Sustainable Solutions: Cattle Countdown: Timed AI Insights with Colin Lynch

In this episode of Sustainable Solutions: From Guelph to Gainesville,  we explore timed AI (artificial insemination) in dairy cattle, with PhD student Colin Lynch, who is studying livestock genetics at the University of Guelp. Hosted by Marisa Crowhurst, University of Florida’s Agricultural Education and Communication masters student. Lynch discusses what timed AI is, how it is used in dairy cattle and the various benefits and implications timed AI has had in the dairy industry. Tune in to learn more!

Transcript

Cattle Countdown: Timed AI Insights

Marisa Crowhurst: [00:00:00] Hello everyone, and welcome to Sustainable Solutions from Guelph to Gainesville, a podcast brought to you by the Streaming Science Project. Streaming Science is a student driven program committed to connecting you with leading experts, exploring how science shapes our world and how we can build a more sustainable future together.

Marisa Crowhurst: Throughout this podcast series, we're not only delving into topics, sustainable agriculture, natural resources, and science communication and literacy. But we are taking an international perspective as we connect with alumni and experts from the University of Guelph Ontario Agricultural College in Ontario, Canada.

Marisa Crowhurst: I'm Marisa Crowhurst, a first year master's student at the University of Florida, and I'll be your host for today's episode. Today, we'll be speaking with Colin Lynch to talk about their experience as a PhD student in livestock [00:01:00] genetics. During our conversation, we'll explore Time Day Eye in dairy cattle, They're exciting work in advancing sustainable practices and their insights into how we can all continue to pursue a more sustainable future in agriculture, natural resources, and broader science.

Marisa Crowhurst: So without further ado, let's dive into our conversation with Colin and explore the sustainable solutions bridging the gap from Guelph to Gainesville. Hi

Marisa Crowhurst: Colin, welcome to our podcast today. 

Colin Lynch: Hello Marisa, thank you for having me. 

Marisa Crowhurst: Absolutely. Can you tell us a little bit about yourself? 

Colin Lynch: Yeah, for sure. So the kind of quick background. I grew up in Dublin in Ireland where I did my undergrad and where I got my, my love for agriculture. So yeah, both parents grew up on farms, but I'm a city kid myself.

Colin Lynch: So that's the ag connection. And then based on that, Honestly, I didn't know what I wanted to [00:02:00] do in university so I kind of looked at the different options and ag seemed to take a few boxes for me so I ended up going doing that and then that's where I found my love for genetics specifically when I was in my third year there and then from there I kind of knew it was something that I wanted to pursue.

Colin Lynch: Further on. So I just kind of looked into doing masters anywhere that would potentially take me in my profit UCD. He, he did his graduate studies at Purdue in Indiana. So he was all for going abroad, especially to North America. So I did the same kind of reached out to a few places in North America. And Guelph was thankfully one of them that actually responded.

Colin Lynch: So they responded to my now current prof Christine, Dr. Christine Bays had an interview. Interview went well, thankfully. And yeah, I've been there the last six years. So two years master's and then four years PhD, hoping to finish up now in come September. 

Marisa Crowhurst: Fingers crossed. So I know you got into [00:03:00] genetics third year, but what got you into dairy cattle genetics?

Colin Lynch: Well, honestly, One of the big advantages, or I wouldn't say advantage, is one of the kind of nice things about livestock genetics in general and quantitative genetics is that the theory can be applied to any species that you look at. So I could take what I've learned and I could go work with dogs, beef cattle, sheep, you name it, anything.

Colin Lynch: You could go into conservation if you wanted to look at potentially, like I don't know, if you're working at a zoo, to do with breeding elephants or whatever it may be. So to be honest, It was that was the project that was kind of available to me at the time was to work specifically with dairy cattle And so I was happy enough to go in learn about it.

Colin Lynch: But again It's the kind of the principles of the thing that were really the thing that kind of gauged my interest specifically So I'm open to working with any species. Basically, that'll have me 

Marisa Crowhurst: Now we know you've got a broad field to work with so for our listeners Can you explain a little bit about [00:04:00] what a livestock geneticist is a little bit about genetics just at a very basic knowledge?

Colin Lynch: For sure. So basically when you're talking about genetics, we're talking about essentially genes and the genes of interest to us as a quantitative geneticist are basically we want to select on traits of the kind of simple one in dairy cattle is something like milk yield. And we know that there's lots of different genes that impact the expression or, or how.

