Guests: Dr. Paul Kononoff, University of Nebraska-Lincoln and Dr. Bill Weiss, The Ohio State University Professor Emeritus Dr. Kononoff’s lab evaluated retrospective feed mixing records collected from eight commercial dairy farms. Data was divided into 28-day periods. Daily TMR nutrient deviation was automatically calculated from feed mixer data as the actual amount of a nutrient fed minus the target amount from the original diet formulation, divided by the target amount. (5:43)
Dr. Kononoff’s lab evaluated retrospective feed mixing records collected from eight commercial dairy farms. Data was divided into 28-day periods. Daily TMR nutrient deviation was automatically calculated from feed mixer data as the actual amount of a nutrient fed minus the target amount from the original diet formulation, divided by the target amount. (5:43)
Crude protein, NDF, fat, and starch were the nutrients evaluated in the study. (13:40)
Variation was positive for every nutrient on the vast majority of days. Dr. Kononoff attributes that to more feed being delivered than the diet formulation predicted animals would consume. Dry matter intake decreased with increasing positive deviation days in starch and increased with increasing positive deviation days in crude protein. NDF deviation did not impact dry matter intake. A narrow range of diets was used in the dataset and the main byproduct feed was high in NDF, so Dr. Kononoff speculates that there was not a wide enough range in NDF to have an impact on intakes. (17:04)
Milk yield increased with increased positive deviation days in starch and decreased with increased positive deviation days in NDF. The pregnancy rate increased with increasing positive deviation days in fat and decreased with increasing positive deviation days in crude protein. Unfortunately, milk urea nitrogen data was not available in the dataset to further investigate the crude protein/pregnancy rate relationship. (20:44)
There was little farm-to-farm variation in the data. (25:08)
As positive deviation days for starch increased, so did feed conversion. The opposite effect was noted for NDF. As positive deviation days for fat increased, feed conversion decreased. This result was a little surprising, as delivering more energy usually improves feed conversion. However, the dataset did not specify the source of fat or fatty acid profile, so there may have been some rumen fermentation interference from fat. (27:08)
Dr. Kononoff thinks it would be interesting to track individual cows through lactation and collect nutrient variation data. Dr. Weiss asks if the correlation between daily farm milk yield and nutrient variation was evaluated; it was not. Dr. Kononoff agrees that there may be some additional correlations that would be interesting to run. (33:22)
In closing, Dr. Zimmerman commends Dr. Kononoff’s work in tackling such a large dataset and looks forward to follow-up research. Dr. Weiss agrees and encourages more data extraction from the dataset. He was also very surprised at the low farm-to-farm variation observed and speculated if that would hold up if there were more variation in diets. Dr. Kononoff reminds the audience that taking a look at the TMR beyond the paper ration and digging into mixing techniques and TMR consistency is as important as evaluating bulk tank information or the amount of milk shipped. (37:20)
You can find this episode’s journal club paper from the Journal of Dairy Science Communications here: https://www.sciencedirect.com/science/article/pii/S2666910224000760
Please subscribe and share with your industry friends to invite more people to join us at the Real Science Exchange virtual pub table.
If you want one of our Real Science Exchange t-shirts, screenshot your rating, review, or subscription, and email a picture to anh.marketing@balchem.com. Include your size and mailing address, and we’ll mail you a shirt.
Scott Sorrell (00:07):
Good evening everyone, and welcome to the Real Science Exchange, the pubcast where leading scientists and industry professionals meet over a few drinks to discuss the latest ideas and trends in animal nutrition. Hi, I'm Scott Sorrell, gonna be your host here tonight at Real Science Exchange. Tonight we're back for the Journal Club with our resident expert, Dr. Bill Weiss, where we're gonna cuss and discuss the latest in dairy research. So, Bill, welcome back to the pub. It's good to see you again. Anything special in your glass tonight?
Dr. Bill Weiss (00:39):
Well, hello Scott. It's good to see you again. And in honor of our Canadian guests, I have a Moose Hat, so
Dr. Paul Kononoff (00:46):
Oh, nice.
Scott Sorrell (00:48):
Excellent. Excellent choice. Good. So, Bill, you always do a great job in, in selecting papers, but I think you did an exceptional job tonight with, with the selection and, and with the speaker. We've got the editor-in -chief himself for the Journal of Dairy Science. Gonna join us here tonight. So would you mind just kind of telling us a little bit about the guest that you brought with you tonight?
