Guests: Dr. Lou Armentano, University of Wisconsin-Madison and Dr. Mike Vandehaar, Michigan State University. Co-host: Dr. Glen Aines, Balchem We are just coming off a very successful Real Science Lecture Series set of five webinars where we unveiled the new 2021 dairy NRC, a chapter at a time, and we can’t wait to have these sit-down conversations with each of our presenters and their guest.
Guests:
Dr. Lou Armentano, University of Wisconsin-Madison and Dr. Mike Vandehaar, Michigan State University. Co-host: Dr. Glen Aines, Balchem
We are just coming off a very successful Real Science Lecture Series set of five webinars where we unveiled the new 2021 dairy NRC, a chapter at a time, and we can’t wait to have these sit-down conversations with each of our presenters and their guest.
Dr. Lou Armentano summarizes the biggest changes from the 2001 edition to the 2021 edition within the energy chapter. He explained that their resource data shows that cows have changed, therefore the maintenance requirements for cows have changed. Since cows have biologically changed with genetic selection, they use more energy to maintain themselves. So that part of the chapter needed to be updated to reflect that change. (14:19)
Dr. Lou Armentano discusses the reporting of fatty acid content in the feed. He expands upon fatty acid digestion, and how those fatty acids affect milk fat. (29:52)
Dr. Mike Vandehaar discusses frame growth, which is the true structural growth of the animal, including muscle, bone, fat, gut tissues and gut fill as well as reserve depletion, which happens in all cows even when they hit maturity. The 2001 model wouldn’t change the equation solution when you entered structural growth as a factor. So they wanted to make sure that oversight was fixed in the new model. (37:33)
Dr. Mike Vandehaar discusses feeding high starch diets versus high fiber by-products to cows through their lactation cycles. (45:47)
Dr. Mike Vanehaar stresses that you can’t just trust the model you have to watch the cows and when you make a diet change try to figure out what the cows are telling you by observing and measuring things like milk production, milk compensation, intake, and body condition score. (1:04:04)
Dr. Lou Armentano says that they have been playing with a functioning model for only about a month and a half so he encourages those who are going to use the new model to speak up if something seems to be a mistake so changes can be made to correct any issues. (1:04:40)
As a reminder, we will continue breaking down the new 2021 8th Revised Edition of the Nutrient Requirements of Animals in podcasts releasing over the coming weeks. Be sure to subscribe so you don’t miss any of the new episodes. If you’d like to pre-order a copy and receive a 25% discount, visit Balchem.com/realscience and click on the NRC series for a link and the discount code.
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Scott Sorrell (00:00:07):
Good evening everyone. And welcome to the Real Science Exchange: the pub cast where leading scientists and industry professionals meet over a few drinks to discuss the latest ideas and trends in animal nutrition. We're just coming off a successful Real Science Lecture series of five webinars where we previewed the new 2021 dairy NRC a chapter at a time. And we're looking forward to having these sit-down conversations with each of our presenters and their guests. Hi, I'm Scott Sorrel, one of your hosts at the Real Science Exchange. Tonight, we have the pleasure of welcoming Dr. Lou Armentano, who tackled the fats and energy sections of the new dairy NRC for us during our webinar series. Dr. Armentano, welcome to the exchange. Glad to be here, Lou, before we dive into the meat of the fats and energy section, tell us what's in your glass.
Dr. Lou Armentano (00:00:59):
Oh, I have, because my scotch is apparently at a FedEx truck , okay. Probably weaving down the road. But this is a local brew. It's a fantasy factory, it's an IPA. And it's made here in Madison. In fact Hopelicious, which is an Ale Asylum beer, this was made in their- Hopelicious has expanded and they made a new brewery and this has made in their old brewery. So this is not made by Ale Asylum, it's called fantasy factory. It's awesome, it's got a picture of a cat with a 50 caliber pistol riding on a unicorn with flames coming out it, so it's an awesome beer. Mike, you've never had this.
Scott Sorrell (00:01:44):
I will definitely put that one on my my short list for sure.
Dr. Lou Armentano (00:01:47):
For sure, especially if you like IPA’s.
Scott Sorrell (00:01:49):
I love IPA's. Lou, it's been 20 years since the last NRC. And now it's, I guess it's called the NASEM. Can you tell us a little bit about the process that was employed and developing the energy and fat section of the new Dairy NRC?
Dr. Lou Armentano (00:02:03):
Well, we did meet as a group. We were assigned the individual chapters. I did the fat chapter and I guess I can say it was a co-author on a couple of other chapters. I actually was not a co-author on the energy chapter. But so we got together and we got our assignments and then we would do our work on our own. And then we would come back and say what we did, and put our ideas before the rest of the committee. And then you begin piecing the whole thing together, because it's all integrated, right? So the protein model had to have energy estimates in it, and those energy estimates had to be the final ones, right? So when they're developing there, everything in this NRC is, is based on some sort of meta regression analysis.To an extreme, it was done somewhat in the last NRC, but to an extreme, that's at least an order of magnitude more. Pretty much every chapter in some sort of regression analysis.
Dr. Lou Armentano (00:03:01):
And in order to do the protein, they had to have the energy values. And in order to have the energy values, Mike had have some estimate of fatty acids digestibility, and he also had to have information from the carbohydrate chapter. So all of this stuff is integrated and sort of at the end, that the protein sort of came at the end because it had to. So really the opportunity to go back and change anything was pretty minimal too. So, it did take a long time and it is integrated, but it's quite the process. I should say, too from an energy point of view- when you introduce Mike this'll be important. Mike was the lead on a USDA grant where we were trying to work on breeding values for improving feed efficiency in cattle . And Mike and I, and Rob Templeman and Kent Weigel and…how could I forget Wendy's name? Like, gosh, but the five has got together and, and a gentleman from Holland, we got together an we did this grant. So during that process, Mike and I really did a lot of talking about energy balance and it became really obvious that the last Dairy NRC was very confusing in terms of dealing with body weights, changes in body energy- it was just really hard to work with, right? And I think Mike really got inspired there to help make the new system simpler. So, so Mike and I actually knew each other in grad school. So we go back a long way. So we've talked a lot about energy during this process.
Scott Sorrell (00:04:36):
Well, I see you brought along Mike along with you as a guest tonight. Why don’t you go ahead and introduce him.
Dr. Lou Armentano (00:04:40):
Well, Mike Vandehaar really doesn't need any introduction. He was you know, blessed by being in graduate school as a young graduate student when I was a senior graduate student. So he had somebody to look up to and I'm sure he still admires me as his hero. He’s a faculty member at Michigan state. Everybody knows Mike is a author of Spartan, a lot of practical experience in energy and also in effort growth. So both areas that needed serious attention from the last NRC.
Scott Sorrell (00:05:13):
Yeah. So, thank you for that. Thanks for joining us tonight, Mike. First tell us what you're drinking, and then what did you find most interesting or perhaps most challenging about being the lead author on the energy chapter?
