Hello and welcome to the webinar on dealing with herd problems in terms of fertility. My name's Jenny Sherwin and I'm gonna talk you through dealing with herd fertility problems today. So we've all been on farms where the farmers have been hinging about the cows not getting calf well enough.
What issues with semen or dirty cows. Lots of different things. So whilst herd fertility issues are common, we now need to think about how we're gonna solve these.
So in terms of that, what do we need to do? Well, actually what we need to do is to determine if there is a problem, where we are in terms of targets and decide whether it's an issue with how many cows are actually serving, so the submission rate, or whether it's a problem with actually are the cows being served, but they're not holding to pregnancy, conception rate, or both. And for that we need data.
We need to determine who is eligible. So for that it's the denominated population of those at risk. So if we use pregnancy as an example, who's at risk of becoming pregnant?
Well, it's those cows who are not already pregnant, who are past their voluntary wait period, and also who have not been marked down as a cull. What we then want to know was who was served when, so the idea of the cow and the date she was served. We can get more information about that as well, but that's the fundamental key part.
And then we also want to know the bit that we're often involved with of was the serf successful? Have we now got a pregnancy? Now I appreciate that there can often be limited data on farm, and if we've got limited data, there are some things that we can do.
So we can have a look at how many PDs we're expecting per visit, and that's gonna give us an indication of the overall performance. How we work it out is to think about the cows in the herd and how many visits we're going to do that year. So for this, we're going to use an example of 200 cows in an all year round herd, and we're going to be going there fortnightly, so 26 visits a year.
In that case, we're gonna be expecting 7.7 PDs per visit. So about 8 PDs each fortnight.
What we then want to know is the percentage of those that go on to become pregnant. And for that, we normally want a target of around 80%. And what that predominantly takes into account is the return serve rate, but it also takes into account the conception rate because that's not possible unless we've got a conception rate of at least.
And at least 40%, sorry. So for this head, we want 80% of the PDs presented. So for us, if we've got 8 cows, we're gonna want about 6.5 PD positives.
Obviously, a conception rate of 40% is often not achievable in our high yielding herds, so we're thinking of more 30, 35% conception rate, which would then lower the target down to more like 60-70%. So we'd probably then be looking for 5 to 6 PD positives. In terms of the number of ONO's or not seeing bullers cycling normally.
That gives us an indication about our heat detection. Are we actually detecting the cows that are cycling fine or not? And it tends to be most prominent.
For that, we normally say think about the herd size, halve the herd size, and divide it by the number of visits. So we're saying that we only want it to be less than 50% of the herd all year round. So for our 200 cow herd, that's 100 divided by 26.
So we're looking at 3.8, so 4 normal O and Os per visit. Another thing we can often get off visits if we go looking for them are the number of cases of reproductive disease, so things like endometritis.
And we use the lactational incidence times by the herd size divided by the number of visits. So if we take endometritis as an example, the target is 10%. So it's 0.1 times 200 our herd size, and then divide that by 26, and that gives us 0.8 cows with endometritis per visit.
So it gives us an idea of where we are and if we're on track. If we're fortunate to have more data, we then start to think about KPIs. And what KPIs should we use?
There's lots and lots of different KPIs available. Quite a lot of them talk about intervals like carving interval, carving to first conception, carving to first service. That's great, but quite a lot of these can be retrospective.
Especially calving interval because we need the cow to calve, get over the voluntary wait period, get back in calf, and then calve again to count. So we're always going to be around at least 12 months out of date. So that tells us about prehistoric.
We're not interested in prehistoric, we're interested in what's happening on this farm now. We've also then got things like carving to first service, which makes it less retrospective. But actually, it's still lagged and it only tells us about one part, and it tells us about whether we're getting that first serving.
And it's also quite skewed by things like the voluntary wait period. The other issue with all of these things is we've got to consider who's our denominator population. Who are our cows at risk for doing this?
If we take carving interval into consideration. Our denominator population of the cows that calve, and then subsequently calve again. That's great, but that's massively skewed by things like culling rate, especially if we're culling for poor fertility.
