year rolling average, right?
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You don't want to use today's prices because of where we sit in this sort of cyclical market.
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But I think when you can capture maybe a full cow cycle, that allows you to at least prioritize the order of these things.
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But until there's some sort of complete revolution or flip over of the market one way or another, if tomorrow we decide that we're not going to penalize, we're not going to penalize fat, or if we say that, you know what, marbling's actually not that important, that'll have a huge impact on the index.
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But as long as those fundamentals stay relatively the same, they're pretty robust to to to price fluctuations and changes, as long as that stuff sort of tracks in the same direction.
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There's not some big, huge shakeup.
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Go ahead, Ben.
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I've been asking all the questions, so I don't want to hog all Dr.
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Rowan's time.
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No, I this I mean Ben a fantastic conversation and appreciate you sharing your thoughts here, Troy.
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I guess I have one as as you're kind of in your extension role and out visiting with producers and extension meetings or county cattlemen meetings, and you're talking about indexes and what are those values.
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I guess I'll go kind of back to the basics here on it of how should producers really interpret an index value, or maybe more so, I mean, how do we use them to compare if if I'm at a sale, right, and I'm looking at the indexes as part of my decision on what bowl am I going to buy, how do I compare what's the best way to compare, you know, an index of lot one versus lot five of two bowls that I'm looking at?
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Yeah.
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So so I uh I talk about indexes to commercial producers as interpreting them just like an EPD for profit, right?
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You're comparing between bowl one and bull five.
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That that index is telling you the estimated profitability difference between those two bowls, right?
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Subtraction just like you would with birth weight EPD, a per cap profitability, given one of these general scenarios, right?
00:20:32.559 --> 00:20:42.160
So for you guys, API assumes that you're retaining your own replacement females, your marketing endpoint is adds carcass weight on a grid, right?
00:20:42.400 --> 00:20:57.920
And so a producer can sort of match the index that's most close to how they market the cattle that they're expecting out of that bull, use those, the differences between values to estimate sort of the genetic potential for profit between these two bulls in a given scenario.
00:20:58.079 --> 00:20:59.920
I think it's useful sometimes, right?
00:21:00.000 --> 00:21:01.759
It's really back of the napkin math.
00:21:01.920 --> 00:21:13.599
But as as a producer sits down and tries to make a decision about how much do I invest in this bull compared to this bull, you know, multiply that index difference times the number of calves you expect that bull to sire.
00:21:13.839 --> 00:21:24.880
And that's a really, really roughed out way to say what we might expect, the the difference that this bull's able to generate in profitability over his lifetime compared to this other.
00:21:24.960 --> 00:21:29.839
And maybe that factors into, you know, that's a$5,000 difference or something.
00:21:29.920 --> 00:21:38.799
Then, hey, I can justify spending an extra thousand or two probably to go home with that that better bull that fits my my overall operation better.
00:21:38.960 --> 00:21:42.400
So those are some ways that I talk to producers about indexes.
00:21:42.640 --> 00:21:49.359
And I always try and emphasize, Ben, the you can get to a good index value in a lot of different ways.
00:21:49.519 --> 00:21:58.960
And so, producers, I think it's really good to focus in on that, but make sure the the pieces, the components of that index that get you there are in the right direction.
00:21:59.119 --> 00:22:14.559
So I'll The example in API, the two, I guess, probably biggest weights in API, and I'm I'm shooting from the hip here, but I would guess stability is a big piece and carcass weight, the big driver on the revenue generation side.
00:22:14.799 --> 00:22:21.839
And so, you know, if if I'm a herd who's selling my calves at weaning, for example, I look at that API index.
00:22:21.920 --> 00:22:24.000
I've got two bulls with the exact same index.
00:22:24.160 --> 00:22:34.880
One bull is in the top 5% for stay, the other bull's in the top 75%, but he's top 5% for carcass weight, the other bulls in the top 75% for carcass weight.
00:22:35.039 --> 00:22:39.920
I have a very clear winner there, even though the index value says the same thing.
00:22:40.079 --> 00:22:52.160
So there's still room for going in and finding the right fit in the context of individual EPDs to make sure that that's aligned with your specific breeding goals or just your needs, right?
00:22:52.319 --> 00:22:54.400
Maybe my cows need an extra shot of milk.
00:22:54.559 --> 00:23:04.079
I can go through and and break ties on indexes with bulls that fit some of those specific genetic pieces that I need to plug into my herd.
00:23:04.400 --> 00:23:09.359
Or kind of helping really fine-tune our selection decisions as well.
00:23:09.680 --> 00:23:11.200
Yep, absolutely.
00:23:11.519 --> 00:23:18.079
Troy, would you maybe touch on you know another way that there's a lot of information in a in a sale catalog, right?
00:23:18.240 --> 00:23:26.480
Or if you're looking up an animal on herdbook, for example, and we have the the EPDs and we have accuracy and we have a percentile rank.
00:23:26.559 --> 00:23:30.960
And you know, uh when we're at a sale, there's a lot of data that we're looking at.
00:23:31.039 --> 00:23:33.839
And you know, we don't always have those percentile tables with us.
00:23:33.920 --> 00:23:41.200
And I know several folks print those off in their in their uh sale catalogs as well as a reference, and then you end up flipping back and forth.
00:23:41.279 --> 00:23:48.559
But could you maybe touch on percentile ranks and and using those in and evaluating genetics as well?
00:23:49.039 --> 00:23:55.119
Yeah, Ben, I I'm a huge, huge proponent for printing percentile ranks in your bull sale catalog.
00:23:55.279 --> 00:23:57.440
I I look at EPDs all day.
00:23:57.599 --> 00:24:07.839
I've I've made it my life's work, but when somebody tells me their bull's EPD from whatever breed is this, I'm usually just stuck scratching my head, right?
00:24:07.920 --> 00:24:11.200
I I don't keep breed average for all of the breeds in my head.
00:24:11.279 --> 00:24:14.160
And your commercial breeder doesn't do that either, right?
00:24:14.319 --> 00:24:18.960
I can pick up a Herford catalog, a Charlet catalog, a Cimentel catalog, an Angus catalog.
00:24:19.200 --> 00:24:25.519
If it's got percentile ranks, it tells me where that animal sits relative to the population that he comes from, right?
00:24:25.680 --> 00:24:31.839
So I don't have to again flip back to that breed average or memorize it for 13 different breeds in my head.
00:24:32.079 --> 00:24:33.839
It really gives me context, right?
00:24:33.920 --> 00:24:37.920
What percent of that breed is better than this bull that I'm looking at right here?
00:24:38.079 --> 00:24:40.559
So a weaning weight EPD of 87.
00:24:40.799 --> 00:24:45.039
Frankly, have no idea if that's good, bad, or ugly in any of these breeds.
00:24:45.279 --> 00:24:56.559
But if I've got a percentile rank, that gives me that instant context that this bull is either average or hey, he's way above average, or he's a bull that I probably want to stay away from on this trade.
00:24:56.720 --> 00:25:04.559
I think that putting percentile ranks in your catalogs and explaining to your commercial customers what that means is very valuable.
00:25:04.640 --> 00:25:08.240
And I'd like to see even more production sales doing that.
00:25:08.400 --> 00:25:19.279
It just simplifies that reading of the EPDs, it gives context, and again, it's all in the spirit of helping your commercial producer understand all of this stuff better and make a more informed decision.
00:25:19.519 --> 00:25:20.160
Yeah.
00:25:20.559 --> 00:25:28.000
Yeah, and you you brought up a trait that obviously we feel very strongly about, and stability is one that's really important in the American Cematol Association.
00:25:28.079 --> 00:25:28.880
And obviously, I know Dr.
00:25:28.960 --> 00:25:33.279
Kreitz is a reproductive physiologist, believes very strongly in it as well, too.
00:25:33.440 --> 00:25:48.319
And and as you talk to commercial producers, and and you brought it up earlier, that we all are kind of attracted to those revenue, you know, decisions or those revenue traits that think about you know adding 10 pounds of weaning weight or 50 pounds of weaning weight.
00:25:48.480 --> 00:25:51.920
Producers seem like they can contextualize that, they can put that in their head.
00:25:52.319 --> 00:25:57.279
But naturally, like most producers, you know, are in the cattle business for the long term, right?
00:25:57.359 --> 00:26:01.039
They're they're not thinking about maximizing profit over the next two to three years.
00:26:01.200 --> 00:26:07.759
We're thinking about those long-term investments and and probably trying to leave the cow herd to the next generation in a better state.
00:26:07.839 --> 00:26:10.400
I'm sure you're you're doing the same thing with your program back home.
00:26:10.559 --> 00:26:12.960
So can you can you talk about stability that way?
00:26:13.039 --> 00:26:20.319
Because it is a trait that we rank really high in our indexes, and maybe that's a little more unique to us than than what some other associations do.
00:26:21.279 --> 00:26:28.000
Yeah, I think that cow longevity really is it's the cornerstone of profitability in commercial herds, right?
00:26:28.160 --> 00:26:35.279
That cow that's able to stay in the herd, she pays herself off and then is profitable for the long term.
00:26:35.599 --> 00:26:39.599
Those are those are the things that we want to want to build programs on, right?
00:26:39.680 --> 00:26:45.359
And and again, your emphasis on stability, I I think is is really commendable.
00:26:45.519 --> 00:26:55.920
The I I I love to present again, my my colleague, former colleague, I guess, Charlie Martinez, has made this really nice chart that sort of shows the payback period of a cow, right?
00:26:56.079 --> 00:27:07.200
I think it's important, especially as we get into higher and higher input worlds, even when the market's good, as our cow cost goes up, that cow sort of pushes back that payback period, right?
00:27:07.359 --> 00:27:09.839
So longevity becomes even more important.
00:27:10.000 --> 00:27:17.759
We need that cow not only to get pregnant as a heifer, right, to rebreed after that first calf, but we need her to stay in the herd.
00:27:17.920 --> 00:27:21.759
We need the udder to stay good, we need the feet to stay good, we need the disposition.
00:27:21.920 --> 00:27:35.839
Stability captures all of that stuff, points us towards that really, really economically important trait of can this cow show up every year, get pregnant, stay functionally sound and fit, and rinse and repeat into the future.
00:27:36.079 --> 00:27:42.640
As I talk to commercial producers, I talk about longevity sort of as a the benefits of that, right?
00:27:42.720 --> 00:27:45.519
On the cost side is heifer development.
00:27:45.599 --> 00:27:50.799
If you've got to prematurely develop another heifer in this cow's place, that costs money.
00:27:50.880 --> 00:27:54.640
Um, that's one more heifer added onto the development bill.
00:27:54.720 --> 00:27:56.160
Um, that's time, right?
00:27:56.319 --> 00:28:01.920
It takes up resources that are not going to produce a calf for at least another year, right?
00:28:02.079 --> 00:28:05.200
So you're pushing back that opportunity at revenue.
00:28:05.279 --> 00:28:11.200
And it's one less calf that you've got to market that year if you're having to develop a heifer in that cow's place, right?
00:28:11.359 --> 00:28:24.480
So whether that's just as a feeder heifer, whether that's as an added value, you know, heifer calf, there's there's sort of a multifold impact of having to get rid of a cow before her time, right?
00:28:24.559 --> 00:28:36.400
And and so again, stayability I think is very, very important and and I think should be a cornerstone of of any index that tries to represent overall profitability, especially when we're keeping back females.
