Breeding for profitable, efficient, locally adapted and sustainable cattle with Troy Rowan University of Tennessee

Currently beef cattle production faces a profitability challenge driven by rising feed costs and efficiency gaps. For cow-calf operations, particularly where producers operate on forage-based systems, the biggest variable cost is cow feed, yet geneticists lack precise selection tools for measuring and improving forage intake and conversion efficiency. 

Today we are joined by Troy Rowan from the University of Tennessee, where he investigates economically important traits in beef cattle with particular emphasis on cow efficiency, local adaptation, and genetic approaches to increasing sustainability. His research focuses on developing novel measurement approaches to create practical selection tools for grazing efficiency.

The opportunity lies in emerging technologies. While decades of work developed feed efficiency tools for concentrate-fed feedlot environments, these measurements don't translate to grazing systems. Rowan's team explores indicators serving as stand-ins in genetic evaluations, using emissions as metabolic proxies, leveraging precision technologies like AI and computer vision to capture phenotypes previously impractical to measure on large animal groups.

Methane and CO2 are important because they are the best indicator of metabolism currently available. Even though they are imperfect metabolism needs to be included to provide an indicator for cow feed costs.

Looking forward, epigenetics and other "omics" technologies promise to revolutionize individual animal management. Rather than just predicting genetic potential passed to offspring, these tools could enable phenotypic predictions based on epigenetic marks, gene expression, or metabolomics. A feedlot receiving heterogeneous cattle could place animals in appropriate pens and management protocols based on predicted disease resistance, growth potential, or feed efficiency—filling in the 70% of trait variation that genetics alone doesn't capture. After measuring 10,000-plus animals to develop robust genetic evaluations, the next frontier is translating that knowledge into practical tools that work across the one the billion plus cattle worldwide, from intensive North American operations to pastoral African systems where data recording infrastructure remains the primary bottleneck.

Transcript

Ash Sweeting: 0:07

Welcome to the AshCloud. I'm Ash Sweeting. Today I'm joined by Troy Rowan from the University of Tennessee, who is investigating using genomics to understand and predict economically important traits in beef cattle with a particular emphasis on cow efficiency, local adaptation, and genetic approaches to increasing beef cattle sustainability. Troy, thank you very much for joining me today. Yeah, good to good to be here, Ash. Genetics is and breeding animals is a something that that animal producers, farmers, ranchers have been doing, you know, since Roman times. And it's also something that many of them love to do, they love to talk about, they're hugely proud of. So do you want to just introduce us to genetics and livestock genetics and and the the power that has to really shape um the animal production systems and the beef production system?

Troy Rowan: 1:08

Yeah, I I think genetics is has always been really interesting to me. I I remember I guess just to go back to where my interest in genetics started, my dad had a bunch of of old books, and and the one that I I loved the most was this like breeds of cattle, just sort of a um a very visual book that sort of explored all the worldwide diversity in in cattle, and it had a bunch of these pictures sort of throughout time, and and it was crazy to see these animals at the turn of the century um look very similar to animals that we've got now. And then pictures from the 1950s and 60s where um our animals were were sort of at belt buckle height, these really tiny, pudgy, um fat animals. And then we got to the um 1980s and 90s, and we had these animals that were just huge, lean, six foot at the shoulder, and then now we're we're back to this sort of um more moderate stage in our beef animals. And so it was just a really cool look at how um intentional selection, even in the absence for a lot of that time um without selection tools, you can make a huge amount of change in an animal's phenotype and performance over a really short amount of time if you're if you're putting effort into making those selection decisions um for one trade or another. So that was always a really cool thing to look at. But as you sort of dig into it and and like you said, all the way back to to Roman times and before then, right? Our um our interactions with the progenitors to cattle, um, pre-domestication, you know, 12,000, 15,000 years ago, um, where we sort of get from this stage of of hunting animals um to actually uh cultivating and living beside them, I think is is that's all genetics, right? We're we're making them fit our lifestyles better. Um, and then very recently, right, we've we've sort of expanded the suite of things that we're we're interested in. Once you've got an animal um tame enough to live amongst you, um then you can start drilling into to making it more productive, and and we just sort of continued that um over the last however long.