Colin Lynch: much milk an animal may actually produce. And so us as quantitative geneticists, we're trying to identify specific animals that have those regions of the genome that are really good for milk production and essentially breed them continuously across generations. So, simply what we're trying to do here is make predictions To how good that animal's natural, or not natural, but their genetic merit is for reproducing the next generation, upon generation, upon generation.

Marisa Crowhurst: That's incredible. [00:05:00] So as a livestock geneticist, or soon to be a livestock geneticist, can you explain the major objective that you are trying to achieve and what that means? 

Colin Lynch: It's a broad range, so up until, it's actually interesting the history of it, so up until around 2000. Most countries anyway, I won't say all countries were basically purely just selecting on milk So continuously improving milk milk milk and the idea there was that any kind of issues that would have happened to fertility or health through this would be made up through management, but Unfortunately, we unfortunately we were incorrect with this.

Colin Lynch: I wasn't a part of this at this stage But they were incorrect at the time and so since then we've kind of had a much more balanced You breeding schemes. So for example, in Canada, we select on roughly 67 different traits across your production. So you've got things like fat yield in the milk, protein yield in the milk, fertility, so conception, things around conception, health.

Colin Lynch: So you've got disorders such as [00:06:00] mastitis or ketosis. And then we have all these assortment of confirmation traits of how the animal looks because we know how that can be associated with other things related to our health and fertility. Also now we're also looking at sustainability. So there's things like reducing the methane production associated with animals to reduce that environmental burden.

Colin Lynch: So we have a big massive kind of pool of traits that we're all kind of collectively selecting on. And so that's kind of one of the big challenges that we have is because each of these individual traits themselves will have a correlation with other traits. So it's a massive balancing act, essentially, because we constantly want to be moving this way.

Colin Lynch: So we can't put too much weight on production, for example, because it may negatively impact our fertility or our health. So it's all kind of. Looking at the best way to balance the dollars going across generation to generation. 

Marisa Crowhurst: I was gonna say, this seems like a handful to juggle that many kind of traits and stuff.

Colin Lynch: Yeah. 

Marisa Crowhurst: So, [00:07:00] do you like to focus specifically on certain traits, or do you guys kind of look at it all and then go where you're going? expertise in this kind of thing. 

Colin Lynch: So kind of where the, from what I've noticed anyway, since I've come in, there's a big trend around specifically to do with health traits and to do with the, again, the sustainability.

Colin Lynch: So feed efficiency and stuff like that to reduce the input versus the output. And with feed efficiency, you're also going to get things like reducing methane and stuff. Cause again, that animal is just that more efficient, but this has come from a point of maybe 10 years ago where there was more of a push on fertility.

Colin Lynch: Trade specifically and then prior to that. It was, again, as I said, it was all to do with production. So based on my timing of coming into the field of livestock genetics, my work is centered around health traits, specifically what I'm doing now with respect to the calf disease traits, while other people are working, a lot of other people will be working towards things like, yeah, feed efficiency, reducing methane and things like that.

Colin Lynch: So [00:08:00] my work specifically, yeah, looking at calf disease traits, but that wouldn't be a kind of an overview of what, you Everyone is looking at there's all these new fancy machine learning methods and stuff like that. People are working with Incorporating like sensor data and stuff. Things are becoming a lot more techie Basically where we can take advantage of a lot more more Technologies out there so that we can bring in more information so we can truly better understand what the animal is doing at any point in time.

Marisa Crowhurst: Absolutely. I know technology is, it's a big thing in agriculture and definitely growing. So how does all of this relate to timed AI, our topic today? And can you talk a little about what exactly timed AI is? 

Colin Lynch: So time to I'll premise this with saying is one of those kind of fantastic technologies that has been developed.

Colin Lynch: Don't ask me the exact year. I want to say the 90s, but I can't actually remember the exact year when OVC, which is kind of the [00:09:00] staple one for many years was developed. And the idea of time to I is essentially to take out the. They take the stress off producers for identifying animals that are in heat, so i.

Colin Lynch: e. they're coming into estrus, they're about to ovulate, so identifying them when they're in heat, so that to breed them within the subsequent days, essentially, so that they become pregnant. They need to become pregnant if they want to produce milk. So, especially in the U. S., more so even here than Canada, you have these very large herds, essentially.