Dr. Bill Weiss (01:12):
Dr. Paul Kononoff. He's a full professor at University of Nebraska. I know he got his degrees at Penn State. I don't know where he got his undergraduate degree. He'll have to tell us. But he has been at Nebraska for, for, for almost forever. He's had a quite active program there in, in a diverse program as well, but a lot on, on energy, which I read quite a bit. And as you mentioned, he's also been editor in chief for I think three or four years, maybe even longer. So that's a very difficult job. And all the authors appreciate your, your efforts in that, even though you might not hear that often enough. So, welcome Paul.
Scott Sorrell (01:53):
Thank you. Yeah, welcome back to the pub. Paul, it's good to see you again. So why don't you tell us a little bit, where did you get that undergraduate degree from?
Dr. Paul Kononoff (02:01):
Yeah, so as Bill mentioned, I'm from Canada and my bachelor's bachelor of Science and Agriculture with a major in animal science was from the University of Saskatchewan. And then I stayed there to do a master's in forge particle size. And that was obviously a good segue into ending up at Penn State for my PhD program. So lots of moose in the great province of Saskatchewan. Bill
Dr. Bill Weiss (02:29):
That's the colostrum factory as well.
Dr. Paul Kononoff (02:31):
That's right, yeah, that's right. Well known for the colostrum production there.
Scott Sorrell (02:35):
Yeah, for sure. Well, I don't wanna forget my co-host, Clay, welcome back. Good to see you once again.
Clay Zimmerman (02:42):
Yeah, thanks, Scott. What's, what's in your glass tonight?
Scott Sorrell (02:45):
Well, you know, I knew Bill always has a beer. So tonight, instead of a bourbon or a scotch, I'm, I'm having a beer and this is a Sam Adams porch rocker. And it's one of my favorites. It's, it's very tasty. It's a little bit like a summer shandy, if you've had those before. It's got just a little hint of lemon. And so I'm, I'm leaving for vacation tomorrow and I, I, I've got, I've got a 12 pack of these to go with me. It's now 11 pack 'cause I am having this one today. So, but it's quite tasty. So looking forward to, to my porch rocker and to this discussion this evening. So, cheers, everyone looking forward to a great podcast.
Dr. Paul Kononoff (03:27):
Cheers. Cheers.
Scott Sorrell (03:36):
The economics of feeding Reashure Precision Release Choline. ReaShure is fed during the transition period. And because it's fed for such a short period of time, it costs just $15 per cow. And yet the benefits will continue to generate income throughout the year. Cows fed reassure produce five pounds more colostrum, which pays for your reassure investment on the very first day of lactation cows. Fed ReaShure also produce five pounds more milk per day every day. That means after the first day, every day is payday. Invest in ReaShure during the transition period and reup your investment on the very first day of lactation. After that, you got it Payday. So Bill, why don't we just jump right into it. The paper you selected tonight is called Estimation of the Nutrient Variation in Feed Delivery and Impacts on Lactating Dairy Cattle. So how, and why did you select this paper?
Dr. Bill Weiss (04:41):
Well, this is a short communication, so it's in actually JDS communications. And again, I really like this journal 'cause they have short papers and they get right to the point. The last couple years of my career, we started looking quite a bit at variation. And then when this paper came out, obviously it's of great interest to me. I I'm hoping people follow up on this area, area of research. 'cause I think there's a lot to be done. And so that's, that's the main reason I pa picked it, just because I have a, a lot of interest in feed variation. Okay. And this, this looked at we when all our work had to do with variation in feed composition, which caused variation in diet composition, but this has more to do with the ingredient, not the ingredients, but the actual TMR making. So if Paul, if you explain a little bit first why you did this experiment. Yeah. And then I want you to dis describe what this, this deviation term you use in here.
Dr. Paul Kononoff (05:39):
Yeah.
Dr. Bill Weiss (05:40):
Let's start with the hypothesis or the, the reason why,
Dr. Paul Kononoff (05:43):
Right. Okay. Yeah. So just a little bit of background. I mean, bill, you always use the term snappy when it comes to scientific writing. And, you know, I have to give Matt Lucy a lot of credit. JDSC is his dream child. And and so he obviously launched this journal with the support of A DSA. And these papers are fun to write because you can get right to the point but but really bring in a lot of science that may or may not have ended up in the, the larger mothership of Journal of Dairy Science. So I just wanted to shout out to JDSC as well. Yeah. And as far as this paper goes. So actually the sponsor of this paper approached us and they said with that they had a lot of feed mixing data on their hands.