00:28:37.039 --> 00:28:37.759
Yeah, absolutely.
00:28:37.920 --> 00:28:44.480
And you know, what we're kind of the core assumption that IGS is built on is that breed associations were not competitors, right?
00:28:44.559 --> 00:28:45.920
We're cross-breeding partners.
00:28:46.240 --> 00:28:50.559
And longevity, obviously, there's there's a really positive response to heterosis.
00:28:50.640 --> 00:28:53.680
And I know that you're somebody that speaks about that quite a bit.
00:28:53.839 --> 00:29:00.960
Our indexes are modeled on, you know, semitals or haploid semitals back on Angus cows to build some of that breed complementarity.
00:29:01.200 --> 00:29:08.000
Can you talk a little bit about that system and and applying that again to those commercial producers that we're trying to build these indexes for?
00:29:08.640 --> 00:29:14.319
You're you're talking about about heterosis specifically or or the index in the context of a coward.
00:29:15.039 --> 00:29:16.559
I mean, it's a dealer's choice, man.
00:29:16.799 --> 00:29:18.319
You take it wherever you want, okay?
00:29:18.559 --> 00:29:19.119
All right.
00:29:19.599 --> 00:29:27.680
Yeah, I I think that that to your point, John, the profitability that's captured by a selection index is is purely additive, right?
00:29:27.839 --> 00:29:34.480
So all those sort of extra benefits that you get from heterosis, the added performance, right?
00:29:34.559 --> 00:29:39.680
There's some free pounds that get thrown in there from that crossbred calf from a direct standpoint.
00:29:39.920 --> 00:29:51.440
That retained female, she's got some heterosis in milk production, but the real money from heterosis comes in those lowly heritable traits, the stabilities, the fertilities of the world.
00:29:51.599 --> 00:30:00.079
They might be 10 or 15% heritable, but the heterosis that you generate can be upwards of 20 or 30 percent, right?
00:30:00.240 --> 00:30:06.240
So that's an extra calf over the course of a cow's lifetime on average, is is what the U.S.
00:30:06.319 --> 00:30:07.599
MARC data would say.
00:30:07.759 --> 00:30:10.720
And so again, that is that's incredibly important.
00:30:10.960 --> 00:30:15.440
You go back and look at the the importance to profitability that stability has.
00:30:15.680 --> 00:30:24.960
If you can squeeze an extra calf out of a cow just by crossbreeding, that's a an enormous opportunity, one that I don't think you can afford not to take advantage of.
00:30:25.200 --> 00:30:36.480
So of course, those assumptions get sort of built into the index as you as you sort of define the commercial herd that you expect your bulls are gonna go out and and inevitably breed.
00:30:36.640 --> 00:30:38.240
That's all getting modeled in there.
00:30:38.319 --> 00:30:47.039
The impacts of heterosis get sort of built into the economic models, but uh again, it's still not a it's not a substitute, it's not fully captured.
00:30:47.200 --> 00:30:56.640
You've still got to implement that cross-breeding program to to take advantage of heterosis, even though you can you can account for some of those things and how you build that index.
00:30:57.440 --> 00:30:58.319
Yeah, Nathalie.
00:30:58.480 --> 00:31:05.200
And it's um, you know, one of those things too for us, like as we're kind of breeding back to Angus cows, we're encouraging some of that that cross breeding to happen there.
00:31:05.279 --> 00:31:08.400
And that that's a a cross that we feel pretty strongly in.
00:31:08.480 --> 00:31:14.400
And I know that as we talk about breed complementarity, yeah, the goal is not to make every breed the same, right?
00:31:14.480 --> 00:31:15.839
But to make them complement each other.
00:31:15.920 --> 00:31:22.559
And and uh something that I'm very passionate about, and I know you feel too, is having discipline on mature cow size.
00:31:22.640 --> 00:31:34.880
And our our you know, API index is probably putting even a little bit more discipline on on mature cow size and not allowing mature cows to continue to, you know, to get to get larger and and potentially more inefficient.
00:31:35.039 --> 00:31:44.880
I think some of that comes back to finding the right fit to go back on Angus cows, but then also some of the data that that you shared are fall focused with us on some of those production efficiencies.
00:31:44.960 --> 00:31:51.920
So you want to talk a little bit about mature cow size and finding cattle that fit that environment uh a little bit more advantageously?
00:31:52.480 --> 00:32:02.720
Yeah, no, I I think mature cow size is is something that we've watched just pretty steadily tick up over the course of time, looking at your guys' genetic trends, right?
00:32:02.880 --> 00:32:06.240
It's a pretty linear trend that tracks the progress that you've made with growth.
00:32:06.319 --> 00:32:09.279
And I always tell folks that that's for good reason, right?
00:32:09.519 --> 00:32:10.799
It's not negligence, right?
00:32:10.880 --> 00:32:11.920
It's just biology.
00:32:12.079 --> 00:32:17.759
And the genetic correlations of these traits is well over 70%, probably.
00:32:17.920 --> 00:32:28.799
And so a selection program that focuses on revenue generating traits, weaning weights, yearling weights, carcass weights, you're inevitably going to drag that mature cow size up along with it.
00:32:28.960 --> 00:32:42.960
And so I think to your point, John, it really comes into balance, and that's where indexes are so valuable is they account for those genetic correlations, they account for the antagonistic economic signals on each of those traits, right?
00:32:43.200 --> 00:32:47.680
More weight, more money, more mature cow weight, higher cow costs, right?
00:32:47.759 --> 00:32:51.359
We're we're operating on these fixed land bases, right?
00:32:51.440 --> 00:32:56.240
The limiting factor of an operation isn't the number of cows, it's the number of acres.
00:32:56.319 --> 00:33:05.200
And so if you can manipulate that cow size to optimize profitability on a fixed land mass, that's that's what we we really want to focus on.
00:33:05.279 --> 00:33:09.039
And so again, mature cow weight is is a tricky one too.
00:33:09.200 --> 00:33:10.640
You you've got to have a little bit of it.
00:33:10.720 --> 00:33:17.119
We can't have these tiny, unproductive cows that are making calves that don't fit into this value chain.
00:33:17.279 --> 00:33:27.359
But at the same time, I think we can we can consider what moderating that cow looks like if we're able to take advantage of that complementary breeding that you mentioned, John.
00:33:27.599 --> 00:33:32.400
Bring that big terminal bull back on that more moderate, mature cow.
00:33:32.559 --> 00:33:37.920
That can be a combination that lets you you sort of have your cake and and eat it too, right?
00:33:38.079 --> 00:33:44.400
We we don't have to pigeonhole ourselves into making one animal that does everything kind of good, right?
00:33:44.559 --> 00:33:56.000
We can we can focus on the maternal side of the business, the terminal side of the business, and and bring those things together in a in a way that that sort of is is optimal to the the full system.
00:33:56.640 --> 00:33:58.480
Yeah, that makes a lot of sense to me.
00:33:58.559 --> 00:34:07.119
And I do think, you know, like you said, being able to balance both those, you know, the the revenue versus expenses and both sides to try to find that Goldilocks kind of trait, right?
00:34:07.200 --> 00:34:14.400
Somewhere in the middle, it seems like uh indexes do a good job of keeping us from going one extreme or another on a particular trait.
00:34:14.639 --> 00:34:24.400
You brought up a term fixed land base, which is something that we kind of customized our agendac license to put in there to try to make sure that we could keep mature cow size in check.
00:34:24.639 --> 00:34:30.559
Can you talk a little bit about that for some producers at home that aren't really familiar with the what a fixed land base might be?
00:34:31.119 --> 00:34:35.199
Yeah, so uh a fixed land base is sort of what it sounds like.
00:34:35.360 --> 00:34:42.079
It's it's the assumption that the the limiting factor and the simulation that's spitting these economic values out.
00:34:42.320 --> 00:34:48.000
The assumption is that there's a limited number of acres that this hypothetical herd is sitting on.
00:34:48.239 --> 00:34:59.199
And if you can manipulate the size of the cow per corresponding intake needs are going to be reflected in a different stocking rate, right?
00:34:59.280 --> 00:35:06.239
We can stock more smaller cows, the same fixed amount of land than we can these really big, large cows.
00:35:06.320 --> 00:35:22.559
And so an index that were to assume just a fixed number of animals, the ability to appropriately model the importance of something like mature cow weight is not going to be reflected in the same way that it is in reality, which all of us are operating on fixed land bases.
00:35:22.719 --> 00:35:25.519
That is the limiting factor of what we can do.
00:35:25.599 --> 00:35:30.079
And so I think that's the the appropriate way to model these things, certainly.
00:35:30.239 --> 00:35:35.840
And yeah, is is really valuable at finding sort of that optimal, right?
00:35:35.920 --> 00:35:38.320
Where you've you can push that cow size down.
00:35:38.480 --> 00:35:55.280
She's maybe going to wean off slightly smaller calves, but at the end of the day, if you can wean off you know 10% more calves, the actual weight, the system-wide efficiency looks a lot better when you you start to calculate things on a per acre basis, maybe as opposed to a per cow basis.
00:35:55.840 --> 00:35:56.480
Absolutely.
00:35:56.719 --> 00:36:00.480
And I one other one that I hear some folks talk about every now and again, Dr.
00:36:00.639 --> 00:36:02.800
Rowan, is the one size fits all, right?
00:36:02.880 --> 00:36:07.440
That we're at, you know, kind of looking at the average environment or the average cow in the country.
00:36:07.599 --> 00:36:09.360
Um, you know, you might look over at Dr.
00:36:09.440 --> 00:36:14.639
Kriggs and like, yeah, you're running your cows on Kentucky Bruegrass, and I've got this, you know, rough fescue here.
00:36:14.800 --> 00:36:16.800
Like, how's that index account for that?
00:36:16.880 --> 00:36:24.239
And can you speak a little bit about the robustness or you know, does do we see major changing in rankings when we go from environment to environment?
00:36:24.320 --> 00:36:27.519
Or how does that usually work on some of these selection indexes?
00:36:27.920 --> 00:36:32.880
Yeah, John, from from my experience, that's one of the things I I get a lot of questions about, right?
00:36:32.960 --> 00:36:39.840
Do we need a do we need a custom index for um for the southeast, for the you know, the high plains, um, for the southwest?
00:36:40.000 --> 00:36:45.599
And in generally, what I've seen is that these indexes are really robust to different environments, right?
00:36:45.679 --> 00:36:48.559
They're they're robust both from a pricing standpoint.
00:36:48.639 --> 00:36:54.000
You know, you could plug in sort of your state's relative prices for each of these things.
00:36:54.079 --> 00:36:59.199
And I think what you'd find out on the other end is that they really look quite similar, right?
00:36:59.280 --> 00:37:02.400
The index that you get out is gonna look almost the same.
00:37:02.559 --> 00:37:09.840
And so again, the big drivers, of course, are gonna be where is the starting point of this herd, right?
00:37:10.000 --> 00:37:14.079
And then cow cost, of course, is gonna vary a fair bit around the country.
00:37:14.320 --> 00:37:39.280
But even then, I think if we appropriately model that, the correlations between indexes sort of optimized for me in Tennessee versus someone in Montana, they're probably gonna look quite similar and coming out the other end because the the core principles that drive those index values, cow efficiency, stability, carcass weight, and quality, those fundamentals all stay the same in their ranking and relative importance.