Ash Sweeting: 3:12

So, you know, you think back to you know old TV shows like the all creatures great and small, anyone who's many people have been to veterin veterinary school, big fans of James Harriet and all that sort of stuff. Um which and then and it's all visual. It's all you might the the most sophisticated bit of um measuring technology you might have was you know a tape measure to measure the height of the shoulders, say, and uh a set of waist scales so you know how much the thing weighs. Um but it's all visual, and you know, the old breeds, you know, the Hereford with the the white face or whatever was also hugely, hugely visual. But now, you know, I guess in the last 40-50 years, but also increasingly in the last five, ten years, the tools available to look at things with much greater resolution have changed. They're just chalk and cheese different. So, how how does that affect things and and what's the opportunities that come from these tools?

Speaker: 4:12

Yeah, that's a um that's something that I don't think I appreciated until fairly recently was the um the act of actually measuring animals in an objective way and including that in selection decisions is a very, very, very modern thing, right? We weren't we weren't routinely running animals across scales to collect weights until you know the 1950s and 60s that were flowing back to to breed societies. Um we didn't have selection tools. Um so we'll uh I'm sure we'll talk about sort of EPDs and indexes, the things that um our producers have that allow us to select on the estimated genetic component of traits. Those are our we sort of live in that world as geneticists now. Um but those are those are fairly new creations, right? Sort of delivered to the industry um at the earliest in the late 70s and sort of picked up steam in the 80s. But um again, our ability to do all that is predicated on the on being able to measure um these phenotypes on big numbers of animals um and sort of uh get those in a central repository. And and our ability to measure more different things, right? A scale is one thing, um, but we've sort of exploded in the number of different things that we've been able to actually capture those objective measurements on um and in turn start to develop selection tools for them, right? So whether that be things like enteric methane, um, whether that be things like um carcass attributes on live animals through ultrasound, um, really the the technologies have exploded there. And I think a lot of our growth in the genetic space in the next decade or two is gonna be how do we leverage things like um AI and computer vision and some other precision technologies to capture um these more granular and precise phenotypes that previously would have never been practical to measure um on big groups of animals.

Ash Sweeting: 6:11

I think if we we take a step back for a second and look at what a ruminant is, and essentially you've got you know you've got the animal, the the lungs, the heart, the organs, all that sort of stuff, but that's also carrying around this mobile fermentation vat that's full of full of hundreds of billions of microbes, um bacteria, protozoa, archaea, etc. etc. fungi. And that's the engine of the digestive system. So you've almost got an organism within an organism, and you know, going back to new technologies that wasn't, you know, until we had genomics, you know, most of those it microbes are highly anaerobic. So as soon as you take them out of the room and they die. So people are trying to culture them for years, and all the interest onions died because you have took them out and put them in touch with oxygen. And now with genomics, um, you actually know what's in there. So you've got these different layers, and then there's some really great work also starting to look at nutrient density of the foods and the beef that's produced, or lamb, or whatever protein is produced. And you know, one of the fascinating things as people start to look at that is how much that varies, and then also how that it seems to be linked to diet and the the um biodiversity of the diet. So you've got another system there that you're kind of adding into the complexity. So I guess I guess where do you see the interactions between those systems and how you monitor them? And then from a practical perspective, you know, how do you not get overwhelmed by all these new sources of data and and information and how do you put it together in something that actually the rancher or the breeder can say, I'll use this for that?

Speaker: 8:01

Yeah, and and I guess I would I would add one more layer of complexity there, um, being environment and management. Um, so so just you know, picking a cow up who's in Tennessee and sticking her out in West Texas, maybe sort of uh around where my wife is from, um, those are two completely different environments, right? We're here, we can graze, you know, 300 plus days a year. Um, out there, cows have to flip rocks over to find uh a little bit to eat, right? Um, the heat's different, the the toxic fescue that our cows graze is another stressor. Um, and then there's this management on top, right? Some um we can have two neighbors whose cattle are managed quite differently, um, and those look like completely different environments, even though um the zip code is exactly the same. So um more supplementation um is gonna change and shift the composition of that um rumen microbiome that you talked about is gonna shift their ability to digest other foodstuffs, it's gonna change that um that cow's efficiency. So there's so many moving pieces there. I think that that as breeders, it's um we have to consider all that. So um we can't just look at our operation in a um in a vacuum. But also I think that a lot of the things and the tools that we work to develop are sort of they're robust to that, right? We try and remove environment um and focus just on genetics, but the the next sort of evolution, I think, is going to be a little bit more environment aware, genetic and genomic predictions. So um layering on some of those management pieces and natural environmental stressors and trying to match specific genetics to certain environments, right? So a cow that really thrives because she's genetically predisposed to have a certain passage rate through her rumen, which allows certain micro uh microbes to colonize it. Um, she's maybe going to be a better fit in sort of a low-quality range environment, whereas another cow might be a better fit in this this sort of high-quality improved pasture environment that we've got in the the east of the Mississippi. So uh again, there's so many moving pieces, but I think our our tools, like you said, we can actually observe the um components or indicators at least of the um the composition of that rumen microbiome. Um we've got more information from satellite imagery that we can utilize to better predict what an animal's environment and management like might look like. So as we figure out how to layer those pieces together and match genetics to specific environments, those are the things that I think I'm the most excited about. Um, but it's a hugely complicated system that's gonna take some time and effort to put all those disparate pieces together. Um, but animal breeders can't just think about um making their calculating their breeding values in a vacuum. Um, there's gonna be a lot more pieces of information and interactions that we've got to consider um if we want to make better tools that allow producers to put animals in the right environments to succeed.