Dr. Paul Kononoff (06:35):
And they wanted to find ways to glean information of it and, and then help their customers as well use the information that, that they're collecting on the farm. And so what was, what was really neat about these, that Bill you mentioned, I do a lot of work with energetics, and we're working with small numbers of animals, and we have really intense measures. What was fun about this data is is that it came, we had large amounts of data. It came from commercial farms and there was large amounts of data. And so that's something that will at least at least in my area of study and the things that we do I don't necessarily spend a lot of time with these kind of data. So that, so that was fun. The other thing I'll mention, you may get a kick out of this, but you know, you off often think about when you get a problem or you start talking to a sponsor about research you also think about the graduate student that would be doing this.
Dr. Paul Kononoff (07:41):
And Addison Carroll, who's the first author we put her on this project and she really tied into data analysis. And even though the genomics people wouldn't call this big data, there was a lot of data for a nutritionist and we'd call it big data. And so you mentioned my PhD from Penn State. When I was at Penn State, I took a class on data management using SaaS. Still have those notes in my office. 1998, I pulled the notes off my shelf and I said, Hey, we gotta tie into this. This is what I know about managing large data sets. And handed her my notes, but then obviously she brought her own take to it. We also engaged with a statistician Kathy Hanford, who's since retired, but is a statistician here at the University of Nebraska.
Dr. Paul Kononoff (08:36):
So it was great to have her come along with us. So you know, when we started looking at the data and just trying to figure out how we can analyze it and, and what information we could glean from it one of the things that we had was obviously feed mixing data. And the sponsor was interested in, you know, just variation in procedures and, and what can we find here? And so as you know, when you start dealing with data we often tie into the mean of of the information and test means, and report means. But what we were really interested in is just the variation that comes with feed mixing. And so as we looked at the data and started sifting through it you know, we came up with the idea of, again, we had large data over several years, but we broke it down into one year to be more manageable.
Dr. Paul Kononoff (09:29):
So we had eight farms and we had all of this feed mixing data, and the data was specifically the variation in adding into the ingredient and then the impact on, on the composition of the rash. And that was fed. So we had to figure out, well, what are we gonna measure and what can we test? And that's where we came up with the idea of, of breaking the data into chunks, essentially monthly chunks or 28 day chunks. And then we just counted the number of days that a particular nutrient ended up in a, in a positive, what we call the positive deviation or a negative deviation. And so basically the, the experimental unit was the number of days of that deviation, either positive or negative within a 28 day kernel of or a window of opportunity.
Dr. Bill Weiss (10:21):
And you used, if I remember right, you the deviation is on mass, not deviation, right?
Dr. Paul Kononoff (10:27):
Yeah, yeah.
Dr. Bill Weiss (10:28):
So like on the deviation and protein, how did you, you get that because the feed that the data would be so many pounds of corn solid, so many pounds of this and that. Yeah. So how did you convert that to, to nutrients?
Dr. Paul Kononoff (10:42):
So, so the company, we didn't have that day nutrient concentration, but obviously the company has information on the chemical composition of those feeds. So we just multiplied by mass, by the most recent chemical composition that that company had for that ingredient. Okay.
Dr. Bill Weiss (11:01):
And what are the composition vary over that 28? Or was corn silage fixed at 8% protein or
Dr. Paul Kononoff (11:08):
Whatever? Yeah, so it would've depended it, it would've varied depending on the information that the nutritionist had inputted or the farm inputted into there. Yeah.
Dr. Bill Weiss (11:19):
Yeah. Why, why'd you, you know, we, we've always done everything on concentration basis, variation, concentration. Why do you pick mass?
Dr. Paul Kononoff (11:27):
Yeah, I, I mean, I think you know, certainly just when you think about what drives the things that matter, I mean, it is the mass of energy and especially these large farms. I mean, they're talking about large volumes of milk produced. And so what we wanted to do is, is, is look at the, the mass of nutrients and then the impact that that has on, on milk production
Dr. Bill Weiss (11:53):
And, and the deviation was from the formulated mass. So, say again, I formulated for 10 pounds and they ate 11.
Dr. Paul Kononoff (12:02):
Right.
Dr. Bill Weiss (12:02):
It would be a one pound deviation,
Dr. Paul Kononoff (12:04):
Right? Yeah, yeah, that's right. It's based on the formulated. And I think, you know, that, that's the neat thing about this study is you know, early in my career, I spent time with, with the feed company based in the northeast. And you know, you drive around with the nutritionist from farm to farm to farm, and you'll look at the diet which is on paper. And often the nutritionist, before we get to the farm, we'd say, okay, well this is what we're feeding at this farm. Super helpful information. But what's really interesting is that page, the way data are presented and and metrics are presented looks the same as you go from farm to farm to farm. But as you know, when you get step out of the truck, farms are different. And that's easy to see what is less easy to see. And this is obviously stuff that you've been doing are just the, the, the feed mixing procedures. And you know, you just don't see that when you're looking at the diet. And I think when you're trying to observe what's going on on the farm and, and troubleshoot, it's easy to think about that diet that you have on paper or a computer screen and not think about actually what's making to the animal pictures very different. And it's a hard thing to conceptualize when you're visiting
Dr. Bill Weiss (13:27):
Paper rations are rarely wrong anymore.