Colin Lynch: Many herds over 500, up to 5, 000, there's plenty of herds in that kind of range. And so it's a lot of work to be constantly trying to ID which animals may be in, at heat at different points in time. So it's really a kind of reduce the kind of labor costs associated with things. So with time, they are, there's essentially, there's a sequence of hormones that are given to the animal so that they ovulate at a predicted point in time.[00:10:00]

Colin Lynch: So that they can essentially group inseminate these animals once they're on the cycle. But if you want to relate that to our genetics. Specifically, there is potential issues around that. And that's because with us as geneticists, the most important thing, and many geneticists will coin this now, it's a nice phrase, is that the phenotype is king.

Colin Lynch: So by phenotype, I mean just the physical characteristics. So, for example, the phenotype of your hair, and I hope I don't get this wrong, is your phenotype is you have a brown hair. 

Marisa Crowhurst: It's actually right here. 

Colin Lynch: Oh, is it? Sorry. 

Marisa Crowhurst: It just looks very brown. No, you're okay. 

Colin Lynch: But exactly. So that, that is your phenotype is the physical characteristics, something that we can measure.

Colin Lynch: And so these phenotypes, and again, I'll bring it back to like the milk yield of an animal. That is, that is a phenotype for that animal. So these phenotypes are uploaded. To our national genetic evaluation boards and based on [00:11:00] all of these daughter records, because all the cows are females, obviously, all those daughter records go towards the sires, i.

Colin Lynch: e. the bulls, genetic evaluation for these different traits. So we have all these thousands of records come up, all these beautiful statistical models are applied, and then we can get a single value for the genetic merit of that sire. But one assumption that we're making is that these phenotypes are actually representative of the daughter.

Colin Lynch: The issue then with Time. di if I related to fertility is by giving these daughters these hormones we're actually masking that animal's true fertility and that's it. To step into another issue to do with fertility is that the current fertility traits that we're using for parameters They're very dictated by management.

Colin Lynch: So a classic one would be calving to for service so that's the time from when an animal calves to when they're serviced again and the kind of the The biology behind the thinking of that is how long does it take for an animal to [00:12:00] recover after calving? Before she's able to sustain a pregnancy again, because the theory or the thinking would be that a farmer or producer is only inseminating that animal once they're ovulating again.

Colin Lynch: But again, that's a complete management decision. If the producer never sees her, if he's like, ah, I'll just wait 80 days, which is common, there's a voluntary waiting period on most, most herds where they'll wait X amount of days before even trying to inseminate. So these traits are, again, very largely.

Colin Lynch: impacted by management and so by bringing in something like timed AI it's fully management at this stage because it's not the animal's innate fertility that's being expressed in the phenotype it's just the response to these hormones essentially and so by These records being included in our evaluations, we may be biasing in some way how our sires are ranking within, within our national evaluations.

Marisa Crowhurst: Can you explain a little bit [00:13:00] about the ranking of the sires and what exactly a sire is for our listeners? 

Colin Lynch: Most of the genetic progress that we're making is on the sire side, and by sire, sorry, I mean the bull. So the bull, the one that's producing the semen that is inseminating the cows that are then producing the calves and then producing milk, obviously.

Colin Lynch: So most of our progress to date has been on the sire side, and that's because a sire can have, you know, Within a year, a sire technically could have tens of thousands of daughters, while on the damned side, on the mother's side, she can only have one daughter, maybe two if she has twins, due to the gestation length.

Colin Lynch: So she can't have more than one, maybe two max calves a year. Whereas on the male side, thousands, hundreds of thousands, even even though they don't quite get last as long to get to hundreds of thousands anymore. But yeah, they can have thousands of daughter each year, so they can pass more of their genetics through the offspring, [00:14:00] through the population.

Colin Lynch: So a lot of progress has been more so made on the sire side and why we concentrate on the sire side. Then for the actual ranking. So within our national evaluations, you can go to CDCB if you're in the States or LactaNet here in Canada. If you look up the lifetime profit index here in Canada, LPI. You got the TPI in the U.

Colin Lynch: S. There's a few other evaluations, Dan, if any of your listeners want to have a look at. And you go see, you'll see bull lists in there. It's essentially rankings of, at the moment, what are the top sires across a multitude of traits. And within them, you'll see So you'll see like, oh, there'll be like milk and then you'll have the components of milk, fertility, all the different fertility traits, health traits, yada, yada, yada, and you'll have EBVs associated with them.

