Guests: Dr. Mark Hanigan, Virginia TechDr. Jeff Firkins, The Ohio StateDr. Hélène Lapierre, Agriculture and Agri-Food Canada Our Dairy NRC series of Real Science webinars was very well received and tonight we are talking about the chapter on protein and amino acids.
Guests: Dr. Mark Hanigan, Virginia Tech
Dr. Jeff Firkins, The Ohio State
Dr. Hélène Lapierre, Agriculture and Agri-Food Canada
Our Dairy NRC series of Real Science webinars was very well received and tonight we are talking about the chapter on protein and amino acids.
Dr. Mark Hanigan discussed the sections his team worked on in the new NRC. First thing was updating the feed library. After the feed library was updated they identified other updates like the microbial equations and RUP equations, adjustability data for the RUP and for microbes as well as composition of the microbes. (6:49)
Dr. Jeff Firkens discussed the difference with the amino acid profile by accounting for protozoa flow which is important for lysine, because protozoa have a lot more lysine than bacteria. So they are attributing microbial protein sources as better sources of lysine. (22:02)
Dr. Hélène Lapierre discussed their updates to metabolic fecal output and urinary endogenous output since the previous data dated back to 1977. The updated data showed a large change. Endogenous urine output was twice as much as it was previously, and fecal output was much lower than it was previously. (36:36)
Dr. Mark Hanigan discussed the new milk protein yield equation and used the analogy of an assembly line. Each nutrient is a separate contributor to the assembly line and without a certain nutrient that assembly line will slow down. Once a little more of that nutrient is provided the assembly line speeds back up. (47:33)
Dr. Hélène Lapierre discussed efficiency and working with cows of the past, and their published data, to provide the specifications for cows of the future that are producing more. So the scaling factor should be based on current herd averages. (58:33)
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 podcast where leading scientists and industry professionals beat over a few drinks to discuss the latest ideas and trends in animal nutrition. Our Dairy NRC series of Real Science Webinars was very well received, and tonight we're talking about the chapter on protein and amino acids. Hi, I'm Scott Sorrel, one of your hosts tonight here at the Real Science Exchange. Tonight, we're welcoming Dr. Mark Hanigan. As the author of this section, we're looking forward to a very lively discussion with him and his guests. Dr. Hanigan, welcome to the Real Science Exchange .
Dr. Mark Hanigan (00:00:07):
Thank you.
Scott Sorrell (00:00:44):
Before we get started, can you tell us a little bit about your background, Mark. And how were you selected to be the author of the proteins and amino acids section?
Dr. Mark Hanigan (00:00:53):
Okay. You know, I really have no idea on that. I mean, it's probably like, who do they think they can talk into this, you know, massive amount of work? And it's too stupid to say no, so.
Scott Sorrell (00:01:03):
Yeah. Yeah, very well. And you're currently at Virginia Tech, tell us what you do there.
Dr. Mark Hanigan (00:01:10):
I am a professor in Dairy Science. I think we may be the last separate department, but we're in the process of merging with Animal Science.
Scott Sorrell (00:01:18):
All right. Very well. Mark, it's been over 20 years since the last NRC, now called NASEM, and I'm sure this required a lot of time and effort from both you and the rest of the team to create the latest edition. Speaking of team, I understand that you've brought several of them with you. Would you take a few moments to introduce them?
Dr. Mark Hanigan (00:01:39):
Yeah, the colleagues you know, people that did a lot of the work as well include, you know, of course, all of the committee members, but Dr. Jeff Perkins from Ohio State has joined us today. And so as Dr. Hélène LaPierre from AgCanada or AgriFood in Canada. I forgot what the, you know, what the appropriate name is.
Dr. Hélène Lapierre (00:01:58):
It's tends to change all the time.
Dr. Mark Hanigan (00:02:01):
Just when you get used to it. And you know, of course everyone did a massive amount of work. So maybe, maybe Jeff and Hélène had a better idea of how much work this was going to be. But in typical fashion, I underestimated the amount of work by at least three or four fold, you know, more than I typically underestimate.
Scott Sorrell (00:02:22):
Yeah. Understandable. So welcome Hélène and Jeff, I look forward to having a lively conversation with you. Hélène, I understand that you're taking this virtual pub thing seriously. What are you drinking tonight?
Dr. Hélène Lapierre (00:02:35):
I had to prepare something that was made from Quebec. So this is a mixture of fresh lemon juice. I was like. And a nut licquor, which is made in Quebec and a bit of maple syrup. So it's really refreshing, really dangerous, in summertime because you just feel it's like lemonade, but a little bit of ice. And it's really, really good. I even brought the bottle to show you because it’s really nice.
Scott Sorrell (00:02:59):
Oh, very nice. Nice. Yeah. Excellent. Really good. Yep. Jeff, welcome. It's always a nice to have somebody from The Ohio State University, I'm a grad myself. Well, wait a minute. I haven’t introduced Clay yet. Clay, he's back in the, in the cohost chair. Welcome. And Clay, what are you drinking tonight?
Dr. Clay Zimmerman (00:03:29):
I have a new apple cider here, courtesy of Stacy, our producer an apple cider from Kansas.
Scott Sorrell (00:03:36):
Mark to get us started, I’d like to dig into the approach and the process you employed to bring the protein amino acid chapter together. What was that process? Where did you start?
Dr. Mark Hanigan (00:03:48):
Well, I'd have to start off and say that, you know, this was an effort, you know, at least on the writing and you know, of the three of us really. And so I, I was not the lead on this chapter action. I was the lead on the modeling chapter. And so I ended up sort of being the caretaker of the equations, I guess, you know, when they were developed, and I did work on some, but it's certainly Hélène and Jeff had a huge part in this and they did way more of the writing on that chapter than I did. I, I made a small contribution.
Dr. Jeff Perkins (00:04:20)
One of the things I'll just jump in and say you know, we started from the beginning developing a bunch of metadata to be used. So there was a lot of, a lot of work. Some of that was by the NDNP, I guess that's how you say it. Mark was a leader of that. I joined in, we, we collected a lot of data and sort of, you would say cleaned it and refined it. And, and a lot of that was done with in coordination with the way Mark’s system was set up so that ultimately it could be used in this larger model. So there was a lot of background, a lot of players involved in that. And that really was a big advantage of this at this time, compared with the last NRC.
Dr. Mark Hanigan (00:05:08):
Yeah. Fortunately, Niel Bateman who was actually a postdoc with Jimmy Clark- and he's passed away now- but he was at LSU at the time. And later he was. He captured all the data from the last NRC. Otherwise, I'm not sure Jeff would have it because he, you know, he's the one that had it.