Dr. Mike Vandehaar (00:05:25):
Okay. Well, I am drinking a beer from Michigan called- from Short's brewery called hoomaloopaliciouss. And it is the best IPA I've ever had. It is my go-to. I'd love to challenge you with this one, Lou, because I've never had anything from Wisconsin close to as good as Michigan IPA,
Scott Sorrell (00:05:48):
Not even spotted cow?
Dr. Mike Vandehaar (00:05:50):
Oh gosh, I love the name but unfortunately, it just doesn't, it doesn't meet up with the name. What I found interesting or challenging was first off, I, as Lou said, I've been interested in energy a long time. I've been… when the first NRC- well, back in the, not the first, the ‘89 was what the sixth edition. I poured through that thing as I tried to make sense of how to put it into a model that could be practically used on farms called Spartan. And then when 2001 came out, I was frustrated by many of the things in there that were hard to implement in a model you would use on a farm. And some of them were hard to even figure out what the equation should be, things didn't always match. But now I understand, you know, it's a huge job.
Dr. Mike Vandehaar (00:06:45):
I realized that we started this in 2014, didn't we? I think so. So I realized I've been one quarter of my academic career, I've been working on NRC. Holy smokes! And that was this one. And this one, I was a reviewer for the 2001, and there were a lot of things that I liked about it and things that I found frustrating. And so when I got asked if I was interested to do this one, I jumped on the chance because I thought there's some things we can do better. And so I found it really fun. I mean, I got, I love working with Lou, Mike Galin, Bill Weiss, Richard Mann. We have just a bunch of people on our committee that are some of my favorite people to see when I go to meetings. So, you know, I really enjoyed the chance to work on this with them. Yeah, there were frustrations, lack of data here and there, but, but overall it was a rewarding experience and I, I feel pretty good about the product we came out with.
Scott Sorrell (00:07:52):
Excellent. Looking forward to the conversation tonight. Tonight we're also walking them back one of our special co-hosts. That'd be Dr. Glen Aines. Dr. Aines, welcome back.
Dr. Glen Aines (00:08:04):
Good to be here.
Scott Sorrell (00:08:06):
So I know you're chomping at the bit on this one. We've been talking about some of the questions you wanted to go over, but first things first what's in your glass tonight?
Dr. Glen Aines (00:08:15):
Tonight, I'm drinking a rum and Coke. This is a Cape Coral Florida. I love the name, The Wicked Dolphin. So yeah, it's quite a, it's quite a nice one.
Scott Sorrell (00:08:28):
Good. Well, tonight I'm drinking a Macallan 12, so I'm going with a scotch and I understand Mike that's actually, you ordered a scotch as well, or one of you then, and it hasn't made it yet.
Dr. Mike Vandehaar (00:08:41):
Like Lou’s, mine is on a UPS truck. It actually has been delivered to my house, but I was never here to sign for it.
Scott Sorrell (00:08:49):
Okay. So I'm having my Macallan tonight. Kind of the backstory of that is in honor of a couple of friends, that'd be Dr. Steve Putney and Dr. Neil Forsberg. We had the opportunity to drink a Macallan, a much older one and much more expensive one. And as I recall, we we've run up some pretty large tabs with that. And I'm also honoring Dr. Neil Forsberg tonight because he, he wrote a book, and it's called Climbing Kilimanjaro: Escaping the metaphorical couch. And so it's available on Amazon, and that's where I got mine. It's a very interesting read, especially if you know Neil. And if you don't know Neil, I recommend a meeting him sometime. He's a great gentleman and a good friend. So with that, why don't we we get started. Mike, can you summarize the biggest challenges in the fats and energy sections from the 2001 edition as a compares to the one that's coming out?
Dr. Mike Vandehaar (00:09:49):
I'll just say the things that I we really wanted to fix with the 2001 version was for one the discount factor was very difficult to make sense of. And it, the discount, applied to the whole diet digested energy value. In the texts, they talk about that it's, that you use a fat corrected TDN value to adjust with increasing levels of intake to adjust or depress the DE value of a feed- of a diet. In the actual program, they didn't do the fat correction. In Spartan, I did it the way the text said to do it, not the way the program did it. And, but it was pretty clear that it depressed adjustability too much at high levels of intake. And what was also kind of frustrating is that at high levels of intake, if you were trying to use this as a formulation program, and you put in that you had a diet that was mostly forage, once you got to a four times maintenance intake, it didn't matter if your diet was mostly forage or half grain, half forage. Because once you had a higher TDN value of the diet, intake was depressed much faster.
Dr. Mike Vandehaar (00:11:24):
Now, of course, cows couldn't eat at four times maintenance intake, but the program as a formulation program didn't know that. So when I would try to use –
Lou (00:11:32)
Mike, you said intake was depressed. You mean, digestability was depressed, right?
Dr. Mike Vandehaar (00:12:38):
Oh yeah, I'm sorry, I'm sorry, digestibility was depressed. But when you're trying to teach students about balancing diets, they don't always have the practical experience to realize that, okay, well, a cows not going to eat enough energy yet if she's fed an all hay diet, right? So how the digestibility depression work was just kind of frustrating, and hard to implement. The other one was that there was some simple things like protein was overvalued in the 2001 system. In the 1989 system, we put everything on it like a TDN value. So protein and starch had the same energy value, but in the 2001 system, protein had a 20, had an energy value that was almost 20% higher than starch because the-. Right, when you look at, when you look at the NE value of protein, or if you would go to the grocery store and you look at protein and carbs, they're both about 4 kCals per gram on the grocery store label, right?
Dr. Mike Vandehaar (00:12:45):
And that's because if you use protein for maintenance, you have to get rid of the amino group as urinary nitrogen. Which takes away 20% of the energy, because we can't oxidize that amino group and get any ATP from it. Well the 1989 system didn't give protein any value other than if it was at maintenance. The 2001 system went completely the other way and said that protein is worth a lot more than starch. So as a formulation program, if you want it to meet the energy requirement of a high producing cow, one way you could do it with 2001 was feeding of a really high protein diet, because protein was worth more than starch.
Dr. Mike Vandehaar (00:13:28):
And we corrected for that. So I was happy we had a- we're working with Ermias Krebbet. We put in a correction so that when you go from DE, you subtract out your expected urinary energy loss, you subtract at your expected methane loss, and you g NE for the diet. I think those are both, you know, are they perfect, right? Will we improve on that in the future, for sure, but we at least have a framework now that can be built upon with better numbers in the future. This is the way biology works. And the old system didn't really work the way biology did. I'm sure there's something else that'll come to mind, but those are a few that, that I was thinking about
Dr. Lou Armentano (00:14:19):
From my perspective, I'd like to make two comments on the big, well, maybe three comments or big changes in the energy chapter. One is that, and using a lot of data from the USDA and also existing published data I believe, the cows have changed. So the maintenance requirement for cows has changed. It hasn't changed because we started figuring out a different way. It changed basically because the cows have biologically changed with genetic selection of cows for being more potential for milk production, and more dairy like, those cows just use more energy just to maintain themselves. So that's a big change. And probably, I think Mike gets defensive about making the change from .08 to 0.1, but probably should have been more because it's still changing. So that's one big thing.