Ash Sweeting: 11:14

So, in terms of that, what what are you currently monitoring and measuring in terms of traits that you're putting into your research and you're in terms of developing these breeding values?

Speaker: 11:29

Yeah, so I guess I'll I'll take a step back and talk really broadly about um sort of our theory as animal breeders is that um we want to have some sort of a plan, right? We want to we want to have goals that we're working towards, and that's gonna differ from operation to operation. Um, whether people retain replacement females, how they market their calves is gonna dictate the traits that go into their selection decisions. And so um my job as a geneticist um, I think is to figure out what the missing pieces are there and how we might be able to measure those um at the scale needed to make selection tools for producers. So um to me, when I sort of look at the landscape of things, anyone who's familiar with um a bull sale catalog or an AI stud book, you open that up and it looks like the back of a baseball card, right? There's there's all these different estimated breeding values for traits from an animal's birth weight all the way through its carcass weight for female fertility, for meat quality, everything in between. And so what I do there is I look and I say, what's missing from this picture? And in our part of the world, in in Tennessee here and overwhelmingly east of the Mississippi, um, we're a cow-calf country, right? Very little feedlot, um, very little slaughter capacity here. So most of our cattle are getting shipped over um out to the west to get fed. Um, so the the biggest dictator of success for producers in this part of the world is how efficient can that cow convert forage into milk into wean calf pounds, right? And and our our best indicator of that at this point is is how big that cow is, right? Bigger cows tend to be less efficient, smaller cows more efficient. And so I spend a lot of time with my extension hat talking about sort of these system level efficiencies. So, you know, you might wean off less pounds of calf per cow, but with the smaller cow, you're actually able to stock more. You wean off more calf pounds per acre, which I think is a better sort of KPI, right? Um, as we try and drill into what efficiency looks like. But as a geneticist, we want to find tools that allow us to tweak things. And the the biggest missing piece in our evaluations at this point is is cow efficiency on forage. Um, so there's been a longtime effort, probably over the last you know, two and a half plus decades, of amassing um feed efficiency records and more of a concentrate feedlot setting. Um and we've we've developed tools that allow selection explicitly for dry matter intake, for feed efficiency. Um, but again, I think that that environment in more of a feedlot setting is gonna look very different from a cow that's efficient out grazing on grass, right? There's way more moving pieces when you put a cow out in her natural environment, right? She's got to be able to deal with environmental stressors. There's behavioral components to foraging. Um, there's been some really interesting work on sort of how range cows graze, huge amount of that appears to be genetically driven. Um, and then there's really just the getting at the core needs of that cow, right? If you've got a lactating cow, how much energy does she need for maintenance? How much is she partitioning towards lactation, reproduction, all these other things that are essential to our profitability? Um, and then you you sort of step back another time and say, what's the biggest cost for a cow calf operation? Um and the biggest variable cost across the board is cow feed costs, and we don't have a great selection tool immediately for just how much a cow eats when she's on forage. So my research program is really interested in finding um and exploring new and novel ways of measuring and predicting cow efficiency in these more forage-based systems. So um we we lean on a lot of technologies from ear tag-based wearables um to measuring sort of the the gases that a cow emits as proxies. Um, it's very, very hard for us to actually go out and measure individual forage disappearance on cows, hugely um impractical. So our goal is to find good indicators um that we could plug in as sort of stand-ins for these traits and genetic evaluations and and hopefully uh deliver tools at the end of the day that allow us to more precisely um say this cow is more or less efficient rather than just saying this cow is is big and this cow is little.