Dr. Mark Hanigan (00:05:26):
And it was, you know, some 10 years later when we sorta drug that back up and worked on getting it all cleaned up and getting all the diets sorted out as much as we could. And when we- that's the problem with all of this is that with entry of data, you're always going to have errors. And that even that data that had been around a long time, we found a few errors in that late in the process as well. And of course, all the data entry that happened to happen was Jeff had a student that worked on it, I had a couple students work on it. Mike had a couple of postdocs from NAMP work on it. I mean, I entered data. I mean, everybody's seemed so it just, it was a large effort to do that, to get started. And then once you have the data, then you can start asking, okay, what, what was wrong with the last set of equations, you know, or the last model and what parts needed to be fixed? I think well then, we decided that pretty much all of them had to be fixed and that they all had, you know, updates that needed to be done.But we didn't, we didn't throw out the system. I mean, it's the, it's the same basic system we had before with more amino acid stuff.
Scott Sorrell (00:06:38):
Can you elaborate on that just a little bit on maybe two or the three big sections that you identified first that you needed to work on first to, to, to overhaul?
Dr. Mark Hanigan (00:06:49):
Well, the whole thing is a bit of a pyramid scheme, right? I mean, you start off with ingredient information, and then you try to track that through the animals. So, you know, we had to, we had to get the feed library updated first. And so Jeff worked a lot with Paul Kanonoff on that and got the feed library updated. And Hélène and I tried to do stuff, but we ended up repeating things quite a few times. It seems like that it was like the movie Groundhog Day where we kept getting up and starting over again. But eventually we got there. But you know, the feed library gets sorted out. Then we identified that there was challenges, both with the microbial equations and with the RUP equations. And so Jeff took the leads on those. He also took the, you know, the lead on the digestibility, we needed to update the digestibility data for the RUP and for the microbes. The composition of the microbes needed to be updated.
Dr. Mark Hanigan (00:07:45):
I then Hélène worked on that as well. Once it gets absorbed, then we needed to divvy it up into the various processes. So I worked on the milk protein equations and Hélène worked on pretty much all the other equations for all the maintenance requirements. And, you know, I think Jeff, probably the growth sort of got left to the end. You know, it sort of trailed along later and kept raising its head again, you know, for, at least for me anyway, on the model side of it/. I t was like a bad stepchild that just wouldn't go away.
Dr. Jeff Perkins (00:08:09)
But I'll jump in for a second, Scott. One of the things that I think was really an advantage moving from the old system to the new one, the old one was really, you know, factorial in nature on a protein basis, but not on amino acid basis, that it, it had sort of a slope response way to do that with Lyceum methionine. The new one, you know, is much more in my opinion, sort of structurally related to a true rrequirements systems. So I think there was, by having virtue of all those data to be able to fill in the gaps that was, we were able to do that. So Hélène and Mark, really, in my opinion, that's what they really added that really beefed up this new one.
Scott Sorrell (00:09:04):
Can you talk a little bit about how you acquired the data. And I'm going to assume that you scoured the databases, you looked worldwide. And what's the process looked like for, for coming up with the data, putting it in a form that you guys can use and input into the models?
Dr. Mark Hanigan (00:09:24):
You know, you guys can feel free to jump in, but I mean, we had, you know, a couple of hundred studies to begin with from the old NRC. And then there were several efforts by multiple people to identify additional data. So, I think Jeff and his student went through and looked through all the flow data that had been published since that time. And anything else that got missed. And then Lou Armentano and people, a couple people in the fat chapter did a search, you know, for all the fat digestibility data. And then I had a student that looked for all the amino acid data. And then we left off you know, the credits list Roget Martineau, who's worked with Len for a lot of years, you know. Did another effort on his side, lus he did a tremendous amount of work on cleaning data and identifying problems. And so I, it just, I mean, there's probably so many people that got involved, it's hard to really lay your hand on everybody. It starts with the literature search, and then entering the data and then checking the data, trying to see if there's any entry errors. And also whether the studies, you know, a few studies got thrown out, cause they just like, there's something wrong with this study. I don't know what it can be, but.
Dr. Clay Zimmerman (00:10:40):
So how, how many studies were included for the protein amino acid model for the NRC?
Dr. Mark Hanigan (00:10:50):
Well it depends on the part, but for the, for the overall effort, we're looking at Hélène, I think it was 365 or 385 studies, something like that. There was over a thousand treatments, and I mean we pay attention to that more probably than the studies. But of course there was like 580, I think we use for rheumatologic flow. And then there was some of those were, you know, fat oriented, fat digestibility. They still had milk production, so we still used it for that, right, but they weren't protein studies. There was some fiber studies as well that we used. So I can't say directly how many of them were protein studies, but certainly more than 600 treatments.
Dr. Hélène Lapierre (00:11:33):
Yeah. And maybe I would, I would just tell as well that through this seven-year process, we've been often teased that it was taking a lot of time to go through all that. But we really felt that we wanted to start from the very beginning to assess where we were going and have like, the right data. So it took a long time. It just seems like database, but, you know, we took a long, long time just to fix that database. And what we'd realize is that sometimes we wanted to have too quick start, but then you do everything, but you realize that there were few things that you were missing database, a few errors, and then you have to restart everything over. So I think that it was really critical to limit of the whole model to have like a good straight database that we could trust, you know, from A to Z. It took a lot of time, and as Mark said, it was like for all of us, it was our time, the time of our students, of our postdocs that weren't supposed to put their time in that, but then we just pulled him in shipping.
Dr. Hélène Lapierre (00:12:40):
Nobody's listening now. But I think that this has to be recognize that yeah, it took some time, but we really worked hard to make it happen.
Scott Sorrell (00:12:56):
You talked about missing data. I'm curious as you're looking at the data sets, and you start plugging things together. Did you identify any huge gaps that we just don't have that information? And so on then I asked you what were those gaps? And then maybe a follow-up question, what needs to be done for the next NASEM that we need to follow up on it to make sure that those, those data are acquired
Dr. Jeff Perkins (00:13:23):
One of the, one of the gaps I feel is there, there really isn't very much reporting of actual amino acids themselves. We had to factor amino acids from different ingredients from a database. And so a lot of people aren't reporting those data anymore. We have to rely on doing that. We have to assume that amino acids have the same degradability in the rumen and digestibility in the small intestine of the RUP fraction as the protein itself. We, you know, one of the limitations we really would like to be able to say one amino acid is different than another, maybe depending on the feed. So, I'm not sure that's going to be fixed in the near future, but it sure would be nice to go back to having actual amino acid flows and some of that kind of thing. That's, that's one thing that comes to mind. So we, we made some, some nice efforts to improve it. I didn't do this, but Hélène, you know, did is, the way that you measure amino acids to account for how, how they're lost during the analysis process, that was very much improved. So there was a lot of real science gains in there that the techniques are there for people to use. So we just need to be able to find some people to get funding to do some of those key studies.