Dr. Paul Kononoff (13:30):
Yeah, yeah, exactly. Yeah.
Dr. Bill Weiss (13:32):
I guess one, one more question for I turn it over to Clay. What, what, what nutrients did you look at and why, why did you pick the ones you picked?
Dr. Paul Kononoff (13:40):
Yeah. Yeah. So we pri we really focused on protein NDF, fat and crude protein oh, sorry. Well, I mentioned protein, but crude protein specifically. And so those were the, the main nutrients we obviously had to decide. We had a whole list of nutrients to, to track, obviously my interest in, in energy oh, I should mention starch as well. So my interest in energy and energy flow through the animal. We certainly wanted to look at fiber and starch. And then of course, because we know crew protein also has major impacts on not only total milk production, but milk composition as well. So not as granular as, you know, you get on some of this formulation software, amino acids fiber digestibility rates or anything, but just really the main nutrients. I always think about Bill, you know, your summit of equation that that was developed to evaluate energy. I mean, the major nutrients are represented in that equation. And I think just conceptually, I often think about these nutrients.
Clay Zimmerman (14:52):
So I have a number of questions just related to the, the materials and methods here, I guess on this. So there were, so there was no sampling that was actually done on these farm of the tmr, correct?
Dr. Paul Kononoff (15:11):
Right. Yeah. So it was just tracking data that was actually coming from the feed mixers what was going into the mixer compared to what was formulated.
Clay Zimmerman (15:21):
So the composition, the, the ingredient composition of these diets is there's one very large proportion of the TMR that's coming from one premixed product, is that correct?
Dr. Paul Kononoff (15:38):
Yeah, that is, that is correct. Yeah. Yeah. So I don't actually, I don't have, it's, it's maybe in the paper, but yeah, there is this, this company does manufacture a premixed product and it, it, most of the time is going in at a fairly large proportion.
Clay Zimmerman (15:54):
So, so do you have any idea how, so how many different ingredients would've been then mixed on farm?
Dr. Paul Kononoff (16:02):
Yeah, I don't, I don't have an answer to that question right now, but you know, you're probably looking at so this feed is primarily based on a wet milling co-product. And so really what you see is a lot of corn silage that come in. Sometimes alfalfa tri silage may be going in there can be some starch in the form of corn or maybe some cereal grains that are added. And then in, in some cases, minerals in trace minerals and vitamins as well. But that all, it all depends on the farm, you know, what they're doing. But a, a smaller proportion of, I would say the usual ingredients that you see kind of here in the northern plains and the southern plains too, most of these dairies were based in basically Nebraska South,
Dr. Bill Weiss (17:04):
You, you had almost, almost every day there's the 90% plus we're positive
Dr. Paul Kononoff (17:10):
Yeah.
Dr. Bill Weiss (17:11):
For all, all nutrients.
Dr. Paul Kononoff (17:12):
Yeah.
Dr. Bill Weiss (17:13):
First question I said is, how can something's gotta go down if everything go Yeah. If those core goes up, something has to go down. Yeah. Or did they just eat more or deliver more than what was programmed?
Dr. Paul Kononoff (17:26):
Yeah. Yeah. So I think, I think that's probably as as simple as that, is that they were delivering more feed than really what was predicted that those animals would actually consume. So you're right, when I looked at it, I thought, oh my goodness something should be going down here, but it's just as simple as that is what they were delivering was a little greater than what was on paper.
Dr. Bill Weiss (17:55):
Yes. Let's get into the, their results here, I guess, and if we start with the intake, what did, what was related to, and we're all, everything from now is deviation
Dr. Paul Kononoff (18:06):
Yes. What we're talking about.
Dr. Bill Weiss (18:08):
So what, what affected intake relative, what deviation affected intake?