Ash Sweeting: 16:05

There's a lot of stuff there. Um there's a lot of stuff there. Um one thing so the uh you know what the cow efficiency at the end of the day it's the miles per gallon of that cow. It's whether you've got the the clunky old 1950s or sixties big car that turns through gas, or you've got something that's much more efficient. Um and in terms of looking at those emissions and trying to work out from that um how efficient the animal is, um I guess where's the where's the science currently in in that space? How much more research do we need to do? What do we already know? And the other side of it, I guess, is you know, what size is the data set? Do we have enough data or do we need more data to be able to understand that better?

Speaker: 17:02

Yeah, that's a that's a good question. Um, and I I think that the the science has been around for a long time, right? So um we've been putting cows in boxes and measuring what comes out of them um since the you know the 1940s, 50s, and 60s. Uh a lot of that foundational um sort of energetics work was done um back in the 60s. And so that's sort of been, we've got a good understanding of sort of how cows use and produce energy. And I'm so far from an animal nutritionist that it's not even funny. Um, you should should talk to one of them a little bit more about what the state of their science is. Um, but our, I guess I as geneticists, we're always sort of like, you know, we need something that's good enough, right? Um we're never gonna put every cow through a respiration chamber, right? That's our gold standard of measuring um so many of the so many of these metabolic um type things, right? But if I can get a cow to go stick her head into a thing, you know, three or four times a week for four weeks, I start to get a pretty good understanding of what that cow's average emissions are. And from those emissions, whether it be methane, CO2, oxygen, um, we can start to piece together um what that cow's metabolism underlying her um really looks like. And so we're still really early stages in beef cattle in doing this. Much more work's been done in dairy because those cows are just more accessible, right? You stick a um you stick a gas sensor in an automatic milking system, the cow's there between two and four times a day. You can amass records really, really quick. Um, a beef cow who's out grazing, you know, a 1500-acre tract of land, um, comes by the water once per day, maybe sticks her head in a machine, you know, once or twice a week, right? Um, amassing records is much more challenging, but I think is is every bit, if not more important due to the sort of heterogeneity of our beef herd, um, due to the variation in grazing systems and forage types. Um, we're still really building um a database that would allow us to start to do genetics on that. Um, but I think is is really, really important um that we we start to develop these indicators of of our biggest variable costs um on these cow cow enterprises.

Ash Sweeting: 19:29

In in terms of that, and this might be I might be asking the nutritionist question rather than the geneticist question, so excuse me for that. But you know, the our metabolism changes also with our health and also with what we eat. So if if an animal then is moving, you know, obviously one of the variables of grazing systems is that the feed source changes every single day. The grass grows, it's hot, and it goes off, or it's dry, or it's frosty, or whatever happens. Um so you've got this ever-changing nutritional quality. And can is is there because you're getting other interactions between the animal and um the animal's metabolism and what where they are. And likewise metabolism will go up if an animal's sick as they start burning energy in terms of driving the immune system, which means their amount of carbon dioxide they're respiring will go up. So it's untangling, I guess, what's the innate genetic side of things plus what's that environment, animal, health interaction. And then also are there opportunities to extract data that a a rancher could use to say my animals are changing, I might need to up my management or or further investigate. And then from the genetics perspective, you need to untangle those as well, because one's one's environmental and one's genetic.

Speaker: 20:59

Yeah, that's a um that's another really good question that gets sort of at the foundation of um of the difference between geneticists and nutritionists. So nutritionists want to understand that environmental piece, right? What's what's driving what I see in this animal? Um, geneticists want to say, once I take away that environmental piece, what am I left with? And what does that tell me about an animal's genetic potential so that I can make a decision about whether that goes on to the next generation or not? And so uh in our case as geneticists, a lot of what we're doing is trying to model out that environmental noise, right? So the the sort of overarching um assumption there is that if I'm measuring one of these phenotypes, um, one of these metabolism phenotypes on a hundred head of cows, they're out in the same pasture, have access to the same forage as the forage dips in quality and quantity, that's going to equally affect all of my cows. I can use that contemporary group to pull out environmental variation. And what I'm left with is trying to disentangle that genetic piece a little bit more. So we're very interested in trying to remove that generally, but I think the the more that we get into it, um, the more interested we're becoming in trying to understand how genetics work across that environmental gradient. Um so are the genetics that make you efficient the same if you are um grazing in the middle of June versus in October, right? Are there are different genetics better at grazing when grass is very plentiful? Um, do some, you know, hold steady and then ratchet up when forage quality dips? Um we're we're starting to become more interested and we've got the ability to start modeling some of those things, at least at the research level. Um, but but that's a huge outstanding piece. And and you mention animal health. Uh I think that that's probably our biggest blind spot right now from a genetic standpoint. How do you measure a phenotype of an animal's ability to stay healthy? Um it's it's really, really tricky, something that that sort of keeps me up at night trying to figure out what that trait is. Um, you know, your ability to resist local pathogens, but I put you in a different environment and you're you're sick right away versus a cow that's very robust to everything. Um it it's it's a big outstanding question and certainly has um impacts on on how we identify these efficient animals and try and move them forward in our breeding programs.