Colin Lynch: These are numbers, and an EBV is an estimated breeding value. And so it's, the estimated breeding value is essentially, it's a, it's an [00:15:00] estimation of the genetic merit of that sire. Relative to the base within the population. So we generally want positive EBVs. And then the rank is based off the sum of all of these EBVs, which are weighted based on how important different traits are.

Colin Lynch: So it's a lot of different math and everything put together, but yeah, essentially the top sire will have the highest EBVs across all of these different traits. 

Marisa Crowhurst: That's very interesting. And so that top sire will be used more often in these cattle herds. 

Colin Lynch: Exactly, yes. Which is another issue as well, though, if we think of like inbreeding and stuff like that.

Colin Lynch: So usually the best animals are mated to the best animals, therefore the offspring will then become the best animals, and then those animals will then become related to each other. So you'll see like, oftentimes, like, The top sires will be cousins of each other and stuff like that, but that's another day's story.

Colin Lynch: I'm sure there's someone else doing inbreeding and the impacts of inbreeding talking on this podcast. 

Marisa Crowhurst: I [00:16:00] know we're gonna have like multi podcast now just from this one. 

Colin Lynch: Yeah. 

Marisa Crowhurst: Okay. Well, so we talked a little about timed AI and timed AI stands for timed artificial insemination, correct? Yes. Okay. So, Why has it become so popular?

Marisa Crowhurst: It seems like from what you've talked about it, it's easier for a production manager to be able to maintain the herd and the breeding production. So is that why it's become so popular? 

Colin Lynch: Yeah, essentially because you're taking away that management element of having to be constantly viewing the animals. If you're, if you can concentrate your time, especially if, if labor is short, you don't have the required time to be constantly looking at your cattle, identifying, making notes of, Oh, I seen this one expressing signs of heat, like standing heat.

Colin Lynch: Have you ever seen cows mounting each other? That's usually a sign that they're, they're in heat and they're going to be ovulating within the next couple of days and they should be inseminated within the next couple of days. So you kind of just use a [00:17:00] blanket, I don't care, not that I don't care, but I don't need to look at this because I know I'm giving her, A shot on this day, a shot on this day, this day, and then I know that I'm inseminating her within two days.

Colin Lynch: And the technologies are so good now that you get similar, you get similar pregnancy rates as to conventional methods in and around up to 60%. I've seen it in many cases, which is which is really good for anything between 50 to 60 percent is fantastic. Most would be down 40 to 50, between 40 to 50 percent, but yeah with efficient time they are being used you can be, you can get upwards of 60 percent if you have a, if you have a good genetic merit herd for fertility.

Marisa Crowhurst: It definitely sounds like it leans heavily on efficiency and so you had talked about some of the implications of timed AI and how it masks the fertility and takes away the innate fertility. Can you talk a little bit more about what you mean about masking the fertility and maybe some more [00:18:00] implications of timed AI from a genetic perspective?

Colin Lynch: For sure. Yeah, so What we're trying to do again as geneticists is yet identify, but what we're doing is we try and take away kind of some of the environmental impacts that are occurring within that trait to fully get down to the biology within that trait. We want to select on that biology. So, We want to identify when it comes to a fertility point of view, animals that are naturally kind of coming back to cycling, naturally able to hold a pregnancy.

Colin Lynch: And we want to see differences between animals too, because if any, every animal performs the same, we have, there's no variation. So you can't improve anything. So we actually need differences in animals so that we can actually identify which ones are better. Okay. This one's better. Let's read that one. We timed, I timed AI, you're actually removing that completely.

Colin Lynch: And so every animal performs the same. So we can't actually make any inferences between different [00:19:00] animals. Another issue is that, and I've seen it from my work is that sometimes animals are selectively used for time. They are. So they'll have animals that they'll use just heat detection on the classical methods of but then they'll identify some problematic.

Colin Lynch: The daughters that have been maybe in previous lactations have had issues and they'll be used as time to AI. And then they'll get, Oh, they'll get these beautiful phenotypes getting uploaded. They'll then be kept in the herd longer as well, which is an issue. So we could potentially be breeding.