Dr. Paul Kononoff (18:12):
Yeah. So the, the two nutrients that we saw affected intake the first one was starch. And so dry matter intake re was reduced with increasing deviation of starch. I think that's kind of to be expected if we will backed in with a hypothesis, we would expect that as you add more starch, you would add more energy, and then that would result in a reduction in dry matter intake. So, so starch was one and as I said, kind of expected. The other one that we saw maybe, maybe expected, but perhaps not always is deviation in crude positive deviation in crude protein enhanced positive D aviation in dry matter intake. And there, I think, I think even Nassim talks about this, about how protein can actually have a positive effect on dry matter intake. And it's often not something we think about, you know, on a lot of farms that we're working with. We work with a narrow range in, in crude protein, but you know, when you're out in the real world and, and crude protein moves that can have a, of an effect on dry matter intake.
Dr. Bill Weiss (19:33):
And NDF didn't, was it a No?
Dr. Paul Kononoff (19:36):
Yeah, because that's another major player.
Dr. Bill Weiss (19:36):
We usually think Why, why do you think it must?
Dr. Paul Kononoff (19:42):
That's a good question. I mean, it didn't sift out in the models that we were working with, so I was a little as surprised especially given that especially given that starch moved you'd think there'd be a dilution effect and we would see something. I guess the one thing that I'd maybe think about, we know NDF affects dry matter intake, but with this particular study as kind of clay mentioned, we worked with a narrow range of diets. And, and so the, the, the main feed is actually fairly high in, in NDF. And so I would speculate that maybe the, we just didn't look at it over a large enough range in NDF to actually have an effect on, on dry matter intake.
Dr. Bill Weiss (20:32):
And this would be byproduct NDF.
Dr. Paul Kononoff (20:34):
Yes, that's right. Forage India. Yeah, that's right.
Dr. Bill Weiss (20:39):
What about, well, we talked about intake. Let's get to the one that pays. What about milk? Yeah.
Dr. Paul Kononoff (20:44):
Yeah. So milk yield, again, kind of as you'd expect with as we saw in dry matter intake, that actually as you increase the deviation in starch on a positive way, you increase milk yield. And so again, that kind of falls in line with dry matter intake. And then this is actually where NDF did come in. We saw that as you decrease the deviation in NDF, you actually did see a positive response in milk yield. And there obviously is some correlations there between NDF and starch, but NDF did come in as something that affect milk yield.
Dr. Bill Weiss (21:27):
And you also with fat, if I remember, fat also had a negative effect or negative relationship. I shouldn't say effect, but read this. Right.
Dr. Paul Kononoff (21:39):
So what we did see is on this is okay, I'm, I'm not, as everybody knows, I'm not a reproductive physiologist, but one of the things that we did see is that as you increased the deviation in fat, so basically more fat, we did see an increase in, in pregnancy rate. And you know, I actually Paul Frickey was visiting Nebraska. He's an alum of University of Nebraska. He just came by to check out his old kicks at the University of Nebraska. But I asked him, you know, Paul, are you surprised? And that, that we see this response? And he kind of said exactly what, what Mimi I hoped him to say or expected him to say, as a nutritionist, we know that that fat can have positive responses in, in pregnancy rates.
Dr. Paul Kononoff (22:31):
And he says, you know, it's been documented in the past and things are tracking in the, the direction that, that you'd expect. So, so FAT did have an influence on pregnancy rate is improving it. The other one is we did see that as there was a positive deviation in crude protein, pregnancy rate did decrease. And again, bill, you know, when we were working on Nasem you know, we had talks about reproduction, and obviously we had limitations in stu nutrition and reproduction studies. But there is some information out there showing that feeding too much crude protein can affect pregnancy rates. And so maybe not expected or maybe that was expected, certainly not at my area of research, but showing that there is some information out there showing that crew protein reduces pregnancy rates, and that is indeed what we observed.
Clay Zimmerman (23:30):
Do you, so related to that, Paul, I'm do you know what the months were in these herds?
Dr. Paul Kononoff (23:37):
So we did, I don't think we actually had mus in, in our data, but you're right, that's obviously something that people, you know, when you're driving around on a truck from farm to farm, trying to link not only nutrition, milk tank, bulk tank data with, you know, what you're seeing on the farm. So, but we didn't have that here. There is mention of months in the paper, you know, saying is that can be an indicator.
Clay Zimmerman (24:06):
So the, so the, the milk component data came from, that was all from the bulk tank data,
Dr. Paul Kononoff (24:13):
Correct? Correct, yes. Yeah.
Clay Zimmerman (24:15):
What about the, what about the milk yield? Was that, was that bulk tank data as well?
Dr. Paul Kononoff (24:20):
So yeah, that was all bulk tank data too. So we relied heavily on you know, the information that we were, we're getting back from the bulk tank data, and then also cow count too on the farms as well. So I think the other thing you that you have to understand is, you know, these are large farms you're dealing with how many cows are on that farm? And we didn't break it down. Some of these farms had, you know, a lot of pens. And so we didn't look at things on an individual pen basis or stage of lactation data. It was pretty gross measures. Basically what's leaving with the farm from what we have on the farm and what we're doing on that farm.