Ash Sweeting: 23:42

On that note, I recently read a piece on human longevity and the genetic component of human longevity. Um this was just in the last week or so. And what the researchers found was the actual genetic component of longevity in humans was much, much higher than previously thought because they um re-crunched data over the last 50 or 100 years where they've taken out diseases, accidents, all those environmental factors that had reduced longevity and tried to focus on what was the genetic nature of it. And um and that then leads back to your appropriate animal for the environment because you know you measure the animal when in springtime, but you can't just lock the animal in springtime for the hot year. They have to go through the summer and the fall and the winter as well.

Speaker: 24:36

Yep, yep. Yeah, and I I think we're again as animal breeders, our uh our biggest thing that we need is in, right? We need numbers of records, and so we we're pretty unpicky with with how and when we get those, right? Um, there's sort of a trade-off, right, between you know record precision and the granularity of it and the number of animals that you can measure. And so we we've got to work and find an optimum. Um, Rob Banks, who spent a long time uh at UNE and AgBU, has sort of worked in in this space of optimized phenotyping and and how we can strategically get the highest quality um while maintaining the the number of records that you need to do genetic evaluation. But but your point stands, right? Would would you rather have, you know, I would rather have 10 records from across the year on every cow. Um, but if I'm left to either get 10% of the cows with 10 records or all of the cows with one record, I'm generally going to choose all of the cows with with one record, um, knowing that that'll probably, you know, if a cow's efficient in spring, the likelihood that she's efficient in fall is is probably pretty high. Um, but again, so much of what we do is try and understand, you know, are those two things different traits? Um, the big question right now, I think, is grazing efficiency the same trait as feedlot efficiency? Um, I would tend to guess, again, not as a nutritionist, but those would seem to me to be pretty different traits. Um, just your ability to digest, concentrate, being quite different than uh that that more uh, you know, the the forage that those grazing cows are gonna have. So uh those are those are all really, really good questions that I think we're all as an industry trying to get our hands on.

Ash Sweeting: 26:28

To go into the weeds on that a little bit, um say you're you know you've monitored the cow in the fall in the springtime, um, and then you're monitoring a different a different mob um later on in the year, and then you also then say you weigh the animal um in the fall, um, and you look at their body condition, something that may be a little less um you know needs less equipment, less less, you know, less less intensive. And can you then correlate if if you've got ones that were doing really efficiently in the spring and then they're still looking healthy and they've still got good weight and their calf weight's good, so obviously the calf's grown and they produce enough milk. Can then you kind of link different data sources and and connect them even though they're not directly um comparable, they're the same data.

Speaker: 27:21

Yeah, for sure. And I I think that's the the really important thing amongst all of this is that we're we're never looking or we're never breeding for a single trait in a vacuum, right? There's always um pushes and pulls. So I could I could have uh you know the most metabolically um efficient cow in my herd, right? She could have the lowest, you know, CO2 emissions, and we're using that as an indicator for cow metabolic efficiency. But if her calf is a hundred pounds lighter than all of the other cows in the herd and she comes through the chute post-weaning um at a body condition score of three and a half or four, um, she's just not eating very much and she's not producing very much, right? And that's the that gets to your miles per gallon issue, right? If um, what is the animal actually doing with the resources that they're taking up? And and so our our selection programs tend to be quite broad, right? We're trying to touch all of the things that affect profitability in a herd. And so um, you've got your revenue generation, right? We can't give up so much on calf weight, um, but we also need to consider these costs, right? And in trying to find that that economically optimized sweet spot, I think is is really what we're trying to do. And and we have selection indexes to allow us to sort of appropriately weight all of the traits and and how they relate to profit so that we're not ending up in a ditch on one side, right? I I worry a little bit about the industry um where we've got pressure to make carcasses bigger and bigger and bigger, um, as unfavorable genetic correlations with cow size and cow efficiency. And so, how do we do all of this in an optimized way that that doesn't put us in one of the two ditches? I I think is one of the big things that we've we've been dealing with in animal breeding since it it became a thing.