Dr. Mike Vandehaar (00:15:14):
Can I comment on that before you go on?
Dr. Lou Armentano (00:15:19):
Of course, yeah. I’d expect it!
Dr. Mike Vandehaar (00:15:20):
So, so yeah. Lou is exactly right on that. Because but what's interesting is our 0.1 came from a re-evaluation of the Beltsville data. And the 0.1 was for the data from night, the mid 1970s to about 1990s. And that's what 25 years ago? And in our feed efficiency project Rob Templamaan’s analysis of trying to understand what are the things that influence dry matter intake in high producing modern cows, cows from the last 10 years, or even five years, it looks like the maintenance requirement is higher than 0.1. Perhaps we should have done that…. Yeah, we just, weren't sure we were ready to,
Dr. Lou Armentano (00:16:06):
It's a everywhere throughout something like this, you know, we say it needs to be data-based, right. Well, the data is all in the past, right. And here's one place where maybe we could've, we could've, and I’m not criticizing what mike did, you know have to justify what you did and the number has to come from somewhere. But it's one of those places where maybe we should have skated to where the puck was going to be instead of where it's been , right. But when you base things on data, you're automatically looking back. I want to say a couple of things about the energy adjustments. So one of the things that became very confusing, so the reading the energy chapter and trying to integrate the fat part into the old energy chapter in the NRC Seven, it literally gave me a headache every time I read it.
Dr. Lou Armentano (00:16:54):
Okay. And I'm sure I've read that more than any times, more than any other person on the earth. And I still confused, okay. Because I think there are things in there that it says it does one thing and it does another, but it's just very, very confusing. And there's also the little exceptions for fat that drive you nuts. It's just impossible understanding. It really is. Well, you know what, I'm kind of, I think if I can't understand it, nobody again, maybe somebody down, but I can't. So I'm glad to change because now I don't have to understand it anymore, okay. A new one's much, much, much simpler. The other thing is that, one of the things about fat in particular, but with every nutrient, the DE to NE conversion was done on a nutrient basis , okay. In the current NRC eight, we get the DE, we adjusted the DE for intake.
Dr. Lou Armentano (00:17:45):
There's still an intake effect that reduces the digestibility, it's just smaller than it was before. Because it was too big before. And then the DE to ME is based on the amount of nitrogen and methane that's going to be produced by that diet, okay? And so it's a holistic integrated evaluation of the diet- not the nutrients themselves. And that's an important distinction because actually for fat, it's very important because in fact, fat has associated effects where the decreases methane production. So it actually has an associated effect that increases the DE to ME conversion for all the nutrients in the feed. Right? So that actually in the old NRC, the fat DE to ME was a hundred percent. Now it's actually more than a hundred percent, if you will which is kind of hard for people to grasp. The other thing I want to say is one more point that people may not quite get. In addition to simplifying the whole idea.
Dr. Lou Armentano (00:18:45):
I mean, intake now is expressed as a percent of body weight, not as a multiple of maintenance, okay. That makes it way easier, and I'm sure no less accurate to do it that way, okay. It's just simpler, okay. And the degree of depression has been reduced- it only reduces carbohydrate, right. Fiber and, and a starch, right. Digestibility, it doesn't affect fat digestibility or protein digestibility, right. So,and the other thing that's different even. So the depression is less, it's limited to certain nutrients, but a really important thing is that the, what is considered the baseline is no longer maintenance , okay. The baseline is now accounting 3.5% of body weight. So that's likely the cow that's producing, you know, a reasonable amount of milk , okay. So when you look at the adjustment, you know, from that cow to a really high producing cow in the herd, it's not nearly as big of an adjustment , okay. And that's partly because the depression is less, but also the base is much higher than it was before. So it'd be easy for people to get confused. You know, in some places it's not gonna adjust at all. While the depression is still there, it's just built into the baseline, 3.5X. You actually can get elevation if you have a lower producing cow, the digestability will actually go up from base if she's eating less than three and a half percent.
Dr. Mike Vandehaar (00:20:10):
Or if she is eating a low starch diet. You’ll definitely see that.
Dr. Lou Armentano (00:20:10):
Yeah,so I think those are important differences that are you know, people just need to realize it's doing that. But the whole thing, and this is so funny. When I read, when I was going over this and reading the energy chapter, it just doesn't give me a headache, you know,nd that's worth a lot , right. If it's more accurate, great. If it's easier to understand, that's great too. So,
Dr. Mike Vandehaar (00:20:44):
But you, but you now drink higher quality alcohol, Lou. Is that part of it?
Dr. Lou Armentano (00:20:50):
Yeah. Well, at the level I drink, it doesn't matter.
Dr. Glen Aines (00:20:57):
The relative importance, nothing in these equations. Where does the data from the literature come on?
Dr. Mike Vandehaar (00:21:03):
Yeah. Good question, yeah. Ermias has had a paper. Well, he was on the paper.And he had, and they had a an equation that was based on amount of fat in the- well, a number of factors that fat influenced NDF adjustability. Predictors of methane and NDF was in there. And one thing that we did do is he went back. I said, because I thought, why don't you have digestible NDF? That's what, that's what matters here, it’s not NDF. If NDF isn't digested, we don't care. It doesn't affect methane, or it shouldn't. And he went back and put in digestible NDF, sometimes they have, I don't know how accurate all of their values were for it adjusted NDF, but that, that equation did a pretty good job of predicting methane. And I heard Lou’s talk from last week and, you know, probably it's possible some fatty acids do more than others. That's not in that equation. So there's room for improvement, but I think we have the basis for a good system to move forward on.
Dr. Lou Armentano (00:22:17):
So, from my perspective, and actually I think maybe Mike's too, you know, we kind of thought, oh, the last one, our NRC was fairly favorable to fat, right. So we definitely didn't go in there trying to make it better. I didn't anyway. You know, we had made this decision early on that the DE to ME would be done at the trial level. So I actually had nothing to do with the I'm not, I'm not questioning the final equation respect, but I had actually nothing to do with the methane equation , okay. The selection of it. And didn't know fat was going to reduce methane that much until the other committee members came back and reported on it. So it's very interesting using all this metaanalysis to drive this makes sense, right? Cause these are trials that people have fed and then recorded.
Dr. Lou Armentano (00:23:02):
Now they're not the average of all diets fed in the world, and not the average of all diets fed in the US. They're just the diets where people happen to measure methane production and also, you know, measure these inputs. So I was a little nervous, you know, cause you, you pick out the best equation and you know, maybe the first equation. So I think the final equation has got, actually, it's got dry matter intake in it. It's got digestible NDF in it, but of course, you know, NDF does make a difference, right. Because it's part of the dry matter intake , right. And then it's got fat. And I thought well, you know, what if like the next best equation, which is like almost as good doesn't that fat in it, okay. But when we were presented this, the five methane producing equations had fat in them.