Dr. Clay Zimmerman (00:14:48):
So from a, as far as the ingredient database, what parameters were new in this version versus the ’01. I know starch was available now, and that wasn't available back in ’01 one. What, what are some other parameters that are new?
Dr. Jeff Perkins (00:15:09):
Well, I guess you know, starch is a big one. And so that definitely is one of the things I hope people can do in the future is do a better job of measuring the residual organic matter fractions. So some of that's laid out. We moved to fatty acid basis, so there's more fatty acid data for digestibility of fat. Just to, I guess, one of the big things is, you know, the previous committee really did a valiant effort to try to get data for digestibility of the RUP fraction. There weren't very many sources of data out there then. And so we really filled in that, so in the previous one, they just signal to users that we don't really know. They had these digestibilities in increments of five units. And so that's much more robust than it was since then, you know, Mark had a hand in, in some of that too, probably. But so anyway, the database itself is much stronger, but we still have a ways to go. In some feeds there weren't very many observations. So some of that could be filled in.
Scott Sorrell (00:16:18):
Just curious if the previous committee kind of left you a handbook guys, these are the things that we were unable to include in the last one. And this is some things you guys need to look at. And then, then did you put a little handbook together, some breadcrumbs for the next committee?
Dr. Jeff Perkins (00:16:35):
Well, I'll go first on that one because I'm in my office and it's kinda messy because that's my comfort zone, I guess. I didn't know I was going to be on camera. I would have cleaned it up a little bit. But anyway, I have this big box sitting to the left, this big whole box full of stuff that Chuck Schwab sent me. I mean, if you, if you actually look through it, it's amazing the amount of work that that was done by him and his colleagues that's embedded in there. So, so there really is a lot of that. I think in the chapters, we try to leave those breadcrumbs cookies for people. So I'll let the others continue on that one.
Dr. Mark Hanigan (00:17:18):
I think in some cases it's, you know, large loaves of bread, not just crumbs. You know, one of the, one of the nice things, I guess, or one of the advances maybe that occurred more on a mechanical side or an approach side is that, you know, the modeling techniques and the approaches that were available and sort of had been worked out 20 years ago, they're nothing like today. I mean, we there's been huge strides in that. Of course there's been huge increases in computing power and that's allowed people to develop these more robust algorithms. And so, because we spent that time and effort a to collect the data, we have the ability to check things. So another data set that got pulled in was Hélène with I think again, Roget and others had collected the data set on splaining metabolism. So I think there was 120 treatment means or something in that you know? And so we had diets where they had fed the diet and then measure portal appearance. So we can take, you know, after Jeff got through with sort of going through the RUP and the microbial digestibility, and then looking at whether the amino acid digestibility was different, we could then go and look and see whether the portal appearance actually balanced with that.
Dr. Mark Hanigan (00:18:27):
And it did, you know, which was the problem before that Jeff was referring to, is that, that when they got done with all this factorial stuff of the RUP and the protein and all the amino acids and everything, it didn't actually match the flow even in the gut. And so they had to do an empirical correction. So not only do we match the flow in the gut. When we got done, we also matched the portal appearance by and large as well. So we were able to crosscheck most everything along the way to have much more faith now. And of course, that just took a lot more computing time and a lot more effort on everyone’s part.
Scott Sorrell (00:19:01):
Jeff you've touched on a protein supply. If we could circle back on that just a little bit, what would you say are some of the two or three biggest changes or revelations related to protein supply that's in this NRC as opposed to the last one?
Dr. Jeff Perkins (00:19:19):
I think one of the big things is the way we compute the passage rate ss very different. The previous one was done in a very robust way. So not to diminish that, but there really is kind of no actual way to measure the passage rate of the so-called potentially degradable fraction. So actually, I'll have to give kudos to Mark on that, but what we did then was make sure that the non-ammonia, non-microbial nitrogen fraction, that's our best estimate of what would be in bypass protein. Then we corrected that for indogenous protein with equations that Hélène had derived. So this corrected version, Mark was able to then derive passage rates that on the, on the whole would be able to resolve, and therefore we'd get RUP’s ups that would match up with and correct non-ammonia, non-microbial nitrogen fraction.
Dr. Jeff Perkins (00:20:19):
That's what helps. So it's sort of like two trains: one from the East and one from the West, and they meet somewhere, you know. And they're laying the track. That really helped that to work. So to me, that was a big advantage. I wish we had a better way to do that mechanistically, but that's just the state of. state of the way it is. We also tried to make the microbial protein equation more mechanistic. The previous one, well, it's not just that one, but also Beef NRC’s and others have done this, they've tried to match up energy in the total track, looking at total digestible nutrients and relate that as the main source of energy for microbial protein produced in the rumen. And statistically, it works out pretty well, but you know, of course, mechanistically you can't have digested carbohydrate in the small intestine and support microbial protein earlier.
Dr. Jeff Perkins (00:21:17):
So, so there's some things like that I think were definite improvements at least. You know, you talked before about the cookies, I think, to leave the groundwork for people to improve that. Like, like one place that we weren't able to do as well as I would liked was to account for starch digestibility is as being affected by how we process grains. We all know that affects things, but we just weren't able to make that resolve in a way that connected all the pieces together. So somebody in the future hopefully we'll be able to do that and lay it in the groundwork of a more mechanistic, microbial protein equation. That's my hope. The amino acid profile is different. Again, working with Hélène and a student of hers, we're accounting for protozoal flow. That's, important, especially for lysine because protozoa have a lot more lysine than bacteria.
Dr. Jeff Perkins (00:22:19):
So we're now attributing microbial protein sources as a little bit better source of lysine. So that of course affects what you have to compensate for in the RUP fraction. So there's some, there's some real nice gains there. I think, I'm sure I'm sure there are gaps that people will help to fill in later. That's, that's kind of where, where I'm at on that. How about guys, do you want to add to that?
Dr. Mark Hanigan (00:22:50)
I think, I think the, on the post absorptive side, you know the milk protein equation was a big challenge really in that, you know, that occupied a lot of our time. And then because of the way we sort of approached, you know, Hélène came up with a way to address, okay, well, how do we now get, how do we back calculate what MP is or what it, what the flow of it is.
Dr. Mark Hanigan (00:23:14):
And, and even today, I mean, I think, I think it's better to think of it as more of a net protein system than it is MP system, because to try to get to the middle you're coming from both ends right? And the middle is the least reliable part probably. But I think the whole system, you know, is, is consistent all the way through. And so whether we have exactly the right requirement and exactly the right MP supply, I don't know. But I definitely know that feed the milk is overall correct.