Ash Sweeting: 29:11

And all as with all these things, I best I guess there's a bit of a pendulum that we'll slip swing, swing a little far to one side and then we'll reconnect it, recorrect, and then go to the other side, and there'll be a bit of backwards and forwards there as well.

Speaker: 29:24

Yeah, yeah. And I I think the the most successful breeders of cattle, um, the people that have been in the business for the longest and are the most successful are the ones that that aren't reactionary in their breeding, right? They've they've got a clear breeding plan, a clear breeding objective. Um, they know what their cattle do well, um, and they're working on you know the suite of traits and and they're consistent through um, you know, right now the market says make cattle as big as possible. Um, but again, that that pendulum is is inevitably going to swing back the other way. The people that stay the course, right, um, they maybe don't get as big of a bump when the market is super high, um, but they're much, much more robust when the the bottom falls out. And so uh again, I think the people that have been in this business, you know, for a hundred plus years that I know have been very, very true to their breeding objective. They focused on um on making really practical cattle um that fit their environment. Um, and then you know, things change along the way. You have to have to pick up a new trait here or there, or switch your emphasis a little bit on things that are are more longer lasting. But um the folks that that are are robust to that pendulum swing are the ones that that stay in the business and are the most successful.

Ash Sweeting: 30:41

You mentioned that as a geneticist, N, as big an N is possible is is good. And so given that there's somewhere between one and a half, one to one and a half billion cattle in the in the world, what's what's what's the N we're looking for?

Speaker: 30:59

Oh, that's a hard question. Um it really depends what your what your end goal is, right? And in that N is very population specific. Um so you know, as a general rule, um, as we're trying to make these selection tools, um, estimated breeding values or expected progeny differences, EBVs or EPDs get used interchangeably. Um, sort of our baseline, um a trait's gotta be heritable um to some degree, right? And your your accuracy of that tool is gonna depend on the heritability of the trait. That's gonna affect how many phenotypes you need to measure. So if there's only one percent of a trait that's driven by genetics, you're gonna have to observe it on a huge number of individuals and able to be able to make a tool that allows you to select on the genetic component of that because it's just such a small slice of the pie. Um, making a genetic selection tool for something that's 50% heritable, um, you can you can have less records there. But um the sort of starting point for a genetic evaluation, I think, is always in that 10,000 records range, is when you can start to be pretty confident um in the tools that are coming out the other end there. But um it's all sort of statistical accuracy and and there's a little bit of risk tolerance that goes with um how much can I um can I tolerate? What's sort of the opportunity cost of not having this tool versus waiting for it to um to be sort of fully mature and and of a sufficient accuracy. So uh again, that's a classic um academic and extension answer, but it it really does depend. Um but I I'd love to measure um the every one point, however many billion cows there are in the world. Um, but again, that comes with its own challenges from um from production system to production system um from breed to breed.

Ash Sweeting: 32:53

And on on that note, we've got the fact that you know a lot of this is not just the animal, but also the animal and its interaction with its environment. And then if we start looking a bit further beyond North America and beyond Australia, and especially when we start to look at the the global south and the tropical environments there and the indigenous breeds there, how because there's so much knowledge created, how how do we use what we're learning and what we've what we're developing to to have the broadest benefits across the global north and the global south?

Speaker: 33:35

Yeah, I I think that that those production environments are all in in such a different um, we're sort of at different places along the um along our progression and in having selection tools. Uh, I do think that that a lot of the you know the iterations of things that we've gone through in the global north, right? We've gone from, you know, in the last since the 1960s, I guess, um, from no performance recording to having genomically enhanced breeding values for, you know, 25 plus traits. Um, I think as we look to the global south and and we do some work in in Kenya with the folks at at the International Livestock Research Institute, sort of where we've gotten to is like we need we need data recording infrastructure, um, is the first and foremost thing, right? How do we capture weights on animals? Um, and once that happens, we can skip a couple steps, right? We don't have to work through the um innovation in how we uh calculate EPDs based on pedigrees. We don't have to go through the however many different iterations of genomic selection that we've developed. Um we can leap a couple steps, and and once you're routinely recording phenotypes on animals, um once we've got um fairly cheap genotyping, which is is well on its way, um, we can develop really robust genetic selection programs pretty quickly. Um, but again, it it takes uh at the end of the day, we've got to measure phenotypes on animals. Um, but the the rest of it, the infrastructure, um, the methodology to actually develop genetic selection tools is there. Um, so I I think that we can we can accelerate that um in a number of ways, but but it's really all about um capturing uh measurements on those animals and and having that in a place that allows you to to do evaluation on it.

