Hello and welcome to the webinar on a herd health approach to calves. My name is Jenny Sherman, and I'm going to talk you through in the next hour different aspects that we can look at focusing on young stock with an idea to focus on heifer rearing, but this is applicable to calves in other systems, including such calves and dairy beef that are going for meat. What we're going to think about is deciding what our aims are and what data we may have going forward and then looking at how we can use some of that data and where does it come from, and we're going to start kind of in a bit of a chronological order, thinking about small calves, but we're also going to think up to that point that they've had a first calf themselves.
So With that in mind, what are our aims? Ultimately, regardless of the system, we want our animals to be alive. We want them to be healthy.
And we also want them to grow at our target growth rate. That growth rate is going to change between systems. It's going to vary between different time points of that rearing phase, and it's also going to vary between farms.
And that is all fine. Ultimately, if we are talking about replacement dairy or beef, we want the animal to carve in at 22 to 24 months of age. Why are we using age?
Well, actually, for most farms we don't have the body weights at the point of carving, and we're using age as a proxy. What we actually want is them to carve in at about 90% of adult body weight, and for that we're talking about third parity animals for that specific farm, and that gives us our target. When we think about these ultimate goals, these are some KPIs which have recently been introduced by Doctor Alex Bra and things to think about, they're aimed at dairy.
But these could easily be applicable to beef, and we're talking about rearing efficiency, so that's out of a cohort of heifers born alive. What percentage of those go on to carve at or below the target age at first carving? And for that we're looking at at least 85%.
We've then got heifer effectiveness, and this is looking more at that. Longer term longevity, which is hugely important from a welfare point of view, but also sustainability and economics, and this is going to be something that starts to come through much more as we move forward into the future. So we've got this heifer effectiveness, and that's saying again, we've got a cohort of heifers born alive.
Criteria one, we've got to carve out or below that target age at first carving. Criteria 2, we want them to complete at least 3 lactations. And for this we're actually looking at it being 3 out of 4 heifers born alive doing that, so it's a pretty high target.
Can we measure these on farm? Yes, we can. So the first thing we need to know is where are we losing animals, and you can see this pie chart on the left.
We've got different lactation stages, sorry. So we know that actually only 90% of that cohort that was measured for this farm survived to first lactation. And then we can see on the other side we've got that green line set for this farm at 680 as their target age at first calving.
And we can see that actually not all of our heifers met that. And again we can then go on to see that actually out of all the heifers born alive, only 18% made it to lactation 4, so we're definitely not going to be hitting that three quarters of the heifer cohort born alive. I think we then need to ask ourselves why.
Is it that we don't have enough heifers surviving to first lactation, and why is that? At what point are we losing them? What are we losing them to?
If we're not getting the age at first carving that we want, is it because we haven't got the right growth? Is it that we've got management practises like serving them later? What's impacting that?
Is it that they've had disease and that's impacting the growth, or is it due to nutrition? And then if we're not getting them to survive to their lactation, why are they exiting the herd? Is it infertility issues?
Is it lameness, ketosis, all of these different things. And actually for this could be quite a good starting point to look at. Again, this is just another farm that we had a look at and you can see this bottom figure of 21% of this heifer cohort born actually did what we wanted them to do.
We've gone from asking for 3 out of 4 to actually only having 1 out of 5 Y. Well, actually, we lost 7% of heifers prior to them becoming pregnant. Why is that what's going on?
We can also see there are numbers that are carving in below target age at first calving on this farm. Again, we've lost another significant chunk of 15%, so we're already on the back foot. And then you can start to see that actually we have quite a high attrition rate on this farm.
Why have we not got longevity going forwards? So, there's some overall KPIs that we can look at. In terms of what data do we have, I like to think about it in terms of what happens to our heifers.
So we're thinking about birth to carving. We've obviously got a lot of things that happen in this time point. The first thing to think about is we're always going to have an age at first carving.
We have to register births. BCMS data is a bare minimum, so we're always going to have an age at first carving. If we've got body weight, superb.
But for most units we won't. To become pregnant, we must have served the cow in the first place. And from that we're gonna have that made in heifer fertility data.
Sometimes that's easier to get, sometimes it's not. Often we'll have an age at their service and hopefully possibly a body weight with that with the target being 60% of adult body weight. Or if we don't have an exact age at their service, we might know when they put them in with the ball if they run a ball rather than AI.
We're gonna know a conception rate if we've got AI going on, and we're also gonna know submission rate. And we can also work out how many were pregnant out of the cohort that went in versus the number that were empty. Another one if we've got people that are keen on weighing is we've got puberty.
So we know puberty happens at approximately 9 months of age at 40% body weight. RRF is hitting 40% adult body weight at that 9 months of age. So you can start to see just from this, we can start to collect some data.