Dr. Lou Armentano (00:23:49):
So, you know, there's really no reason to pick any other equation. They're all gonna have fat, and all with a negative effect on methane. But it wasn't necessarily from a database, that was just to determine the effect of fat on methane , right. It was, it was from a broader database because we needed one equation that would estimate methane from all the dietary inputs. And it's one of the things that happens. I mean, we have the intake equation. We're not doing the intake chapter right now, but the intake, the NRC predicts intake based on diet , okay. Has one based on cow and a new one that's based on diet. And Mike Allen put that equation together. And when he did fat, did not show up in that equation , right. So he looked for all the things that a diet that affected intake, and fat didn't when you're looking for what overalls a diet and explains things, you have to realize things in diet are correlated.
Dr. Lou Armentano (00:24:47):
So you don't want to put a lot of correlated independent variables in an equation. Well, both Mike Allen and I, when we did searches in the literature where people fed fat, that was the nature of the experiment. We did find it, a reduction in intake. So those are two different questions, right? I mean, one is sort of limiting itself a little bit to cause and effect equations where you fed fat. But then of course, we didn't look at all the other things that were changing in these diets, right. But then if you look for predicting intake overall, fat didn't make the grade compared to other things in the diet that were more important and presumably were correlated with fat. So same thing happen with the methane. We know methane production, we have one methane production equation that affects the energy chapter and affects fat energy values and affects environmental issues. And it's the best one we can come up for and fat is not in there by a fluke , okay. If the top five equations include fat, it belongs in there. Now whether we have exactly the right number, whether itreflects all fats, you know, equally… that's a good question.
Dr. Mike Vandehaar (00:25:52):
I like, Lou said, I'm not a proponent. In the past, I’d not been a big proponent of adding fat to every dairy cows’ diet. One of the, one of the things that, yes, the model is integrated, but on the one hand. So it's integrated, but to some extent we haven't seen how all of it fits together until just recently when the software was really developed. So up until that point, I, yeah, I assumed fat was going to increase the NE density of a diet, not just because it has a higher energy density itself, but because of its effect on decreasing methane. But now that everything's together, we see that, and part of me was thinking, oh gosh, I didn't, I didn't mean to put together a model that did that, but that's what it is. And the only thing I would add to that is, you know, both Mike’s, Mike Allen has had a, a nice publication from 2001 that showed that fat can reduce intake.
Dr. Mike Vandehaar (00:27:06):
And this latest paper that we did on looking at dietary factors that can affect to predict intake. One of the things that was difficult about that paper was that what we feed cows is highly correlated with how much milk they produce. So the diets for- and we, in that model milk production is important for predicting milk intake. So you had to come up with some complicated, advanced statistical techniques to try to make sense of how do you deal with the fact that we have, we have cows fed that are producing a lot of milk, or fed different types of diets than the cows that are not producing much milk. And I would advise people to read the paper Suza Et al. No, I think Mike’s first, he's the first author. So Mike's first author on a paper. I did, I advise him to read that, but it's a, it's a nice paper. It does the best job of any equation yet at predicting intake based off diet and animal factors, but it's not perfect. And we know that high-fat diets can depress intake sometimes. So when people use the model, they just need to be aware that, okay, remember, fat can depress intake. This model doesn't show that in that intake prediction. So don't just do the thing that looks best to the computer. Use your head.
Scott Sorrell (00:28:42):
So far, gentlemen, we've just been talking about fat as an energy source and we'd lumped0 we at Balchem- lumped the fat and energy sections together, just cause we wanted to limit this to five webinars in the series. But, but we know that fatty acids are much more than energy, or can be. They're very biologically active compounds. And I'm thinking back to some research done by one of your colleagues, Dr. Vondahaar and Adam Locke. How did you guys account for that? Or did you account for some of those issues in, in the NRC.
Dr. Mike Vandehaar (00:29:20):
Thinking about partitioning and how some fatty acids can alter partitioning? We did not even attempt. We talk about it in the energy chapter, but we did not attempt to model that. That's something I hope that we can do a better job of in the future. You know, we know that starch can affect partitioning of nutrients between, say body repletion and milk production, but we did not attempt to include that. Lou?
Dr. Lou Armentano (00:29:52):
So we have a literature view that's hidden in an Australian nutrition journal that looked at different fatty acids. So one thing that's definitely in the new NRC is we switched from ether extract to fatty acid. And so when we did that, we are actually reporting the fatty acid content of the feeds. And we do that because we think it's important , okay. The crunching number part of the model just talks about total fatty acids digestion, and it does not in any way, you know, if you change oleic to linoleic to linolenic, the result of the model is not going to change , okay. However, the chapter talks quite a bit about how those fatty acids do affect milk fat. And you know, the, the bottom on the story is that whenever you feed CA 18 fatty acids, and generally there's not a lot of feeding of C 180.
Dr. Lou Armentano (00:30:54):
So when you feed C181 or C182 or C183, what always happens is that the milk C18 yield okay, goes up. But the milk short chain fatty acids that are made in the mammary gland goes down , okay. And that effect is clearly bigger for linoleic acid , okay. As a matter of fact, you would need so much linolenic acid that you actually start reducing milk C18 yield. So it's sort of a curvilinear response. So that's discussed in the chapter. You know, there's a report that shows you the fatty acid levels in the chapter. And, you know, by all means, if you run this model, right, and then the cows do something different, right. You know, the cows were wrong , okay. Guaranteed, right. Or your diet is not really what you said. It is. No, it's not, you know, the model's wrong , okay. But if the model's not predicting something, there's gotta be a reason for that.
Dr. Lou Armentano (00:31:57):
Okay. And so that's when you need use your brain, and you need to look at the intakes the cows are actually doing. You need to look at, you know, is my linoleic acid content in this diet too high? Maybe it's too low. It's an essential fatty acid. Right? So those outputs are there. They're fairly prominent and people, and they're fairly accurate now. Cause we have, you know, actual measurements of fatty acid and to measure total fatty acid, you measure the individual fatty acids and they add them up. So, so the individual fatty acids data is there. People should react to it, but the model is not going to do it for them. Another thing that's also very useful in that respect is that you can now get from DHI, you can actually get a profile of the deNovo, the C16, and the C18 fatty acids in milk.
Dr. Lou Armentano (00:32:49):
And that's very informative about what's going on. Because what's happening most of the time when you add oils is that the C18 in milk is going up and the shorter fatty acids are going down. And if you look at total fat, you would say nothing's happening , okay. But something is definitely happening almost all the time. And that's again, discussed at length in the chapter, but there's not a single numerical equation in there that would do it in the model. So you have to use this. Has anybody noticed that that Mike Vandehaar, the Dutch guy talks with his hands all the time, and that Lou Armentano, the Italian guy, doesn’t?
Scott Sorrell (00:33:25):
Hey Glen, I was going to ask you. You listened to Dr. Armentanos presentation. What was maybe the key takeaway that you came away with, key learning from that? And then what's a couple things that maybe weren't answered.