Clay Zimmerman (24:56):
And the farms were basically, what, three, 3000 to 30,000 cows,
Dr. Paul Kononoff (25:02):
Right? Yeah, that's right. Yeah. Yeah. Eight farms in total. I should mention that. Clay.
Dr. Bill Weiss (25:08):
I guess one thing that really kind of surprised me in table two, you, you show the stuff by farm, the deviation by farm, and they're actually with one or two exceptions, they're all about the same. Yeah,
Dr. Paul Kononoff (25:22):
Yeah.
Dr. Bill Weiss (25:22):
There was very little de deviation farm being farm, farm to farm deviation. Did that surprise you or, yeah. Do you think it's a factor of the diets being kind of common, but
Dr. Paul Kononoff (25:33):
I mean, I think that that's a factor of how people generally deliver feed on the farm. It's pretty similar, you know, it's pretty common to overfeed those, deliver those nutrients. There was one particular farm, farm six that stood out, and so there was some difference. But you're right for the most part, those farms are fairly consistent in what was being delivered. But there's always a, you know, there's always one farm that really sticks out that farm Six, for instance, they clearly had some some, some decisions that were made on a consistent basis that was different from the rest of them.
Dr. Bill Weiss (26:13):
So, you know, on a consistency basis, you'd argue probably farm six may have been the more consistent 'cause they're less. But I don't know how big the negatives
Dr. Paul Kononoff (26:23):
Are. Right. Yeah, that's true. Yeah.
Dr. Bill Weiss (26:28):
I guess the other, just outta curiosity, you know, we're, as nutritionists, we're generally much more concerned about deficiencies, negatives, and you gave positives. So did you, why, why did you just choose positive? I would've choose negative deviation, just 'cause I always think those are worse, but it's curious why you picked positive deviations. Neither one is,
Dr. Paul Kononoff (26:50):
Right. I mean, yeah. Yeah. I mean, so we basically I think, I think the reason why the positive came in in is because that's, that's how it was calculated. So if the deviations came in negative, we would've calculated that. So, so I know. Yeah.
Dr. Bill Weiss (27:06):
Yeah.
Clay Zimmerman (27:08):
So, so Paul, the other other thing I thought was interesting, you looked at feed conversion.
Dr. Paul Kononoff (27:13):
Yeah, yeah.
Clay Zimmerman (27:15):
And so as the deviation went up with starch, it improved feed conversion, right?
Dr. Paul Kononoff (27:23):
Right. Yeah. So that basically says that as you increase starch, you're gonna improve feed conversion. And so I think, again, that's probably what we're expecting. What you're getting there is simply more digestible energy for those animals to, to utilize it and then to convert that into, into milk. But on, on the feed conversion thing we also saw a very similar in, in in responses to milk yield. We also saw a reduction in, with the reduction in deviation in NDF, we saw improvements in feed conversion as well. So the more NDF you added feed conversion did go down.
Clay Zimmerman (28:09):
So, so Paul, when it came to feed conversion, as the deviation increased with fat in the diet, it reduced feed conversion. Why, what do you think is happening there?
Dr. Paul Kononoff (28:22):
Yeah, that one I think took us a little bit by surprise. You would think that as you added more fat, that you would supply more energy and feed conversion would increase. But I think you know, one thing that I should clarify is we don't know the source of that fat or even the fatty acid profile. There was one particular farm where there was an extremely high deviation of fat. They were feeding a lot of fat. And so it's, it's very possible that on that per, you know, in, in some of these cases, what you're doing is just for lack of a better description, screwing up the rumen in these really unusual far out cases. And then this is why we saw reduction in, in feed conversions. So aside from maybe just an anomaly in in in room and fermentation, I don't have a good explanation on why that may have happened.
Dr. Bill Weiss (29:22):
Yes. You know, another possibility with this type of data is collinearity. Yeah. It's correlated to something else that is actually causing It. Right?
Dr. Paul Kononoff (29:31):
Yeah.
Dr. Bill Weiss (29:32):
You know, since this isn't a designed experiment, you always have that risk in all these experiments.
Dr. Paul Kononoff (29:37):
Right? Yep. Good point.
Scott Sorrell (29:39):
Paul, were there any other significant unexpected results from the data?