We've obviously also got weaning to kick in and we've also got lots of things we can measure throughout the entire time. Exit rates, you know, when our heifers leaving the system, if at all, hopefully not at all, but is it within the 1st 28 days? Is it prior to weaning, post weaning, after serving?
Starts to give us an idea as to what's going on and the reasons on these farms and what might be behind them. The other interesting one to think about is, is it a mortality? So we had a death on farm, or have we had to cull them, for instance, for infertility reasons?
And don't forget that sales can often impact these numbers, so making sure we know the destination and the reasons for exit is really important. And don't forget stillbirths, you know, this is still included in this area. And a really important one to look at and for us it's normally calves that die either at the point of carving or within the 1st 24 hours.
We've obviously also got morbidity rates and there's lots of different diseases, you know, the main diarrhoea, respiratory disease that we think of, but don't forget things like lameness. And with this, we want to look at what's the overall prevalence. Is it high?
Is it low? Is it increasing, is it decreasing? Which direction are we going in?
Is it showing any signs of seasonality? New versus recurrence really important that we've got high environmental pressure with lots of new cases, or actually, do we just have a handful of animals that we're really struggling to get on top of and we just have a high treatment rate because of recurrence? Who is it?
Is it a certain parent, a certain age group? Is it a certain time of year, or a certain time point in that rearing system? So lots of things to look at.
And then obviously we've also got daily live weight gain, so we can also have a look at weight data as we go through. So with that in mind, let's start at the mortality rate. So stillbirths are always good to have a look at.
Not always recorded well on farm, but I do think it is really important for us to get an idea of this. You can see these are some dairies I've taken the names off the bottom that we've benchmarked for stillbirth. We've put a target of less than 5%, and that meets most supermarket contracts.
If you read within the literature, the UK average prevalence is estimated to be around 8%. But you can see we have a wide variation between stillbirths. Another thing that's good to do is to plot it for a farm over a year.
So here we've put the number of carvings in there, and with this farm we've split it between first lactation and then any adult cow carvings, and all of those are read off the left hand axis and of the two bars. And then we've put the percentage stillbirth for each month as the lines, which is right off the right hand axis. This is good because it gives us an idea of when, when are the problems kicking off.
So for this herd you can see we have peaks in June, July, August, so something's kicking off in summer. And we can also see here differences within parity, which we know parity is a risk factor for stillbirths. So it allows us to start to discriminate and decide which area we want to focus on.
So for me, for this head, we want to know what happens in the summer, especially with the older cows. And also we seem to be going on a bit of an upward trend at the back end of 2021, start of 2022 in the first lactation heifers. And when we start to think of stillbirths, there's lots of different reasons, and with that lots of different risk factors.
But this allows us to start to break the data up. So like in that previous graph we've had a look at the parity. That's a huge risk factor.
We can look at is it animals with an older age at first carving? Is it ones with twins, is there a certain bulb that's being used? Are we getting premature?
Quite often it ends up unexplained, and we've got lots of issues that we know about in humans, but we can't measure in calves, you know, premature or placenta. When it dissociates issues with the umbilical cordplanitis, all of these types of things we need to have a think about. But actually with a bit of data, if we can start to get our farms to record it, most of us have a diary or on a clipboard somewhere, and if they can write me a reason, that's really useful.
So on this farm, these are their 19 they had for 2022. We can see that actually 6 are unknown and 6 are dystopia. OK, we've had a few large calves.
Maybe we need to look at, was there a certain bug? You know, but there isn't anything here to say, oh, it's a small heifer carving in or anything like that. So it starts to get us to go forward and to take that data in a positive direction.
Don't forget you can also look at costs. So the estimated cost is around just under 700 pounds, and these are the different costs for it. And actually you can see again for those farms that we estimated, we had a look at how many were stillborn and then we based it on whether the calf that died was a dairy replacement or a beef replacement based at the cost of that time when we did the meeting and then worked it out cost per 100 calvings for that year.
And it just starts to focus that mind on actually stillbirths are quite important, and yes, they are sadly part of life, but if we can reduce them, if it's due to poor sire selection or due to slow carvers because of hypocalcemia or not enough tension at carving, we can start to put things in to mitigate and to reduce that. Other mortality rates to think about are just general deaths, so this is UK data taken from BCMS from 2011 to 2019, and this is calf mortality rates up to 3 months of age, split by whether you're dairy or non-dairy, and whether you're male or female. And you can see sadly that data is fairly static unless you're a dairy male where you've slightly dropped off a chance of of mortality in the 1st 3 months, but it's still pretty high.
And we can see also with the graph on the right that we have a seasonal impact. So again, we've got stuff to start to benchmark it to, as well as benchmarking to other farms. In terms of what mortality you measure, mortality within one month of age is normally a common one due to the impact of neonatal scours, and you can see here we've used the proxies of a denominator of the total number of births for each month, and then we've just got the total number of deaths as the orange bars again already off the left hand axis.