Dr. Glen Aines (00:33:39):
I think probably the fatty acid conversation was probably one of the most interesting parts of it, quite frankly. You have a situation where essentially fatty acids are not suppressing digestability correct?
Dr. Lou Armentano (00:33:53):
Fatty acids are not depressing NDF digestibility. And that that's not really changing anything, that was just sort of overblown in a couple of reviews, right? The fatty acids that depress NDF, they just believed were like C14 , okay. Which also causes cows to crash miserably, or at least in the US.
Dr. Glen Aines (00:34:12):
Well, I think that the challenge for the nutritionists is going to be, as you say, maybe using the noggin a little bit, because if the model doesn't tell them, if you feed too much fat, I mean, conventional wisdom used to be whatever 6 or 7% would kind of be your upper limit. Because everybody was concerned about depressing dry matter intake and whatnot, that some people are gonna look at the model and make bad decisions, right.
Dr. Lou Armentano (00:34:40):
We did leave several percent of the chapters sort of recommendations.
Dr. Glen Aines (00:34:43):
Okay. So that was, that was actually one of my questions. Is there still some guidance in the system?
Dr. Lou Armentano (00:34:48):
Yeah. So, I mean, we actually left 7% in as an upper limit. It's rarely economical, you know, even to be quite honest, I think it's really economical to go to that level , okay. Most people do that because they receive a reproductive benefit. Which may or may not be there, and I certainly can’t come to a conclusion about that. But you also have to realize that the dataset- so, so there was, you know, I should say that there was one, there was a slight depression of NDF digestibility, okay. I think there was one unit depression for each unit of fatty acid that was added , okay. So it’s not none, and that was for the, I think, free oil. Palmitic acid, for example, increases digestibility, which is very interesting , right. And you have to realize that, you know, scientists are, I think are very conservative.
Dr. Lou Armentano (00:35:33):
Okay. When I do an experiment, I like to hit it with a hammer. So I'll, I'll go some real negative controls and some... because I want to see a difference , okay. You know, and then I, I, I want to look at the treatments in between the negative and positive control that are different. Scientists are pretty conservative, so there's just not a hell of a lot of data out there where people were feeding more than 7% fatty acids , right. Because they've all been told not to. With a linear effect, if you go much beyond that, then it may be a problem , right. But it's not really, you know a cutoff, like a cliff, say. And I think that's what's very confusing about the fat thing. People want to still think of it as a ramp. Right? Well, this depression in short chain fatty acids starts right away.
Dr. Lou Armentano (00:36:15):
Okay. As soon as you start C18 to a diet, you can get a diet as low in fat as you possibly can, which means taking out corn, right. Which is 5% fatty acid and putting in cornstarch, which has no fatty acid , right. You can do that and get a diet down to about one and a half percent fatty acids- it's hard , okay. when you add C18 to that diet, C18 goes up, right. You're very low levels of C18 in the diet, meaning exogenous C18 that goes up. But the short chain fatty acids go down, okay- right away. So it's not, it's a balance of those two streams, but they're both pretty darn linear across the range. So I don't know if I'd cut your question off, but that was…
Dr. Glen Aines (00:36:57):
No, no, no. That's, that's perfect. ,
Dr. Mike Vandehaar (00:36:59):
And I would just point out too, that the, if you use the 2001 model, you could do the same thing, right. You could have added extra fat. We had no, there was no maximum upper limit that it would prevent you from trying to feed , right. So the software is no different this time. The other we'll let you do stupid stuff.
Dr. Glen Aines (00:37:22):
I understand that. The other, the other thing that I was going to ask you guys about was if I remember, and I interpreted it right. In the presentation, you talked about that there's now an impact of frame growth on, I assume it's on-0 and I might've gotten it wrong- on lactation and milk production.
Dr. Lou Armentano (00:37:42):
Mike, go ahead.
Dr. Mike Vandehaar (00:37:43):
Yeah. And the previous NRC, they talk about this in the chapter, okay. That, that prema-parris cow could have both frame growth, which is true structural growth of the animal. Muscle, Bone, everything. Including some fat, including some gut tissues, including even some gut fill. That happens in a prema-parris animal, as well as you can have reserve repletion, which happens in all cows, even when they hit maturity. they talk about that in the chapter. In Spartan, I did with the chapter said, but I guess I didn't even know this. Lou filled me in on this, that if you use the model that came along, the disc that came along with the 2001, it did not let you put in any brain growth for a two year old.
Dr. Lou Armentano (00:38:32):
Well, you put it in, and you said how she was, and you said how big she was going to be when she was older. But if you change that gap, nothing changed in the program. I mean, I don't, you know, we do experimental diets and we use heifers, but we don't formulate different diets for the heifers and the cows , right, when we do an experiment. So I never really specially formulated diets for heifers. So I didn't know that for the longest time. And then I decided, I think probably when we started doing the feed additions, I don't even know when, but I just decided to change it maybe for a class. And I noticed it didn't matter if the heifer wasn't full full-grown or not in the computer part of the NRC model. It just didn't matter. So that was just an oversight, I think. But, you know, we have to realize that this brain growth is important, right?
Dr. Lou Armentano (00:39:20):
I mean even third,- I think it was came out of a study, a multi-university study. And even in third lactation, most of these cows are at weight, their full, mature weight, okay. And what, 40% of our cows might be first lactation animals , okay. So they're having a significant growth lag. They're still growing in second lactation and a little bit, they're still, you know, it's not much, but they're growing in third lactation. So you know, I compare a third lactation cow is more like you know, an 18 or 19 year old, you know. Their major growth has done, but they're still growing a little bit. So most of our cows, you know, still have some frame growth left, right? Yeah. More so, aged cows are going to be a little bit rare around here for some reason. So I'm going to pat Mike on the back, if he's not waving his arms too much. Most of the terminology; shrunk weight and empty weight… there were so many terms in there that just didn't need to be in there , right. And then he'd just gotten copied over from the beef model was the simplest way to do it. And it's not unreasonable, right, for dairy guys to turn to the beef people for growth, but it was just, you know. Their system's a little different, and it was just so unneedlessly complicated that, and then the separating out this change in frame growth from a gain, it's much better. Mike, you've done a great job. Now you can take credit for it and say exactly what you did, but it was great.
Dr. Mike Vandehaar (00:40:51):
Thanks. I just want to make one point about it though. And that is that because of the different efficiencies-. Because reserve repletion is-. The energy density of reserve repletion is much higher than the energy density of frame growth. Probably about twice as high, but the efficiency of converting ME to the retained energy of reserve repletion is also about twice as high as the efficiency of converting ME to the actual retained energy of frame growth. So in the end, if the animal gaining weight, it takes about six megacals of net energy per lactation to gain a kilogram, regardless of whether it's true frame or reserves. What is different is that the frame growth requires more protein, requires almost twice as much protein.
Dr. Glen Aines (00:41:40):
Yeah. Like the question, I guess, ultimately comes to the practical application. If you've got most of the, the average cow is what 2.6 2.8, lactations in a herd. The vast majority of your herd is physiologically immature. And then how should, you know, nutritionists be looking at it?