And the other thing is the fact that we don't know whether a cohort had time to complete that interval. So if we think of calving interval, if they, if a cow calved last winter, then actually by now you would hope if she carved the first of January last year, she would be in calf. But actually if we know that the calving index for that herd is 400 days, she's not had time to complete that interval.
The other thing with a lot of KPIs is we talk about the mean. The mean calving interval, the average calving interval. The problem is that most reproductive data is positively skewed, and these outliers have a strong impact on the mean.
So because of that, we've resorted to using the medium, which is great. That tells us what that middle 50% cow does if we line them up from best to worst. The issue is, it doesn't tell us what's going on with the other cows.
It doesn't tell us how wide our ranges are, what the shape of the distribution looks like. So it's very difficult to work it out. So quite a lot of the KPIs we use can be fairly flawed.
So for data analysis, what do we like to use most? The one that's the most useful and least retrospective from my point of view is the 21 day pregnancy. Right, so basically saying out of all of those cows eligible to become pregnant, how many actually do?
So with that in mind, I'm gonna walk you through some of the parameters we use and I'm gonna show you using all year round carving herd that we, currently do some work with. So for all year round carving heads, we assume you know they're carving 12 months of the year, 24/7 type thing. However, the key thing to remember is it's not consistent and it's not constant.
We get peaks and we get troughs. It might indicate previous issues with fertility that happened 12 months before, and it's now a catch up. It might relate to the fact that we suddenly carved some heifism.
What it means is we've got changing environments for these cows that the environment they're carving into and then the environment they're going into as we affect stocking density. So, 21 day pregnancy rate. This graph can be quite intimidating to look at at times, but once you've got your head around it, it's quite easy.
Key thing to note is it's 3-week periods ending, and that's the date. So everything's done per 3 weeks. And we chose 3 weeks because that's the Easter cycle length.
You can do it for 14 days, 28 days, however you want to do it. So, the bars are the number of cows, and that's right off the left-hand side. The lines are rates and percentages, and that's read off the right-hand side.
So the yellow bars are the number of cows eligible, and that means in this case eligible to come pregnant. So they're not already pregnant. They're past the voluntary wait period and they've not been marked as a cull.
The blue bar is those cows that are in the yellow bath that have been served in that three-week period. And the green bar is, out of those cows that were served and eligible, they've now also become PD positive. So from this we can work out some key parameters.
We can work out our submission rate, which is out of the eligible cows, how many of the cows went to be served. And you can see here that this is represented by this line. It's done as a rolling er, so this is a 9 week rolling.
It gives us an idea of what's going on over time, and it's done as a rolling rate because whilst this herd has a larger number of herds, if you were doing this for 100, 150 cow herd, at times you can have small denominators which massively skew results. So it's to give you larger denominator populations to base decisions on. And actually you can see here we started off fairly static with our server rate.
We went up slightly, but then actually we've had this steady decline, and this steady decline from summer 2020 to February, January, February time 2021 resulted in a change of just below 60% to around 45%. However, we've obviously made a change around the start of 2021. And we've now got this increase, and what's really nice is that actually it doesn't take that many blocks to go through to be able to show the farmer the impact this has had.
Because ultimately, to change the submission rate, you have to be serving more cows, and that involves putting more effort in. So being able to show the benefits of that effort is really good. We've then got the conception rate.
So that's out of those served, so the blue, how many become green, become pregnant. That isn't indicated as a line on here. However, that's something we can look at in more detail shortly.
The green line on here is your rolling 21 day pregnancy rate. So that's out of those eligible who goes on to be pregnant. The target for that is 20, 25%, so we're saying that 1 in 41 in 5 cows that are eligible, we want to become pregnant.
And you can see, you know, it's been fairly static. We have a slight increase over October, November time, December time. It plateaus again, tiny little drop and then fairly plateaus.
What's slightly sad for this farmer is the fact that he's put this effort in. He's increased his submission rate by around, not quite 20%. However, we've not seen this change here.
This line doesn't match that one. So therefore, what's driving fertility more here is going to be his conception rate because it's not ultimately ending up with more and more pregnancies by serving more cows. So it must, must have a big impact of the fact that the cows are not holding.
So I also like to put numbers on it. This is great to do, especially with block carvers, but it's also A bit of an easier way to eyeball it. These are each of the blocks.