Our target is less than 5%, and we've done the mortality rate as a percentage. And you can see that there are different times where we have issues and seeing where those are. And again, this is something that's quite comfortable to benchmark against.
So we actually have a couple of farms in that bottom left hand corner here that are 0%, which is superb, but we've got a couple of farms that are well over the 5%. So actually these guys will need to do more with. OK, so morbidity rates.
Where are we gonna get the data from? So it's going to come from the fact that the farmers spotted the animal that it's sick, and then I'm sure we've all seen dairies dairy diaries like this, and often we have to figure it out. Online software has made our life hugely easier.
It's easier to pull the data off, but it requires that farmer to write it down to accurately identify the animal and write it down on the right date rather than saying it's in my head. For me, young stock, especially if we're talking pre-weaning, they need their own diary, or medicine book for all of this to be recorded on, or exceptionally good online software. Recording.
So when we talk about it, you know, we're talking about treatment records most of the time, and that relies on somebody detecting disease being present and treating it, and that's really important to remember. Just because you have a low treatment rate doesn't mean everything is hunky dory on the farm. It could be that they're just pants at early detection, and actually they don't treat a calf, say, for pneumonia until it sounds like Thomas the Tank engine, and at that point our cure rates aren't going to come through.
And in those situations you may see high levels of recurrence. Because we're just not good enough at that early detection to get the best cure rate. And that's why looking at levels of recurrence versus new cases is really important to determine, have we got potentially detection problems, have we got treatment problems, if we've got high recurrence versus actually what's going on with the environment if we've got a high new case rate.
And again, looking for seasonality, what is it when we spike. Is it to do with the environment, is it to do with the ventilation not working? Is it to do with lots of calves suddenly appearing, because actually we always end up with a block of carving in October, say, cos we suddenly carve a load of heff is in.
It's taking all of that in mind. If we don't have robust treatment records. It's not the end of the world, but it's not, not helpful because it doesn't allow us to look at things over time.
But we have got the ability to look at snapshots, and those often come in the form of the Wisconsin Health scoring, which you can do for pneumonia and both diarrhoea or thoracic ultrasound. The key things with those to remember are, they're definitely not foolproof. You could do it today and come back 3 days later and the story will look completely different.
But it might help us detect some subclinical infection and start to at least get us on that idea as to where we are. And don't forget other methodologies. So we are starting to see a lot more precision data coming through, whether that's behavioural, whether it's to do with feeding.
So automatic feeders is superb. We get data about whether they've drank all their milk allowance, how many visits they've had, and actually that behavioural stuff often kicks in much before we see the clinical signs. We've also got future technologies coming forward in terms of pyrexia detection as well.
And also looking at the behaviour aspects in terms of what the calf is doing, it's activity and things like that. So this should only hopefully get better, but I think understanding the issues behind some of our data helps us decide how much weighting to put on them. So this is just an example where we had them write it down in a diary, and we can have a look.
It's definitely not fantastic. We haven't got any denominator here, so we don't know how many carved, carved cows carved in one. But what we know is that it's all year round and they are pretty steady.
So actually from this you can start to say, well, the main colour I can see is scours, it's yellow. So for me, I might hate the shed and think that oh my God, the ventilation's appalling, but jumping up and down about BRD at the minute isn't gonna be on that farmer's radar. I need to go in and I need to start talking about colostrum and cleanliness.
The other thing I can see is that it's dropped in June, July and August. So actually, It, what's happened, what's different about those? We can also see that towards the end of the graph this unknown category started to pick up what is this red, what do they mean by unknown and more history questions are required here.
And we can see that also assuming we've got the same number of cars, January 2019 was nowhere near as bad as January 2018. So a lot of information, often not the best of data, and it points us in the right direction of where to go and have a look and discuss further. If we've got the data, putting it in a graph like this, so actually we can, we've got them all tallied, you can see here that actually for this year there's 140 treatments for 300 calves at risk.
So we're just shy about 50%. I think it's about 47 cases per 100 calves per year. So that's that's a pretty high treatment record and actually if we start to plot it, we can start to see where the big numbers kick in.
And you can see that unsurprisingly, with this being pneumonia, February, March, we're not so keen, summer, we go lower. And it's picking up these patterns that are really important to see where it changes over time, as well as knowing where our overall prevalence is. And again, we can plot this, so this is the same graph as before I did.
Apart from this time we've put a 3 month rolling average on. A 3 month rolling average can be really helpful if you have small numbers of animals coming through. The problem we've got is if you've only got 10 eligible calves at a time.
The difference between one animal and three animals being sick is massive in terms of being 10% versus 30%. Actually, if we combine that over 3 months, we can start to see better trends. So a 3 month rolling average can be really helpful to see the way it's going, and you can see we spiked up until spring.