00:37:39.440 --> 00:38:00.320
So again, there might be a little bit of switching, but I would be very confident that something that's sort of based on a an overall nationwide average, especially understanding that now, more frequently than ever, our cattle are going into different environments, that that that's going to be highly representative of an animal's potential for profit.
00:38:01.039 --> 00:38:02.719
Now that's that's a good point, right?
00:38:02.880 --> 00:38:09.119
Because if you're selling bulls and you know that all over the country, right, it's it's nice to have that one size fits all.
00:38:09.199 --> 00:38:16.159
You know, if we were to build a custom index for for my environment, maybe it provides uh a little bit more for me to tailor it.
00:38:16.239 --> 00:38:24.960
But then if I'm you know, a commercial producer comes to my sale and your sale, and we both have got customized indexed to our one environment, they really can't make a comparison, can they?
00:38:25.440 --> 00:38:26.400
No, exactly.
00:38:26.480 --> 00:38:36.480
And and the other thing that I always talk about when we talk about environment is that so much of environment, and specifically I think related to cow costs, trying to model that.
00:38:36.719 --> 00:38:39.440
Ben and I don't live awfully far from each other.
00:38:39.679 --> 00:38:51.280
You know, our the sort of fundamental climate, the temperature, the precip, the forage type overwhelmingly is quite similar, but we could have vastly different cow costs at the end of the day.
00:38:51.440 --> 00:38:58.719
If if I'm hypermanaging my cows and they're they're you know, we're we're pushing those costs up significantly, right?
00:38:58.800 --> 00:39:01.679
They're getting supplemented strategically, they're getting this and that.
00:39:01.760 --> 00:39:05.760
And Ben is running things a whole lot leaner on his operation.
00:39:05.920 --> 00:39:20.480
We can manipulate that cow cost in a really, really big way that makes those environments, even though from the, you know, the climate data would say that they're almost identical, they can look really, really different just in how we implement management at each of our places.
00:39:20.639 --> 00:39:36.400
So again, there's so many moving pieces to that genetics and environment interacting that I think it's almost impossible, even if you did something regionally, to have it be truly robust to all the management pieces that don't neatly fit into environment necessarily.
00:39:36.800 --> 00:39:40.960
Kind of on on all that, Troy, though, that the genetics and the environment.
00:39:41.920 --> 00:39:52.320
Talk a little bit on the unphenotype and the importance of submitting phenotypic information, birth weights, weaning weights, mature cow weights.
00:39:52.559 --> 00:39:54.639
That's those are still important, right?
00:39:55.280 --> 00:40:04.960
Yeah, no, an index built on EPDs that are built on a lack of true phenotypic records, it's not going to be a good index, right?
00:40:05.119 --> 00:40:17.519
So uh all of this stuff goes back to being able to capture, if you want to represent profitability in an index, you have to capture and record the phenotypes that drive profitability, right?
00:40:17.599 --> 00:40:19.119
There's no substitute for that.
00:40:19.280 --> 00:40:21.679
You can't build it on retrospective data, right?
00:40:21.760 --> 00:40:24.960
It has to be a constant influx of phenotypes.
00:40:25.119 --> 00:40:28.559
And so again, these phenotypes are as important as ever.
00:40:28.880 --> 00:40:33.039
Genomics doesn't absolve us from continuing to collect phenotypes.
00:40:33.199 --> 00:40:39.760
An index doesn't mean we don't need to collect phenotypes on the individual components that make that index up.
00:40:39.920 --> 00:40:42.639
In fact, I think it makes it even more important, right?
00:40:42.800 --> 00:40:50.079
The things like John was talking about, whether they be mature cow weight records, dry matter intake, we know how important those things are.
00:40:50.400 --> 00:41:01.280
How can we capture more phenotypes, make those tools more valuable, give us a better idea about what the real distribution of genetic potentials across our population is?
00:41:01.440 --> 00:41:11.519
That allows us to make a more effective selection index, allows us to move that sort of big picture breeding goal forward in a better way.
00:41:11.679 --> 00:41:13.760
So, so you're you're absolutely right, Ben.
00:41:14.239 --> 00:41:20.000
Phenotypic capture is as important today as it's ever been, maybe even more important.
00:41:20.320 --> 00:41:41.360
And as you you sort of expand the suite of traits that you are capturing phenotypes for, getting those sort of critical mass where you feel comfortable about the accuracy of that, the representativeness of the population in that, it allows you to plug that into the index faster, be more responsive to those real industry signals that are getting created.
00:41:41.599 --> 00:41:44.800
John talked about red meat yield at the very beginning.
00:41:44.960 --> 00:41:47.519
That's something that I think everybody is excited for.
00:41:47.679 --> 00:41:49.039
What is that going to look like?
00:41:49.199 --> 00:41:50.719
How's that gonna fit into the index?
00:41:50.800 --> 00:41:52.079
What's the signal gonna be?
00:41:52.239 --> 00:42:11.760
As soon as we can start capturing these phenotypes, getting these tools developed that are reliable and accurate that puts us in a good situation when the switch flips and there's a clear economic signal for something like that, we can find ways to fit it into that index and move the population forward in a way that's economically optimized.
00:42:12.079 --> 00:42:12.960
Absolutely.
00:42:13.280 --> 00:42:22.079
Troy, you you travel a lot and have a chance to visit with a lot of producers across the country and get in really now across the world.
00:42:22.159 --> 00:42:24.639
I see you traveled to South Africa recently.
00:42:25.039 --> 00:42:29.920
What are some of those common misconceptions that you hear about indexes?
00:42:30.000 --> 00:42:34.639
And how do you kind of address maybe some of those concerns that you've heard in your travels?
00:42:35.840 --> 00:42:43.519
Yeah, I was actually I was sitting in a meeting with Breed in South Africa just last week as they were discussing the creation of their index.
00:42:43.679 --> 00:42:51.519
And there was lots of these questions about like, well, I think that this value should be, we should have a little bit more emphasis on this or a little less emphasis on this.
00:42:51.840 --> 00:42:59.199
And to me, that's the core of an economic index is that you let the economics tell you the importance of the traits, right?
00:42:59.360 --> 00:43:09.199
So I think there's we have these perceptions about our breeds, we have these desires sometimes to push a trait forward, to put our finger on the scale a little bit.
00:43:09.440 --> 00:43:19.039
But if at the end of the day, the core need that these indexes serve is to help a commercial producer make a more profitable decision.
00:43:19.280 --> 00:43:34.079
We've got to let the profitability sort of work itself out and let that tell us the importance of these traits rather than trying to take our sort of internal biases, our thoughts about the breed, our thoughts about where things are or where they need to go.
00:43:34.239 --> 00:43:38.639
If we want a truly economic index, let the economics do the talking.
00:43:38.880 --> 00:43:42.559
There are some cases where, you know, maybe a breed has a problem, right?
00:43:42.639 --> 00:43:43.920
And we need to fix this.
00:43:44.159 --> 00:43:45.599
You can change some things.
00:43:45.840 --> 00:43:48.079
We call this a desired gains index.
00:43:48.159 --> 00:43:55.119
And you can say, listen, maybe I'm I'm a breed and I've got a docility problem and it's keeping my bulls from being able to get purchased.
00:43:55.280 --> 00:43:59.440
We're gonna take a little bit of weight away from this trait and this trait and this trait.
00:44:00.320 --> 00:44:02.320
Add into that docility value.
00:44:02.719 --> 00:44:12.559
Challenge there, right, is that if you can't put a real economic value on that, not truly optimized, and you're taking away selection pressure that you could put on other traits.
00:44:12.719 --> 00:44:14.559
So there's trade-offs when you do that.
00:44:14.800 --> 00:44:33.119
Again, sometimes it's it's necessary, but other times I think it can can work against folks when you start doing too much shuffling around to those things rather than letting that that economic optimization do the the hard part of putting weights on these traits.
00:44:33.599 --> 00:44:35.280
Yeah, that that makes great sense.
00:44:35.360 --> 00:44:43.440
And sometimes too, and particularly like some of those traits that are economically relevant that are going to be in the index, the opportunity for improvement is there, right?
00:44:43.679 --> 00:44:46.800
I think back, you know, API started in 2005.
00:44:47.039 --> 00:44:51.119
At that time, about 60% of cattle in the country graded choice and prime.
00:44:51.280 --> 00:44:53.360
So there was a lot of opportunity to move up.
00:44:53.519 --> 00:45:00.320
Um, you know, today we're we're bumping up about 90%, which is you know just tremendous, the genetic progress we've made in that time.
00:45:00.480 --> 00:45:05.760
So sometimes the index is telling us that, hey, there's there's maybe not as much low-hanging fruit there.
00:45:05.840 --> 00:45:11.920
So, you know, potentially, but you know, you might select, put more emphasis somewhere else just because of the progress that we've made.
00:45:12.000 --> 00:45:16.559
And that that's part of the benefit of of having an index that can weight some of those things as well.
00:45:17.199 --> 00:45:25.760
Yeah, no, I I think that's a really good point, John, is that is that these indexes aren't necessarily they're they're meant to be stable but not static, right?
00:45:25.840 --> 00:45:30.320
And so the fundamentals of the industry are they change over time.
00:45:30.480 --> 00:45:36.000
And if we're doing our job as an industry, the the cattle that make the industry up are going to change over time, right?
00:45:36.079 --> 00:45:42.880
And I think that as we make these indexes and we adapt them, you've got to keep that in mind that it's underlying cowherd, right?
00:45:43.039 --> 00:45:48.000
As we've we've fixed some of these issues or we've taken advantage of some of these premiums that were there.
00:45:48.239 --> 00:45:53.920
Um the reality of when we initially made the index to today might look quite different.
00:45:54.159 --> 00:46:00.239
And the opportunity, I think, is is maybe greater on some traits than others to push those forward.
00:46:00.400 --> 00:46:05.760
There's more juice to squeeze out from a profitability standpoint as we think about genetic improvements.
00:46:05.920 --> 00:46:07.199
So you're absolutely right.
00:46:07.360 --> 00:46:16.559
We we've got to have these indexes that are sort of living and responsive to the uh to the industry, but that are stable enough that you can build breeding programs around them, right?
00:46:16.639 --> 00:46:19.199
We don't want something that changes every year, right?
00:46:19.360 --> 00:46:23.519
As something goes up and down, the index looks completely different, that's worthless.
00:46:23.679 --> 00:46:30.480
But we've also got to have indexes that are are flexible enough to adapt when the fundamentals of the industry have changed.
00:46:30.800 --> 00:46:31.280
Well, Dr.
00:46:31.360 --> 00:46:34.159
Rowan, I really appreciate your insight today.
00:46:34.320 --> 00:46:43.199
I know that I've certainly enjoyed our conversation here and great to catch up and really take something that can seem pretty complicated, right?
00:46:43.280 --> 00:46:59.119
There's a lot of information that's going into a selection index, but it's great to distill that down, kind of get back to the basics and what is that you know, actually predicting and predicting profitability or at trying to estimate profitability of those animals or of their progeny, rather.
00:46:59.360 --> 00:47:01.840
As we start to wrap up here today, uh Dr.
00:47:02.000 --> 00:47:04.800
De Klerk, any any uh final questions for Dr.
00:47:04.880 --> 00:47:06.480
Rowan as as we wrap up?
00:47:06.880 --> 00:47:07.440
No, sir.
00:47:07.519 --> 00:47:08.719
I appreciate your time, Troy.
00:47:08.800 --> 00:47:10.320
This was a great conversation.