Dr. Paul Kononoff (29:45):
I don't know if they were a lot of really unexpected results that we haven't covered here. I think I, you know, truly impressed with you know, it's so easy for us to sit in our offices and think about how easy it is to do this. One of the things as I was refreshing my memory on this study is it was even done, you know, during the covid times. And it, it really is amazing that you see, you know, what people are able to do, how much food they're able to produce on these farms, and really, you know, how good of a job they actually do in making sure that these animals are, are fed and cared for. So I don't know if I'm surprised at anything, just impressed with what, you know, the dairy sector does on a daily basis, even in the face of the challenges.
Scott Sorrell (30:41):
Anything else, gentlemen?
Clay Zimmerman (30:44):
So Bill, I know you've done a lot of work in this area of feed variation. So how, how, how do the findings here compare to what you've seen in, in some of your previous work?
Dr. Bill Weiss (30:57):
It's, again, this is a source of variation. We never looked at. We've, we've always looked at ingredient variation, and this is the step above, and all this variation is additive. It's independent, so it doesn't, I shouldn't say additive. These are independent. So this is just one more source. I would like Paul to go back and redo this on a concentration basis though, too. because then again, that's, we can start looking at where, where can, if variation is that important, where, where are major control points? Yeah. Is it in the feed selection? Is it in TMR making whatsoever would like that just so it fits into this other data in addition to what he did here. It'll add back into the data we have and other. So
Dr. Paul Kononoff (31:46):
Sounds like a good companion paper certainly gives us something to do here. And we'll make it snappy too, I promise.
Dr. Bill Weiss (31:56):
Yeah. Guess the other one thing that I, I didn't see in here is, is dry man dry matter or intake deviation. To me that would be, again, is that just the one driving the bus or is there more to that than, than that?
Dr. Paul Kononoff (32:11):
So the, the dry matter content of the diet
Dr. Bill Weiss (32:15):
Is that, well, that, that what, when you do the concentration work, if you do it, yeah I would definitely look at that, right? Yeah. But then you'd also should have, I'm assuming dry matter intake deviation.
Dr. Paul Kononoff (32:27):
Yes. Yeah.
Dr. Bill Weiss (32:28):
And so if, if that, if they all always ate more than they programmed, is that what's causing, and then the differences you're seeing is related to concentration? Yeah.
Dr. Paul Kononoff (32:39):
Yeah. Yeah. In other words,
Dr. Bill Weiss (32:40):
The reason protein did this and starch did that would be a concentration deviation. I think I'd encourage you to write a companion paper. I think it would be great. I'll even review it. So editor, I'll review it.
Dr. Paul Kononoff (32:55):
We'll give you a, we'll give you journal loyalty points to that bill, so then your next paper in either JDSC or JDS will be free.
Scott Sorrell (33:07):
Well, that is that, gentlemen? Anything else?
Dr. Paul Kononoff (33:11):
All right. No, I think I know I was waiting for Bill. Bill always asks what you would do differently in this.
Dr. Bill Weiss (33:16):
That was my line. I thought that we were gonna have one more round of questions. So that's what I was gonna ask
Dr. Paul Kononoff (33:22):
You know, I was thinking about that. I was prepared for it. Obviously we were recipients of these data. And so what we would do differently, I'm not, I'm not really sure. I think one of the things I'd you always think if you had more data or more granular data, it would be better. I'm not sure this is the case, but it really would be fun to track individual animals over the course of lactation with some of these changes. And so, you know, if I had my wishes, we would have some individual cow data that obviously we didn't have. But I think that that can kind of help us understand biologically what's happening just on a more granular basis.
Dr. Bill Weiss (34:05):
Yes. That did raise one last question. Did you have daily, not cow, but daily farm milk production rates
Dr. Paul Kononoff (34:12):
Or, yes. Yeah, we did.
Dr. Bill Weiss (34:14):
Was that core, was the variation in milk yield related to the variation in any of these nutrients, or did you look at that?
Dr. Paul Kononoff (34:23):
So we actually didn't, didn't look at that. No, that's a really good point. We certainly just looked at the number of deviation in that 28 day period. So I think, yeah, there's some correlations that, that some gross correlations we could look at that we haven't. That's a good one.
Clay Zimmerman (34:40):
So the yeah, the THI for all, for all of the, for the eight farms and the, the, the one herd was definitely lower, you know, compared to the others. Yeah. But was there any did you look at it, was there any seasonal effect related to the deviation?
Dr. Paul Kononoff (35:01):
Yeah, so as you'd expect we ab yeah, we absolutely did see the, kind of the regular expect, you know as the temperature humidity intake or index increased, we did see reductions in, in milk yield. So that is indeed something that we saw here. I think, you know, in our statistical models you know, we, we included time in the statistical model, which obviously is you know, covering that effect of the THI, but it certainly did influence it. But that influence was covered by the, the time effect in our statistical model.