We dropped from April onwards. We went really nice and low over the summer, and now we're going back up that we're in autumn. The other thing to do is to look for that recurrence that we've been talking about.
So here we can see we've got a cohort of heifers, how many animals have been treated how many times. And actually you can see here the farmer will be banging on about how there's loads of calves he's treating repeated times. He is remembering the threes and fours, OK, of which there is probably 12 calves there.
OK. Well, we've got about 62 that didn't require any treatment. And actually, our cure rate's pretty decent.
Because there's about 54 that have only had one treatment. So actually for me, Yes, the farmer is upset about this recurrence. We're never going to get 100% cure rates.
What is driving a pretty high new infection rate when we, when we've got more calves being treated than not, and actually most of them only require one treatment. So again, going back to that environment, what is there, what's going on? Why are we picking up lots of new cases?
The other question of who. So the farm on the left is an indication where pen 1 and 2 are in one shed, pen 3 and 4 are in the other. I actually hate pen 3 and 4 from a ventilation point of view, but it's PE 1 and 2 that get pneumonia.
I need to ignore Pen 3 and 4 that are in the different shed. They are not my problem at this time. I need to go to Pen 1 and 2 and work out why there is a high infection pressure there.
Similarly, it's worth splitting it across a number of treatments, so on this unit, they stay in paired housing till 3 weeks of age, and then they switch into group housing, and you can see around that 22 to 42 days is when we get our spike. Is it to do with the stress of the housing switch? Is it to do with the housing per se we put it in?
Are we mixing groups? Who's in that airspace? What is going on?
I don't care about the 1st 3 weeks of life here. I want to know what the 22 to 42 day old calves are and what is going on there. Again, we can work out because all of these are treatments, it's an incident, so cases per 100 cars per year, and we can work this out.
The one thing I would say is that I am really anti benchmarking carbs, because if we look at this, you could say, oh, farm E, they must be absolutely cracking, you know, they're really low, treatment rate. Farm D, oh my lord, they're over 100 treatments per 100 carbs per year. Actually, both of them could be really bad, but at least Farm D treats them and farm E doesn't.
So I think for me, this is a big no. We don't have a standard way of diagnosing. In terms of pneumonia and scour, but especially pneumonia.
So I would, I'd really avoid this because unless everybody is working off the same protocols and the same indicators, it, it's really hard and it can be really demoralising for people that are doing a fantastic job. And it's also working out what those treatments are. And different people using longer or short, short term antibiotics, and some people just going in with NSAIDs to start with.
It's quite a difficult one to do. But one thing we can do is we can look at AMU for our calves. I would say that actually this has got slightly harder with the more use of category D, the prudent antibiotics, if we don't have a diary and we can allocate it to certain animals, doing that off just what's brought from the vet can be really tricky.
Where things like yoursomyrosins and your fluenicols, which are really only ever used in calves res respiratory diseases are easier. So there is a very handy tool here created by the team at Nottingham, which you can use to calculate your AMU and it will give you your mix per populated population correction units, your defined daily dose, your DVD, a DVD, sorry, and then your defined course dose. I also tell you if any of them are the highly important critical antibiotics.
Yes, it's not great in the fact that the population corrected unit isn't really designed for calves, and we've not worked out our at-risk days properly, but again, it's an indicator and everybody's on that footing. So here's an example of a farm that had 61 cases per 100 cows per year. We looked through their data and actually we can see the amount of drain resfluor and alamycin that they've used.
And actually you can see that this is contributing to 9.15 MBs per PCU in the Hertz. Preor targets less than 20, so we're at nearly half of what we need just based on our prune weaning calves getting respiratory disease.
You can see that there's 2 for the defined daily dose, so on average our calves are being exposed to 2 days of antibiotics, which is the equivalent of 0.4 courses per calf. There's a large amount going in there and actually when you start to add that up, you are starting to look at over 1.5 litres of antibiotics going into calves.
That's a hell of a lot. We need to do something about that. This you can benchmark and it and it's good.
The key thing to point out is if they use oral antibiotics, it will massively shoot up the Ms per PCU, which is what happened with farm H. But equally farm D used a hell of a lot without using oral antibiotics. But you can start to work out why is it high, where are you, what are you using those antibiotics for, and benchmarking and a bit of peer pressure is always a really handy thing as well.
So in terms of our mortality and morbidity, think about where your data comes from. Some of it's really reliable, some may be less so. Make sure we know who our denominator population is, so who is eligible to be counted in this, and that can be done by kind of done off births or by selecting a cohort of heifers and following them through.
Enumerator, who are we treating and what do we exactly mean by treatment, and remember the pattern that recognition. Where, who knew versus lots and changes over time and seasonality and in terms of benchmarking mortality in AMU, yes, definitely for me it's a no for disease treatment at the minute. We just don't have that gold standard and we honestly for things like BRD, we're just not good enough.