00:47:10.800 --> 00:47:13.039
Well, Troy, I know you're you're a busy guy.
00:47:13.199 --> 00:47:16.559
You just got back from South Africa, going all over the place.
00:47:16.719 --> 00:47:24.000
We were so thankful and grateful that you were able to join us in Fall Focus and Bozeman here in August and a wealth of information.
00:47:24.239 --> 00:47:30.239
Certainly uh appreciate your time and sharing your wisdom with listeners to the podcast here today.
00:47:31.039 --> 00:47:35.440
Oh, well, I appreciate you guys having me and and always enjoy talking about these things.
00:47:35.599 --> 00:47:38.639
The conversation about indexes, I think, is really important.
00:47:38.880 --> 00:47:48.320
And I think we've still got a lot of work to do, not just on making indexes that are really high quality, but on making sure that they're getting used by the commercial producers that they're made for.
00:47:48.639 --> 00:47:48.960
All right.
00:47:49.119 --> 00:47:50.719
Well, thank you for your time, Daytroy.
00:47:50.800 --> 00:47:56.480
Thank you for everything you've done for the American Semitol Association and IGS, and we appreciate your help and your collaboration.
00:00:04.719 --> 00:00:11.919
Welcome everybody to another episode of the Prophet Through Science podcast hosted by the American Semitol Association with Dr.
00:00:12.000 --> 00:00:13.599
John DeClerc and myself.
00:00:13.759 --> 00:00:23.760
And John, today's a really exciting day and fortunate to have our first guest joining us all the way from the state of Tennessee, the University of Tennessee, Dr.
00:00:23.839 --> 00:00:24.719
Troy Rowan.
00:00:24.960 --> 00:00:29.839
Troy actually joined us for Fall Focus in Bozeman, Montana back in August.
00:00:30.000 --> 00:00:33.200
And fortunate to have Troy join us on here today.
00:00:33.359 --> 00:00:42.079
And Troy, if you wouldn't mind for folks that are listening in here, would you give us a brief introduction on yourself and your role at the University of Tennessee?
00:00:42.799 --> 00:00:43.840
Yeah, thanks, Fan.
00:00:43.920 --> 00:00:49.200
And thanks for making me the and trusting me with being the first non-Sementol employee to be on the podcast.
00:00:49.280 --> 00:00:51.119
So we'll uh we'll see how that how this goes.
00:00:51.200 --> 00:00:54.159
And um maybe after this you'll decide to keep everything in-house.
00:00:54.240 --> 00:00:57.119
But but excited to be here and talk with you guys today.
00:00:57.280 --> 00:01:20.079
But like I said, Troy Rowan, um, a faculty member here at the University of Tennessee, have a split research and extension appointment and focus broadly on beef cattle, genetics, and genomics, have a real interest in cow efficiency, how we can use phenotypes to move our breeding programs towards making more efficient cows, which I think will certainly feed into our conversation today.
00:01:20.239 --> 00:01:24.079
But have been at Tennessee for about five and a half years now.
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And and yeah, have always admired what happens at the Cementol Association, all the way back through my PhD working with data sets and collaborating with you guys.
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So again, love our partnership that we have and excited to chat with you today.
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Yeah, thank you, Troy.
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I know we first got to meet each other when I was wrapping up my graduate work at Kentucky and uh had some mutual friends from a faculty standpoint between Kentucky and Tennessee.
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And one of the things that I've always admired about you and your your role in extension is taking really complex topics and sharing that information that's really easy for folks to understand.
00:02:02.400 --> 00:02:22.719
And we'd like to kind of visit today with you a little bit about genetic evaluations and EPDs and and really selection indexes and those tools that are available for breeders, for registered breeders, for commercial bull buyers that are out in the marketplace as they make their genetic decisions for for their herds.
00:02:23.520 --> 00:02:30.159
And as we kind of just hop right into it, would really like to focus today on selection indexes.
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You know, the American Semitol Association and International Genetic Solutions would produce several economic indexes, the all-purpose index, the terminal index, for example, and would just like to kind of get your thoughts as you know, give us the basics.
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What really is a selection index and what's it designed to do?
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Yeah, I think that at its core is selection index is really just a combination of traits, a combination of EPDs rather, that are weighted and economically optimized to maximize profit in a given scenario, right?
00:03:04.400 --> 00:03:07.120
So the two ways that you guys develop indexes, right?
00:03:07.199 --> 00:03:21.039
You define an endpoint, you define whether you're replacing your own females in the herd, and then you let the economics do the talking in figuring out how to weight each of those individual EPDs such that you can arrive at a single number.
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So I think for a lot of producers, selection indexes are really valuable because instead of looking at 15 or 20 traits that we know are profit drivers, but focuses our attention just on a single number.
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When we think about sort of the information overload that a lot of commercial producers get when faced with the bull sale catalog, I think that indexes are really useful at helping them focus their attention on generally the suite of traits that would help them be the most profitable in their given scenario.
00:03:50.240 --> 00:04:00.960
And of course, there's a whole sort of subfield of animal breeding that goes into the calculation of these weights and the simulations that live behind.
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But at the end of the day, they're they're tools that allow producers to directly select for the genetics that are going to make them more profitable.
00:04:09.360 --> 00:04:10.159
And so, Dr.
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Rowan, I guess that that comes to the question like who are we really building these selection indexes for?
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Is it for commercial producers to simplify decisions, or is this something that you know ASA should sit around and say, we think semitol cattle need to be as a you know more docile or have more red meat yield or have more fertility, and we're trying to drive the breed in a particular space or direction.
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What is a selection index created for?
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What audience?
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Yeah, to to me, the selection index is is made for the commercial producer, right?
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It's it's made to simplify that decision.
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The assumptions that it makes as it tries to optimize profitability are all commercial in nature, right?
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They they assume some sort of a base level of cows.
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There are simulations that are happening behind the scenes to calculate these things that sort of play out all of these scenarios.
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Prices go up for one thing, corn gets more expensive, labor changes in the feedlot, right?
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All of these things are going to slightly change the emphasis, maybe that in the real world, right?
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In the real commercial world where we're sending these cattle, the feed yards, we're bringing them these heifers back into our herds as replacements.
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There are real economic drivers of those things.
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And again, that's in the commercial herd, I think, is where we design these things to function.
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So, and again, in addition to helping those producers make that decision maybe a little bit easier, right?
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Instead of trying to sort through 15 different EPDs, really it helps them drill into a single value.
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And I think that's just incredibly helpful as we think about trying to simplify the genetic decision-making process for our commercial producers.
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As a seed stock producer, I think these things are certainly they're useful, right?
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Just as with commercial producers, your focus as a seed stock breeder is making the right type of bull to go into that commercial herd that's your customer.
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And so being aware of these things, I think is is incredibly important.
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Building breeding, I guess, breeding objectives around these things solely.
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I would sort of tell a seed stock producer, you've got, you know, the 15 or 20 different dials that you can turn individually, right?
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You can understand how they affect that index at the end of the day.
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But your job is sort of another level up as far as complexity goes.
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There's lots of different ways to get to high index value.
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Making sure that those things are aligned with your breeding program is really, really important.
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So I think as Cimitol sits down and thinks about your future, certainly those conversations should come into, you know, how might this thing affect profitability for our commercial producer?
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But at the end of the day, I think you make those tools, your breeders move those forward, but that decision making, that decision support that the index provides is really tailor-made for that commercial producer.
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I think that's great.
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I think about my dad, you know, he's got a hundred commercial cows, and he maybe looks at EPDs twice a year, you know.
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And if we're going to simplify the decision, I think we need to simplify the decision for a guy like him that's buying commercial bowls instead of our seed stock producers that have all those different tools at their hand.
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And I I really enjoyed a research study that you were a part of with one of your former colleagues, Charlie Martinez there at the University of Tennessee, where you guys did a simulated bull test and eyes don't lie.
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And you talk about those 15 to 20 dials to commercial producer, they study every one of those, or can you tell us a little bit about the results of that and how that ties into this discussion?
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Yeah, John, that's one of my favorite things that we've done using Dr.
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Martinez's eye tracking technology.
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Our question is really just we put all this stuff out there in a bull sale catalog.
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We expect that people are utilizing it to some degree.
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Let's just let their actual behavior, their actual eyes, tell us what they're doing.
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And what we find is there's an enormous amount of variation, right?
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It's just like when you think about the genetics inside your breed, right?
00:08:01.360 --> 00:08:14.800
There's some people that are on each side of that normal distribution, all the way from people that when you put a sort of online auction in front of them, they spend about 10 seconds looking at the animal's phenotype and then make their decision.
00:08:14.959 --> 00:08:24.240
Others that study absolutely every last piece of data three times, they take the time to digest it and then make a decision and everything else in between.
00:08:24.399 --> 00:08:31.680
And one of the big takeaways related to collection indexes, we were curious, are are people looking at these things?
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And I think there's probably our our sample would have been biased to the southeast here, but people are gonna look more at things that they understand, right?
00:08:41.679 --> 00:08:50.080
And a lot of times that's phenotypically evaluating the animal, that's a a birth weight EPD, a weaning weight EPD, and that's it.
00:08:50.240 --> 00:08:56.480
And so we find that was probably the most common was that that phenotypic evaluation, birth weight and weaning weight.
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And and for good reason.
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I think that when we talk about profitability, that's a function of revenue and costs.
00:09:03.039 --> 00:09:11.440
I think our producers have a really good handle on their costs, the things or on their on their revenue rather, and less of a good idea about their costs.
00:09:11.519 --> 00:09:20.000
And so those things, the live calf and the weaning weight that make that check bigger as they deliver cattle to the stockyard at the end of the year, that's easy for them to understand.
00:09:20.240 --> 00:09:29.039
All the other stuff, I think um, sometimes it just piles on, and it's easier just to skip over it all together than to try and sort through everything.
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And so I think probably not a ton of time being spent looking at those indexes, but we've we've been trying recently, sort of given all this.
00:09:37.759 --> 00:09:48.240
One of the things we tested is if you just invert the EPD profile, put your indexes at the front, weaning weight and birth weight at the back, do people spend more time actually looking at indexes?
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And we find that they do, and we find that not only do they spend more time looking at them, but they end up making better buying decisions at the end of the exercise.
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So encouraging us that there's some little things that we can do, maybe in addition to all the education that we do from an extension standpoint, you all do through your breeders to educate their commercial customers.
00:10:08.799 --> 00:10:24.639
But again, just pointing to the fact that that these indexes really are invaluable tools for that commercial producer, making sure they have an understanding of what they are, how to use them, and then putting them front and center so that they can use it to help make that decision more effectively.
00:10:25.120 --> 00:10:29.519
Yeah, you made a really good point that I think is is hard for some folks to recognize, right?
00:10:29.600 --> 00:10:40.559
We have all sorts of, you know, choice prime premium is worth X amount of dollars, or feeder calves are worth X, but it's a lot harder to get at, well, how much grass is my cow eating every day, right?
00:10:40.720 --> 00:10:48.960
Or when we put a set of cattle in the feed yard and and we're looking at what their true fee to gain efficiency is when we maybe don't have some of that data, you brought it up earlier.
00:10:49.039 --> 00:10:50.480
We're trying to measure profit, right?
00:10:50.559 --> 00:10:53.600
So we need both the revenue and the expense side.