This is the number eligible, number served, and number pregnant, just taken from those bars. We work out the submission rate by saying that 63 divided by 86 is 73%. We've worked out the conception rate by saying that 8 pregnancies were generated from 63 serves, and the 21 day preg rate by saying that 9%, which is 8 PD pluses out of the 86 eligible.
Here is a nice story, minus this flip here. We can say to our farmer, look, you've increased by 20% pretty much. Your submission rate, massive pat on the back.
Well done, and we'll look into how he's done that shortly. You can also have a look at the conception rate. So It's highly variable.
You know, we've got some massive differences going on here. Conception rate is much harder to change. It's multifactorial, and again, we'll discuss it more later.
But it's quite a tricky one to get what's going on on this farm. And then we've got the 21 day pregnancy rate, which is basically the conception rate times the submission rate. And therefore, despite the fact that we've seen this boost in submission rate, because our conception rate is variable, we're not quite getting the outcomes in terms of pregnancies that we want.
So on this farm, both submission rate and conception rate are playing roles. So, let's have a look at the submission rate in more detail. So this is the first service submission rate.
So on this graph, the purple bars and the number of serves due. So in June 21, we reduced 6 1st serves because it's read off this on this axis. In terms of submission rate, we want to know the percentage.
So that's right off the left-hand axis and you can see that just over, so 50% of those were served. So 6 of 3 of the 6 cows got a first serve 8 at 2 by 24 days after the voluntary wait period. And then the green line is this 3 month rolling average.
We put a line of faith through the middle to see where we roughly are over the past kind of two years. You can see that we sit at around 50%. We had a dip in the summer of 2019.
But that dip, we don't see, well, we get a dip here, so we still get a bit of a dip, but it's not as prominent as before, so there's been an improvement there. But it's wiggly, we have good times and we have bad times above it. First serve submission rate can be quite variable.
So for instance, in the month where we've only got 6, it's quite easy to skew the results. However, the target for this is 80%. And even if we removed all the skews and went for the best results we've got, we're still 20% off that.
So we can then look at that in terms of what days in milk are our cows getting our first serve. So days in milk are on the X axis and the number of first serves is that this is a 12 month period. It is on the y axis.
The green box is our target. That's 24 days after the voluntary wait period, so it's that first Easter cycle. And we want 80% in there.
You can see that we're far off 80%. You know, we've got this massive tail and stew. It's really shifted this way.
And actually, if we put another box in, you would say that 90% of the cows, plus, are served by the second Easter cycle. So why is that? Why aren't we getting the first, but we're getting the 2nd?
This is a common scenario, and this is often where you come in. Vets go on farms you presented with those O and O's. They're normally cycling, so we jab them.
We jab them. The farmer knows to see them. He spots them, he serves them.
Happy days. So actually this is the impact of their routine fertility vet, so she's definitely earning her money, but what we want is these cows to be in here. And why is that?
And then this comes down to his heat detection method. This guy solely relies on pedometers. It's got a beautiful high yielding shed with lots of space.
So therefore we need to talk about how we pick them up. Is it that we use ancillary methods like tail paint and even tail painting a different colour, for a first serve? Is it that we add in an observation?
You know, go down when it's quiet with a cup of tea and just go and have a walk around. Or is it that you play with the sensitivity of the pedometers and acknowledge that you're going to get some false positives, but hopefully you catch more of them. But there's a massive wind to be made there with submission rate.
Then got the return serve, and this is exactly the same graph as before, in terms of where you read the bars and the numbers, but this is for after a serve, do we have a return serve 18 to 24 days later. She's not pregnant, obviously. And here, if we do the eye of faith again, we sat at around 35, 36%.
Target here is 55 to 60%. So again, we're not where we want to be target wise. And again, we have some troughs and dips, and we have a slight dip here in winter.
And a dip again the previous winter, but again, not as marked. So there is some seasonality, but again, not hugely marked and is improved, which is great to see. The other thing is, we've got this from from February time, this increase in this return service submission rate.
Great, we can show the farmer. And if we think back to that 21 day pregnancy rate and we saw that increase in submission, we know it's now coming from the return service. So his efforts of going out, he decided in February he was going to go out, create a list of cows when they should return, and then go and hunt them down.