So, the other one to think about is daily life weight gain. In terms of data collection, we can do scales or we tapes, and we tapes have been shown to be fairly decent in pre-weed calves, and actually this is research we did at Nottingham. And the key thing is it's the same person that does it and that we have that it's a population tool.
We're not looking at it for one animal for daily live weight gain and that we've got a decent space of time between the two readings. So for example, you can see the line that says age between two weighings is 28. And actually you only need 18 calves on average to be absolutely sure that there is an absolute error of less than 5050 grammes per day, in the daily life weight gain.
So again, a useful tool to start to get farmers to use and one for me for pre-weaning a birth, a weight at, around 4 weeks of age, so just buzzing, disbudding, sorry, is an excellent excuse to get a weight. I'm weaning tell you a hell of a lot of what's going on. If we don't have weights or you have the ability to go and take some snapshot weights using a weight tape, you can start to plot them.
If you put a line of best fit, the gradient of that will give you your daily live weight gain, and then you can also start to see where you think your target weight would be at different time points and how many animals have met that or not met that. A great example of this is this is a block carver who has, as you can see, 3 different breeds going on. And actually we set 6 month wait targets and determine whether they're above or below.
And you can see that actually we're fairly split and some of the jerseys were really struggling. So actually for us that's an intervention point of do we change what we're doing in terms of nutrition for these guys. And similar idea for the bulling weights, and actually we quite like this on this farm.
We've got our target of weight and weight at a certain age, and we can work out who's failed to meet it, who's achieved the target, and this is beautiful. We've got lots of overachievers, so we don't need to worry so much, but we've also got some at risks, and it's all these at risks likely to gain that weight and how much weight do they need to gain in the run up to that 390 days. In terms of daily live weight gain, I often play with it in Excel, but if that's not your thing, again, the shiny app has the ability to do it.
There's also an Excel sheet on there that you can use as a template so it automatically uploads. It will give you some mean weights, which are great, and you can split them by heifer or ball calves or if you want different cohorts, just call one a heifer and one a ball. And then you'll get the box plots.
Now this is really important. I'm really anti-averages, especially with daily live weight gain. Oh, this group got an average growth rate of 0.65.
Yeah, that's great, but actually, so for instance, if you look at the red, we've got almost 0.2 kg. Per day difference in growth within that box, that red box, and that red box represents that middle 50% of carbs.
So actually we don't have a very uniform group and we've got a massive spread of growth rates within our calves, which is not what we want. We want them all to be growing really nicely and as close to each other as possible, so it's a beautiful uniform group that we look at. This again is a really good thing of showing following a cohort through.
So this is birth to OAD, which represents 4 weeks of age in this herd because they'll go to once a day feeding, and then April is the time they wean, and then April to July is out at grass post weaning. And we can start to see that actually, whilst the overall, which is our yellow, is above 0.8 kg a day, it's taking us quite a while to get there and they're not overly performing well in that 1st 4 weeks of life.
Which isn't surprising given the milk that they feed, and it is about where we'd expect them to be saying that they're January, February born. However, what's disappointing for this head is that actually the OAD to April tends to be much higher. So this year we didn't do quite so well in the run up to weaning.
So why? What went wrong? And also we can then work out whether it's in line with our targets for the growth rates at that time point based on what we're feeding.
So here's an example of that. So here are some jerseys. We know that they want the jerseys to carve in just shy of 500, and I know what they feed them.
So I can work out what I, where I think we can get some growth. And we also need to think about feed conversion efficiency, which we don't have time to discuss today. But we can start to see that actually we start off on higher daily life weight gains, and that's because we overwinter on fodder beat where we never get great growth rates, and actually it's trying to stop us having to push really hard at the end.
And with this, it's then really important to measure, so you can see here that we measured them September to October, so on autumn grass and the growth rates were only down at just shy of 0.5% and we wanted it to be at 0.8.
We then measured October to April the grey, and you can see that we were just over the 0.6, and that's because we started to feed silage with the fodder beet and that's good. We were better than where we wanted to be.
However, we did have some calves that required ketchup, and that's what that yellow. 2025 carbs is. And actually, we nail it over 1 kg a day as a medium, which is the lime, but we've got quite a big spread.
But actually, We weren't planning on being more than 0.7, but that's because we've had to play catch up. So it's knowing that, and it means we can alter what we do.
So that group of 25 got cake as well as spring grass. But it meant we reached bulling weights. So I suppose for me, in a bit of a nutshell for daily life weight gain.
If you're gonna do target similar to this farm that we've just discussed, Work it out, start to put numbers in, start to see where they should be coming forward. It gives you an idea. We know that we want them to be 90% body weight carving.