Dr. Jeff Perkins (00:23:45)
Yeah. That's a good point. One of the points I made at the discover conferences, you know, we really never measure digestibility of our RUP. We, we predict it in there with all the data, even though the flow studies that we picked through to get these, almost nobody sticks in an ilial cannula and derives that. So, so we had to predict it that the data are more robust. And if they're off a little bit, then they're off in a way that self-correcting by the time you get to milk protein in the way that Mark described. I, I really feel like that's an advantage in the community was always, I'm always really careful to try to account for that. So anyway, whatever it's wrong, hopefully it's wrong in enough of a way that, that sort of self-correcting and, and we get good prediction results.
Scott Sorrell (00:24:37):
Did you guys talk any about the next NRC and what the process had looked like? I mean, this last one took seven years, right? And it was should we start now? You know, should we start that process?
Dr. Mark Hanigan (00:24:55):
The original goal was to have them every 10 years. So yeah, you're about a year away from getting started because it takes a year to raise the money and get the committee selected, takes another year to get going. So, yeah, I think he needs to start next year.
Scott Sorrell (00:25:06):
Yeah.
Dr. Jeff Perkins (00:25:08):
Different people though.
Dr. Mark Hanigan (00:25:11)
Yeah. I was gonna say, Jeff said he’s chair the next one.
Dr. Hélène Lapierre (00:25:14):
I’ll just say that was just that that's another point, too, that we didn't really have time with the pandemic happening and everything. Just to have like a wrap up meeting with the whole committee that we're supposed to have once everything will be published and we'll have the software organized and everything that we're still working on, it will be to add like a post-publication meeting where we're going to put all those thoughts together. And as you said, how should we make it happen for the next time? But what we really tried to do in this version is to have, like, I would call it really a biology based model on which it's going to be easier to add things, or to change things or to move things compared to what it was before. So that's one thing that Mark really worked hard on just to make sure that is going to be much more easier actually for the next committee to take what we've been doing and just move things along as knowledge will be gained as we know that it will happen.
Dr. Hélène Lapierre (00:26:15):
So I think that hopefully with that basis, it's going to be easier to make additions, deletions, or whatever then than it was before. So, but as you said, this is really a big challenge is to think of how that should be done the next time so it doesn't take as long. And that it's, as Mark was saying, it was really a much larger commitment for everybody involved than we all thought it was to be when we on. So we'll see how it goes, but we really need this wrap up meeting actually just to give a hand to- not to give him, but to help the next committee.
Dr. Clay Zimmerman (00:26:57):
Iis the ingredient database, is that an ongoing project? Or do you, do you start from scratch with that as well?
Dr. Mark Hanigan (00:27:07):
It's, it's sort of an ongoing project. Basically, National Animal Nutrition Program does a lot of that work for us. Well, at least the start of it, okay. They gather up commercial data and they, they create a, you know, an average ingredient. Of course, then there's a lot of fill-in stuff like, you know, you're not getting, are you getting KD’s and KP's from that database. So, you know, that's stuff that Jeff had to work on. And the amino acids don't come from that database, you know, at least today. And so that was stuff that, you know, we had to get from elsewhere. So it is ongoing and they're collecting it from commercial labs. So I think the, you know, the proximate nutrients are much more reflective now than what they used to be.
Dr. Jeff Perkins (00:27:55)
And, and one thing that, you know, as it becomes more data-driven as we get more and more new data that will be added, they're also reflecting more of the nutrient composition of the feeds that are being used today. So what comes to mind, for example, there's the corn silage that was fed 30 years ago, isn't the same as the sorn silage today. There's more starch and so on. And so it needs to be ongoing to help, to continue to you know, sort of converge to be closer to what we're actually doing. So a lot of improvements in that way. Like mark said, with the feeding, feed testing, you know.
Dr. Mark Hanigan (0028:38)
What I think, I think it should eventually, for the next committee, it may allow them to, to consider some regional differences too. Cause I'm pretty sure Jeff, the corn silage that we grow in the south, you know, with our, our weather conditions is not the same corn silage you grow, you know, in the upper Midwest, either. And we never get as good a production out of it as you guys do.
Scott Sorrell (00:28:59):
You guys talk about the changes in feed stuffs. Our company, Balchem, recently did a survey where we analyzed methyl donors in, I think it was corn, soybean, I don't remember what else- Clay. We found out that it was significantly lower than what was currently in the NRC. So, I mean, I can imagine that if it's true of methyl donors, it's gotta be true of a lot of different nutrients, even some that we may not be routinely analyzing for.
Dr. Mark Hanigan (00:29:31):
Yeah. I think once you get off the approximate nutrients, then our level of confidence in what we have goes down considerably. Just to come back quickly to that, the overall process, I mean, industry needs to push for NASEM to do another one. Okay, maybe if you, if we think we really need to start in another two or three years, it has to be, I think, pushed by industry. Because who else is going to do it? Right. It requires some money, it requires a commitment, it requires somebody to ask them to do it. They aren't set up to do it automatically. They're set up to respond to requests.
Scott Sorrell (00:30:07)
Yeah. Good point.
Dr. Clay Zimmerman (00:30:10):
Yeah. And during the, during the discover conference, the last session there was discussion around us. I thought that was a really good discussion at the end of the conference.
Dr. Mark Hanigan (00:30:20):
But if everybody sits around and waits for the next one to happen, that's not going to happen, okay. Somebody has to actually drive the process forward.
Dr. Jeff Perkins (00:30:30)
You know the nutrient requirements are supposed to be established on data. You know, you don't get to just say, I think it should be about this. You have to do the best you can to find those data. And we're not supposed to add safety factors, and we're not supposed to be doing fudging. And that means that we need to have improvements where they're needed. So I couldn't agree more with what Mark said. It needs to be derived from industry for industry.
Dr. Clay Zimmerman (00:30:56):
Jeff, how does, as far as the protein supply piece again, how's non-protein nitrogen factored, factored into the equation.
Dr. Jeff Perkins (00:31:05):
It's basically, if it's all soluble protein, there's a certain amount of that that flows to the intestine. And I, maybe Mark wants to address it or Hélène, but I don't think we really address it as much as maybe we could. But at least this year, we allow some of the so-called A-fraction of the soluble protein to pass to the intestine. And Mark derived that equation that maybe he's better to talk to talk about this questions.
Dr. Mark Hanigan (00:31:05)
Yeah. I mean, I think that again was more of a concerted effort because one of Jeff's colleagues at Ohio State worked on that as well for a little bit. But essentially, I mean, there's, there's several pieces of informational literature that suggest that some of this a A-fraction should pass, right? It can't all be degraded. And even if you take like a thousand percent per hour of degradation rate, and you pair that with like a 15% liquid passage rate, 10% should pass.