It's working. He's getting the benefits he's seeing more serfs. But is he seeing them at the right time?
And that's where this inter-service interval comes in. We're looking at when after a serve do we serve the cow again. We want 18 to 24 days after survey because that's a natural Easter cycle.
So, therefore, our target for this is normally 55 to 60%. If we don't get that, we'll go for the 36 to 48, because that's just double the normal cycle length. So we've missed one, but now we've correctly identified the second.
What we don't want is the 49 day plus because that often represents multiple missed heats. It can also represent foetal death or abortion, but commonly it's just multiple miss heats of the cow that just continues plodding through without a serve. We've then got this 2 to 17 day, and that often indicates when a cow has been served and she's not truly an estress.
It doesn't matter if it was serve A or serve B that was incorrect. One of them was. You can get it in cows with follicular cysts, but to drive it at a herd level, you would need a lot of cows with follicular cysts.
So it's normally heat detection accuracy. We've also got this 25 to 35 day bar. We've traditionally thought of this as kind of an extended interval.
So it might be late embryonic death, incorrectly identified or missed heats, or the fact that we've used prostaglandins after an early negative PD to bring them cycling again. Look at the in-service intervals on a number of serves at the bottom by how many are done per day, so the y axis, you get this pattern. So, you can see here we've got the spikes in the 18th to 24th, but then we get this second spike that sits here.
What's that about? Well actually, if we zoom into that a bit closer. You can see that these are these yellow ones as well that box that 25, 26, 27 days.
So these cows appear to have a longer easter cycle length. This was highlighted by research by John Remnant, which showed actually these cows are normally cycling, and it's common in high yielding cows. So actually these are natural cycles and you can count, you can almost take those off and count them in the 18 to 24 day bar.
Because those are normal, normally cycling cows. So as we said, this guy made a change to the management of his return serves in February 2021. So what we need to do is also look at the impact of time.
Our job is to monitor the efficacy of the changes that have been made. So if we look at this graph, 2020, we had about 37% of return serves happening at 18 to 24 days. We're now up to 43%, 18 to 24 days.
And you can see that the 36 to 48 day bar was around 25%. Well, we've now dropped it by 10% to around 15. So that's brilliant.
Hopefully, that means those cows have gone into the 18 to 24 day, and we're not missing that cycle. Perfect. 25 to 35 day bars pretty solid.
49 day plus, we've dropped a couple of percentage. Not as much of an impact as we'd want, but it's still going in the right direction and hopefully over time that will become lower. It's also harder for this guy to drop this because he has, a sweepable.
That often gets these girls. The thing we have done is this 2 to 17-day bar has doubled. So what that's saying is that whilst some of these 36 to 48 day bars, some of them we're serving again and we're serving them at the correct time and serving them earlier, whoopee, actually some of them are ending up here.
And it means that one of the two serves wasn't performed at the correct time and we've got some inaccuracies with our heat detection. So we've almost compensated how accurate we are with our desire to serve more cows. This can work fine.
Assuming that one of the two serves is correct and it actually manages to hold. However, the target is less than 5 to 10% depending on what you read, and this guy is close creeping up to the 15%. So For me, having a chat here to the guy about which cows are they pulling out of serve, what bullying signs are they using, how do they know when they put a hand in to serve that she's ready to be served, as well as talking about playing with things like milk progesterone to help them determine whether she's truly in heat or not would be useful things.
So we know that his submission rate, his first service submission rate, is fairly pant, but we've got some room to improve that. The return says its improvements are working, but it is coming at the cost of accuracy of heat detection. Which again we can chat, have a bit of a chat to him about.
So now we need to have a look at his conception rate. So this is his overall conception rate for all serves. It's done by month on the X-axis.
The number of serves which are the bars on the left axis and the percentage on the right axis. Reds are all the number of PD negatives, green are all the PD positives, and the rolling 3-month PD positive as a percentage. So the conception rate is the green line, and that's red off the right-hand axis.
Eye of faith, and it's a true eye of faith here, guys. We put our trend line in to see what's going on. It's great.