These guys here, they're serving in May. I want them at 60% because it's so much harder to grow them once they're adults, you know, you feed conversion efficiency from this point onwards. It's gonna be 8 to 10 kg in for 1 kg of growth.
Here, 2 kg in for 1 kg of growth. 3 to 4 kg in for a kilo growth. Oh sorry, no, that's still 2 kg in for a kilo growth.
This starts to go to 3 and then 4 kgs in. So you can start to see that actually this is why we've weighted it to grow higher at this end. But sit down with a farmer and see, you know, I would prefer for them not to overwinter on for a beach.
That is always going to happen on that unit, so we are going to compensate further up the line and also make them grow more down here. Think about your data collection, so make sure it's consistent, make sure if it's scale it's calibrated, . Weighing a known weight is a good idea, weighing a person less of a good idea.
And again, if you're using weight tapes, the same person. Snapshots can be really useful as to how we reached the ideal body weight for this head, and we can fit that line of best fit to give us an indication of daily life weight gain. Following them over time is really important.
Throughout rearing, having them as a cohort and following them at each stage can be massively beneficial in terms of making sure the heifers grow well, bearing in mind that 50% of skeletal growth is in the 1st 6 months of life. And also doing it between cohorts over time. Are we getting better or are we getting worse at this?
What's changed? And please avoid averages, especially with daily life weight gain. We need to look at what's going on within that range to get a nice uniform cohort.
So thinking about other things that we can measure. Colostrum data, that's great. We all want our farmers to be measuring colostrum quality on the dairy side and actually research is coming out from the Edinburgh group, but actually it's really important to consider in terms of beefies as well.
But it's literally a pass fail, so don't try and do averages or anything like that. Who's passed, who hasn't. Are we happy with that number?
And if we're not, why not? Let's look at the dry cows or how are we going to deal with it in the short term. Similar idea with total proteins, at the minute, most people use the cutoff of 55.
And plot it as a yes no. There is research coming out by Lombard Eal in 2020, where we've now split it into excellent, good, fair, and poor. If you've got guys like this farm which are cracking and it's a beautiful graph to look at, maybe push them harder for the excellent.
Equally, if you've got somebody that's struggling with the 55 cut off, let's stick with the 55, which is the cutoff for poor between poor and fair on that Lombard scale, and let's make it realistic. But you can start to see how we can plot this over time and see if the wheels start to fall off. Also, if you're doing it routinely, it's a really good thing to write it on the whiteboard in the calf unit or if you've got a WhatsApp group for that farm, whack it around there and nobody wants to see any failures, so actually it's quite a good motivator to have it there and visual.
Don't forget body condition scoring, this is really for older animals. We do have animals that are . Have a tendency to get fat, especially if heif is are ever fed dairy cow refusals that normally has 20-25% excess energy in it.
Not great, but actually this is between cohorts and yes there's not large numbers of them between the cohorts, but we can start to see that we're going in a better direction. And again, snapshots not the best. We need this more often, but again, an underutilised tool in general, but especially within heifers.
Personally, I wouldn't start body conditions to growing until about 9 months of age. Hess has struggled to put fat on early on in life, and also it's quite difficult. By the time they're at least 6 months of age, if the rumen's been developed right, they're starting to have pretty close to adult capacity.
And then from 9 months of age we're at that puberty stage and we need to start to think about the condition of them going into bullying. Sorry, this is the table. I'm not quite sure it's here.
It should have been the one above, that's a Lombard so giving you the different cutoffs for the colostrum for the serum IGGs with the equivalent total proteins, . So if people want to push you farmers, crack on. Other things we can do is we can work out our predicted day live weight gain off what they feed, so you can see here these guys feed 8 litres a day at 12.5%.
And we've gone for a newborn calf, so they're not going to be eating any concentrates. Concentrates don't kick in till about 3 to 4 weeks after they're offered. So why we should offer them from birth.
Bog standard 24% crude protein, 20% fat oil, 6% ash. Target growth rate of 0.8%, and we've put the environmental temperature at 10.
This animal should fly and grow at 1 kg a day. If we drop the temperature down, that's gonna change. If we drop that down to 6 litres, it's gonna change, so you can start to see this is what we're feeding, what should we be getting in terms of those target growth rates.
And you can create something like this, and basically all I've done is I've picked certain weights, estimated how old they'll be at that. These guys are feeding the same amount of milk throughout, so this is how much energy it's supplied. And then I've worked out for 15 degrees, 10 degrees, and 5 degrees how much energy that animal needs to grow at 0.8 kg a day.
And I've worked out the predicted the live weight gain of milk alone. And then worked out if they need any extra concentrate. So here, 75 kg calf there I think that is a 6 litre, 15% bog standard commercial milk placer.
Needs to be 1.5 kgs a day. Well, I mean they should be smashing up to 1 at that point of room development as well.