Scott Sorrell (35:48):
So I think this is a good good place to call last call. And, and with that, this has been a great discussion. I've enjoyed it and I know our listeners will enjoy it as well. What I'd like to do is just kind of go around the horn here and ask you guys what are, what are a couple key takeaways, key learnings from, from the, the paper. And clay, if you wouldn't mind, I'd like to start with you.
Commerical/Closing (36:18):
New research is changing everything we thought we knew about coline's impact on the cow and her calf and top scientists have a lot to say about it. They're presenting new research that supports choline as a required nutrient to optimize milk production choline as a required nutrient to support a healthy transition choline as a required nutrient to improve calf health and growth, and choline as a required nutrient to increase colostrum quantity. This new research is solidifying choline's role as a required nutrient for essentially every cow, regardless of health status, milk production level, or body condition score. Learn more about the science that is changing the game and the choline source that is making it happen. ReaShure Precision Release Choline from Balchem, visit balchem.com/scientistssay to learn more.
Clay Zimmerman (37:20):
Yeah, so I do want to reiterate, I love these short communication papers as well. They're, they're quick, easy reads and they tend to be very practical. So they're, you know, they're very relatable to the field in general. It's amazing. I mean, I'm amazed at the, I mean, this is a huge database, right? All of these eight, eight farms, but a lot of cows, lots of data here. So it's I'm very very intrigued by the findings here. Be curious to see some more follow up on this. Because there is a lot to learn here, but excellent job sifting through all this, all the data that's out there and, and and, and putting this paper together. So thank you. Thank you. Thanks Clay. Thank you, Paul.
Dr. Paul Kononoff (38:21):
Yeah, thank you.
Scott Sorrell (38:23):
Thanks for those comments. Clay, bill, anything you'd like to add?
Dr. Bill Weiss (38:27):
I'd like to mirror what Clay said. I think there's, you know, with this huge data set, I think there's a lot of stuff you could extract from this. I encourage you to, to look at other, other things from this data. 'cause It's, it's a, a unique set of data. You won't have this much data, I think, any place. Yeah. And I guess the one thing, if I had to pick just one that surprised me is this table two, and that's how these eight farms with one exception were, were just so similar. The deviations were just so similar. I was expecting a huge farm to farm variation. Me too. And that surprised me, which is both good and bad. As far as research goes, but, it really surprised me. I guess I would like, if you ever think of doing this again as maybe more diversity in dice to see if that consistency held up over greater, greater variety of dice. But it's, that just amazed me, that floor that gave.
Dr. Paul Kononoff (39:25):
Yeah. Yeah. No, I think, I think you're right. And, you know, you suggested or kind of indicated that, that that may have been in part because of the major feed that was on most of these farms could have driven that consistency. As far as a takeaway here, I think what I was just really, you know, when I think about this study and I mentioned it before, is just how you know, you, you can think about the, the rations that you have on paper on your computer, but just to you know, really dig into whether it's physical observations or in-person observations on how mixing techniques differ from farm to farm or trying to get, you know, some data as well. So I think that's as important as looking at the bulk tank or the amount of milk shipped is trying to glean some information on what the feeders are doing and just how consistent that is. 'cause It can be wildly different from what's on paper.
Scott Sorrell (40:23):
Yeah. Well, Paul, thank you for joining us this evening. This has been a lot of fun. You're always a great guest. Thank you for sharing this information, Bill and Clay, it's been a lot of fun. Always enjoy seeing you guys. And to our loyal listeners, we thank you once again for joining us. I hope you had some fun. I hope you learned something. We hope to see you next time here at the Real Science Exchange, where it's always happy hour and you're always friends.
Commerical/Closing (40:51):
We'd love to hear your comments or ideas for topics and guests. So please reach out via email@anh.marketing at balchem.com with any suggestions, and we'll work hard to add them to the schedule. Don't forget to leave a five star rating on your way out. You can request your Real Science Exchange t-shirt in just a few easy steps, just like or subscribe to the Real Science Exchange. And send us a screenshot along with your address and t-shirt size to anh.marketing at balchem.com. Balchem’s Real Science Lecture series of webinars takes place on the first Tuesday of every month with the top research and nutrition topics that will impact your business. We also include small ruminant, monogastric, and companion animal focused topics throughout the year. Visit balchem.com/realscience to see the upcoming topics and to register for future webinars. You can also access past webinars and search for the topics most important to you.