Dr. Mark Hanigan (00:32:00):
Okay. And statistically that's what we ended up driving was 10% without any guidance, right. That's, that's what it comes back as. So, so I, I think that helped a little bit you know, the KPs were too high. They should, you know, again, and I think Jeff was alluding to this, when we measure KP most of the time, and certainly what the old NRC was based on was the KP for mark particle passage. Well, that doesn't mean that's what the protein passage rate is, okay. That's what those marked particles are. And of course those are heavily influenced by fiber nutrients. So, so we ended up with a different KP than what we had before, and it essentially reduces the RUP content of all of the feeds pretty much. Although, you know, more for the concentrates than it did from the forages. Correct, Jeff?
Dr. Jeff Perkins (00:32:57)
Yeah. They're, you know, they're, they're both decreased. So, you know, I don't exactly remember which was more, but yeah, both.
Dr. Mark Hanigan (00:35:50)
We basically, just that one actually didn't require much discussion. We just did it, then went down the road, right. And then once we had it, allowed us to get, you know, arcadias and so.
Scott Sorrell (00:33:15):
Jeff, before we leave the, the protein supply section. So all the changes that were made from one version to the next, how will that manifest itself in the field? What will nutritionists and dairy farmers see that's different from what we were doing before, based on your recommendations?
Dr. Jeff Perkins (00:33:36):
Well, one of the things I think, you know, we grounded all the flow data into action. You know, we used all actual flow data to do that. And one of the things that that really strikes me is, you know, I've heard it said before, the best source of protein for a cow is starch. But as you add more starch, you tend to, you know, actually lower the efficiency of microbial protein synthesis. So you don't get as, according to the prediction, you don't get as much as you might think. So one of the things I think we're going to see, is we really need to have a good balance between rumen degrade starch and, and, you know, effective fiber and, and forage. So we're going to, I think it's going to emphasize the role of forage, not just to keep the rumen healthy, but also as a source of microbial protein.
Dr. Jeff Perkins (00:34:29):
So that's one of the things I anticipate will work out. There's some issues with digestibility of the RUP. We have to use book values that we had. Like I said, we, we don't have a bunch of data from studies for that. I, so I think on the, on the, on that standpoint, people are still going to have to be testing for it to make sure that the sources they're using are appropriate and, you know, highly digestible of the RDP fraction. So I think we're going to continue to see testing for those kinds of things. And I guess that's the best way I can answer it. Maybe if one of the other two wants to chime in and help me out, but that's, that's how I would think of it.
Dr. Clay Zimmerman (00:35:13):
Jeff I'm curious, what, so what methods do you recommend for testing RUP digestibility?
Dr. Jeff Perkins (00:35:20):
That's a tough one. So, you know, we're still trying to figure out the best way to do this for all feeds. So I kind of like the mobile bag technique as a way to, for some feeds that they, they, there's really not a way to do otherwise. So we're going to be doing some of that. But that's really hard to make work in the field. So we're still stuck kind of with the same techniques. There's the so-called three-step or the verification kind like that. The Cornell folks have there’s that's pretty similar to that and pretty well worked out. So I think we're going to still see the same kind of approaches. I don't see a lot of changes in what we're doing, just that we need to continue to keep doing it.
Scott Sorrell (00:36:11):
Hélène, if we can kind of maybe start talking a little bit about the protein and amino acid requirements. I understand that you were fundamental in writing that section of the chapter. Can you talk a little bit about some of the challenges that you had with that?
Dr. Hélène Lapierre (00:36:26):
And actually I'm just going to go one step back. If you look at the outputs of net protein that were predicted by previous models, like the metabolic fecal output, or urinary endogenous proteins, actually. All that was based on a very good paper from Swanson, but that dated from ‘77. So we thought that this knowledge actually should, there was enough knowledge actually to update those predictions. And also, I think we have a better assessment of what actually is metabolic fecal protein and what should be endogenous urinary protein. So really started from the biology to revisit those numbers. And they were really changed a lot. like endogenous urine output is twice as much as it was previously. Whereas the fecal output is much lower. And also, again, based on biology, we decided to assign to each of the proteins which is being exported out of the animal, which is creating a demand on net protein or on the amino acid in efficiency.
Dr. Hélène Lapierre (00:37:35):
That would be similar across the functions, again, based on, on biology of how amino acids are being handled within the animal. So that meant that we needed not only to quantify the net protein that was secreted or gained, but also what was their amino acid composition? So we spent a fair bit of time on that. And we also assigned to those amino acid composition that we obtained from protein neutralizers, as Jeff was saying, before the recovery factor to account for incomplete recovery. We all know that, it's not new, but we kind of forget all things that when, you run hydrolysis for 24 hours, you get numbers for all of the amino acids, but actually, it's more of a compromise. It's not good, it's not perfect for an amino acid, but if you want to rank feed ingredients, that's going to be good.
Dr. Hélène Lapierre (00:38:33):
But if you really want to make a cycle analysis and see how much is being digested, how much goes into milk, how much is being absorbed, then you have really have to take into account that you're protein hydrolysis actually does not yield the total amount of each amino acid, which is into the protein. So we also added that factor into the amino acid composition, and then by a factoral approach, we could estimate what was the net output or the net demand of each amino acid. And then based on the database that we had, and on some studies, there isn't that many looking at us this analysis, what was the efficiency of utilization of each of the amino acid. Because there was a large variation, which depends not only on the amino acid supply, but also on energy supply, which is something that was not considered in the previous model, where we had to fix deficiency.
Dr. Hélène Lapierre (00:39:31):
So I would have liked to pushl bit more the issue with the efficiency of use, but, you know, we needed to put, to edit at some time point. So we'll publish papers later on, but at least we came with the target efficiency that we proposed for a, what we call it, inadequate energy supply that would complement the prediction of protein yield that Mark developed. Just to have a guideline of, you know, what should be deficiency for each amino acid that we should target. And if you calculate efficiency based on the prediction of the supply and the output of milk that you want, what would be the needed theoretical efficiency. And if it's much larger than the target efficiency, it's telling us that the supply might be limiting, but it's not just like one amino acid. I said, we want to look at in a holistic manner as a whole, including the energy supply a well.
Scott Sorrell (00:40:30):
We had a webinar with Dr. Chris Reynolds from Redding University. And there was a question asked, Mark, related to your comments about that there's not really the whole barrel and stave concept is no longer valid. And they were, the question was-. And Clay, I don't remember exactly what it was, the notion of individual amino acid requirements, now passe.
Dr. Clay Zimmerman (00:40:55)
Single limiting amino acids.