Colin Lynch: infertility into our herd because we're allowing, or we're, yeah, we're allowing these animals to stay in the herd by artificially giving them hormones so that they can become pregnant. I also feel as consumers, and this is everyone each to their own, I think everyone should be aware of what's going on.

Colin Lynch: We, consumers, Generally don't like the sound of any hormones or anything kind of being used when not fully necessary. If [00:20:00] it's not due to sickness or something like that, I can confirm that from everything that I've read, there's no impact on the, the animals like production in terms of there's no it doesn't get transmitted into the milk or anything.

Colin Lynch: These are all natural hormones that the, the animal is producing themselves, but we're just giving them a boost so that they can become fertile. Within that period of time. But yeah, the main issue, as I say, again, is that we're potentially biasing those evaluations or those sires lists, and that we're potentially breeding infertility into our herd where we could potentially fingers crossed.

Colin Lynch: It never comes, this comes to this point, but if AI, you could get to a point where animals couldn't naturally get into. heat by themselves or get into, become pregnant by themselves because we've pushed them so far in terms of maybe production and stuff like that. I don't think that that's going to be the case, but I could use turkeys for [00:21:00] example, turkeys can't be naturally inseminated anymore.

Colin Lynch: They have to be artificially inseminated because of, because of breeding. So it is, it is something that we should be aware of. I don't think we're close to it in the dairy industry as of yet, but it's something that I do think should be talked about more and solutions should be put together for it.

Marisa Crowhurst: Absolutely. I know that myself as a dog breeder. I'm a preservation dog breeder. And so a lot of what I do is I, you know, you breed to. breed better dogs of that variety and so when you were talking about the breeding of the cattle and you said how Some of them may not be ideal for breeding but then with this time day I they're actually kept in the herd longer.

Marisa Crowhurst: Whereas maybe they should have been cut from the herd So that's very interesting To hear because you know you if like, for example, if a dog can't get pregnant, you know, we cut them from the breeding program, and so that is such an interesting perspective to me. So I know we talked about the [00:22:00] efficiency and the benefits of timed AI, and you've mentioned some implications of timed AI, but do you feel like this is something we can improve on using timed AI to make it more efficient, but also Benefiting the genetics and the breeding side, or do you think this is kind of a, we're just going to focus more on efficiency and we may just like the turkeys lose that breeding ground.

Colin Lynch: Yeah, no, it's a, it's a, I don't think there's. a clear cut correct answer because it is something that I've kind of given a lot of thought to obviously over the last couple of years, but it's been a few years since my master's, but because I think it's, it is a fantastic technology and it could be something that ideally I would say it is you shouldn't breed a replacement in your herd.

Colin Lynch: Based off time they are based off an animal that you were forced to give time to I too. So if you have a fantastic care that's producing plenty and plenty of milk, one of your top milkers and you just want to get her back in calf. [00:23:00] Yeah, do it. Possibly use a beef semen as well. That's also becoming much more popular now.

Colin Lynch: It's, it's been very popular back where I'm from in Ireland for a while, but it's becoming more and more popular in the U. S. and Canada, whereby if you identify a cow that you know that you don't want to breed a replacement of, and so by replacement I specifically mean a calf that's going to eventually join your milking herd in the coming years.

Colin Lynch: If you just want to produce a calf off that that you may sell off to beef or whatever oftentimes producers now once they identify cows like that They'll actually cross her with beef semen so that the calf is actually more of a beef animal because it'll be going towards beef So that could be one reason for for one good merit for using Time to eye on those type of animals that you're not too worried about the offspring being included in your milking herd then from a genetic evaluation kind of point of view, I feel that either there's kind of, there's a couple of methods that we could go about.

Colin Lynch: I think 100 percent [00:24:00] starting yesterday, the, whether an animal was bred using timed AI, yes or no, should have, should be included in all kinds of records and any uploading, just so that we have the potential to, you know, Correct word at some point of view. So when I was actually initially doing my study, I had the data that I had collected there and I didn't collect, sorry, the data that was collected.

Colin Lynch: I had just over 4 million breeding records. And in that I had just, I think, 5, 800 different breeding codes. And so breeding code was like a letter that within each herd was associated with whatever the breeding type was. And by breeding type, it could be anything. It could be, it could say TimeDI, which was great and made my life easier, but it could be OVsync, it could be if like a seeder specifically was used, if it was heat detection, but these were all done by the producer.