Dr. Mike Vandehaar (00:42:01):
You know, f you have the ability to do a two year old group, you should do a two year old group. And now, the program will support that those animals ought to be fed a higher protein diet.
Dr. Glen Aines (00:42:13):
The high percentage of your herd is going to be first and second lactation. Yeah,
Dr. Mike Vandehaar (00:42:18):
I think second, your second lactation. Yes. They're still growing, but it is relative to their total requirement. It's not, I don't know what the energy bit would be, but it's maybe 10%. So in the end, it probably, you could just have all of your lactation two and older fed the same diet, but it, it would, it would promote the idea that we really ought to have a two year group, two year old group.
Dr. Lou Armentano (00:42:47):
So college professors love to think of all the different groups of cows we could feed. And then nutritionists, is at least the ones in Wisconsin, you know, their model seems to be, well, if we only make one diet, we can't possibly get it to the wrong path , okay. So there's a lot of one PMR , okay. That's me. And then grouping animals, you know. If you try to group animals in a rotary parlor and trying to find the cow, she's in the wrong pen. I mean, you know, it's a nightmare. But we did some, well Victor Cabrera- thank you- did a field survey of how animals are grouped, right. And the two most common grouping strategies is a post fresh group , okay. And first lactation group. So that first lactation group is actually grouped , okay. They're actually physically separated. All right.
Dr. Lou Armentano (00:43:42):
So maybe now, if we came up with different protein levels for that group, they actually might feed a different diet to that group. It's not that they can't, it's not that they're going to have to group them all purposes, I mean there’s an awful lot of first lactation groups, because you know, people can see they're smaller, right. They might have, I know in our barn, we actually have some smaller stalls, a little bit shorter stalls that we put the first lactation animals in, and then they stay a little cleaner being in an appropriate size stall. And they're not around, you know, bigger cows that can beat them up when we have all Holsteins, right. If we have Jerseys, well then the Jerseys beat up the Holsteins, but with the heifers and the cows they're kept separate, so they could be fed separately. But it is sometimes like, it's like trying to push a wet noodle uphill to get people to feed multiple diets. So you're, you're grinning. So I, I assume seen this, oh yeah. There's a lot of opportunities for this post fresh group. You know, they're kept separate for management, and that's a place where you can focus on putting fairly expensive ingredients that, as long as they're not going to be in the diet for, you know, 300 days are well worth the risk, right. In that early post fresh group. and then the heifers could easily be treated differently.
Scott Sorrell (00:44:56):
How long would they be in that post fresh group Lou? Ideally?
Dr. Lou Armentano (00:45:00):
Mike, I think that you probably a better idea than I do. I think the numbers, you know, set- Mike’s putting up a three. So I assume 3 weeks. So yeah, 30 days, you know. And I think if a cow's doing well, she gets out, and if a cow's doing crappy, she stays in. And of course the pen is a certain size, right. So, I mean, all these things are, you know, you have a whole bunch of cows calving, then it might be two weeks where, you know, for the healthiest one. So you know, the flow of cows. Well maybe now with double off sync, we get cows pregnant in the middle of August more than we used to, I don't know. But you know, the flow of cows is not steady, right?
Dr. Glen Aines (00:45:48)
Well, there's a lot of other reasons for grouping like that, grouping cows. There's a lot of other reasons for grouping those cows, nutritionally speaking.
Dr. Mike Vandehaar (00:45:53):
Can I give a good example of one? This is when the, when you put into this model that you could feed a high starch diet that allows for greater intake in cows, but a diet that was say high in soy hulls or some other by high fiber by-product feed with lower starch, actually in this model will give you almost as high of an energy density as a high starch diet. Because starch depresses NDF digestibility. You feed that by-product fiber, you no longer have as much of a depression in NDF adjustability, and the energy value of a high soy hull diet and a high starch diet could be about the same, but the high starch diet typically allows cows to eat more. So when you get to cows that are not quite as limited by gut fill in how much they eat, the model would support the idea that this is the diet you ought to include more high-fiber by-product feeds. Because you get almost as much energy out of them as maybe a high starch diet.
Dr. Mike Vandehaar (00:47:04):
So feed- save your high starch for the, for the peak cows. And then if you need to, if you can find fiber high fiber by-product feeds cheaper, use those in your later lactation cows that have already hit their correct body condition score and you will also in the process, because the high fiber doesn't partition as many nutrients to body tissue, you could keep the cows from getting as fat. So, I think this model will say, we don't have that part, the partitioning part in the model, but if you read the chapter that idea gets promoted, and it makes a lot of sense to have another diet for cows that have hit their, their body condition score, where you want them to be. Milk production has maybe started to tail off a little bit. Give them a high fiber diet. And by high fiber, I don't mean forage fiber necessarily, but a diet that will still promote milk.
Dr. Lou Armentano (00:48:07):
You know, the database really doesn't have a lot of late lactation studies that, you know, people do crossover studies. and we tend to want to get reasonable bulk production when we're doing those studies. So we don't do a lot of crossover studies with late lactation animals, and we don't do a lot of full lactation studies. I think, you know, I don't get out enough. But we did do- I was involved with some field studies, reproduction studies, and management studies. And it's surprising when BST went away, you know, we expected to see a lot more over conditioned cows coming in, and we don't see that. So, there's something going on with intake regulation, I think, and maybe it's management oriented, I don't know. But if one group, if we want to feed one group PMR’s you know, if you just look at the calculation of how much cows are supposed to eat, and then how much energy is supposed to be the diet, some of them should be getting pretty fat.
Dr. Lou Armentano (00:49:09):
And yet they don't. So I know that Victor Cabrera was doing some energy calculations and I said, well, your energy can't bow. And so we looked at just sort of projecting the intake using the days in milk depression, which hasn't really changed a lot in this new version. It's the same. And you know, if you looked at how much energy density would be there for the one group PMR if you fed that animal out to 300 some days, she should be getting fat. So something must be happening there. So the obvious, the difference between partitioning of energy, there must be a, some sort of lipotat that's telling the animal that it’s getting too fat. I apparently lack that, but cows seem to have that.
Scott Sorrell (00:49:52):
Yeah. Lou, you kind of touched on the fact that there is not a lot of post fresh cow data. You guys can only build recommendations around data that you have. What what's some of the biggest gaps you identified in terms of research? What gaps do you need to fill with research? What would that be? Okay,
Dr. Mike Vandehaar (00:50:14):
So post, you mean not post-fresh but post-peak, right? Right. Well, partitioning, I think is one thing that we need to get a better handle on. And I think that we could start to, if you look at starch, especially fermentable starch, which even our intake equation doesn't have fermented starch in there, it has total starch. We just didn't have enough data to really use fermented starts well in the intake equation. But that, I think that by the next NRC, hopefully there will be at least the ability. If we're going to- right now, we predict energy allowable milk, right. In the model we could, we could do we could perhaps say, okay, here's the energy allowable milk, but based on the fermented starch, maybe unsaturated fatty acids in your diet. Here's what I think, here's what the model could predict would happen to partitioning and therefore milk yield. I think that would be an interesting thing to see if we could move toward that. We won't get it right, at least not completely right in the foreseeable future.