It's nice to see we're going in an upward trend. We do fluctuate around it, but we do have an upward trend, and that's really nice to be able to show this guy that he's going in the right direction and that the last 18 months have been worth whatever he's been doing effort wise. You can see that we've got this big increase kind of.
Over the more winter months and we're now starting to drop off again. And actually, if we look back, not as obvious, but we have the same thing back in 2019 and 2020. There's some form of seasonal effect going on here, and we need to kind of keep an eye out for that and try and have an understand as to what happens in those months on this farm.
We can also split it into 1 serves and second serves. First serves can be quite variable. It's often because we've got low denominators, it makes it harder to interpret, but this one is pretty spiky, .
And doesn't really follow the pattern of the overalls. If we look at the return serves, that does follow the pattern of the overalls and exaggerates this increase, and it's this increase that we see both over both winter periods of 2019, 2020 and 2020 to 21. That are very similar to what's driving the conception rate on this farm.
So it's the return services again that are really driving this. So when we think about conception rate, there's lots of different factors that affect it. We've got semen quality, and that isn't just are there enough good swimmers that are of the right morphology, but it's also how we're storing the semen and is it sex can often impact on it.
Operator technique, whether it's human or bull, are they doing what they are meant to be doing. Nutritional status. What's going on?
Have we got any signs of post calving diseases, any metabolic diseases around peak yield? How is our carbon, our cow in terms of negative energy balance? Good timing of AI.
Are we serving cows at the right time point? The endemic diseases, the impact of things like lameness and mastitis, what's happening with those? Are they dragging our conception rates down?
And then infectious diseases BVD lepto IVR, Neosporra. I'm not going to go into details on those, but knowing the status of the herd, doing sentinel screens, things like that are really important to rule these out. Especially if there is a sudden change in conception rate.
And then we've also got environmental issues that are things like what's happening to the cows in terms of their environment. Are they pasture? Do they change housing at any point?
Are there signs of heat stress? And these quite commonly are highlighted in terms of time points over the year. So this is one of my more favoured graphs to look at for conception rate.
So what we've got here is percentage of MPD, so pregnancy diagnosed by month, and here it gives us the number of serves per month. This blue line here tells us our overall conception rate of about 21%. The dark solid blue in this bar tells us a percentage positive, but hatched with a percentage negative, or when it's closer to, closer to time, it's the uncertain.
So we can look at it over time and unsurprisingly it doesn't look dissimilar to the red and green graph, but we can see here that October and November were real drivers and January was high. December dropped back down to average. We do have drops below average in the summer, so maybe we've got some heat stress going on.
But for me, it's also concentrating on the positives. We like to concentrate on negatives as vets, but what, what is it about October and November that made them stand out? We've also then got factors affecting semen quality.
And for that, it, you know, we often want to look at the ball. Farmers are obsessed at knowing which ball's the best. Key thing with this is you can see on this herd, this is a 12 month period.
This guy has used a lot of balls over the 12 months. This is ordered in terms of the most number of serves on the left to the least on the right. You can ignore the vast majority on the right because there aren't enough.
You can see here, you'd think this bull is absolutely cracking. Yeah, that's great. You did 4 serves.
That's not going to really help us. What we're interested in are the ones on this side. And actually, if we look at those 1st 3 bars, we've got decent numbers of serves, and actually, we do have an impact here.
So what's going on? I quite like taking it down to the figures to see what's going on. So all I've done is put the bull, how many PD positives he had in the year, how many total serves he had in the year, what the conception rate was, and then for my benefit with not knowing the bulls as well on this herd, I've just put in what they are.
So these are the two that as vets will automatically jump to the bad numbers. These are the two that we We're gonna worry about DGOH Dante, 55 serves. Yeah, all right, denominator, but I mean, normally, 340 serves, 17% conception rate.
However, what we need to think about is the fact that he has probably done. Over half the Serbs in this herd. So, therefore, is it because the semen quality is rubbish?
Or Is it because of the cows he's been used to serve it on? Is it that everybody has one serve of the others, and then after that first serve, we use him? Is it that we use the sext AI on our first lactation animals, but then only use beef on our second lactation plus?