Colin Lynch: So, if you had to [00:25:00] hazard a guess, guess how many different variations of the word OVsync I had within my data? How many different ways do you think you could spell OVsync? 

Marisa Crowhurst: I feel like there can't be that many, but the way you're saying this is making me think there is probably Whatever you think it 

Colin Lynch: is, multiply it by 10.

Marisa Crowhurst: Oh no! I don't I feel like 50? Is that too much? 

Colin Lynch: 303. 

Marisa Crowhurst: Oh my goodness. 

Colin Lynch: No, because you can have like, because it doesn't have to be the full word, but it could be like O V S O V SYNC like O V and then S Y N C A or SYNC as in like S I N K, all these different ways because it was the producer uploading them themselves.

Colin Lynch: And so I had to go through nearly manually using a, essentially I would search. For like pattern recognitions and stuff and change things through that big, big, long script that I had to write, but I essentially was assigning all of these different breedings to whether they were timed AI or heat [00:26:00] detection.

Colin Lynch: So that's one first thing that I would ask for like DHI Dairy Herd Improvement to kind of incorporate that into their dairy comp system. To, yeah, just an extra column there was like time they are or natural, yes, no, nice and simple like that so that if we possibly at some point only wanted to look at records that were naturally done or by natural, I mean, classical heat detection methods, no hormones used to use for our actual predictions as of now.

Colin Lynch: Another way could be there may be some way even though I don't like it as much is to Make a correction based off whether time day I was used or not, though. We have so many fertility records. Now we've been collecting fertility pretty intensely for a long, long time now that I think we still have a well, sorry.

Colin Lynch: I'm speaking from a Canadian point of view. Time BI is way more is a lot more used in the US. So maybe this isn't as as plausible, but we could just use the natural heat detection records [00:27:00] for the actual evaluations. Another one could be that will have. to fertility index is essentially where we'll have a time to AI fertility index and we'll have a heat detection index, because there's probably some balls that respond better to the timed AI system.

Colin Lynch: Like they, the daughters they produce are more in tune with the hormones being induced essentially within them through the, the injections and stuff like that. I don't know inside and output. There is a possibility for that. So there is a possibility for, for two different indexes overall. So yeah, there's a few different ways we could go about it, but my main point would be, and we need to record this, record what is going on specifically within each breeding, 

Marisa Crowhurst: you mentioned recording it is, is this not something commonly recorded now or are farmers and producers not recording if they're using timed AI or is it just not as common?

Colin Lynch: For this, for the U. S. I can't speak of, I can speak of for Canadian farmers that they [00:28:00] are recording, they're putting down that breeding code, as I mentioned, but the breeding code, the 303 different versions of OV Sync, like it's, a program's not going to just pick up on that innately, like, oh, that is OV Sync, because, It's just, they're not, it's not going to be built in.

Colin Lynch: Probably now, if you stuck it all into chat GBT, it would be able to, but when I was doing this initially, chat GBT wasn't an option. My masters would have been way quicker probably if if it was available, but so they are collecting it, but there's no consistency in how things are being recorded. So there's no simple.

Colin Lynch: What is the type of breeding done? That's all we need. And the producers can keep themselves. They can have their own their own breeding codes that they feel comfortable with using. But we also need a column in there so that it states timeDI, yes, no. Nice and simple. A one if, let's say one if timeDI was used, a zero if not.

Colin Lynch: That would be perfect. I don't think it adds too much to the [00:29:00] time that it would take to actually make these recordings, but it would make a big difference for us with respect to the evaluations. 

Marisa Crowhurst: Yeah. I definitely think that would help with keeping the accuracy of the evaluations and hopefully some other researchers not having to do all that hard labor with over 300 different codes.

Marisa Crowhurst: Looking back at timed AI and your thoughts on it, do you think some of your viewpoints on timed AI and its implications, do you think that's kind of a controversial topic with the dairy industry because of how efficient timed AI is? 

Colin Lynch: Yeah. Usually, my thinking of it is that if people were to read the The Heather, like, for example, if you wanted to do me dirty, you could put up a equal with the title of this and really annoy people by it.

Colin Lynch: Like again, I'm glad you kind of, you, you asked that question because I always do want to premise it with is I'm not saying that timely is a terrible technology and that we shouldn't be doing it. I'm just saying that we need to be careful with it from purely a genetics point of view, that [00:30:00] we could run into issues down the line.