00:10:53.840 --> 00:11:05.039
I hear some of our producers, because our some of our expense side EPDs are newer traits like mature cow weight and dry matter intake, and they get a little nervous about putting those into the index.
00:11:05.120 --> 00:11:14.159
And you know, I I think you know, one thing to me is if you had an accountant that said, Hey, I'm I'm not gonna worry about how much money you spend on your cattle, I'm just gonna worry about how much you sell them for.
00:11:14.320 --> 00:11:14.639
Yep.
00:11:14.799 --> 00:11:17.679
Yeah, he might be popular accountant, but you still could go broke, right?
00:11:17.759 --> 00:11:29.519
So tell us, is it important that even though maybe some of those indexes we or some of those traits we don't have as many phenotypes on, do we still need to have those into the index to do a good job of estimating profit?
00:11:30.080 --> 00:11:32.159
Yeah, no, it's that's absolutely right, John.
00:11:32.240 --> 00:11:36.879
If you don't have a handle on both sides of the profit equation, you're not actually calculating profit.
00:11:37.039 --> 00:11:41.360
And yeah, you end up looking like some of these, uh, some of these startup companies, right?
00:11:41.440 --> 00:11:49.759
They said we've got however many million dollars of revenue, um, but they don't say uh what their their expenses have been as they've blown through all their venture capital cash.
00:11:49.919 --> 00:11:51.600
So it's the same thing on an index.
00:11:51.759 --> 00:11:55.919
You've got to have both sides of that profitability equation represented.
00:11:56.000 --> 00:12:06.080
And and again, I think to your point, there's good reason, right, to say, hey, we've got an order of magnitude or two orders of magnitude, more of these records on the output traits.
00:12:06.240 --> 00:12:15.679
But I think still, from everything I've seen, your even early stages evaluations on things like mature weight, which is going to be one of the big drivers of cow costs.
00:12:15.840 --> 00:12:18.960
Those are well-informed, accurate EPDs.
00:12:19.120 --> 00:12:35.360
Um, I think that skipping out on those, just because they're maybe not to the um the same degree of phenotypes accumulated as something like weaning weight, um, skipping over those, I think you lose way, way more than you gain from maybe a little bit different accuracies in those EPDs.
00:12:35.440 --> 00:12:45.120
So, yeah, it's absolutely essential that you try and represent the full profitability equation, all those sources of revenue, as many of those expenses that you can.
00:12:45.360 --> 00:12:52.399
And then I think that's sort of the next frontier for us in genetics is figuring out how do you fill in the missing pieces, right?
00:12:52.559 --> 00:12:58.559
Where are we using some sort of an approximation for a trait that we'd be better off getting a little bit closer to it?
00:12:58.639 --> 00:13:02.720
Or is there some expense that's just not at all getting accounted for here?
00:13:02.879 --> 00:13:13.519
And then you continue to develop and innovate in those spaces and make tools that allow you to more comprehensively capture the profitability of an individual animal or a system.
00:13:13.919 --> 00:13:15.039
Oh, yeah, there's no doubt.
00:13:15.279 --> 00:13:21.679
And luckily, I mean, there's some more technology coming on board that we can hopefully do a little better job of capturing some of those phenotypes.
00:13:21.919 --> 00:13:32.240
Yeah, one thing I do think is interesting is some of our breeders will look at a relative emphasis chart and they'll look at a trait like DMI and they'll say, boy, there's a lot of relative emphasis on there.
00:13:32.399 --> 00:13:36.240
But maybe since we don't have as many phenotypes on there, there's not as much spread.
00:13:36.320 --> 00:13:38.240
So, practically speaking, Dr.
00:13:38.399 --> 00:13:45.200
Rowan, is that going to have maybe as as much of a difference on the index if there's not as much spread in individual trait?
00:13:46.080 --> 00:13:53.200
Yeah, so I think that's a really important thing to sort of to bring to mind, John, is the the difference between that relative emphasis.
00:13:53.279 --> 00:13:59.360
So the the pie chart that we're all familiar with looking at, I really like to look at sort of the selection response graph.
00:13:59.440 --> 00:14:10.799
So it basically puts everything on sort of a standardized, a standardized scale and says, if you were to select for this index, how would traits in the population change over X number of years?
00:14:10.960 --> 00:14:15.200
And to me, that gives you a lot better indication of what the index is actually doing.
00:14:15.360 --> 00:14:24.480
To your point about dry matter intake, maybe not a ton of spread, even with a fair bit of relative emphasis, you might not see a huge amount of change in the population.
00:14:24.720 --> 00:14:30.000
Whereas for traits where you've got lots of spread, depending on the heritability of the trait and all those things, right?
00:14:30.159 --> 00:14:31.759
Lots of moving pieces.
00:14:31.840 --> 00:14:39.679
Um, but you'll see that some respond much more strongly, even to relatively small relative emphases in an index.
00:14:39.759 --> 00:14:43.200
But but again, lots of moving pieces that can drive that.
00:14:43.600 --> 00:14:45.039
Well, I think that's a great way to put it.
00:14:45.120 --> 00:14:47.840
I'll I'll try to use an analogy here based on my guest.
00:14:48.080 --> 00:14:50.960
You know, I got two guys in the fescue veld who have good football teams.
00:14:51.039 --> 00:14:51.279
All right.
00:14:51.440 --> 00:14:52.000
So if Dr.
00:14:52.159 --> 00:15:00.799
Rowan, if if Josh Heipel, your football coach, was saying that we're gonna bring in a brand new quarterback, and the number one trait that I'm looking at is completion percentage.
00:15:00.960 --> 00:15:07.039
And there's three guys he's looking at, and that's the most important thing, and the spread is 65 to 68 percent.
00:15:07.200 --> 00:15:20.720
And you know, maybe his third most important trait is rushing touchdowns, and there's one guy that's got 10, and the other two have got two and three, even though that's the third most important trait for him, that might be what makes the decision because there's not as much spread there.
00:15:20.879 --> 00:15:22.240
So, is that two questions?
00:15:22.320 --> 00:15:24.480
Is that similar on something like on DMI?
00:15:24.639 --> 00:15:33.039
And then also, if there's not as much spread in DMI, does that mean that there's not as much genetic potential or difference there, or are we just not capturing it yet?
00:15:33.360 --> 00:15:36.720
Yeah, it's uh I think that's a that's a a good analogy, John.
00:15:36.879 --> 00:15:44.879
And and yeah, so the the spread there is is also going to be a a bit of a function of of the heritability of the trait, the number of records, right?
00:15:44.960 --> 00:15:56.000
As you as you record more dry matter intake records, you'll that normal distribution will start to make itself a little bit more apparent than it does in the early days of data collection.
00:15:56.080 --> 00:16:02.399
And and yeah, that certainly has an effect on the response to selection that an index is is going to generate.
00:16:02.559 --> 00:16:12.799
So again, the relative emphasis can maybe push some of that along a little bit more, trying to identify those animals that are just a little bit in the right direction compared to average.
00:16:12.960 --> 00:16:18.960
But again, as you you get more records, you observe more of that spread, those EPDs will consequently get more spread.
00:16:19.120 --> 00:16:30.159
And then as you readjust these indexes in the future, that that relative emphasis might look different and and as will the corresponding response to selecting with that index.
00:16:30.399 --> 00:16:32.720
Well, I'll tell you another question we get quite a bit.
00:16:32.799 --> 00:16:38.000
Now we we obviously put together, worked with CabalFax to build like a uh historical prices.
00:16:38.240 --> 00:16:46.559
We look, we got over the last 20 years, we use the last 10 years to build uh to to convert to Z-scores to use kind of a future model here.
00:16:46.639 --> 00:16:52.240
But in in your estimation, what seems to have more of an impact on an index?
00:16:52.320 --> 00:16:57.519
Is it the actual genetics and the way the cattle are performing, or is it more some of those economic assumptions?
00:16:57.600 --> 00:17:07.440
Because we'll have people quibble with us that hey, carcass dollars weight should be worth five dollars more than what you're you're modeling, or um, you know, your cow costs are a little higher or lower.
00:17:07.599 --> 00:17:13.920
But practically speaking, for producers, what seems to be driving more of the differences in these indexes?
00:17:14.640 --> 00:17:40.400
Yeah, it's a it's a good question, and and I think it's pretty trait dependent in the the playing around I've done with with making indexes, the the the things that consistently surprise me are how cow costs can really, depending on how big your cows are and how much it costs to keep an animal unit around, that can can take you know a fairly small amount of the index if you're you've got those really small, moderate cows.
00:17:40.480 --> 00:17:44.240
Um they're big and and efficiency is maybe a little bit out of whack.
00:17:44.319 --> 00:17:50.240
Um, that downward pressure on cow size can can really, really take up a big pizza of the index.
00:17:50.480 --> 00:17:57.839
But generally, when it comes to pricing, right, as long as there's not a huge flip in the prioritization of things, right?
00:17:57.920 --> 00:18:01.599
So there's not, you know, let's say carcass weight, right?
00:18:01.759 --> 00:18:04.079
Carcass weight is is valuable.
00:18:04.240 --> 00:18:09.759
The scales at which you get premiums for big carcass weights or or small carcass weights, right?
00:18:09.839 --> 00:18:13.759
All that, all those factors, I guess, they sort of come out in the wash, right?
00:18:13.920 --> 00:18:20.640
Equivalent over$5 a carcass weight is very different than equibling about$100 in cow cost, right?
00:18:20.720 --> 00:18:28.720
So the better you can represent economic reality is always good, but there's also a fair bit of statistical noise around those values, right?
00:18:28.799 --> 00:18:32.400
And that's why you use that 10-year rolling average, right?
00:18:32.559 --> 00:18:37.680
You don't want to use today's prices because of where we sit in this sort of cyclical market.
00:18:38.000 --> 00:18:46.559
But I think when you can capture maybe a full cow cycle, that allows you to at least prioritize the order of these things.
00:18:46.720 --> 00:19:02.480
But until there's some sort of complete revolution or flip over of the market one way or another, if tomorrow we decide that we're not going to penalize, we're not going to penalize fat, or if we say that, you know what, marbling's actually not that important, that'll have a huge impact on the index.
00:19:02.640 --> 00:19:13.680
But as long as those fundamentals stay relatively the same, they're pretty robust to to to price fluctuations and changes, as long as that stuff sort of tracks in the same direction.
00:19:13.759 --> 00:19:16.319
There's not some big, huge shakeup.
00:19:16.720 --> 00:19:17.680
Go ahead, Ben.
00:19:17.839 --> 00:19:20.480
I've been asking all the questions, so I don't want to hog all Dr.
00:19:20.559 --> 00:19:21.119
Rowan's time.
00:19:21.839 --> 00:19:28.160
No, I this I mean Ben a fantastic conversation and appreciate you sharing your thoughts here, Troy.
00:19:28.240 --> 00:19:40.240
I guess I have one as as you're kind of in your extension role and out visiting with producers and extension meetings or county cattlemen meetings, and you're talking about indexes and what are those values.
00:19:40.400 --> 00:20:06.559
I guess I'll go kind of back to the basics here on it of how should producers really interpret an index value, or maybe more so, I mean, how do we use them to compare if if I'm at a sale, right, and I'm looking at the indexes as part of my decision on what bowl am I going to buy, how do I compare what's the best way to compare, you know, an index of lot one versus lot five of two bowls that I'm looking at?
00:20:06.960 --> 00:20:07.359
Yeah.