Dr. Mark Hanigan (00:40:59):
I mean, all the mechanistic data, you know, that's been collected at the cellular level. And even at the, even some of the tissue level says, yeah, that's not how it works. So it's certainly an easy way to describe that process because it is sort of a complicated one. So, you know, I wish I could come up with some analogy that would go into every textbook in the world for the next 50 or 80 years, as well. And not only for animals, but it's for plants and everyone, I mean, everybody used that. In fact, it derived plants originally. So I think you can think about it from an evolutionary standpoint, if that cow was, or, or any mammalian organism, was that sensitive to any single amino acid, they would've all fallen over every time there was a little shortage of food supply and died, right.
Dr. Mark Hanigan (00:41:51):
And it would have died out as a species. So it had to evolve methods to mitigate a deficiency, or what we think was a deficiency, right? So if the cows eating a diet and it's short a methionine, it's going to say, well, I'm going to conserve methionine. I will shut down use of methionine as much as possible, just supplying to tissues and I'll shut it down other places. And I'll ramp up my affinity from methionine at the mammary and try to protect my investment in this calf, right? And so you have all these mechanisms that always resist that, you know. If you put too much methionine in, it does the opposite. It increases its catabolism, it resists taking it up at the mammary. I mean, it does still take up more, and you push some more milk out, but it's not as steep of slopes in general as what one you would have envisioned with the old sort of, very fragile system, I guess you might call it. So, you know, I think that that concept, you know, at the very least has to be changed. Lou chastised me after the meeting, you know, in Chicago and said, if you really want something new to, you know, to be taken up by people, you can't base it on the old concept. You just have to get rid of the old concept, and blow it up and then build a new one. So I was trying to morph it into a leaky barrel, but he doesn't like that.
Dr. Hélène Lapierre (00:43:11):
I thought it was a good idea. I think it's for a concept of efficiency is interesting, too. Because one amino acid isn't that limiting per se, but the cow cannot put out more than it digests, or that it absorbs, unless it's taking some from it's the reserves. So there is always liked a theoretical limit that we have to acknowledge. And I think this is where the calculation of the efficiency is making some sense. as you know, if you're having an efficiency of 95%, you know that if the cow is achieving that it's going to be taking some of the amino acid in its muscle, or it will not get there. So, but you know, between 75% or 70% or 80%, depending on the amino acid, there's a margin where the cow can adjust actually. And I think this is really where in the future, we will have more studies and we'll have more studies.
Dr. Hélène Lapierre (00:44:12):
Yeah, because in one of the questions you asked first, what type of study we were missing. And this is one of the things that we were really craving of, were those type of studies on amino acids where you would have only one amino acid moving at different doses. Because often we have like one level here, one level there. So, but what happens between the two levels? We don't have a clue. Or we have all of the amino acids moving together. So the response that we observed, we have to pull some hypothesis and to derive some equations. But there was very, very limited number of studies where actually we had enough data that we're moving one amino acid supply at different levels is really something that we do miss. And also, I think we all needed to start somewhere. So first people were looking at like a silo of picking an amino acid and a silo of energy. But now I think we're really trying to merge these two because well we'll see it with the questions with Mark, and also in the estimation of the efficiency of it utilization of MP or amino acid energy, is really a key factor in the efficiency of utilization of the amino acidas well. So I think that we need to merge the two now. And also we get into the type of energy. And so a lot of work for new scientists.
Dr. Jeff Perkins (00:45:36):
Scott, can I just add a comment? I think, you know, I really have to give kudos to my two colleagues. They really improved the efficiency concept, and the quantification of how to use it. That was, you know listeners need to remember that that was a fixed value in the previous NRC. Everyone knew that it shouldn't be fixed if you feed more protein than the efficiency of transferring that into milk goes down and vice versa, if you feed less. So that's really a big improvement and the other thing, whatever model he winds up to show it, the leaky barrel or whatever, people need to remember that not all of the amino acid goes into a protein that needs that amino acid at that place. There's a whole bunch of amino acid that does a bunch of other things. Like you talked about the C1 transfers.
Dr. Jeff Perkins (00:46:30):
I just read yesterday from my amino acid metabolism class, that something like only 20% of methionine is actually used in, in protein synthesis. So these other processes get prioritized as you shift as you know, like what Mark was talking about, whether it's maintenance of the calf or maintenance of genetics for milk protein yield. So again, my kudos kudos to my colleagues that that's really, I'm not sure people are gonna see it, but that's really there. And it's also the foundation of what Chris Reynolds and his colleagues are trying to do to lower protein. And that is how do you, you know, how do you maximize efficiency and not have some, some kind of issue backfire through that? So anyway, I just had to say that, so.
Dr. Clay Zimmerman (00:47:21):
So Mark, can you maybe, you know, for our listeners here, can you remind us what, what is the what's the new milk, protein yield equation?
Dr. Mark Hanigan (00:47:33):
It's just a simple multiple linear regression equation. Okay. It was like 13 terms and quadratic term. And but it, you know, basically it treats each of the nutrients as separate contributors to the overall, right. So if you think about protein synthesis, it’s like an assembly line, right? We can't assess well, we right now we, we-. Most of us know if you're trying to buy a car, you can't assemble a working car if you don't have enough chips to go in the car, okay. It doesn't matter how many wheels you have, how many doors you have, how many seats you have, the cars still will not go, okay. And so, you know, I think that's the same kind of idea here, is that that assembly line can sort of stutter maybe. And then I'm not saying that this is actually what happens, but you can think about it that way, that it will stutter every time it gets to one of those nutrients that it needs that is in shorter supply.
Dr. Mark Hanigan (00:48:29):
And it will slow the overall line down. And particularly if you start thinking about it of as thousands and thousands of lines that are operating in that tissue, you get some of them slowing down. Okay. And you, you provide a little more nutrient and it goes up. So the equation has to reflect that. It has to have this independent and additive stuff. And then there's some quadratic stuff that is interactive. Nut by and large, that was the best equation. Okay. It wasn't, it wasn't magnificently better than, you know, some of the other approaches that may be considered more older concepts, but it did come out statistically the best approach. And it fits all of our conceptual data.
Dr. Clay Zimmerman (00:49:08):
So from a, from a nutrient standpoint I'll paraphrase here. So milk protein yield, it's an energy driven process. And the key amino acids, methionine, lysine, histidine, isoleucine, and leucine. Is that correct?
Dr. Mark Hanigan (00:49:30):
Yeah. Did we miss anyone Hélène? We had, we had five of them I think that ended up in the equation. It's not to say the other ones are not important. It's just, the data were not adequate to pull them out uniquely. So we had isoleucine, leucine, histidine, lysine, and methionine, yeah. Those are the five.
Dr. Clay Zimmerman (00:49:53):
So one question that we've been getting from people is, is there a weighting to those amino acids as far as milk protein? Yes.