Ash Sweeting: 35:30

And then that I guess it feeds back into whatever the local conditions are and their ability to and the ease to capture capture those phenotypes and that information and and communicate it and the the the capacity and the skills of of the local population to to do all that work as well.

Speaker: 35:51

Yeah, and and that's uh I don't know, every time that I'm I'm in Africa, I'm I'm just amazed that cattle can live there, right? And the um the pastoralists that we work with are such good herdspeople. And and again, the ability to keep cattle alive in conditions like that, working against parasites and um every yeah, every tick-borne um and flyborne illness you can imagine. Um, those animals are survivors. And so uh again, the ability to layer um genetic improvement on top of these animals that are so adapted to such a harsh environment, I think there's there's tons and tons of potential um in um in the global south to to make those systems more productive. Um but again, it really all comes down to data recording um and data infrastructure. But but again, there there's so much potential there um and there's a lot of energy. Uh I think specifically focused on the um on red meat, so whether that be whether that be beef, sheep, or goat, um, to to really get those programs off the ground and and get those populations uh better able to utilize those natural resources.

Ash Sweeting: 37:01

I think something to also add about the whole Africa side of things is the ability of that continent to grow grass is is pretty impressive. And I I was in Africa last year and um I saw a a book on on the different grass species and native grass species, and I was I was pleasantly surprised by how many of those native African grass species you may find in a feed store um as improved pastures um in eastern Australia and all that sort of stuff. So yeah, there's things, there's things more than more things go backwards and forwards each way than when we often give credit for.

Speaker: 37:38

Yeah, and and the same with the cattle too, right? I I think about um if you want a model for animals that are incredibly robust to disease in stressful situations, you don't have to look any further than than some of those native African breeds, right? And so are there, you know, is there genetics that um some of our um, you know, the global north could pull out of there um that would help uh solve some of our challenges with um emerging animal diseases or um increased you know extreme weather events, whatever that looks like, um, that those animals have been dealing with for the last you know tens of thousands of years and are well adapted to. So uh again, I think there's so much two-way um exchange of of knowledge, exchange of of germplasm and genetics, um, but it's it's really exciting when those things start to come together.

Ash Sweeting: 38:31

One of I think the most exciting areas of of science that we're learning more and more about um from my perspective is epigenetics. And that whole so genetics, please add detail if this is not 100% correct. But my understanding is genetics is the hard-coded DNA, and the epigenetics is how those genes are actually expressed. So the example, the way I think about it, the example I use um is that a a child can be born with a certain eye colour, and at a later stage in life their eye colour will change, but their DNA hasn't changed, their genetics haven't changed, but the way those genes have expressed, been expressed has changed. And that's that epigenetics. Where where do you see or do you see, and if if you do, where do you see potential in in in new and emerging areas such as epigenetics to kind of give us you know even even greater tools to to improve the uh the industry.