Dr. Lou Armentano (00:51:26):
So you know, one thing about, and this is the reason why transition diets have always been an issue, right? One thing about post fresh experimentation is that it's almost got to be like, it's almost gotta be like a randomized type statistical trial, okay. We do a lot of Latin square studies or some sort of switchback study, or at the very least we do a cover and adjustment for the data , okay. That's because identifying where that cow is in terms of reduction level really removes a lot of variance in milk production from the data. So that makes the trials much more powerful. And I think people think that these long-term open-ended studies are more powerful. They're not okay there. I mean, the biggest source of variation is cow. And I mean, what if for these genetic numbers, we, we needed like tens of thousands of cow records.
Dr. Lou Armentano (00:52:26):
Okay. Cow is a big, big variable , okay. The other thing that I'll say is I like some of the things that Mike Allen and now Adam Watt’s doing it. Maybe, maybe Mike Vandahaar as well. This looking at the level of production of the cows before the treatment, and then seeing how they respond to the treatment. Cause there are some really interesting interactions there. And of course that's very important from a term of grouping, right? If you have high producing and low producing cows, then they could respond differently to the diet. First of all, you'd like to have them in different pens, right? So you can actually see the different response. Right? Well, I suppose you could follow the cows individually by their- you couldn't follow their intake, but you could follow their production.
Dr. Mike Vandehaar (00:53:11):
Yeah. That's where the high producing cows will produce more on a high starch diet. The low producing cows did the same thing, whether the diet was high in starch or high in high fiber by-product feeds.
Dr. Lou Armentano (00:53:26):
And some of that's level of production, some of that stage of lactation, it's, it's hard to separate out.
Dr. Mike Vandehaar (00:53:32):
Can I say one thing about your- you mentioned that cow is the biggest variable. Now we have nutritionists don't do a very good job of using the information they have available to them from genomics. Because if we could, instead of always using pre, you know, the first two weeks of, of milk production for a cow is your pre-treatment and that's your, that's your covariate. And then you apply treatments and you see what happened. We could do more stuff where we started using what's the genomic prediction for milk production in this cow as our covariate in a model with different treatments.
Dr. Lou Armentano (00:54:13):
Yeah. It certainly helps, but it's not as, it's not as powerful as the actual.
Dr. Mike Vandehaar (00:54:17):
Not as good, not as good, I know. But we don't, we often don't even try. And we really ought to.
Dr. Lou Armentano (00:54:22):
A lot of people also misunderstand here. You commonly heard this phrase that if you got a pound at peak, it's worth 200 for the lactation. So, you know, you have to realize if you have two different treatments, right. And they're assigned to two different groups of cows in a, in sort of a long-term study. You might get, you might get a significant difference and maybe you got four pounds different at peak, right? Well, you got a significant difference. And there was a four pound difference in the groups, but maybe two pounds of that was due to the treatment. And two pounds was random , okay. You never know , right. You can have a six pound treatment response and a minus two pound random response , right. So, but some of that difference is random, and that random difference is going to persist for the whole lactation, right, whether you give them a treatment or not , right. So, you know, nobody probably ever would do this, but just take two random groups of cows and split them. They're going to behave differently. You know, even if they have the same walking distance to the pan, or, you know, the same pen size, you know, same stall size, they just react differently just because random, you know. And hundreds of cows is not enough to wipe out random cows, or those are ranked different from each other.
Scott Sorrell (00:55:38):
Gentlemen, has there been any key discussion areas we haven't covered yet that, that the audience needs to hear?
Dr. Mike Vandehaar (00:55:44):
You know, I was going to say one thing earlier about the value of protein in the model. In the previous model, the only non-protein nitrogen was probably overvalued, especially if you look at some pasture-based systems where the grass is fertilized with a lot of nitrogen, you can get pretty high NPN levels in that grass. And in the previous model they gave urea, urea was a special feed. It got, it, got an energy value of zero. All other protein sources, regardless of how much NPN they have in them, we didn't worry about it. So when you look at the feed test lab gives you an energy value for a feed, it may have a lot of NPN in it, and it just predicts the energy value based on nitrogen, not worrying about whether it's true protein or not. With the new system, you'll be able to account for that.
Dr. Lou Armentano (00:56:42):
I would like to say that this is a model, and it is not reality, right. The map is not the terrain. We use an awful lot of existing experimental data , okay, which by its nature is an empirical, and more of an associative than a causative , okay. There are a lot of causative things that clearly have happened in individual experiments, okay, that are not included in the model , okay. They just don't show up in the general associations for one reason or another , okay. So people, and I think the chapters discuss, I know the fat chapter does, and I think the carbohydrate chapter does. I mean it talks about critical group experiments and things that people have seen. They've really seen this, this really happened in the experiment, that's why they do statistics. And every time you're on a farm, it's your own little experiment.
Dr. Lou Armentano (00:57:38):
Right. So when we do meta analysis, we always put study in there , right. The study has gotta be treated as a random variable, it takes out a lot of noise , okay. But guess what, when you go back to the farm, it's like, you're another study. So all the noise is added back in again , okay. So, I would always go to farms: I think the model's very usable. It's very understandable. It captures broad strokes. And then the parameters come from real-world data where we have, you know, measurements, right. It's where these things were measured. And the treatments that are applied were, you know, whatever funding source the person had to work with , right. Or whatever, right. It wasn't necessarily your farm or a random selection of farms in the US or random selection of farms, you know, in the upper Midwest where a lot of the data comes from.
Dr. Lou Armentano (00:58:29):
So this is where, you know, my Ed Gallon’s written several papers called Minds over Models, and I think this is where you just need to be aware, you know. There's thumb rules and they're great , okay. You know, thumb rules are there to designed to keep you out of trouble , right. But those, the flip side of the thumb rules is conventional wisdom. And conventional wisdom, some people say that CW, I say, usually it's BS , okay. A lot of times it's just flat wrong , okay. And you know, one example I can point out too is, you know, if we look at total fat in milk, we come up with this one story. It's not true , okay. Clearly not true if we look at the individual short-term and long-term fat, so people need to be able to think out of the box , okay. The model is another kind of box.
Dr. Lou Armentano (00:59:18):
Okay. It's a good box, but it's a box. Listen to the cows, Terry Howard always told me he listened to the cows. They don't lie , okay. And you are going to see there's another thing in the whole energy carbohydrates section. I think that there's was a tension in the committee, and I think there's a tension in the industry. And that is, you know, are we losing acidosis? Right. You know, we see if the black and white cattle come in and they have a lot of liver abscesses, does that mean our herds are routinely asked about it? I think those of us who've measured pH on cows that we think are reasonably healthy, right, in our university herds, you know. We, our pH 5.5, we don't go off. We go, yeah., that's kind of what you expect it to be, you know, a couple hours after she ate a big meal.