And that's the same. Here at the if it's 10 degrees. However, 5 degrees, we've hit the lower critical temperature for a calf of this age.
It needs 0.72 kgs, so we can start to see if we're having seasonal issues, if we're having issues with growth at different time points, why that might be in terms of diet and don't forget disease will also impact this and stress. Another thing that's interesting to do is if you are keen to have a look at the cost per kilo of gain.
I normally do this just off milk feeding, especially if benchmarking for the 1st 4 weeks, assuming that my clients will give concentrate from birth. And again, I can use that calculator to predict their daily life weight gain based on what they feed, so how many kilos they're going to put in over that 1st 4 weeks. And how much milk it's costing them.
So the organic guys, these guys are feeding whole milk, the conventionals were all on milk replacer, and that varies with the percentage of solids in and how many litres. And actually, you can then work out, right, if it's cost 50, just over 50 quid to grow 12.6 kg, it's 4 pounds per kilo gain.
And actually you can start to see that the guides that are feeding more actually have the much lower pounds per kilo gain, and it's a very good way of motivating people to feed more. So this conventional chap here, oh. Sorry, in farm E, he was feeding 8 litres, 2 pounds 10.
This person here is feeding 4 litres. Again, just food for thought, and again, if you know how much concentrated groups having and how much milk replacement they've had over a certain period and you know they're weaning weight, you can work out how much pounds per kilo it's cost and if economics motivates your farmer, that can be a way to do it. And again, easy to benchmark because we're all on the same level playing field, assuming we've got decent weight data.
Other things to think about is temperature inside and outside the building. It does vary and also it gives us an idea of that cold stress that the calf is under. And again, you can see how it alters.
This is a farmer that was keen to have a place, so this is a normal igloo. This is the inside shed because the igloos were backed into a shed. And then we put two heat lamps in an igloo and how it increased the temperature.
And similarly, you can do the same for relative humidity, and again, this is the same farm that I've nicked the data from. Why is this important? Well, actually it tells us a lot, so we can work out how much we need to feed them, but also it impacts disease.
So this is unit we can see the average temperature, which is red off the right hand axis and the average humidity for the day read off the left hand axis, and we've got the number of pneumonia treatments that happened over the period of December. We had an outbreak in December and required a lot of treatments over two certain days. And actually, if we look at the relative humidity in the run-up to that, our relative humidity was being sat at 100% as an average for the day.
That's because the drain kept blocking. And, the farm didn't always believe the impact of the drain. This data proves it.
So actually, any ancillary test data you've got can be really useful and, and definitely worth playing with. And again, you can see these are just played with in Excel. It, it's nothing fancy in terms of software.
So For the last 1015 minutes, I want to think about our ultimate outcomes as to what we want to measure. Again, that run up to service, we've got the weights. Are we meeting the target weights, how many above, how many below?
What's our daily live weight gain, and how does that compare to the rest of the time? But with that we've got the maiden heifer fertility, and that's really important to think about and we compare it from year to year. This is easier in block carvers, so this is a block carver but can also be done in all year round.
You can just create little cohorts, little blocks, just make sure you've got a decent number of heifers in them. So we can see that all of our heif is conceived in both years. Age at first service matters less for a block carver because we're just going to start serving on this day pretty much regardless.
But you see, our conception rate differed. 2020 born, we're near 80% compared to just over 2/3 for 2019, and most of that was driven by those who conceived the first serve, which much poorer of 2019 in comparison to 2020. And that also then meant we had fewer helers conceiving after 450 days.
So, when we look at maiden conception rate, we can look at first serve versus second serve, we can look at different balls. Just remember quite a lot of these are very low numbers. If we're using .
Fall on farm rather than AI, it's always going to look a lot better for those guys cause we don't record most of their serves, but you can start to get an idea if you've got two operators, you can compare operators. It's the same idea as if you were working with a the adulthood. Here the conception rate target would be over 60%, and it really does need to be over 60% or that there is something wrong.
Again, we can look at heifer in service intervals. Exactly the same idea. We've got a really nice return serve rate at 18 to 24 days, so 55% plus, a low 2 to 17 day bar.
If it's high, that indicates we've got, for heat detection because one of the two serves was wrong, and we obviously really can't afford to get these 49 days plus cows coming through. And then just very quickly let's have a think about carving heifers. So we talked about age at first carving, so we can plot that.
And as we've said, we can work out how many above and below, and then we can start that will feed into also our, rearing efficiency. Key thing again is don't take averages. You can see this farm's got quite a long tail going this way, and actually that's going to alter our average.
And if we did a median versus a mean, we would have a bit of a difference. But actually just just eyeball it like this. Where's it looking, what's it looking like?