Colin Lynch: From including all of this information within our current evaluations and my work to get on to it has shown that so what I basically did was I identified sires that had that had a sufficient amount of daughters with both time they I records just time they I and heat detections and I split them up so I did I ran an evaluation essentially with just time they I records and then I ran an evaluation with just heat detection records, naturally.

Colin Lynch: And so if there was no bias of time to eye within our evaluations, we'd expect the sires to basically rank the same. Cause I had, I think I had minimum 20 daughters. I can't remember off the top of my head, something like that, but we had a good chunk of, of daughters that we'd expect basically. And it was the same sires for both groups.

Colin Lynch: So let's just say I had for simplicity, I had a hundred sires here. And then I had a hundred records for, let's say 20 records for each of those hundreds, either. So 2000 here and [00:31:00] 2000 there ran the evaluations. And if there was no bias of time, they, I would expect the sires to basically rank the exact same.

Colin Lynch: Because by rank, again, the list of them, like the performance would be at the top, like regardless of what was used, the best would be still the best if there was no bias. But what I found was that there was significant, large re ranking as in the correlations for some traits was like 0. 35. So there's quite a weak relationship between them.

Colin Lynch: So they're trending in the same way, but there's a lot of re ranking essentially going on, meaning that with the way we're currently doing this. There's possibility that maybe the top ranked sire should actually be 10th or, or whatever. So we're not being, as you kind of mentioned, as accurate as we possibly could be.

Marisa Crowhurst: And you're seeing that with the bias and the time day eyes making those differences. 

Colin Lynch: And I'm, yeah, I'll premise my, my one as well, like mine is a very extreme what I did because I, I used fully just heath versus fully just time [00:32:00] day. In the real world, it's split some way. It could be 50 percent time DI, 50 percent sorry, 50 percent heat detection, 50 percent time DI actually used within the evaluation.

Colin Lynch: I can't imagine it would ever be that extreme where there's a sire in the current evaluation that has only a hundred percent timed AI records or a hundred percent. So that bias is probably being somewhat diluted by having that balance, but there is for sure somewhat of a bias there. The extent of it, we can't, can't estimate estimate as, as of now, but it's something that people within my position should be aware of.

Colin Lynch: Concerned and just interested in kind of looking at further, I would say. 

Marisa Crowhurst: Yeah. I mean, I think this is definitely some great knowledge to share with our listeners and perhaps others in the dairy industry. So what are some of your next steps for this research? What are you looking at doing in the future?

Colin Lynch: That's good. Good. Another good question. So we're going to kind of look at, we've done some genetic [00:33:00] correlations between the traits. And so. There's a thing called phenotypic correlation and then a genetic correlation. So phenotypic, again, if you look at the traits like milk yield, so you'll let, let's say two different traits, like fat yield and milk yield, for example, have a pretty strong genetic correlation, something like 0.

Colin Lynch: 8. And so, phenotypically, the higher your milk yield is, the higher your fat yield would be. So, but for genetic correlation, it's more so about the regions of the genome, the genes that make us all up, how much they are in common between respective traits. So, and that's really what we want from a genetics point of view, because we're making selections on that gene level, is what we're interested in doing.

Colin Lynch: And so, for, I want to say, for a couple of the traits, the genetic correlation was pretty high, so if we improve one, we're improving the other, but for other traits, not so much. And that's something that we need to kind of look and delve in further, is looking at the genetic correlation between these two traits.

Colin Lynch: Because, if the genetic correlation is one, [00:34:00] then selecting one will improve the other and then we don't have to worry too much, but we need to have more kind of concrete evidence to ensure that maybe there is a difference between them and that would go, if there is a difference, then we have to look at, okay, alleviating that difference or selecting on the two traits differently.

Colin Lynch: Then another thing would be, and it goes back to that collection, we need to get more accurate information in. We don't want some guy like me sitting down. Arbitrary, not arbitrarily, but somewhat being like time, the heat detection over all these because I make mistakes and I made tons of mistakes and I'll be the first to admit that because it wasn't a simple thing to do.

Colin Lynch: But yeah, it's kind of infrastructure is really important. And that's another thing for people that kind of wonder what we do. 90 percent of what I do is combing through data, making sure things make sense because you know yourself, human error, people are uploading this, it happens. things can be wrong. So I have to comb through the data, make sure everything's structured, everything [00:35:00]makes sense, so that the statistical technologies that we apply can actually run properly on them and we're getting proper results from it at the end of the day.