00:20:07.519 --> 00:20:15.359
So so I uh I talk about indexes to commercial producers as interpreting them just like an EPD for profit, right?
00:20:15.599 --> 00:20:18.240
You're comparing between bowl one and bull five.
00:20:18.559 --> 00:20:24.880
That that index is telling you the estimated profitability difference between those two bowls, right?
00:20:25.119 --> 00:20:32.400
Subtraction just like you would with birth weight EPD, a per cap profitability, given one of these general scenarios, right?
00:20:32.559 --> 00:20:42.160
So for you guys, API assumes that you're retaining your own replacement females, your marketing endpoint is adds carcass weight on a grid, right?
00:20:42.400 --> 00:20:57.920
And so a producer can sort of match the index that's most close to how they market the cattle that they're expecting out of that bull, use those, the differences between values to estimate sort of the genetic potential for profit between these two bulls in a given scenario.
00:20:58.079 --> 00:20:59.920
I think it's useful sometimes, right?
00:21:00.000 --> 00:21:01.759
It's really back of the napkin math.
00:21:01.920 --> 00:21:13.599
But as as a producer sits down and tries to make a decision about how much do I invest in this bull compared to this bull, you know, multiply that index difference times the number of calves you expect that bull to sire.
00:21:13.839 --> 00:21:24.880
And that's a really, really roughed out way to say what we might expect, the the difference that this bull's able to generate in profitability over his lifetime compared to this other.
00:21:24.960 --> 00:21:29.839
And maybe that factors into, you know, that's a$5,000 difference or something.
00:21:29.920 --> 00:21:38.799
Then, hey, I can justify spending an extra thousand or two probably to go home with that that better bull that fits my my overall operation better.
00:21:38.960 --> 00:21:42.400
So those are some ways that I talk to producers about indexes.
00:21:42.640 --> 00:21:49.359
And I always try and emphasize, Ben, the you can get to a good index value in a lot of different ways.
00:21:49.519 --> 00:21:58.960
And so, producers, I think it's really good to focus in on that, but make sure the the pieces, the components of that index that get you there are in the right direction.
00:21:59.119 --> 00:22:14.559
So I'll The example in API, the two, I guess, probably biggest weights in API, and I'm I'm shooting from the hip here, but I would guess stability is a big piece and carcass weight, the big driver on the revenue generation side.
00:22:14.799 --> 00:22:21.839
And so, you know, if if I'm a herd who's selling my calves at weaning, for example, I look at that API index.
00:22:21.920 --> 00:22:24.000
I've got two bulls with the exact same index.
00:22:24.160 --> 00:22:34.880
One bull is in the top 5% for stay, the other bull's in the top 75%, but he's top 5% for carcass weight, the other bulls in the top 75% for carcass weight.
00:22:35.039 --> 00:22:39.920
I have a very clear winner there, even though the index value says the same thing.
00:22:40.079 --> 00:22:52.160
So there's still room for going in and finding the right fit in the context of individual EPDs to make sure that that's aligned with your specific breeding goals or just your needs, right?
00:22:52.319 --> 00:22:54.400
Maybe my cows need an extra shot of milk.
00:22:54.559 --> 00:23:04.079
I can go through and and break ties on indexes with bulls that fit some of those specific genetic pieces that I need to plug into my herd.
00:23:04.400 --> 00:23:09.359
Or kind of helping really fine-tune our selection decisions as well.
00:23:09.680 --> 00:23:11.200
Yep, absolutely.
00:23:11.519 --> 00:23:18.079
Troy, would you maybe touch on you know another way that there's a lot of information in a in a sale catalog, right?
00:23:18.240 --> 00:23:26.480
Or if you're looking up an animal on herdbook, for example, and we have the the EPDs and we have accuracy and we have a percentile rank.
00:23:26.559 --> 00:23:30.960
And you know, uh when we're at a sale, there's a lot of data that we're looking at.
00:23:31.039 --> 00:23:33.839
And you know, we don't always have those percentile tables with us.
00:23:33.920 --> 00:23:41.200
And I know several folks print those off in their in their uh sale catalogs as well as a reference, and then you end up flipping back and forth.
00:23:41.279 --> 00:23:48.559
But could you maybe touch on percentile ranks and and using those in and evaluating genetics as well?
00:23:49.039 --> 00:23:55.119
Yeah, Ben, I I'm a huge, huge proponent for printing percentile ranks in your bull sale catalog.
00:23:55.279 --> 00:23:57.440
I I look at EPDs all day.
00:23:57.599 --> 00:24:07.839
I've I've made it my life's work, but when somebody tells me their bull's EPD from whatever breed is this, I'm usually just stuck scratching my head, right?
00:24:07.920 --> 00:24:11.200
I I don't keep breed average for all of the breeds in my head.
00:24:11.279 --> 00:24:14.160
And your commercial breeder doesn't do that either, right?
00:24:14.319 --> 00:24:18.960
I can pick up a Herford catalog, a Charlet catalog, a Cimentel catalog, an Angus catalog.
00:24:19.200 --> 00:24:25.519
If it's got percentile ranks, it tells me where that animal sits relative to the population that he comes from, right?
00:24:25.680 --> 00:24:31.839
So I don't have to again flip back to that breed average or memorize it for 13 different breeds in my head.
00:24:32.079 --> 00:24:33.839
It really gives me context, right?
00:24:33.920 --> 00:24:37.920
What percent of that breed is better than this bull that I'm looking at right here?
00:24:38.079 --> 00:24:40.559
So a weaning weight EPD of 87.
00:24:40.799 --> 00:24:45.039
Frankly, have no idea if that's good, bad, or ugly in any of these breeds.
00:24:45.279 --> 00:24:56.559
But if I've got a percentile rank, that gives me that instant context that this bull is either average or hey, he's way above average, or he's a bull that I probably want to stay away from on this trade.
00:24:56.720 --> 00:25:04.559
I think that putting percentile ranks in your catalogs and explaining to your commercial customers what that means is very valuable.
00:25:04.640 --> 00:25:08.240
And I'd like to see even more production sales doing that.
00:25:08.400 --> 00:25:19.279
It just simplifies that reading of the EPDs, it gives context, and again, it's all in the spirit of helping your commercial producer understand all of this stuff better and make a more informed decision.
00:25:19.519 --> 00:25:20.160
Yeah.
00:25:20.559 --> 00:25:28.000
Yeah, and you you brought up a trait that obviously we feel very strongly about, and stability is one that's really important in the American Cematol Association.
00:25:28.079 --> 00:25:28.880
And obviously, I know Dr.
00:25:28.960 --> 00:25:33.279
Kreitz is a reproductive physiologist, believes very strongly in it as well, too.
00:25:33.440 --> 00:25:48.319
And and as you talk to commercial producers, and and you brought it up earlier, that we all are kind of attracted to those revenue, you know, decisions or those revenue traits that think about you know adding 10 pounds of weaning weight or 50 pounds of weaning weight.
00:25:48.480 --> 00:25:51.920
Producers seem like they can contextualize that, they can put that in their head.
00:25:52.319 --> 00:25:57.279
But naturally, like most producers, you know, are in the cattle business for the long term, right?
00:25:57.359 --> 00:26:01.039
They're they're not thinking about maximizing profit over the next two to three years.
00:26:01.200 --> 00:26:07.759
We're thinking about those long-term investments and and probably trying to leave the cow herd to the next generation in a better state.
00:26:07.839 --> 00:26:10.400
I'm sure you're you're doing the same thing with your program back home.
00:26:10.559 --> 00:26:12.960
So can you can you talk about stability that way?
00:26:13.039 --> 00:26:20.319
Because it is a trait that we rank really high in our indexes, and maybe that's a little more unique to us than than what some other associations do.
00:26:21.279 --> 00:26:28.000
Yeah, I think that cow longevity really is it's the cornerstone of profitability in commercial herds, right?
00:26:28.160 --> 00:26:35.279
That cow that's able to stay in the herd, she pays herself off and then is profitable for the long term.
00:26:35.599 --> 00:26:39.599
Those are those are the things that we want to want to build programs on, right?
00:26:39.680 --> 00:26:45.359
And and again, your emphasis on stability, I I think is is really commendable.
00:26:45.519 --> 00:26:55.920
The I I I love to present again, my my colleague, former colleague, I guess, Charlie Martinez, has made this really nice chart that sort of shows the payback period of a cow, right?
00:26:56.079 --> 00:27:07.200
I think it's important, especially as we get into higher and higher input worlds, even when the market's good, as our cow cost goes up, that cow sort of pushes back that payback period, right?
00:27:07.359 --> 00:27:09.839
So longevity becomes even more important.
00:27:10.000 --> 00:27:17.759
We need that cow not only to get pregnant as a heifer, right, to rebreed after that first calf, but we need her to stay in the herd.
00:27:17.920 --> 00:27:21.759
We need the udder to stay good, we need the feet to stay good, we need the disposition.
00:27:21.920 --> 00:27:35.839
Stability captures all of that stuff, points us towards that really, really economically important trait of can this cow show up every year, get pregnant, stay functionally sound and fit, and rinse and repeat into the future.
00:27:36.079 --> 00:27:42.640
As I talk to commercial producers, I talk about longevity sort of as a the benefits of that, right?
00:27:42.720 --> 00:27:45.519
On the cost side is heifer development.
00:27:45.599 --> 00:27:50.799
If you've got to prematurely develop another heifer in this cow's place, that costs money.
00:27:50.880 --> 00:27:54.640
Um, that's one more heifer added onto the development bill.
00:27:54.720 --> 00:27:56.160
Um, that's time, right?
00:27:56.319 --> 00:28:01.920
It takes up resources that are not going to produce a calf for at least another year, right?
00:28:02.079 --> 00:28:05.200
So you're pushing back that opportunity at revenue.
00:28:05.279 --> 00:28:11.200
And it's one less calf that you've got to market that year if you're having to develop a heifer in that cow's place, right?
00:28:11.359 --> 00:28:24.480
So whether that's just as a feeder heifer, whether that's as an added value, you know, heifer calf, there's there's sort of a multifold impact of having to get rid of a cow before her time, right?
00:28:24.559 --> 00:28:36.400
And and so again, stayability I think is very, very important and and I think should be a cornerstone of of any index that tries to represent overall profitability, especially when we're keeping back females.
00:28:37.039 --> 00:28:37.759
Yeah, absolutely.
00:28:37.920 --> 00:28:44.480
And you know, what we're kind of the core assumption that IGS is built on is that breed associations were not competitors, right?
00:28:44.559 --> 00:28:45.920
We're cross-breeding partners.
00:28:46.240 --> 00:28:50.559
And longevity, obviously, there's there's a really positive response to heterosis.
00:28:50.640 --> 00:28:53.680
And I know that you're somebody that speaks about that quite a bit.
00:28:53.839 --> 00:29:00.960
Our indexes are modeled on, you know, semitals or haploid semitals back on Angus cows to build some of that breed complementarity.
00:29:01.200 --> 00:29:08.000
Can you talk a little bit about that system and and applying that again to those commercial producers that we're trying to build these indexes for?
00:29:08.640 --> 00:29:14.319
You're you're talking about about heterosis specifically or or the index in the context of a coward.
00:29:15.039 --> 00:29:16.559
I mean, it's a dealer's choice, man.
00:29:16.799 --> 00:29:18.319
You take it wherever you want, okay?
00:29:18.559 --> 00:29:19.119
All right.