You can also work out the cost of it. So if you can work out how much it costs per day to rear an empty bulling heifer. You can then work out How many days they were then either open for longer or if they carved less than 22, how much money was saved, and you can see it doesn't have to be spot on, but actually you can see here that by having these 34 animals carved earlier, we theoretically have saved around 6000 pounds.
This assumes our animals are carving in at the right weight and that they go on to produce the right amount of milk. However, we can also see that we've got a large number of animals down the bottom that are costing us a lot of money in terms of what we're rearing them for far too many days, and that's going to increase the time it takes for that animal to pay back its debt and therefore it's going to be well into the second lactation before these animals are clear and they start making the farm a profit. Also, research has shown that these girls are much less likely to survive.
So, they really, we really do need to push that 22 to 24 months. And you can see on this farm having this spread, it's probably estimated to cost in the region of 10,000 pounds. Another one to have a look at is your first lactation yield.
This really gives you an idea of how we got the heifers grown well enough and therefore they were ready to carve in and fly, as well as actually I always think of heifers as our little teenagers. How well have we transitioned them into that world of being an adult, being a grown up. Because actually that is a huge change, you know, we've got environmental change, structural change.
Welcome to the world of milking, oh, we're gonna get you pregnant again, lots of new stresses. So actually what we want is for them to be producing 80%, sorry, the black line seems to have wiggle down a bit on that graph. So our targets 80%.
1st lactation heifers give 80% of what the adult herd gives. And this again is for 10 different farms, and we've got the cow yield versus the heifer yield and you can see that actually for some of them over here. Like these, these guys got over 90%, like, they're heifers.
Storming And it is doable, but at the other end, we've got somebody down here where not even 3/4 of their animals, so they're not even giving 3/4 of the, adult cow yield. So we have got variation and it literally is just comparing these two numbers. But it can tell us a lot as to how well the hef is are faring in that first lactation.
The other thing to think of is we, we need to get them into second lactation. Again, you've got welfare, sustainability, economics. So what's their fertility doing?
Exactly the same thing to look at as we do in adult cows. 21 day preg rate. Which is this green line here is a 9 week rolling average and that is also then split into your submission rate, your conception rate.
You can see on this unit for the first lactation animals. Since mid-September, they started to drop off to December. What happened?
We were at 30%, we're now at 20%. Is this the same as what's going on with the adult her cows? In which case?
Whatever is going on is happening to all the cows, but if it's just happening to the first lactation animals, does it actually reflect that we've got issues with the first lactation animals that are calving in? And again, lots of things to start to think about. The last one to think about as well is monitoring longevity.
So that there is a lot of research coming out about longevity. So the Vires published a paper in 2020 in the Journal of Dairy Science, which is a really interesting read if anybody has time. .
And it's basically looking at how long is optimal for a cow to stay in the herd, and with this we're looking at the impact of things like If we keep them in for longer, we've got decreased chance of getting genetic potential in, which is true, but equally, if your cows are surviving well, then she's probably phenotypically very good for your herd, . If we've got cows surviving longer, we've got a lower replacement rate required, so actually we can sell more calves for beef. We've obviously got the fact that the older she gets, the more likely she can see.
So it's all a balance as well as increasing yield as they get older. Anyway, the answer is that by the looks of from the clever math that's been done, parity 5 is where we want to get them to. So actually, we need to start to look at how long are they surviving.
Research based in the UK has indicated that actually quite a lot of our heifers are not surviving to the end of first lactation. That's massive welfare. Why we should never be wanting to cull a first lactation animal.
Because they haven't paid us back for starters, and what have we done to them to break them? So monitoring this is really important, and then this goes back to that heifer effectiveness that Alex Back brought in the KPI that we discussed about, about how we want 3/4 of heifers to carve in at or below the age of target age at first calving and survive to lactation 3. Just have a look at where they're exiting.
The one thing I would say with some of this, especially if we're looking at it from a heifer point of view, the heifer effectiveness is quite historic, you know, we're looking at cohorts at the minute that were born in 2016. 1718 to start to get data. So this is gonna take quite a long time to change, but actually not necessarily, you know, if if we're culling heifers for lameness.
Why are they getting lame, what's going on there. So for me, this is also a massive thing for us to look at in terms of a herd health approach in of ensuring that our animals hang around for long enough. So that has been my webinar.
I'm sorry it's been a quick quiz through, but I just wanted to show you lots of the different aspects that we can do, and I think for me, the answer is take whatever data you've got and please have a play. Software systems are getting better at analysing data, but equally stick it in Excel and have a play, see what you've got. Please remember we've got the Herd Health Shiy app.
That's brilliant, it, it really does save time, . And if your farmer has data, please interact with them, give it to them back, or else one day they will stop collecting it for you. Please, if you do have any questions, my email is there, Twitter, I'm also on Instagram.
Otherwise, thank you very much for attending, and I hope it's been of use to you. Thank you.