Colin Lynch: So yeah, starting with infrastructure and then looking at the kind of genetic correlations among these traits. 

Marisa Crowhurst: Absolutely. I think human error definitely plays a large part in a lot of our research that we're doing. Do you think you can tie in for us listeners a little bit about how your research plays into sustainability?

Marisa Crowhurst: I know sustainability is such a hot topic in not only the cattle industry, but in the agricultural industry in general. 

Colin Lynch: Well, yeah. So the time to ice off is all just about essentially just that efficiency factor. We want that animal to be back in calf ASAP basically as soon as possible, depending on, again, there, there'll be people that would be coming at me.

Colin Lynch: No doubt, because there's some producers that. Won't breed for ages because we've kind of been stuck with this. And I'm not sure if you've heard of the, the [00:36:00] three Oh five day lactation. So usually animals are milked for not usually not anymore. Anyway, in North America, 305 days, then they're, they're milked for 305, they're dried off and then they're bred within 60 days of that to have a calving interval of 365 days.

Colin Lynch: So for a year, basically, but that's only important in pasture based system. And I know I'm getting off topic here a little bit, but that's only important in pasture based systems like Ireland, because we have our animals out in pasture most of the year. So we have to line things up with the grass growing season because when an animal calves, that's when they need their most energy rich diet, because they go from not having that much of a an energy requirement relative to then They're soon, they're producing 20, 30, 40, 50 pounds of milk within a day.

Colin Lynch: And so you want the most high, high nutrient grass available to them from a pasture based system. But [00:37:00] in intensive systems like we have in North America, it's not as important. So because the grass, they're inside the whole time, the climate's the same the whole time, basically. During summer you might have a bit of heat stress.

Colin Lynch: But there's, there's alleviations for that too. So it's a lot more consistent. So you can, you can milk animals for, for far, far longer if you want. So from that point of view, it's not kind of as people may say as important, but I still think it's it comes back to ensuring that that animal is efficient in everything that it's about in terms of it's less work for the producer just to be able to identify naturally an animal that's naturally innately fertile, inseminate, next one round.

Colin Lynch: So, That, yeah, the kind of the harmony of everything is there and then that we're also kind of getting away from the need of having hormones within our system. So maybe less, well, you could see it as being sustainable, but more so the [00:38:00] image of dairy being more sustainable, I would say. 

Marisa Crowhurst: Absolutely. Well, that is incredible.

Marisa Crowhurst: Colin, thank you so much for your time today. Did you have any last minute major takeaways that you wanted to share with our listeners about your research or about anything that you wanted to talk about? 

Colin Lynch: Do I have anything else that I want to say? Do I trying to think, is there any final message that I could, could think of to One thing I would say, and this is probably a little off topic, but agriculture is One of the better fields, I would say, to be in.

Colin Lynch: Some of the best people I've ever met are in the agricultural field. As I mentioned at the beginning, I'm a city kid, and so a lot of city kids like myself will go into agriculture and they'll feel like the odd person out, no doubt. I'm like, all these people talking about farming, talking about, oh my tractor's this and that.

Colin Lynch: I'm like, what the hell are you guys talking about? But what I do want to premise that is, is that there's a ton of positions. There's ton of like [00:39:00] jobs, roles that are super, super interesting from people of every background. So if you're listening to this and you do have an interest in agriculture, but you didn't grow up on a farm.

Colin Lynch: Don't let it stop you from looking and exploring further positions in it, because, yeah, the best people that I've ever met are grassroots farm people and stuff like that. The most easygoing. 

Marisa Crowhurst: Well, I absolutely agree. From someone who also didn't grow up on a farm, I feel like the ag and natural resources field has been very welcoming, and I think that's a great message to share.

Marisa Crowhurst: Thank you for listening to the Sustainable Solutions from Guelph to Gainesville series on the Streaming Science Podcast. Make sure to check out our website and social media for more of our work. If you enjoyed this episode, we encourage you to tune into other episodes in our series and to visit the University of Guelph OAC webpages and social media for more info.

Marisa Crowhurst: Once again, I'm your host, Marisa Crowhurst. Thanks for listening. For more information about this episode, visit the links in our show notes. And thank you, [00:40:00] Colin.