So we need to be careful rather than blaming the bull. We need to think about how they're used, why they're used, and when they're used. One of the things that's quite easy to do, and we've put some of the balls together so that all the dairy balls, the black and white dairy balls are in green, nor blue because he's got so many he's in yellow by himself.
Let's just have a look at when they're used and what it does to kind of the conception rates. So we can see that we, we do get changes, and we can see that no blue on this farm has been phased out slightly in favour for black and white dairy, and we have had some increases in conception rates. However, October and November of last year were our kind of key performance months for conception rate, and you can see that the ratio of yellow to green is flipped between the two months.
So whilst Nor Bloom may play a role, He's not the only reason by a long way. So we just need to be a bit careful when we're interpreting the bull as deciding, is it the semen or is it what we do with the semen from that bull that's important. So we've got operator techniques.
So on this farm that I've been walking you through, these guys don't use operator technique, operator recording, and it's a recommendation that has been made to them. It's not a witch hunt. We don't want to say you're rubbish, it's all your fault, but it also points us at who might need an extra hand and helps us rule it out.
This is a herd that do record the different operators, and I've blanked out the names and denominators so they can't be identified. But just to be careful, so this operator here looks much worse than the rest. And instead of going, you shouldn't serve cows, blah blah blah, we also need to think about the fact that this person might have served only 8 cows and got 1 pregnant, where the person up here served 453.
And what is it about those 8 cows that this person served? Are they difficult ones? Are they the ones towards the end of lactation or early lactation?
You know, we just need to be a bit careful and sensitive around this. But I do think it's worth talking about and I think the recommendation of AI courses can always be helpful. Another one is the nutritional status.
So, we often look at serve number, and I quite like serve number. We often use it as a proxy for metabolic disease. And you can see we've got this pattern that's quite common here where we're getting this increase in conception rate as the serve number increases.
And if we look at that in numbers, you can see that really nicely here, and that might indicate that our cows are under some form of metabolic stress. Whether it's from calving or whether it's from peak lactation, but once they're over that, we seem to be heading in the right direction. We've then got this drop here at 4th 4th serve, and why is that?
Well, actually for this herd these guys have a housing change. Once they get to around this 440 days of milk, which is when the 4th serve tends to happen, they swap, sheds, so they move from the nice high yielding shed to the older low yielding shed, and actually at that point it's this change. And it's quite stressful, and actually it's one of the things that we've recommended to them in terms of how they move their cows across.
So we can actually look at it in terms of days in milk. So we've started at 38 days in milk, which is our voluntary wait period and they've gone up in intervals of 21 days. And actually, you can see here, The 1st 3 blocks are pretty stationary.
But that's not what happened with the serves. But then we've also got to think about the fact that the daisy milk impact is impacted by things like the first er submission rate. Which we know isn't brilliant, and we know that a chunk of them are not being served.
I kind of been served at that 2nd estress. So actually combining those two first two columns probably puts us at where we would expect to be. Again, we're seeing this drop off at that kind of 140 day point.
So what we've done here is, all we've done is we've taken the days in milk at which 1st, 2nd, 3rd, 4th serves have happened at. And we've used a box and whisker to give us an indication of where our median and mean is, but also to give us an idea of the distribution. And you can see that that first serve distributions to the box, which gives us a middle 50% of cows, is much tighter and it gets wider and is pretty wide by the time we get to the 4th serve.
With a variation of, oh, what's that? About 50 odd days. Between the middle 50% of cows.
So the thing that we can see here is that 75% of cows, that's 75th percentile. Are served by around 85 days in milk. So actually these two bars account for the first serve.
And then if we look again, these two are gonna account for our second serve. So actually with daisy milk, just be a bit careful. But what's interesting is here this is possibly less indicative of metabolic disease around calving and could indicate potentially issues more if we're still seeing it by 100 days in milk at around peak yield due to peak yield happening 6 to 8 weeks post calving.
Another impact to that is obviously then the yield. And that can is included within the metabolic because you're thinking of type 1 ketosis. This is just it represented as numbers, but actually we can see in this head.