Dr. Lou Armentano (01:00:06):
And so I don't know if the people would get a lot of milk out of cows or just the ones who managed to feed a diet that maybe nutritionally acidotic, but somehow all the other management comes in and they can, you know, surf on that edge and go really fast for a long time. And then other people find they have to back off because they can't manage it. I don't know the answer to that , okay. But I always talk about when you're feeding carbohydrates here, you know, to get more energy in the cow, that energy comes in the form of volatile fatty acids and volatile fatty acids are acids , okay. You know, they have a PKA of 4.8, they're acidic. So it's impossible, it's impossible to have all the energy going into a cow without having a high concentration of volatile fatty acids in the rumen. Just like it's impossible to optimize microbial protein production without having a level of ammonia. That's going to spill over into the urine, right. There's just innate inefficiencies there that if you want to operate, you know, full speed, then you have to live with some inefficiencies. And ultimately they're not inefficient cause high production, you know, dilutes out maintenance so much that, that’s like your fixed costs. And in fact it is economically and biologically efficient to go to these high levels. But yes.
Scott Sorrell (01:01:31):
All right. And with that, Stephanie has called last call. So what I'd like to do is ask each of you to kind of give us, you know, one or two key takeaway messages for, for our audience. And Glen, why don't we start with you?
Dr. Glen Aines (01:01:46):
Well, you know, I still think that the fatty acid discussion is going to be something that a lot of nutritionists are going to have to bone up on, and it's going to be a lot of new stuff on that. The other one, I think that is, I don't know, I think it's going to become more important is this, this whole, you know, ecology thing, right? The methane reduction in methanogenesis, and that somehow in some ways, some shape, some form, I think will push us towards better understanding of the impact of specific fatty acids on methane production in the rumen. I don't know what that looks like, but I think there'll be a lot of interest in that. Clearly in many states already there there's concern about the levels of methane production, and there'll be some efforts to utilize some of that information to maybe help.
Dr. Lou Armentano (01:02:36):
Yeah. Interesting that we really downplayed the effect of fat on NDF digestion, yet it's having a major effect on methane production , right. And that's kind of interesting, right? It's you know, maybe that's because it does it on protozoa, I don't know, right?
Dr. Glen Aines (01:02:52):
You brought that up earlier. I thought that was a good point.
Dr. Lou Armentano (01:02:54):
Well, and if it decreases the protozoa, what's it doing to choline flow? You know, I mean, I, you know, there's all those things are, and I’ll put a little plug in there for Balchem- but I mean, yeah, I think the other things would be hard. This, this is the energy fat discussion, but people wrapping their mind around the amino acid balancing is going to be a real educational opportunity, I think for people, too. So I think the energy chapter honestly, is just so much more straightforward and simple and easy to understand that it's actually going to be a relief
Dr. Glen Aines (01:03:22)
look forward to reading it.
Dr. Lou Armentano (01:03:28)
Yeah. It's, it's I didn't really realize Mike was that good a writer, but it's actually really quite good.
Dr. Mike Vandehaar (01:03:30):
I did have help from Bill. I just got to point that out.
Dr. Lou Armentano (01:03:34):
Bill's a fantastic editor.
Dr. Mike Vandehaar (01:03:35):
Bill kept cutting stuff.
Dr. Lou Armentano (01:03:37):
But I, you know, mostly chunk up the success of the energy chapter to the fact that Mike and I have been discussing it for so long that, you know, he finally learned something.
Dr. Mike Vandehaar (01:03:45):
Yeah, and for sure Lou does deserve almost all the credit for it. Probably.
Dr. Lou Armentano (01:03:50):
I think that was obvious anyway, but. No it really shows that Mike had taken the previous NRC and incorporated into Spartan , okay. That kind of nuts and bolts dealing with the model and at the same time dealing with reality it, it really showed. Very good job on the energy, Jeff. Right.
Dr. Mike Vandehaar (01:04:13):
Thanks. My last comment would be, is even though we think it's an improvement, I still would. I agree with what Lou had said. You know, don't, don't just trust the model. You gotta, you gotta watch the cows. And when you make a diet change, try to figure out what they're telling you. Sometimes it's hard to measure, but that doesn't mean you shouldn't try. Watch, watch the milk production, watch milk composition, watch intake, watch body condition score, whatever you can measure is going to be helpful, but watch the cows.
Dr. Lou Armentano (01:04:50):
And just realize we have not been playing with a functioning model for 10 years, okay. We've been playing with a functioning model for about a month and a half, okay. You know, so if you see something, say something is all I would say. Because there's bound to be, there's bound to be mistakes there. And I think the NRSP nine will do a good job of, of you know, fixing things that were just plum wrong, , right. And then identifying things that need to be improved.
Dr. Mike Vandehaar (01:05:17):
So both Mark Hannigan and I are on NRSP nine. And we'll, we'll be looking at if we see things that just seem like they need to be fixed soon, we'll be able to do that. Not of course to the official NASEM, but we can change the software if we need to. So, and it'll be some sort of revision of the NASEM software.
Dr. Lou Armentano (01:05:42):
And NRSP nine is separate from the national academy, but there'll be sort of hosting this. And they made some improvements in the last software. You know, for example, I think it was written to run-on DOSS or something like that, but we made it would work in windows 10 there. So that's absolutely essential that somebody there writing on this, and there's a place to put, I don't wanna call them complaints, but you know, observations.
Scott Sorrell (01:06:12):
Lou, any final comments? Yyou want to put a bow on this horse?
Dr. Lou Armentano (01:06:16):
Just, you know, as good as the energy chapter is, the fat chapters clearly better. But other than that. Again, Mike really oes talk with his hands a lot, which I find very, yeah. No, I appreciate Balchem doing this, it was fun experience. It was very useful.
Scott Sorrell (01:06:35):
Yeah. A lot of fun and yeah, I want to thank you, gentlemen. This has been long awaited, but it certainly did not disappoint. I also want to thank our loyal listeners for stopping by at exchange once again, and hopefully you heard something new and maybe something you can even take back to the farm to help your customers. As a reminder, we continue breaking down the new 2021 8th revised edition of the Dairy NRC over the next coming weeks. Be sure to subscribe so you don't miss any of the new episodes. If you'd like to pre-order a copy and receive a 25% discount visit Balchem.com/realscience and click on the NRC series for a link and the discount code. If you like what you heard tonight, please remember to hit the five star rating on your way out. And don't forget to request your Real Science Exchange. T-shirt just like or subscribe to the Real Science Exchange. Send us a screenshot along with your address and shirt size to anhh.marketing@balchem.com. Our Real Science Lecture series of webinars continues with the ruminant focused topics on the first Tuesday of every month. Visit balchem.com/realscience to see upcoming events and past topics. We hope to see you next time here to Real Science Exchange, where it's always happy hour and you're always among friends.
Dr. Lou Armentano (01:08:02):
And where's my bottle of scotch?