00:29:19.599 --> 00:29:27.680
Yeah, I I think that that to your point, John, the profitability that's captured by a selection index is is purely additive, right?
00:29:27.839 --> 00:29:34.480
So all those sort of extra benefits that you get from heterosis, the added performance, right?
00:29:34.559 --> 00:29:39.680
There's some free pounds that get thrown in there from that crossbred calf from a direct standpoint.
00:29:39.920 --> 00:29:51.440
That retained female, she's got some heterosis in milk production, but the real money from heterosis comes in those lowly heritable traits, the stabilities, the fertilities of the world.
00:29:51.599 --> 00:30:00.079
They might be 10 or 15% heritable, but the heterosis that you generate can be upwards of 20 or 30 percent, right?
00:30:00.240 --> 00:30:06.240
So that's an extra calf over the course of a cow's lifetime on average, is is what the U.S.
00:30:06.319 --> 00:30:07.599
MARC data would say.
00:30:07.759 --> 00:30:10.720
And so again, that is that's incredibly important.
00:30:10.960 --> 00:30:15.440
You go back and look at the the importance to profitability that stability has.
00:30:15.680 --> 00:30:24.960
If you can squeeze an extra calf out of a cow just by crossbreeding, that's a an enormous opportunity, one that I don't think you can afford not to take advantage of.
00:30:25.200 --> 00:30:36.480
So of course, those assumptions get sort of built into the index as you as you sort of define the commercial herd that you expect your bulls are gonna go out and and inevitably breed.
00:30:36.640 --> 00:30:38.240
That's all getting modeled in there.
00:30:38.319 --> 00:30:47.039
The impacts of heterosis get sort of built into the economic models, but uh again, it's still not a it's not a substitute, it's not fully captured.
00:30:47.200 --> 00:30:56.640
You've still got to implement that cross-breeding program to to take advantage of heterosis, even though you can you can account for some of those things and how you build that index.
00:30:57.440 --> 00:30:58.319
Yeah, Nathalie.
00:30:58.480 --> 00:31:05.200
And it's um, you know, one of those things too for us, like as we're kind of breeding back to Angus cows, we're encouraging some of that that cross breeding to happen there.
00:31:05.279 --> 00:31:08.400
And that that's a a cross that we feel pretty strongly in.
00:31:08.480 --> 00:31:14.400
And I know that as we talk about breed complementarity, yeah, the goal is not to make every breed the same, right?
00:31:14.480 --> 00:31:15.839
But to make them complement each other.
00:31:15.920 --> 00:31:22.559
And and uh something that I'm very passionate about, and I know you feel too, is having discipline on mature cow size.
00:31:22.640 --> 00:31:34.880
And our our you know, API index is probably putting even a little bit more discipline on on mature cow size and not allowing mature cows to continue to, you know, to get to get larger and and potentially more inefficient.
00:31:35.039 --> 00:31:44.880
I think some of that comes back to finding the right fit to go back on Angus cows, but then also some of the data that that you shared are fall focused with us on some of those production efficiencies.
00:31:44.960 --> 00:31:51.920
So you want to talk a little bit about mature cow size and finding cattle that fit that environment uh a little bit more advantageously?
00:31:52.480 --> 00:32:02.720
Yeah, no, I I think mature cow size is is something that we've watched just pretty steadily tick up over the course of time, looking at your guys' genetic trends, right?
00:32:02.880 --> 00:32:06.240
It's a pretty linear trend that tracks the progress that you've made with growth.
00:32:06.319 --> 00:32:09.279
And I always tell folks that that's for good reason, right?
00:32:09.519 --> 00:32:10.799
It's not negligence, right?
00:32:10.880 --> 00:32:11.920
It's just biology.
00:32:12.079 --> 00:32:17.759
And the genetic correlations of these traits is well over 70%, probably.
00:32:17.920 --> 00:32:28.799
And so a selection program that focuses on revenue generating traits, weaning weights, yearling weights, carcass weights, you're inevitably going to drag that mature cow size up along with it.
00:32:28.960 --> 00:32:42.960
And so I think to your point, John, it really comes into balance, and that's where indexes are so valuable is they account for those genetic correlations, they account for the antagonistic economic signals on each of those traits, right?
00:32:43.200 --> 00:32:47.680
More weight, more money, more mature cow weight, higher cow costs, right?
00:32:47.759 --> 00:32:51.359
We're we're operating on these fixed land bases, right?
00:32:51.440 --> 00:32:56.240
The limiting factor of an operation isn't the number of cows, it's the number of acres.
00:32:56.319 --> 00:33:05.200
And so if you can manipulate that cow size to optimize profitability on a fixed land mass, that's that's what we we really want to focus on.
00:33:05.279 --> 00:33:09.039
And so again, mature cow weight is is a tricky one too.
00:33:09.200 --> 00:33:10.640
You you've got to have a little bit of it.
00:33:10.720 --> 00:33:17.119
We can't have these tiny, unproductive cows that are making calves that don't fit into this value chain.
00:33:17.279 --> 00:33:27.359
But at the same time, I think we can we can consider what moderating that cow looks like if we're able to take advantage of that complementary breeding that you mentioned, John.
00:33:27.599 --> 00:33:32.400
Bring that big terminal bull back on that more moderate, mature cow.
00:33:32.559 --> 00:33:37.920
That can be a combination that lets you you sort of have your cake and and eat it too, right?
00:33:38.079 --> 00:33:44.400
We we don't have to pigeonhole ourselves into making one animal that does everything kind of good, right?
00:33:44.559 --> 00:33:56.000
We can we can focus on the maternal side of the business, the terminal side of the business, and and bring those things together in a in a way that that sort of is is optimal to the the full system.
00:33:56.640 --> 00:33:58.480
Yeah, that makes a lot of sense to me.
00:33:58.559 --> 00:34:07.119
And I do think, you know, like you said, being able to balance both those, you know, the the revenue versus expenses and both sides to try to find that Goldilocks kind of trait, right?
00:34:07.200 --> 00:34:14.400
Somewhere in the middle, it seems like uh indexes do a good job of keeping us from going one extreme or another on a particular trait.
00:34:14.639 --> 00:34:24.400
You brought up a term fixed land base, which is something that we kind of customized our agendac license to put in there to try to make sure that we could keep mature cow size in check.
00:34:24.639 --> 00:34:30.559
Can you talk a little bit about that for some producers at home that aren't really familiar with the what a fixed land base might be?
00:34:31.119 --> 00:34:35.199
Yeah, so uh a fixed land base is sort of what it sounds like.
00:34:35.360 --> 00:34:42.079
It's it's the assumption that the the limiting factor and the simulation that's spitting these economic values out.
00:34:42.320 --> 00:34:48.000
The assumption is that there's a limited number of acres that this hypothetical herd is sitting on.
00:34:48.239 --> 00:34:59.199
And if you can manipulate the size of the cow per corresponding intake needs are going to be reflected in a different stocking rate, right?
00:34:59.280 --> 00:35:06.239
We can stock more smaller cows, the same fixed amount of land than we can these really big, large cows.
00:35:06.320 --> 00:35:22.559
And so an index that were to assume just a fixed number of animals, the ability to appropriately model the importance of something like mature cow weight is not going to be reflected in the same way that it is in reality, which all of us are operating on fixed land bases.
00:35:22.719 --> 00:35:25.519
That is the limiting factor of what we can do.
00:35:25.599 --> 00:35:30.079
And so I think that's the the appropriate way to model these things, certainly.
00:35:30.239 --> 00:35:35.840
And yeah, is is really valuable at finding sort of that optimal, right?
00:35:35.920 --> 00:35:38.320
Where you've you can push that cow size down.
00:35:38.480 --> 00:35:55.280
She's maybe going to wean off slightly smaller calves, but at the end of the day, if you can wean off you know 10% more calves, the actual weight, the system-wide efficiency looks a lot better when you you start to calculate things on a per acre basis, maybe as opposed to a per cow basis.
00:35:55.840 --> 00:35:56.480
Absolutely.
00:35:56.719 --> 00:36:00.480
And I one other one that I hear some folks talk about every now and again, Dr.
00:36:00.639 --> 00:36:02.800
Rowan, is the one size fits all, right?
00:36:02.880 --> 00:36:07.440
That we're at, you know, kind of looking at the average environment or the average cow in the country.
00:36:07.599 --> 00:36:09.360
Um, you know, you might look over at Dr.
00:36:09.440 --> 00:36:14.639
Kriggs and like, yeah, you're running your cows on Kentucky Bruegrass, and I've got this, you know, rough fescue here.
00:36:14.800 --> 00:36:16.800
Like, how's that index account for that?
00:36:16.880 --> 00:36:24.239
And can you speak a little bit about the robustness or you know, does do we see major changing in rankings when we go from environment to environment?
00:36:24.320 --> 00:36:27.519
Or how does that usually work on some of these selection indexes?
00:36:27.920 --> 00:36:32.880
Yeah, John, from from my experience, that's one of the things I I get a lot of questions about, right?
00:36:32.960 --> 00:36:39.840
Do we need a do we need a custom index for um for the southeast, for the you know, the high plains, um, for the southwest?
00:36:40.000 --> 00:36:45.599
And in generally, what I've seen is that these indexes are really robust to different environments, right?
00:36:45.679 --> 00:36:48.559
They're they're robust both from a pricing standpoint.
00:36:48.639 --> 00:36:54.000
You know, you could plug in sort of your state's relative prices for each of these things.
00:36:54.079 --> 00:36:59.199
And I think what you'd find out on the other end is that they really look quite similar, right?
00:36:59.280 --> 00:37:02.400
The index that you get out is gonna look almost the same.
00:37:02.559 --> 00:37:09.840
And so again, the big drivers, of course, are gonna be where is the starting point of this herd, right?
00:37:10.000 --> 00:37:14.079
And then cow cost, of course, is gonna vary a fair bit around the country.
00:37:14.320 --> 00:37:39.280
But even then, I think if we appropriately model that, the correlations between indexes sort of optimized for me in Tennessee versus someone in Montana, they're probably gonna look quite similar and coming out the other end because the the core principles that drive those index values, cow efficiency, stability, carcass weight, and quality, those fundamentals all stay the same in their ranking and relative importance.
00:37:39.440 --> 00:38:00.320
So again, there might be a little bit of switching, but I would be very confident that something that's sort of based on a an overall nationwide average, especially understanding that now, more frequently than ever, our cattle are going into different environments, that that that's going to be highly representative of an animal's potential for profit.
00:38:01.039 --> 00:38:02.719
Now that's that's a good point, right?
00:38:02.880 --> 00:38:09.119
Because if you're selling bulls and you know that all over the country, right, it's it's nice to have that one size fits all.
00:38:09.199 --> 00:38:16.159
You know, if we were to build a custom index for for my environment, maybe it provides uh a little bit more for me to tailor it.
00:38:16.239 --> 00:38:24.960
But then if I'm you know, a commercial producer comes to my sale and your sale, and we both have got customized indexed to our one environment, they really can't make a comparison, can they?
00:38:25.440 --> 00:38:26.400
No, exactly.
00:38:26.480 --> 00:38:36.480
And and the other thing that I always talk about when we talk about environment is that so much of environment, and specifically I think related to cow costs, trying to model that.
00:38:36.719 --> 00:38:39.440
Ben and I don't live awfully far from each other.
00:38:39.679 --> 00:38:51.280
You know, our the sort of fundamental climate, the temperature, the precip, the forage type overwhelmingly is quite similar, but we could have vastly different cow costs at the end of the day.