That the vast majority of the cows are produced in between 20 and 49 litres, and that's not having an impact on the conception rate. Does drop a few percentage once you get to 50+, but that is a small minority of the herd and isn't going to be the reason. We're still a good 10% below target with the cows that are between 20 and 5 and 49.
We have got a better conception rate here at the 10 to 19 litres, but those are gonna be the cows that are much more likely to be later in lactation. Because this is a herd that's producing over 9500 litres. So we can also look at AI timing.
How are we getting on with where we serve them? And actually, you know, we can see that what we've got here is this green line represents the overall conception rate of around 21%. The yellow bar is the total number of serves.
And then we can see what the conception rate does for each of those. And again, here it is tabulated and effectively. This 18% down here, which is where they were serving more of them at the wrong time, does have a much lower, has kind of that 4% lower than the 22%.
But our 49 day pluses again, possibly because they are later in lactation at that stage have a higher one. So I suppose for me, the argument here is with this farmer, if we're increasing the number of serfs that are building up here, we are gonna drop our conception rate lower than where we are already, and that could cause us some problems. So after that quick whiz through, it's what have we learned?
We've learned that something happens in autumn. What it is we don't know. And with that, we've also got the housing move.
And that kind of falls under the, the serve number, but I've put up here because it is an environmental issue that these guys moved before that 4th serve and it crashes the conception rate. We've got potential ball issues, but I don't think we can start blaming him yet and immediately. We don't know about the operator because these guys don't record it, and that can be easily fixed.
We've got indications that we may have some metabolic issues, and we could do with looking at some fresh cow records, which we'll go on to shortly. Infectious diseases, they're not presented here, but these guys do lots of routine screens and are vaccinated for IBR. So much less likely here because we know where we stand.
So, we know we've got low levels of mastitis on this head, so we're not worried about that. The impact of lameness, we can't really measure due to the fact that we don't have regular mobility scores. So again, We don't know, but at the minute isn't going to be the biggest win or the lowest hanging fruit.
And AI timing, we've got this potential impact. If he continues to increase his return service submission rate by incorrect identification of cows, then we are going to see an impact. So if we're looking at reproductive diseases, what do we want to look at?
So these are the common ones that we look at, and we've got to think about where this data comes from. So LDAs are quite easy, you know, we're the ones that go out there, we ping the cow, we chop her open or toggle her and we fix it. It's in our records.
We can get those, that's fine. And for me thinking about the denominator for this, it's carvings. It's that carving time period, so.
And it's a number of carvings over your set time, whether that's 12 months as we've done here. Metritis and endometritis. So metritis is gonna be if it's less than 21 days, endometritis more than 21 days.
They're often commonly combined on computer systems because farmers call them whites or will discharge dirty, things like that. And it's great if we've got fresh cow checks and we're going in and we're checking cows and we're putting hands in, but actually quite often. We're only going in and, you know, it's when we've got an O and and we put our hand in and go, oh, she's a bit dirty, that we spot it.
So metritis and endometritis are quite often, underreported and underdiagnosed. So if we think we've got metabolic issues, they may well be an area to think about going and having a bit of a more of a look for. Milk fevers, again, you know, we all know the clinical milk fever, and farmers are good at picking those up and treating them.
However, subclinical milk fever massively goes undetected on farm, and that has a big impact on the uterine environment. And the other thing to say is if you have what looks like quite a lot of milk fevers, often because we use the calcium boogluconate treatments as our way of measuring it. It's very difficult to decide what's a kind of proactive prevention versus what's actually treatment, you know, is it that we treat all four lactation cows are above with calcium at calving?
Is it that we give them twins, calcium at calving and things like that? And retained retained foetal membranes, you know, you think, well, that's easy. The farmer can spot them, but it's making it sure it's a time period.
There isn't a set RFM rule. People do the time to their own devices. For me, it's if the RFMs have been there for more than 24 hours.
But again, it's making sure what farmers record them and that there's consistency within the farm staff. That's one of the biggest issues we've had. In terms of getting some data from all of this for different herds.
Don't forget, you know, metabolic diseases. People are quite often doingneers and BHBs. BHBs are great because you can do it with the handheld metres.
And again, easy, nice representation. So these are historic ones, but you can see that, you know, we've got a decent chunk of red, so above target for both the BHBs and the Nephers. And again, just remember your denominated population.