Speaker: 39:32

Yeah, epigenetics are um they're they're still sort of the wild west um from a livestock perspective, right? I think the the human community has poured a lot of resources into them in the context of disease in particular. Um, but but yeah, we're we're born with the exact same set of DNA um that we've got now, minus and plus a few mutations. But but again, the the turning on and off of genes, um, it's something that we've known about for a long time and and probably the the application, even though we haven't called it epigenetics, but um we call it uh in cattle it'd be fetal programming. So basically, you know, what what a a mom's plane of nutrition is when she's pregnant um is gonna dictate in a lot of ways the performance of that calf, right? And there's been a lot of work in this, um, sort of the intersection of of reproduction and nutrition, understanding how to sort of optimize um how we develop heifers, right? Does maybe some early nutrient restriction actually help that heifer cope better downstream? Um, you make some of those epigenetic changes um that that turn genes on or off. Um, and then downstream uh she's better able to respond to that environmental challenge. And so um I think there's that's something that's been on our mind. We probably haven't, as animal scientists called it, um, epigenetics. But for me, the the biggest opportunity from a genetics standpoint is really changing how we think about um prediction. So um up to this point, like I've said, we we record a phenotype, we pull that environmental variation out, and we've got an estimate of what an animal can pass down to its offspring, right? So we're we're very focused on um not individual animal performance, but how will this animal's offspring perform compared to this animal's offspring? And so I I think the the exciting thing about epigenetics or understanding things like we talk about gene expression, transcriptomics or proteomics or metabolics, we've got all these omics, right? And the exciting thing for me is how do we take these and turn them into phenotypic predictions for an animal, right? Um pull a sample of blood early on in an animal's life and say, you know, based on these epigenetic marks, which are are becoming more tractable to measure at scale, like I've said, we're always um we're always interested in in. And, you know, those are previously researched tools. Um, there have been some advancements in how we do that that I think will allow us to screen large numbers of animals for um some well-known epigenetic marks in the near future. So, given that information, instead of just predicting um what's the additive genetic potential of this animal, can we start to drill down on some of these things and say, hey, this animal um has a lot of genetic potential, and it appears based on these epigenetic marks that this animal had a really favorable fetal environment, and this animal got all of the benefits of, you know, it was vaccinated, it had um good forage management of its dam, and it got all these benefits, right? This animal has the potential to grow really fast, have a really um high meriting carcass, or be a very productive uh female, epigenetics and and some of these other um omics things are are gonna allow us to drill in on predicting individual animal phenotype and managing them accordingly, rather than just um saying something about the genetic component of a trait that that really might not be that heritable. So um in the future, and this is is a ways off, but I can see you know, a feedlot gets a group of animals, um, they're pretty heterogeneous, maybe from 10 or 15 different operations, but they can start placing animals in individual pins and managing them as such, right? These animals based on these epigenetic marks or this gene expression that we see, unlikely um to get sick, even if they uh there's a big outbreak around very robust immune systems. We can maybe put them in our antibiotic free pin over here. These animals were maybe a little bit higher risk based on those things. Um, and they can do that in sort of a a structured and um, I guess, standardized manner. So I think it Unlocks a lot of opportunities for doing individual animal management based on phenotypic predictions.

Ash Sweeting: 44:06

That that is that is absolutely fascinating. That's very, very interesting. So uh almost of an addition like an epigenetic um selection tool to what are those animals going to be more likely, you know, you could put an extra extra X percent starch in a diet and push some heart higher because say you think they have uh the epigenetics to cope for that. That that that's absolutely fascinating. That's really interesting.

Speaker: 44:31

For sure. Yeah, and it it's really filling in that, you know, if there's if a phenotype's 100% and you know, feed efficiency is is 30% heritable, right? That we capture with our selection tool, there's another 70% that we can can start to fill in the the missing bits about as we try and predict how that animal will actually perform. So I I think it's really, really exciting and and has enormous implications for how we we manage our manage our cattle.

Ash Sweeting: 44:59

We're almost coming up to time. So before we go, is there anything else that you'd like to add that we haven't already discussed?

Speaker: 45:07

Oh man. Um, not that it wouldn't take uh an hour plus. Uh I love these conversations that are are just sort of open and free-flowing, and and I can talk about cows and um and and beef production um all day, but but I I think I'm I'm really bullish on um on sort of the future of the beef industry. I I think that there's so much um meat that's left on the bone for us to get at from a genetics perspective. Um there's there's a whole suite of traits that we probably haven't been able to measure or touch in the past. Um we're starting to get the tools to get there. Um and at the end of the day, it's about making the best use of our natural resources, right? Um, living in in harmony with the environment, um, putting these large ruminants in positions that they were designed to do, right? Um and that's turned grass into high quality protein. Um people still really like to eat beef. Um, and so I I think we've got a we've got a great story. We've got improving tools and in genetics, especially, right? It's it's this continual progress over time. Um, it doesn't happen overnight, but the the progress that you make is cumulative, right? We're we're not backsliding every generation, we're building on improvements, and and for me, that's the exciting part is that we've you know we've had hundreds of years of selection to build on, um, but we're not losing that. And and as long as we're working in the right direction, I think it's good news for um for beef production um as I look forward. So uh it gets me out of bed every morning and and nothing I love to talk more about it. So I appreciate the opportunity to come on today, Ash.

Ash Sweeting: 46:47

It's been an absolute pleasure, Troy. Thank you so very much for joining me today. You bet. Thank you for listening to the AshCloud. Please subscribe to Ashcloud if you've enjoyed this podcast, where I will continue to discuss food sustainability with guests who bring a deep understanding of the environmental, political, and cultural challenges facing our society and creative ideas on how to address them.