Dr. Mark Hanigan (00:50:05):
Yeah. Each of those terms in the model has its own coefficient. So the two, the ones that you can expect to see bigger responses per unit of absorbed supply are methionine in histidine. They both generate about a two to one response, right? Two grams of protein per gram of absorbed. Isoleucine, leucine and lysine are all closer to one. So if you're going to add something, you know, if it's expensive, you know, then pick the one that has the biggest response, right. If lysine is cheap or something else is cheap, you know, I know like there's been interest in, in tryptophan threenine because, or at least just tryptophan if remember, right. Cause it's really cheap to produce, I guess it's a by-product of some other things they're already doing. So they'd love to have tryptophan. Well, there's no data, you know, to Jeff's point, there's no data. Like I think the feed data maybe is okay on tryptophan. There is not a shred of believable tryptophan data from mouth to milk, okay. After that, I mean, maybe milk's okay and mouth’s okay. But nothing in between, there was nothing cross check on that all the way through. It showed up as significant in the milk protein equation, but none of us trust it. Okay. So it could just be an artifact of something else.
Dr. Hélène Lapierre (00:51:22):
Yeah, and I would just add, this is my bias, obviously, you know, check for what is the less expensive, but the least expensive, but then just make sure that you check the efficiency, because if you put a lot of lysine, for example, because it's really cheap, but then you don't have enough methionine, you can end up with a nice level, but your efficiency of methionine is that 95%, because you put all cheap stuff that has a lot of lysine, but not enough methionine then, you know, you're going to be away from the target efficiency. So it really needs to be balanced with the two concepts.
Dr. Mark Hanigan (00:51:58):
Yeah, and that's something that we struggled with and that we didn't really have adequate data to address is that, that sort of interaction, right? I mean the stats say there's not really a very strong interaction, but we know there is. When you get really far away. I mean, if you do if you create an imbalance, I’ts going to have negative effects on the animal. You know, maybe that's it only at intake. I don't know, but we know you can't just like, say, okay, well let's put in, you know, a kilogram of methionine, okay, even if we could afford it, it's, it's not going to make the animal operate well. So yeah, we struggled with, how do we sort of come up with this idea that there's a range, right? We don't have to be right on this specific number, but there is a range of the ratios that probably need to occur amongst the,.
Dr. Mark Hanigan (00:52:45):
And I think, you know, Hélène's idea of using these target efficiencies helps you keep on that, you know, sort of on that ridge, maybe you might call it. You know, you got ridge those wandering around. And so you have a ridge there that's these target efficiency. We can drop off of that ridge and still be fine, but we can't drop off too far, okay. And that, that's the part we just, we couldn't pull out of the data. There's just, the data just were not strong enough to tell us how far away. Because everyone fed that according to the old NRC in most cases, right? So we don't have a lot of data off the top of that ridge. You know, we got lots of data along that ridge, but not off the top.
Dr. Hélène Lapierre (00:53:24):
And also for those amino acids that were not included, for example, they didn't come out being significant. I think a lot of that is due to the fact that a large part of the database actually, where we had one amino acid that was increased, the supply was increased. Often, it was like a change in supply so that was all of the amino acids that were also increased. We didn't have that much data, for example, on phenylalanine. And when you look at the data, for example, maybe like the phenylalanine content of the microbial protein is so high, actually that most of the times we do have plenty of in phenylalanine. But we knew, we know that if we run a deletion study of phenylalanine, we might deprive, we might decrease milk protein yield. But in theory, it is very unlikely that this will happen, which in normal dairy ration. So couldn't, we couldn't catch that with a global equation. It didn't come out to be significant, but if you run two or three studies where you delete phenylalanine, it’ s going to be crucial, and it's going to be really important to maintaining milk production. But generally in the whole dataset we have because of this limited number of studies that we have, with just one amino acid changing, we could never catch that statistically.
Dr. Mark Hanigan (00:54:43):
So even though phenylalanine is not in the equation as a driver, that doesn't mean you can ignore it, I think is what you're saying. Okay. It still needs to be along that ridge someplace.
Dr. Clay Zimmerman (00:54:53):
Jeff, I want to go back to your comment you made a few minutes ago about how only 20% of methionine is used for milk protein synthesis. So if a cow is deficient in methionine, what's the priority for, for use for, from methionine? What's it being used for metabolically?
Dr. Jeff Perkins (00:55:12):
Well, I was reading, it was actually for humans. So it's, you know, human, a bunch of human nutrition people. So I don't want to transfer that to milk protein, but I was just making the point. Like it's a bunch of it's used for cystine or other functions and that the, all those functions can get be reprioritized as they need to be. So otherwise, I guess I think it’d probably would be better, maybe Mark, you want to try to answer that question on the efficiency?
Dr. Mark Hanigan (00:55:43)
Yeah. I mean, you know, I, I think that it seems like the more we go down this road that there's not this, you know, we would like to say, okay, well, first we're going to do this. And then second we're going to do that. I don't think the cow or any of the organisms work that way. It says I got to do all these things at the same time, and I'm going to manipulate and slide things one way or the other a little bit, but not turn off in general.
Dr. Mark Hanigan (00:56:05):
I mean, you know, unless things get really bad, right? I mean, if you restrict nutrients enough, she will turn off milk production, okay. And same way with gestation that it'll abort if you make the deficiency enough. So I think it's in terms of the methionine thing, I think there's a certain amount that has to be used for that methyl donor, you know. And it probably is going to depend on other things as well. And I, I really don't know, but you know, there's a lot of these nutrients that we don't know that much about, and we certainly don't know the interactions, okay. We're- even with the amino acids, we're still trying to figure out the primary drivers and we just don't have enough data to figure out those interactions. We can't keep doing. And I know we're, we're trying to stay a little bit more applied here, but we can't keep doing two by two factorials forever and think that we're going to work out very many of these interactions on the surface, right. We're going to get two points, but to try to pull that out and make a surface response to answer your question- really hard to do.
Dr. Clay Zimmerman (00:57:08):
Maybe, maybe one final question is, you know, obviously, you know, we have herds out there averaging well in excess of a hundred pounds of milk a day. And you know, certainly lots of cows, and even groups on dairies that are 150 pounds or more. I'm curious how robust is the database, you know, from a requirement standpoint for these high end producers?
Dr. Mark Hanigan (00:57:36):
We had, I think the upper end of the milk, Hélène if remember it, is like 110 pounds or something like that. 115. We don't have a ton of data up there, but we have data. But you know, if you have a herd that’s averaging 100, then you’ve got pens that are averaging 120, right. Which means individuals are, are at 150 or something that. We don't have any data like that. I mean, there might've been, and those treatment means, you know, when, when they were put together, but we can't recreate that out of treatment. So I think we have better mechanisms captured now, which would give one a little more confidence that it will operate okay somewhat outside of its range. But I think it goes back to buyer beware. We define what the range is. Okay. You have to decide how much faith you're going to put in it when you start operating very far outside of that.