You know, you might not make a change based off, say, the December, January readings when you've only got 10 cows in that herd, but actually, if you combine them over a few months, then they will give you a better indication. So what are we gonna do with this farm? So we've said the first service submission rate holds some key wins for us.
We're nowhere near the 80%. We're around 50% if we're lucky. So how are we going to improve it?
As we said before, you could use ancillary heat aids, brushes, scratch pads, Kars. One of the problems we have with those these days is the fact that we have brushes, and I do not mean that in a negative way. I really like the welfare aspect of brushes, but they do have a tendency to set off some of our heat detection aids.
So then we're looking at things like the addition of observation and making sure that if people are doing observation, they know the right size. The van Erdenberg system is really good for for the point collections and also for just a bit of a refresher for staff. It's one of those things that you could always do for kind of a 20 minute chat at the end of a routine and get all the staff together and talk through what we decide is correct.
And actually, that would be really helpful for this return serve accuracy. Whilst it's great, we are getting some improvements. It is coming at a cost to us.
And that cost is also going to have an impact on our conception rate. So in terms of accuracy here, it's making sure that everybody knows what a bulling cow is, for the people that are serving, knowing if she is right from the palpation. And also thinking about if we're not sure, can we add in a milk progesterone test?
These are variable costs, but they're normally around 4 or 5 pound a cow, depending on where you get them from. So actually, that comparable to, that you only need to save a few straws to make it useful. And then we've got to think about the conception rate on this farm.
We've got to investigate the possibility of metabolic diseases, and we can see from here that we've had signs of, type 2 ketosis due to the fact we've got high prepartum neus and high BHBs. We've got a higher than, well, around target metritis, endometritis, but as we've said, that's most likely underreported. And also we've got a higher than we'd like LDA rate.
These all point to the fact that we've got metabolic disease going on. That and the fact that we do get a slower increase in, conception rate at the start. Again, how are we going to do that?
Well, that's going on farm. That's doing things like measuring feed space, looking at stocking density, looking at grouping of housing, going back to the dry cows. What are they being fed?
How are they housed? And actually this guy's just put in a new dry cow shed and it'll be interesting to see if we get any the changes that we want off the back of that. We can also do body condition scoring.
We can look at changes in body condition score, and it's also important to look at it across different parities as well. We don't have time to discuss it today, but this herd actually poses some really interesting things where there's metabolic disease is obvious in lactation to and above cows, but actually doesn't appear to be present. In first lactation cows in terms of calving disease, but actually, we do get a 10% drop in conception rate for serve 1 to 2 in the first lactation cows, and that may well be related.
To how they are integrated into the herd, and that's where this discussion of integration into the heifers comes in at the bottom. Other things for these guys to do record the operator dead easy. If they're not keen to do that, let's do some AI refreshes all around.
It's not gonna hurt. We talked about that first, that 4th serve drop, so the movement into the new yard, rather than doing it individually, let's do it in groups. Let's think about the stocking densities in there.
Is there space to open up any more loafing areas. Etc. There's always that cloud of the bull.
So yeah, check some semen shores, look for the motility. And see what's going on. So lots of things that we can go and do and hopefully this farm has been a good way to highlight to you how we can use this data to point us in the right direction of what to go and do and where to go and look and what to talk to the farmer about.
So with that Mine, I'm going to draw this to an end, but these are kind of my key points. You can do it in lots of different ways, collecting fertility data. Just think about the pros and cons of the data.
Where does it come from? How reliable is it? How retrospective is it?
Who's in that eligible population, and how skewed is it by our outliers. And with this, think about how reliable the data is. Do you trust it?
Where does it come from, and think about what your data is. And then I always like the KISS principle in life, just keep it simple, have a really methodical approach. The approach I've gone through with you, that is the order of slides that I look at.
That is how I, how I tend to do these things. So look for trends, look for the anomalies. And just try and see what's going on on that farm.
I'm sorry I couldn't be with you live today, but if you do have any questions, please feel free to email me. My email address is ginny.herwin@ Nottingham.ac.uk.
Otherwise, thank you for joining us, and I hope you enjoyed the webinar.