00:38:51.440 --> 00:38:58.719
If if I'm hypermanaging my cows and they're they're you know, we're we're pushing those costs up significantly, right?
00:38:58.800 --> 00:39:01.679
They're getting supplemented strategically, they're getting this and that.
00:39:01.760 --> 00:39:05.760
And Ben is running things a whole lot leaner on his operation.
00:39:05.920 --> 00:39:20.480
We can manipulate that cow cost in a really, really big way that makes those environments, even though from the, you know, the climate data would say that they're almost identical, they can look really, really different just in how we implement management at each of our places.
00:39:20.639 --> 00:39:36.400
So again, there's so many moving pieces to that genetics and environment interacting that I think it's almost impossible, even if you did something regionally, to have it be truly robust to all the management pieces that don't neatly fit into environment necessarily.
00:39:36.800 --> 00:39:40.960
Kind of on on all that, Troy, though, that the genetics and the environment.
00:39:41.920 --> 00:39:52.320
Talk a little bit on the unphenotype and the importance of submitting phenotypic information, birth weights, weaning weights, mature cow weights.
00:39:52.559 --> 00:39:54.639
That's those are still important, right?
00:39:55.280 --> 00:40:04.960
Yeah, no, an index built on EPDs that are built on a lack of true phenotypic records, it's not going to be a good index, right?
00:40:05.119 --> 00:40:17.519
So uh all of this stuff goes back to being able to capture, if you want to represent profitability in an index, you have to capture and record the phenotypes that drive profitability, right?
00:40:17.599 --> 00:40:19.119
There's no substitute for that.
00:40:19.280 --> 00:40:21.679
You can't build it on retrospective data, right?
00:40:21.760 --> 00:40:24.960
It has to be a constant influx of phenotypes.
00:40:25.119 --> 00:40:28.559
And so again, these phenotypes are as important as ever.
00:40:28.880 --> 00:40:33.039
Genomics doesn't absolve us from continuing to collect phenotypes.
00:40:33.199 --> 00:40:39.760
An index doesn't mean we don't need to collect phenotypes on the individual components that make that index up.
00:40:39.920 --> 00:40:42.639
In fact, I think it makes it even more important, right?
00:40:42.800 --> 00:40:50.079
The things like John was talking about, whether they be mature cow weight records, dry matter intake, we know how important those things are.
00:40:50.400 --> 00:41:01.280
How can we capture more phenotypes, make those tools more valuable, give us a better idea about what the real distribution of genetic potentials across our population is?
00:41:01.440 --> 00:41:11.519
That allows us to make a more effective selection index, allows us to move that sort of big picture breeding goal forward in a better way.
00:41:11.679 --> 00:41:13.760
So, so you're you're absolutely right, Ben.
00:41:14.239 --> 00:41:20.000
Phenotypic capture is as important today as it's ever been, maybe even more important.
00:41:20.320 --> 00:41:41.360
And as you you sort of expand the suite of traits that you are capturing phenotypes for, getting those sort of critical mass where you feel comfortable about the accuracy of that, the representativeness of the population in that, it allows you to plug that into the index faster, be more responsive to those real industry signals that are getting created.
00:41:41.599 --> 00:41:44.800
John talked about red meat yield at the very beginning.
00:41:44.960 --> 00:41:47.519
That's something that I think everybody is excited for.
00:41:47.679 --> 00:41:49.039
What is that going to look like?
00:41:49.199 --> 00:41:50.719
How's that gonna fit into the index?
00:41:50.800 --> 00:41:52.079
What's the signal gonna be?
00:41:52.239 --> 00:42:11.760
As soon as we can start capturing these phenotypes, getting these tools developed that are reliable and accurate that puts us in a good situation when the switch flips and there's a clear economic signal for something like that, we can find ways to fit it into that index and move the population forward in a way that's economically optimized.
00:42:12.079 --> 00:42:12.960
Absolutely.
00:42:13.280 --> 00:42:22.079
Troy, you you travel a lot and have a chance to visit with a lot of producers across the country and get in really now across the world.
00:42:22.159 --> 00:42:24.639
I see you traveled to South Africa recently.
00:42:25.039 --> 00:42:29.920
What are some of those common misconceptions that you hear about indexes?
00:42:30.000 --> 00:42:34.639
And how do you kind of address maybe some of those concerns that you've heard in your travels?
00:42:35.840 --> 00:42:43.519
Yeah, I was actually I was sitting in a meeting with Breed in South Africa just last week as they were discussing the creation of their index.
00:42:43.679 --> 00:42:51.519
And there was lots of these questions about like, well, I think that this value should be, we should have a little bit more emphasis on this or a little less emphasis on this.
00:42:51.840 --> 00:42:59.199
And to me, that's the core of an economic index is that you let the economics tell you the importance of the traits, right?
00:42:59.360 --> 00:43:09.199
So I think there's we have these perceptions about our breeds, we have these desires sometimes to push a trait forward, to put our finger on the scale a little bit.
00:43:09.440 --> 00:43:19.039
But if at the end of the day, the core need that these indexes serve is to help a commercial producer make a more profitable decision.
00:43:19.280 --> 00:43:34.079
We've got to let the profitability sort of work itself out and let that tell us the importance of these traits rather than trying to take our sort of internal biases, our thoughts about the breed, our thoughts about where things are or where they need to go.
00:43:34.239 --> 00:43:38.639
If we want a truly economic index, let the economics do the talking.
00:43:38.880 --> 00:43:42.559
There are some cases where, you know, maybe a breed has a problem, right?
00:43:42.639 --> 00:43:43.920
And we need to fix this.
00:43:44.159 --> 00:43:45.599
You can change some things.
00:43:45.840 --> 00:43:48.079
We call this a desired gains index.
00:43:48.159 --> 00:43:55.119
And you can say, listen, maybe I'm I'm a breed and I've got a docility problem and it's keeping my bulls from being able to get purchased.
00:43:55.280 --> 00:43:59.440
We're gonna take a little bit of weight away from this trait and this trait and this trait.
00:44:00.320 --> 00:44:02.320
Add into that docility value.
00:44:02.719 --> 00:44:12.559
Challenge there, right, is that if you can't put a real economic value on that, not truly optimized, and you're taking away selection pressure that you could put on other traits.
00:44:12.719 --> 00:44:14.559
So there's trade-offs when you do that.
00:44:14.800 --> 00:44:33.119
Again, sometimes it's it's necessary, but other times I think it can can work against folks when you start doing too much shuffling around to those things rather than letting that that economic optimization do the the hard part of putting weights on these traits.
00:44:33.599 --> 00:44:35.280
Yeah, that that makes great sense.
00:44:35.360 --> 00:44:43.440
And sometimes too, and particularly like some of those traits that are economically relevant that are going to be in the index, the opportunity for improvement is there, right?
00:44:43.679 --> 00:44:46.800
I think back, you know, API started in 2005.
00:44:47.039 --> 00:44:51.119
At that time, about 60% of cattle in the country graded choice and prime.
00:44:51.280 --> 00:44:53.360
So there was a lot of opportunity to move up.
00:44:53.519 --> 00:45:00.320
Um, you know, today we're we're bumping up about 90%, which is you know just tremendous, the genetic progress we've made in that time.
00:45:00.480 --> 00:45:05.760
So sometimes the index is telling us that, hey, there's there's maybe not as much low-hanging fruit there.
00:45:05.840 --> 00:45:11.920
So, you know, potentially, but you know, you might select, put more emphasis somewhere else just because of the progress that we've made.
00:45:12.000 --> 00:45:16.559
And that that's part of the benefit of of having an index that can weight some of those things as well.
00:45:17.199 --> 00:45:25.760
Yeah, no, I I think that's a really good point, John, is that is that these indexes aren't necessarily they're they're meant to be stable but not static, right?
00:45:25.840 --> 00:45:30.320
And so the fundamentals of the industry are they change over time.
00:45:30.480 --> 00:45:36.000
And if we're doing our job as an industry, the the cattle that make the industry up are going to change over time, right?
00:45:36.079 --> 00:45:42.880
And I think that as we make these indexes and we adapt them, you've got to keep that in mind that it's underlying cowherd, right?
00:45:43.039 --> 00:45:48.000
As we've we've fixed some of these issues or we've taken advantage of some of these premiums that were there.
00:45:48.239 --> 00:45:53.920
Um the reality of when we initially made the index to today might look quite different.
00:45:54.159 --> 00:46:00.239
And the opportunity, I think, is is maybe greater on some traits than others to push those forward.
00:46:00.400 --> 00:46:05.760
There's more juice to squeeze out from a profitability standpoint as we think about genetic improvements.
00:46:05.920 --> 00:46:07.199
So you're absolutely right.
00:46:07.360 --> 00:46:16.559
We we've got to have these indexes that are sort of living and responsive to the uh to the industry, but that are stable enough that you can build breeding programs around them, right?
00:46:16.639 --> 00:46:19.199
We don't want something that changes every year, right?
00:46:19.360 --> 00:46:23.519
As something goes up and down, the index looks completely different, that's worthless.
00:46:23.679 --> 00:46:30.480
But we've also got to have indexes that are are flexible enough to adapt when the fundamentals of the industry have changed.
00:46:30.800 --> 00:46:31.280
Well, Dr.
00:46:31.360 --> 00:46:34.159
Rowan, I really appreciate your insight today.
00:46:34.320 --> 00:46:43.199
I know that I've certainly enjoyed our conversation here and great to catch up and really take something that can seem pretty complicated, right?
00:46:43.280 --> 00:46:59.119
There's a lot of information that's going into a selection index, but it's great to distill that down, kind of get back to the basics and what is that you know, actually predicting and predicting profitability or at trying to estimate profitability of those animals or of their progeny, rather.
00:46:59.360 --> 00:47:01.840
As we start to wrap up here today, uh Dr.
00:47:02.000 --> 00:47:04.800
De Klerk, any any uh final questions for Dr.
00:47:04.880 --> 00:47:06.480
Rowan as as we wrap up?
00:47:06.880 --> 00:47:07.440
No, sir.
00:47:07.519 --> 00:47:08.719
I appreciate your time, Troy.
00:47:08.800 --> 00:47:10.320
This was a great conversation.
00:47:10.800 --> 00:47:13.039
Well, Troy, I know you're you're a busy guy.
00:47:13.199 --> 00:47:16.559
You just got back from South Africa, going all over the place.
00:47:16.719 --> 00:47:24.000
We were so thankful and grateful that you were able to join us in Fall Focus and Bozeman here in August and a wealth of information.
00:47:24.239 --> 00:47:30.239
Certainly uh appreciate your time and sharing your wisdom with listeners to the podcast here today.
00:47:31.039 --> 00:47:35.440
Oh, well, I appreciate you guys having me and and always enjoy talking about these things.
00:47:35.599 --> 00:47:38.639
The conversation about indexes, I think, is really important.
00:47:38.880 --> 00:47:48.320
And I think we've still got a lot of work to do, not just on making indexes that are really high quality, but on making sure that they're getting used by the commercial producers that they're made for.
00:47:48.639 --> 00:47:48.960
All right.
00:47:49.119 --> 00:47:50.719
Well, thank you for your time, Daytroy.
00:47:50.800 --> 00:47:56.480
Thank you for everything you've done for the American Semitol Association and IGS, and we appreciate your help and your collaboration.