Dr. Hélène Lapierre (00:58:33):
But I would just say that in terms of efficiency, actually, even the cow is producing more, actually it's going to eat more. And I think that in terms of efficiency, we should be pretty much on the same type of global organization. And also Mark, you developed like a scaling factor to predict those milk protein yields for the cows. Actually, as we were discussing, we had this discussion a few times, you know. We were working with cows of the past. We published data for cows of the future, and obviously the cows that have given us their data are producing less than the cows that we want to feed now. And Mark developed a factor actually to take into account of that the prediction actually needs to take, to include the factor that is thinking to account the ruling on average of, of the, of the herd.
Scott Sorrell (00:59:26):
You know, I guess we're kind of getting to the end here. Is there anything that we haven't covered related to the protein and amino acid chapter that you think the audience needs to know about?
Dr. Mark Hanigan (00:59:36):
I, you know, I think just to reiterate that, you know, both at the microbial level and at the milk protein level and probably at growth and the other functions as well, there's this interaction, okay, between energy and amino acids, you know. We only, we only have an energy and protein at the microbes, but that doesn't rule out the fact that there might be amino acids that are, you know, that those microbes will respond to that we just can't see. But I think we have to stop thinking about this first limiting train. I know you asked me that question before and I sort of got off into the barrel stuff, but if we continue to think that way from an application standpoint, you're not, you're going to be frustrated with this model because you'll be able to change the energy and think that, okay, well, I'm going to, I'm going to have this big change and it will be held up by the other nutrients that are there. So it requires a little bit different thought process.
Scott Sorrell (01:00:33):
Very well. With that, we'll call last call. Going to ask each of you to give us kind of one or two real key takeaway, applied messages from the chapter that a nutritionist or dairy farmer can use right away. And Clay, why don't we start with you?
Dr. Clay Zimmerman (01:00:53):
So, yeah, I mean, I'm certainly, I'm excited about the, you know, the approach here to milk protein yield and being able to apply that in the field with the new equation that's there. I do want to ask you a question to the group though. Maybe for my last point here, I am curious, and Scott alluded to this a little bit earlier, but I'm curious. How do you think this will be applied in the field as far as software. Do you see the NRC, the NASEM software being applied? Or, you know, is there a plan in place to basically get this into some of the other models that are, some of the other software programs that are out there?
Dr. Mark Hanigan (01:01:49):
Well, I know from the, when we were looking for programming support to do the software, you know, there was, there was a couple of the companies, the commercial vendors that did express interest in providing that support. And, you know, at least one of them, you know, was going to charge less money, provided they could get a license to put it into their software. So, so I think that, you know, is the route because a chunk of the money does come from industry, of course, those vendors do not want one of them acting as a competitor in the marketplace, nor does NASM want to do that. Okay. They don't, they don't have software support. In fact, if something breaks on that software, National Animal Nutrition Program has to fix it most of the time. Okay. Because it just, they just don't, they don't have it. It's, it's like an end product. They don't even really, wouldn't even be bothered if there was no software, it's us that say we have to have software to teach and do extension. So that software is a teaching and extension tool- it is not a commercial application. You're not going to be working with more than just a herder too. So I hope it gets put into some other programs and done correctly.
Scott Sorrell (01:03:04):
Hélènee, what kind of words, wisdom do you have for our audience?
Dr. Hélène Lapierre (01:03:06):
Well, I think that we need to learn how to use the efficiency utilization, how to calculate it. How to deal with it, and better learn how it's being affected by the energy, as well as the amino acid supply. I think we gave some target efficiencies, but as for each amino acids, but as Mark was saying, it's likely not just like a very tiny number, you know, we don't have like the uncertainty around it. There is some uncertainty around it, but I think that really to make sure that we're not trying to make the cow make more use of the amino acid that she has absorbed than what she can. So I think that this is like a type of guideline that we need to keep in mind and we're happy that we could provide it for all the essential amino acids in a factorial manner. So I think that's interesting. But because if we find, for example, afterwards that we have a better quantification of methionine, for example, by Smithfield on or whatever, we can just add it in terms of the model and we can have another efficiency or another requirement and, and work with that.
Scott Sorrell (01:04:18):
Thank you. Jeff, how about you?
Dr. Jeff Perkins (01:04:21):
Well you know, just thinking and listening to what the other two said, I, I think, I think it will be used and compared. One of, one of the strengths I think will be when people go to run simulations, they'll see what one, what this model predicts compared with another one, and then they'll try those under different situations. And they'll figure that out. I'm always mindful of what somebody who I know who fed a lot of cows who goes, I really liked the models because they helped me think. And, so I think there's going to be a certain amount of that, that people are going to get used to, and they're going to continue to try. The other thing I wanted to point out is we have a charge to use data, but we also are scientists, and we also want it to work.
Dr. Jeff Perkins (01:05:11):
So we tried to find the best way that we could to, to make all those things function. And so one of the, one of the key points that I think is going to lead you into the future, maybe it's more indirect answering your question. There's going to be study after study, after study, that does comparisons, and study after study that now is designing new objectives based on what this model says. And I think, that's a big game that's eventually going to be helping people into the future. So at least, at least that's my hope.
Scott Sorrell (01:05:46):
Thank you. Dr. Hannigan, final words.
Dr. Mark Hanigan (01:05:52):
I'm glad it's done. My wife thinks that it will never get done. She doesn't think we're done yet, nor will we ever be.
Dr. Mark Hanigan (01:06:00):
But you know, I, I certainly want to you know, give a lot of credit to Bill (Weiss) and Richard, as well. I mean, particularly Bill, I mean, on the software year and all of the, he, he pretty much did a lot, in my opinion, did almost all the editing on the whole book and it's, I think 525 pages or something like that. I mean, he just read through this modeling chapter again this week. And so I have trouble reading through that, okay. I just don't want to look at it again. He's read through it several times. So really got to give a lot of credit. But from a field application standpoint, you know, I think that you know, the old equations were too sensitive. They weren't correct on average, and they also were too sensitive. So it told you if you were short on protein, you're going to have this massive loss of milk production and, and it didn't happen.
Dr. Mark Hanigan (01:06:55):
Okay. And it also told you, you're going to get this massive gain if you fix the problem. And that didn't happen either, okay. So, so I think the, you know, probably the take home message is that, you know, probably explore a bit more outside of the bounds. I'm not saying you should do stupid things, but the cows are pretty robust. So if you have a situation that calls for deviating a little bit from what you might, the model says you should do, I would give it a try. And you just make sure you position things with the producer correctly, that, Hey, we're going to try this. This might happen, but it's not going to be as bad as what you would've thought.
Scott Sorrell (01:07:28):
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